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In 2026, Owner.com generates $120,000 in new ARR per BDR, per month.

So what’s the one thing they added to get there? There isn’t one.

The easy answer is they bought an AI SDR and the number went up. Plenty of teams bought the same AI SDRs in the same window, and their per-rep numbers didn’t move like this. If the tool were the cause, everyone who bought it would see the jump. They don’t.

What moved Owner’s number was who owned the AI, and the call on what to build versus what to buy.

Last year I broke down part 1: how Owner scaled outbound to $30M+ ARR. Back then each BDR was sourcing about $72,000 in new closed-won ARR every month.

Today it’s $120,000 on average. That’s $1.44M per BDR, per year (source: GTM Atlas). Their top performer hit $174,000 in closed-won in a single month.

Owner is redefining what an outbound team can produce in 2026. That’s why they’re worth studying. The lesson lives in how they think.

Today let’s unpack what changed.

P.S. If you missed part 1, read it here.

Who Owner is, in numbers

Restaurant software, SMB sales motion, ACV around $8K to $10K, in-office XDR team.

The scale tells you the rebuild is real: $50M ARR in September 2025 to about $100M ARR in May 2026. Headcount 258 to 357 (+38% past 12 months). Sales team 64 to 93 reps (+45% past 12 months). Their average AE closes $2M+ in ARR per year on a $150K OTE, and 4x CW ARR / AE / Month vs competitors.

That growth traces back to two structural decisions:

1. One team owns AI

2. Buy the commodity, build the differentiator

Decision 1: one team owns AI

Most companies do AI the decentralized way. Every rep and every manager goes off to figure out their own prompts, their own tools, their own workflows. It feels empowering. It produces a hundred mediocre half-built things, and it eats rep time. A rep using ChatGPT to research is a rep not on the phone. That’s why you see surveys where companies adopt AI everywhere and report zero impact.

Owner went the other way. One central team owns AI for the whole GTM function. In September 2025, they hired a GTM AI lead: YG Hong. A trained expert in this category gets closer to 20x the output of an amateur, not the 50% or 100% you’d assume. So the owner bought the expertise instead of asking each of their reps to become an amateur prompt engineer.

The ops and AI lead sit with the reps to find the bottleneck, build, measure it, and feed it back. Observe, build, measure, repeat.

Decision 2: buy the commodity, build the differentiator

The second layer. Owner buys infrastructure: Salesforce, Snowflake, Anthropic + OpenAI, Momentum, Salesloft, Datalane, Avarra, 1Mind.

They build the intelligence layer itself:

• Pre-Call Research (PCR) agent,

• ML lead scoring

• Intelligent routing model,

• Cerebro (Salesforce config in plain language),

• The Gardener (master business-context file)

• Churn review agent.

Their build test, and they only build when most of these are yes: it’s not uptime-critical, it’s custom to their ICP, the engineering ROI is big, they own the resulting intelligence, and no vendor will ever build it for their niche.

These two decisions are the whole story. Everything below only compounds because one team owns the loop.

Now let’s unpack what that loop produced at each stage of the funnel.

Stage 1: rebuild the BDR’s day

At most companies, only 30% of an SDR’s time is spent on prospecting, and 50-60% of their hours go to list-building and research.

That is the non-prospecting activities and time Owner went after first.

The central team shadowed outbound BDRs work and built one tool to absorb all of it. They call it AI PCR, pre-call research. It drops a personalized brief into Salesloft before every call: a website audit the prospect can see (a score out of 100, the revenue the problems are costing them, a problem list), plus the nearest high-performing Owner customer for local social proof.

The rep dials more, and each dial lands warmer, because the opener is built on something real about that specific restaurant. More connects, and better ones. Kyle Norton (Owner's CRO) frames it as a 5x improvement in decision-maker connects from where they started, now 20 to 30 a day on 150 to 200 dials.

The build cost: one engineer, two weeks. One team built it well, instead of fifteen reps building it badly fifteen times. Kyle calls AI PCR a large part of how the BDR team gets to $120,000+ in closed-won per month.

That lift only exists because one team owned the loop and pointed it at the boring 60%. That’s the first lift: more meetings into the funnel.

Stage 2: rebuild the AE’s day

AEs were spending to 30 minutes before every demo manually researching each prospect. That did two things: it cut the time they could spend with customers, and it slowed qualification, especially when a restaurant owner wanted a demo within hours.

The same pre-call research that warms the BDR’s dial also preps the AE’s demo. The Grader report audits the restaurant’s online presence before the call and hands the rep a personalized business case. Instead of “you need a better website,” the rep can show the specific problems likely costing this owner money. The buyer walks in already problem-aware.

The result: aes stopped burning 20 to 30 minutes per demo, freed that time for more customer conversations, were better prepared, and saw a close-rate lift on top.

If the AEs close more of the meetings the BDRs source, every sourced opportunity is suddenly worth more. That’s the second lift, stacking on the first: more meetings in, and each one worth more. The win-rate work below is the same loop, one stage downstream.

Stage 3: the talent system

Owner ran AI scoring across 150 of their sales calls to find what actually correlated with winning. The surprise: discovery barely moved the needle. Ten of the twelve highest-correlated skills were end-of-call mechanics. Discovery scored zero of twelve.

Here are the 12, grouped by where they happen on the call:

End of call (8 of 12):

• Ask for the sale

• Book the next call, even a placeholder

• Help the prospect see the value of continuing

• Agree on a real purpose for the next call

• Gauge whether the prospect is leaning in

• Ask for the next meeting to move the buying journey forward

• Give next steps with clear reasons and value to the prospect

• Make an assumptive close (fill out the form together)

Pricing (1 of 12): concise, transparent delivery that reiterates value.
Prospect engagement (1 of 12): the prospect leaves with a clear idea of next steps.
General (2 of 12): rep stays in control of the call, reads the room and simplifies language.
Discovery (0 of 12).

One caveat before you copy that. Owner sells to SMBs on a 1 to 6 day sales cycle, 1 or 2 calls to close. In that motion the deal is won or lost in the last few minutes of the call, which is why end-of-call mechanics topped their list. So don’t copy their answer. Copy their system: score your own calls and coach whatever separates your won deals from your lost ones.

Here’s what they do, in four steps.

1. Hire for DNA, then audit it. They hire raw, coachable people, often straight out of school, for drive, coachability, organization, and curiosity. Then every quarter they run a hiring autopsy: line up interview scores against actual attainment and re-fit the scorecard.

2. Manufacture the craft instead of buying it. An oversized enablement team installs skill faster than you could hire it, using a four-step loop borrowed from MMA coaching: introduce, isolate, integrate, inspect. Most enablement skips isolate and integrate and jumps straight from “here’s the skill” to “now go do it.” That gap is why training doesn’t stick.

3. Let the data tell you which craft to coach. The 150-skill analysis pointed at end-of-call mechanics, not discovery. So they ran three sequential modules on closing only, ten weeks, with calls scored daily, changing one thing at a time. Win rate moved from 38% to 52%.

4. Wire it into one signal system. The Skills Intelligence Center, an Owner build, marries the human read of a call with the machine read, and feeds both coaching and hiring off the same signal.

Talent and enablement compound together. Owner hires coachable people, then runs them through a real coaching machine. Raw talent without the machine stays raw. The machine without coachable people has nothing to work with. Put both on the BDRs and they book more, ramp faster, and stay longer. Put both on the AEs and they close more of what the BDRs source. Two stages lifted at once. That’s the compounding.

That explains another part of the $120K closed-won per BDR per month story. Owner got more precise about who could actually operate inside the machine, then made each of those people better.

Why it compounds instead of adds

The jump from $72,000 to $120,000 came in small lifts at each stage of the funnel, and the lifts multiply.

Hire the right people. Coach them on the skills that actually correlate with winning. Hand the research to AI so reps (XDRs + AEs) get selling time back, which means more conversations. The conversations are better prepared, so better conversion. More of those meetings close, because the AEs got better at closing. Reps ramp faster and stay longer.

Move four stages of the same funnel 10 to 15% each, and the gains multiply. $72,000 to $120,000 in closed-won per BDR per month. $1.44M a year, per rep.

With a team of 15 BDRs. At $72,000 each, that team sourced $1.08M in new closed-won ARR a month. At $120,000 each, it’s $1.8M. Same 15 reps, $720,000 more every month, about $8.6M more a year. That’s the lift from compounding alone, before they hire a single new rep.

And they are hiring. Their sales team grew from 64 to 93 reps (past 12 months). So the two effects stack: each rep produces more, and there are more reps producing. Every new BDR now ramps into a $1.44M-a-year seat instead of an $864K one. The per-rep gain is the multiplier. Headcount is another multiplier on top.

This is the sub-two-hour marathon of outbound.

In April 2026, Sabastian Sawe won the London Marathon in 1:59:30, the first official sub-two-hour marathon in history, breaking a barrier the sport had called impossible for decades. Overnight it reset what every coach and runner believes a body can do.

Owner just did that for an SDR team. $1.44M sourced per BDR a year is the new proof of where the outbound ceiling sits in 2026.

What you can actually take from this

Even if you don’t have 100 reps. Or you don’t have an in-house GTM engineer building you AI pre-call research in two weeks.

Good news: the lesson doesn’t need any of that. The lesson is the order of operations.

First, give the structural decision its due. Pick one owner for AI and systems on your team, even if that owner is you for now. The compounding only starts when one person owns the loop instead of every rep building their own.

Then find the bottleneck. The manual work on your BDRs and AEs, the contacts added to a sequence by hand, the research before every call.

Then pick two compounding bets, not ten. Take the busywork off your reps so they sell more, and fix one stage of the funnel with depth, the way Owner fixed closing. Two stages moved on purpose beats ten tools bought in a panic.

This is the same equation I keep coming back to: strategy, leadership, talent, systems, enablement, and outbound process. Multiplied, not added.

So when someone asks what one thing Owner added, the honest answer is still: there isn’t one. They put AI under a central team, pointed it at the boring 60% of the job, coached the humans on the one skill that moved the number, and let it compound.

You can build a smaller version of that system this quarter.

Hope you enjoy this was helpful!

See you in the next newsletter.

Elric

P.S. Two things.

• Building systems like this is what I do with clients right now. If you want help finding where your funnel leaks and fixing one stage with depth, reply and tell me what your motion looks like.

• And I’m building something new (the Pantry) to upgrade the paid newsletter: a way to build these systems without me in the room. The frameworks I unpack in breakdowns like this one, plus the workflows to run them inside your own company.

Resources:

• Kyle Norton's newsletter & Podcast The Revenue Leadership Podcast,

  • Don’t Buy Your GTM Brain

  • Thoughts on a centralized vs decentralized AI transformation

• Part 1: How Owner.com Scaled Outbound to $40M+ ARR

• How Owner.com Scaled From Founder-Led Sales to $30M ARR

• GTM Atlas: Start with the data

• SaaStr: How Owner.com’s CRO Is Closing $2M+ in ARR Per Rep With AI

• SaaStr: Inside the AI Agent Stacks at SaaStr, Owner.com & Klaviyo | SaaStr AI Annual Final Day

• Scaling Your GTM Playbooks Using Avatars with Rob Yuen & Matt Huang

I ran the same 1,400 US contacts through all 10 B2B mobile data providers. Then I checked every number twice. Once for whether it is a real mobile. Once for whether it belongs to your prospect. I paid $2,521, because no vendor will hand you these numbers.

I ran it because of how teams actually pick a provider. They go on opinion. The one a rep used last job, the one a founder saw at a conference, the answer to “what’s your favorite data provider?” in a Slack thread. Almost no one tests. So I did.

No single tool is enough. The real question is not which provider is best, but which cover the right people for who you call.

What I measured, the same way for all 10: coverage, valid-mobile rate, right-person match, predicted reachability, and cost per right-person number.

The short version

No provider is both wide and accurate, and 1 in 4 of the mobiles you get back is the wrong person. So you do not pick one, you stack two: lead with Wiza for reach, add the one provider that fits who you call, and validate the line and the name before you dial (about $0.10 a number). The rest of this page shows you which second provider fits your persona.

The 4 numbers that matter

• The widest and the most accurate are never the same provider. The widest returns a valid mobile for 1,211 of 1,400, or 87%; the most accurate is the right person 69% of the time and reaches far fewer people. The top-right corner of the chart is empty.

• One in four mobiles is the wrong person. Of every valid mobile, 60% are the right person, 25% belong to someone else, and 15% cannot be confirmed without a call.

• High coverage, low right-person coverage. You can find a number for 1,390 of 1,400 (99%), but a right-person mobile for only 957 (68%).

• Bad data costs a 10-rep team about $53k a year. 50 dials a day, 1 in 4 on the wrong person, at an $85k wage, before dead numbers.

That empty corner is the whole story. Finding a number is the easy part. Reaching the right person on a working mobile is the hard part, and it is the only part that books meetings.

What this benchmark is. A head-to-head test of 10 B2B mobile data providers that sell phone numbers for cold calling and outbound:

• Apollo

• ZoomInfo

• Lusha

• LeadIQ

• LeadMagic

• Upcell

• Wiza

• Forager

• People Data Labs

• RocketReach

The same 1,400 US contacts, run through every provider at the same point in time. Together they handed back 3,446 distinct phone numbers, an average of 2.46 per contact. The sample is balanced across:

• 7 leader personas: security, engineering and product, finance, HR, ops, sales, and marketing.

• Seniority levels: C-level, VP, and Head-of.

• 2 company sizes: mid-market (50 to 1,000 employees) and enterprise (1,000-plus).

• 100 contacts in each persona-by-size cell.

I put every number through the same two checks. First the line type: using Trestle, a service that labels each number a real mobile, a landline, or a VOIP line. Then the name on it, using IPQS, a fraud-and-identity tool that returns the name actually tied to a phone number, which I matched against the prospect. That is what makes these results measured rather than claimed. People Data Labs is shortened to PDL throughout.

Why me, and why you can trust the numbers. I help GTM leaders build and scale outbound teams, I do not sell a data product, and no provider here paid me. I paid for the test myself, about $2,521, because the vendors will not hand you these facts. They do not publish coverage, and the one thing they do claim, that the number is a mobile, is wrong a meaningful share of the time.

Who this is for. Outbound to US B2B contacts you can find on LinkedIn. If you sell into a market where your buyers are not on LinkedIn, like local businesses or restaurants, these providers cover far less of your list and this benchmark will not match what you see. Vertical and SMB data is a different problem and out of scope here.

What bad data actually costs you

You already validate emails before you send them, because spraying un-verified addresses wastes sends and burns your domain. Phone data is the same. Dial an un-validated list and about 1 in 4 of your calls lands on the wrong person, on top of the dead and non-mobile lines. That is the quiet SDR tax: reps spending their day on numbers you could have dropped before they ever lifted the phone.

The math, scoped to what an SDR actually does, because they are not on the phones all day. Say two hours of cold calling, around 50 dials. One in four lands on the wrong person, so about 12 of those dials, roughly half an hour of selling time, are wasted every day, per rep, before you count the dead numbers. Across a 10-rep team that is five hours of selling gone daily. In salary, at an $85k on-target wage, that is about $5k a year per rep at two hours of calling: roughly $16k for 3 reps, $53k for 10, $106k for 20. Dial more and it climbs, dial less and it shrinks. None of it shows up as a line item, it shows up as reps who are not booking enough. More reps will not fix it. The fix is to validate the line type and the name before you dial, about $0.10 a number, so reps only ever call numbers that are real and likely the right person.

And the wasted salary is the smaller half. The bigger cost is the pipeline you never build: every dial spent on a stranger is a conversation that did not happen with a real prospect. A quarter of your team’s calling capacity going to wrong numbers is a quarter less pipeline, and that is lost revenue, not just lost payroll.

Why coverage numbers lie

Every provider advertises coverage: how often it returns a number. That is the widest, weakest layer, and it is the only one they talk about. A real number gets four questions, not one, and each one is harder than the last. The rest of this benchmark walks down them in order.

• Coverage: did a number come back at all. Almost always yes, 99% of your list.

• Valid mobile: is it actually a mobile line, not a landline or VOIP your dialer treats differently.

• Right person: does that mobile belong to your prospect, or to a stranger.

• Reachable: will the right person even pick up.

Coverage rate is how much of your list a provider matches, and it is the easy layer everyone passes. The other three are where providers separate. Measure all four, or you are measuring the easy one.

Layer 1 · Did a number come back?

Wiza returns a number for the most people. Forager the fewest.

Coverage is the easy layer, but it is not flat. Wiza returns a phone number for 94% of a list; Forager for 38%. This is before any check on whether it is a mobile or the right person, just “did a number come back.”

Layer 2 · Is it a valid mobile?

Not every number labeled “mobile” is one.

The one claim providers make is that the number is a mobile. It is the only checkable claim, and it fails often. A share of what gets labeled “mobile” is a landline or a VOIP line that no dialer will reach the way you expect. RocketReach is the worst offender: one in four of the numbers it returns is not a valid mobile.

Strip out the landlines and VOIP and you are left with the coverage that actually matters: the share of your list you get a real, dialable mobile for. RocketReach shows the fall clearest, a number for 81% of your list but a real mobile for only 845 (60%). Wiza, cleaner, holds 1,211 (87%). This is the coverage the rest of the page runs on, the hero chart at the top and the scorecard both use it.

Layer 3 · Is it the right person?

One in four mobiles belongs to a stranger.

Once you have a working mobile, the question that decides everything: does it belong to your prospect, or to a stranger? This is the hard layer, the one no vendor reports, and the one that separates the providers.

The leaderboard flips here. Wiza is the widest on coverage but sits mid-pack on accuracy at 59%, and RocketReach is the reverse, narrow coverage but the highest right-person rate. No provider tops both, which is the whole finding. Pool every provider together and the aggregate tells the same story from the other side.

I checked the name on every valid mobile in the test (via IPQS). 60% were the right person. 25% belonged to someone else, a wasted dial and a polluted CRM. 15% I could not confirm without a live call. The best provider money can buy still hands you a stranger one time in four. And the honest limit, mine included: no data check confirms the number is still your prospect’s. People change jobs and numbers. Only the call confirms it, which is exactly why the right-person rate is the number to buy on.

Layer 4 · Will they pick up?

Pickup is the same for everyone.

The last layer, once you have the right person’s mobile: will they even pick up? This is the one you cannot buy your way out of. You cannot raise the pickup rate, because it is capped by buyer behavior, not data. TitanX, across hundreds of millions of dials, finds only about 20% of any B2B market ever answers a cold call, no matter how many times you try. And whichever provider’s number you dial, the predicted reachability is the same.

You can see it in SureConnect’s prediction. It forecasts a reachability tier for each number without ever dialing it, unlike TitanX, which scores from a team making real calls. Only about 3% are a predicted live answer (P1), about a third voicemail (P2), and over half predicted unreachable (P3). Both reachable tiers, the live answer and the voicemail, sit at the same rate for every provider. There is no provider whose numbers answer more.

So the pickup rate is not yours to improve. The only thing you can change is how many right-person numbers you put in front of your reps, and that swings a lot by provider. If only about 1 in 5 of any market will ever answer, your one lever is to spend those scarce pickups on the right people instead of strangers. That is the goal of this whole benchmark. Better data does not raise the answer rate. It decides who is on the line when someone does answer. The provider that hands you more right-person numbers hands you more conversations. Full stop.

One more trap, and it is the sharpest one: reachable is not the same as right. Of the numbers SureConnect predicts will answer or hit voicemail, 1 in 6 belongs to a different person. And on the ones it is most confident will answer live, it is worse: more than 1 in 4 is a stranger, the same wrong-number rate as the list overall. A reachability score tells you a line will pick up; it cannot tell you whose line it is. That is why the right-person check is its own layer, and why a cheap “will it connect” score can never stand in for it.

No provider is both wide and accurate

That is the chart you saw at the top, and now you have walked the layers behind it. Coverage against quality (of the mobiles a provider returns, how many are the right person), every provider plotted, and the corner you would actually want, wide and accurate at once, is empty. The widest provider is only mid on quality. The ones whose mobiles are cleanest reach far fewer people.

That is the structural reason there is no single best provider, and why the stack starts at two. Everything that follows is how you pick your two for who you call.

The scorecard: all 10, ranked by how many right people they deliver

One base shows up on top every time: Wiza returns the most right-person numbers, for every persona and both company sizes. That is a count of right people delivered off the full list, not a per-number hit-rate, and it is the number that drives conversations. So the table is sorted by it.

How to read it: Right people reached is the bottom line and the sort. It is already net of every kind of waste, so it is the only column that decides. The rest show why it is high or low. A returned number fails two ways, and both are useless to a rep: Not a mobile, a landline, VOIP, or dead line, and wrong person, a real mobile that belongs to a stranger. Mobile coverage is how many real mobiles you get. Right people per valid mobile is, of those, the share that are actually your prospect; the rest are the wrong person. Multiply the two and you land on Right people reached. Forager is the clean example: only 2% not a mobile, but 36% of its real mobiles are still a stranger, so it ends at 24%. Right person = an exact name match (IPQS) on a valid mobile. Cost = per right-person number, current pricing. n = 100 per persona-by-size cell, single run, May 2026. ZoomInfo is a borrowed-account estimate ($0.60/record, excludes its $15k/yr floor). Capability for Apollo and Lusha is from provider docs; the rest are confirmed from the test returns.

Reach and quality are different vendors.

Wiza reaches the most people, but of the mobiles it returns, only 59% are the right person. RocketReach is the cleanest at 69%, though it makes you filter the most non-mobiles to get there: about 1 in 4 of the numbers it returns is not even a mobile. PDL and Forager are right behind, at 66% and 64%, and cleaner. Those three reach far fewer people than Wiza. So the buy is base plus complement: Wiza for reach, then the provider that adds the most new right people on top of it. That second provider is RocketReach overall, though Apollo and ZoomInfo are within a few contacts of it. No single tool is enough, which is exactly the point: identity is the hard layer, no provider owns it, so you cover the right people by stacking the two that fit your market.

Explore it for who you call (Interactive dashboard)

The same data, filtered to your buyers. Pick a persona and size; every view recomputes.

Open the interactive dashboard here: https://outbound.kitchen/research/mobile-data-benchmark-2026

Best for what you care about most

The full board is above. If you just need the quick pick for your one priority, here it is.

• Cheapest usable numbers: LeadMagic ($0.11 per right-person number), self-serve and direct, is the cheapest by a wide margin. Upcell ($0.27) is next, and the cheap way into it is through Clay.

• Cleanest mobiles: of the mobiles a provider returns, RocketReach’s are the most likely to be the right person (69%), with PDL and Forager just behind (66% and 64%). PDL and Forager get there with few non-mobiles; RocketReach makes you filter more first. When a wrong dial is expensive, these hit the prospect most often per mobile dialed.

• Most coverage: Wiza reaches the most of your list (1,211 valid mobiles, 87%, the most right people delivered), which is why it is the base of almost every stack.

• A full record, not just a number: PDL and RocketReach return verified email, company, and job history alongside the mobile, so the credit does double duty.

And whatever you stack, run it as a waterfall ordered by cost: lead with the cheap, accurate ones and layer the wide one to mop up. Same reach, about 10% less.

Build your stack: find your row

No single second provider wins everywhere, so here is the whole decision in one table. Find who you sell to, take the two names, start there. That is the stack.

Lead with Wiza, then add the one partner for your row. Two providers get you to between 50% (HR, the hardest persona to reach) and 68% (marketing) of the right people. The third barely moves the line, so only add it when your dialer says you are running dry.

One cut I ran and left out on purpose: company size. I split the same data by mid-market (50 to 1,000) versus enterprise (1,000+), and the provider picture barely moves between them. The same names sit in the same corners. What actually changes the answer is the persona, who you call, not how big their company is. That is why this table is by persona and not by company size.

What a third provider actually adds

Stack the providers in order and each one adds less new reach than the last: Wiza gets you to 51% of the right people, a second provider takes you to 60%, the third adds three points, the fourth two, the fifth barely registers.

That does not mean cap your stack at two. If you run credit-based tools in a waterfall, you only pay for the numbers you actually use, so adding a third or fourth as a cheap fallback costs you almost nothing. It just changes why you add one: never for more coverage, since a third mostly hands you numbers you already had, but because that one is cheap and catches the few your first two missed. The decision is cost, not count. The only thing to avoid is paying for a second expensive contract to chase those last few points.

The reason is overlap. Some providers are largely reselling the same underlying numbers, so a second or third often returns mobiles you already have. Most pairs overlap 20 to 30 percent, and the most redundant, like Upcell and Lusha or LeadMagic and Upcell, overlap about half. Stack two that overlap heavily and the second barely widens your reach, which is exactly what the curve above shows.

Run it as a waterfall, and order it by cost

A data waterfall sequences your providers in priority order: you call provider A, and only on a miss call provider B, so you pay for the second provider only on the gaps the first one left. That is how you get two providers’ reach without paying twice for the overlap. Clay, n8n, and Cargo all build one.

The question is what order to run them in. There are a few ways to sequence a waterfall: by cost (cheapest provider first), by coverage (the provider that returns the most right people first), or greedily by best cost per new contact. I modeled all three, and cost-first wins. The cost and coverage orders both reach the same 68% ceiling, but cost-first gets there for about 10% less, because the widest provider’s coverage overlaps the cheap ones, so leading with it bills you for numbers a cheaper provider would have caught anyway.

So: quality decides which providers, cost decides their order. Lead with the cheap, accurate ones (LeadMagic, Apollo), then layer Wiza to catch what they missed. This order is for this list.

What a usable number really costs, and what a credit buys

Sticker price per number is the wrong metric. You pay for every number, but only the right-person ones are usable, so the real cost is cost per right-person number.

But even that is not apples to apples, because a credit does not buy the same thing everywhere.

LeadMagic at $0.11 returns only a phone number. People Data Labs and RocketReach cost more, but they also return verified email, company, and job history alongside the number. Price the number against what else comes with it.

How you actually buy each one (and the catch)

The number is only half the cost. The other half is how you are allowed to buy it. Most of these are cheap and self-serve. Two force an expensive annual contract.

This is the hidden lever. ZoomInfo is the one you cannot just swipe a card for. Upcell is reachable for a fraction of its direct minimum if you pull it through Clay’s credits instead of signing direct. ZoomInfo has no self-serve at all, which is why I had to borrow an account to include it. Everything else you can start on this month for the price of a lunch.

What it cost me, and the Clay tax

I paid about $2,521 to run this. The same run today would cost roughly $4,990, nearly double, for one reason: in 2026 Clay raised its credit price 2.65x, from $0.016 to $0.0425 a credit. The five I ran through Clay got 11% to 166% more expensive overnight. The four I bought direct sit outside Clay, so that hike does not touch them. Even the scoring step, SureConnect, jumped from $880 to $2,337 from the same increase.

The takeaway: where a vendor sells direct, buying outside Clay stops your cost from depending on Clay’s rate, that is Apollo, Lusha, LeadIQ, and LeadMagic. The exception worth knowing is Upcell: it re-priced its credits to match Clay’s new rate, so the cheap way in is through Clay first (about $0.15 a number), and you only graduate to its roughly $2k direct deal once it has earned a place on your list. Among the Clay-routed five, only RocketReach also softened the hike; Wiza, PDL, and Forager pass Clay’s full increase straight through.

What I actually took away from this

The honest headline: no single tool is enough. There are winners by category. One provider leads on coverage, another on accuracy, another on cost. None leads on all three, and the gaps between them are smaller than the marketing suggests. Every provider has a strength and a soft spot, so the win is picking the two that fit who you call, not hunting for the one that does everything.

The bigger lever is what you do with the data after you buy it. No vendor can tell you whether a number is the right person on your specific list, that is the gap this whole benchmark is about, so your own dial results are the only ground truth there is. A team that validates numbers before dialing, never calls a dead one twice, and feeds those results back into which providers it keeps will beat a team sitting on “better” data with no loop, every time. The provider choice gets you in range. The feedback loop is the actual edge.

I built it with a team that books most of its meetings on the phone. We stacked a couple of mobile providers, validated each line was a real mobile before anyone dialed, scored reachability with the tools made for it (we tried TitanX and SureConnect), and fed every call result back into which provider earned the next list. That loop is the part that compounded.

The recommendation, in one place

If you read nothing else, this is what to do.

1. How many providers depends on how you pay. For reach, two get you most of the way and a third barely adds, so you do not need five for coverage. But if you buy credit-based tools and run them in a waterfall, you only pay for the numbers you actually use, so stacking several as cheap fallbacks costs almost nothing extra. The trap is contracts: do not sign multiple annual deals like ZoomInfo or Cognism. A clean setup is one contract tool as your base if you already have one, plus credit-based providers layered in a cost-ordered waterfall. For any provider that still locks you into an annual contract, run the experiment on your own list first with the month-to-month and credit providers, and sign the year only once the coverage proves out for your market.

2. Run them as a cost-ordered waterfall. Lead with the cheap, accurate ones and layer the wide one to mop up. Same reach, about 10% less spend.

3. Buy direct where you can. Apollo, Lusha, LeadIQ, and LeadMagic sell their own credits, insulated from Clay’s hikes. Through Clay you can pay up to 2.65x more for the same number.

4. Track the source. Stamp every number in your CRM or sequencer with the provider and date, or you will never know which one is earning its spend.

5. Validate before you dial, then score. Run each number through a line-type and name check (IPQS, Trestle) to confirm it is a live mobile that belongs to the prospect, about $0.10 each. That gets you most of the way on identity; the live call is the final confirmation. Then score for reachability (SureConnect, TitanX) so reps dial the best numbers first.

6. Close the loop. Pull dispositions back from the phone system, was it the right person, someone else, did they pick up, and feed that into which providers you keep.

The stack gets you in the game. The feedback loop is how you win it. The benchmark gives you the starting lineup; your dispositions tell you who to keep.

If you are already in one of these situations

• You already pay for ZoomInfo. Keep it for what it is good at, the full record: email, firmographics, org charts. Do not lead your dialer with its mobiles. They are mid on quality (53% of their mobiles are the right person) and the most expensive per usable number ($1.27). Use it as the enrichment base, layer cheaper, higher-match-rate mobile providers on top, and put ZoomInfo last in the waterfall.

• You run on Clay. Most of the credit-based providers are one toggle away: Wiza, Upcell, RocketReach, PDL, Forager. Order your waterfall cheap-and-accurate first, then Wiza for coverage. LeadMagic and Apollo are cheaper bought direct, so pull those in with your own keys. Upcell re-priced its credits to match Clay’s new rate, so start it through Clay before its direct deal.

• You are starting from zero. Begin with Wiza plus the one partner for who you sell to (the lookup above), bought whichever way is cheaper for that vendor. Add a third only when your dialer says you are running dry.

What I did not test, and why

• Waterfall aggregators (FullEnrich, BetterContact). Left out on purpose. They stack the same underlying providers I tested, so of course their coverage looks higher. It is not like-for-like, it is someone else’s stack with a markup. The waterfall section above is how you build the same thing and keep the margin.

• One-year-contract providers (Cognism, SalesIntel, Seamless.AI). I could not get access without signing an annual contract, so I left them out rather than guess. ZoomInfo is in only because a friend ran my list through his account.

• Live calling. I did not run a calling census, and even one has a ceiling: a dial only tells you the truth about the people who actually answer, and most will not. The pickup numbers here are predicted, not confirmed by an actual call. A future round with a human caller could measure real pickup, but it would still only confirm the share who pick up, not the rest.

Behind the numbers

The methodology

Elric Legloire, Outbound Kitchen. I help GTM leaders build and scale outbound teams and write a newsletter on it. I do not sell a data product, no provider here pays me, and none sponsored or reviewed this. Self-funded, about $2,521 out of pocket.

The sample. 1,400 US B2B contacts scraped fresh from LinkedIn in May 2026. 7 leader personas (security, engineering/product, finance, HR, ops, sales, marketing) at C-level, VP, and Head-of. 2 company sizes (mid-market 50 to 1,000, enterprise 1,000+), 100 contacts in each persona-by-size cell. Input to each provider: first name, last name, LinkedIn URL, and company name for some.

The depth. Those 1,400 people came back as 3,446 distinct phone numbers, 2.46 per contact across the 10 providers. I did not score one number per person and move on. Every number got checked on its own for line type and for the name on the line, which is what makes the right-person rates here measured rather than claimed.

How I sourced each provider. Phone numbers through my own Clay credits for five (Upcell, Wiza, RocketReach, Forager, People Data Labs). Direct via their own API for three I pay for (Lusha, Apollo, LeadIQ). My own account API for LeadMagic. ZoomInfo through Jed Mahrle’s account, since it is annual-only and I could not buy it on its own. Thanks Jed. If you want the sharpest outbound newsletter going, read his: Practical Prospecting.

The checks.

• Coverage: a number came back.

• Valid mobile: Trestle classified the line type, mobile versus VOIP versus landline. That is the only thing I used Trestle for.

• Right person: IPQS is a fraud-and-identity tool that returns the name tied to a phone line. I took that name and matched it to the prospect, counting only exact matches as right-person (nicknames and truncated names that I could not confirm exactly are counted as not-confirmed, which is the strict, conservative call).

• Predicted reachable: SureConnect predicts whether each number will connect, without dialing it. It is a prediction, not an actual pickup, and I report it separately, never folded into the funnel.

Right-person definition. Right person is an exact IPQS name match on a valid mobile, IPQS only, no second validator promoting partial matches. It is the strict definition, which is why these right-person rates read lower than a vendor’s own marketing number. The published dataset uses the same definition, so if you recompute it, you land on these numbers.

Limits. Single run, one snapshot in time. US, LinkedIn-sourced contacts only. The per-persona and per-size cuts are 100 to 200 contacts each, so treat those as direction, not gospel. The numbers move over time, so treat this as a snapshot.

Run it yourself

Filter the dashboard to your personas and company sizes, and every number recomputes for your exact segment. Sort all ten providers by coverage, by right-person accuracy, or by cost per right-person number. Put any two head to head. Read the best two-tool stack for who you actually call. Then download the full 1,400-row dataset and run the whole test against your own list.

Open the 2026 US B2B Mobile Data Benchmark:

Open the Benchmark

Hope this benchmark was useful!

See you in the next newsletter.

Elric

[Live Webinar] Why account scores tell you too late

Account-level ABM waits for the score to spike. By then the buying group has decided and the wrong messages have already landed. Forrester’s Kelvin Gee joins Influ2 CEO Dmitri Lisitski to break down what orchestrating around individual buyers looks like instead. June 3, 12 PM ET.

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In the past 2 weeks, I saw 4 GTM leaders (CRO and SDR leaders) asking about how to increase show rate. And I saw this on LinkedIn:

If you are using Calendly or Chili Piper and sending automated reminders, that's exactly why your show rate is sitting at 30 to 50 percent. Just remove the automation, run the handoff manually, and the show rate climbs.

Which I disagree with the framing that automation is the problem, I worked at Chili Piper. So I've seen our internal data and with customers.

So if you have 30-50% show rate, it’s a meeting quality problem.

The meeting was bad before it got booked.

P.S. For paid members, the full meeting-quality diagnosis scorecard with if-yes / if-no branches is at the end of the newsletter, including what to fix for your outbound team.

Show rate is a defect rate

Think of show rate the way a kitchen thinks about a plate sent back from the table.

When a dish comes back, you do not fix it by training the server to apologize faster. You walk back to the line. You find the station where the dish was made wrong. You fix the process there. The complaint at the table is just where you noticed the defect. The lever is upstream.

The meeting got booked. That part worked. But what got booked? A prospect who said yes to escape a cold call. An invite titled “Intro call” with no agenda and no reason to attend.

Reminders confirm a meeting the prospect already wants to attend. They do not create the reason to be on the call.

Show rate is the receipt at the end of a meeting-building process. Fix the process, not the receipt.

Bad meetings are created before the calendar invite is sent.

Once you accept that show rate is downstream, the question changes. The right question is: where in our process are we building meetings prospects don’t want to attend?

So if an outbound team has a 30–50% show rate, I’d first look at the sales process:

Is the account in ICP?

Show rate is the first place a bad account shows up, but it is not the last. The same account that no-shows the first meeting also tanks opportunity rate, deal size, and close rate downstream. If your SDRs are booking outside ICP to hit activity quota, you are building defects on purpose. Audit the booked accounts against your ICP definition before you audit anything else.

What is the channel used for booking the meeting?

Meetings booked via cold calling versus email or LinkedIn have a different show rate. Don’t expect the same show rate.

Did the prospect name a problem?

Curiosity is what got the prospect on the call. That is outbound’s job. But curiosity is not enough to make someone show up next week. A stated problem is. If the SDR cannot repeat one clear sentence the prospect said about their pain, the prospect was only interested. They were not committed. That meeting is weak.

How far out is the meeting booked?

The farther the meeting is, the weaker it gets. Same week is strong. 14 days out is risky. After 14 days, you are betting that the prospect will still care about the same problem. They probably won’t.

Did the prospect accept the invite live, on the call?

A live accept is the cheapest, strongest commitment device you have. If your reps are not asking for it, you are leaving show rate on the table.

Does the invite explain why you are meeting?

“Intro call” is not a reason. The prospect’s stated problem is a reason.

The data points upstream, not at the reminder

At Chili Piper, where I worked, we ran 80 to 90 percent show rate on outbound meetings, around 200 meetings per month. We had automated reminders, sure. We also had the meeting-building process I just described. The reminders were a layer. The meeting-building was the foundation.

Gong data, cited in Cold Calling Sucks, breaks it down by rep tier.

• The average rep runs a 56.9 percent show rate on cold-call-set meetings.

• The top quartile runs 72.5 percent. 1.3x the show rate.

Tito Bohrt analyzed 6,414 meetings and found the same directional pattern on calendar distance. Same-week beats next-week beats two-weeks-out. The meeting was a stronger commitment at the time of booking.

• Booking meetings 0-1 days out results in the highest show rate, peaking at 80.79% for day 1.

• The show rate stays relatively strong for the first few days, around 75%, but starts to dip by day 5 and steadily declines to around 60% by day 14.

As always, compare apples to apples. If you sell to people on the ground, like restaurants, don’t expect to get 90 percent of show rate. Your show rate will look different from selling to office buyers who spend all day in their calendars.

So comparing show rates without segmenting by industry, persona, and channels can be misleading.

Show rate shows the result. Meeting quality is the lever.

Show rate is the metric you might check every Monday. Track it. If you want it higher, fix the meeting before you fix the reminders.

Outbound creates demand. We're here to give the best experience for prospects by creating curiosity on how we can help them fix their problems and change. If prospects won’t show up to the conversation, it was a politeness response on a cold call.

The fix is on the line.

That's it for today.

Hope this was helpful.

Elric

What’s coming

US Mobile data benchmark for 2026: Finally, wrapping this up, the benchmark will be sent this week.

For the paid newsletter

I’m still building The Pantry. It’s the next version of the paid newsletter.

I’ve been spending on how to design the next phase, don’t want to be just another “hub” just for content + templates like the paid newsletter. And working on the MVP.

Here’s what the membership will give you:

• Self-assessments and trainings that work like a kitchen audit. You walk your line, find the station slowing the whole pass down, and fix it this week.

• AI workflows, prompts, and skills you can run today. Example: scraping attendee lists from tradeshows and conferences.

• The methodology, built in the open. You’re on the journey with me as I turn what I do with customers into SOPs and frameworks.

• The system runs on any CRM, any SEP, any market. With AI you depend on tools less anyway. You build your own layer on top.

• Trained on real reps. What I run with customers, what I test, what top teams do. It gets sharper every month.

New: Pricing when Pantry launches: $79/month or $499/year.

To everyone who supported the paid newsletter until now: thank you. You made this possible. That’s why your current rate stays, a small way to say thanks.

• Already a paid subscriber? Nothing changes as long as you stay subscribed. (If you cancel and come back later, the new rate applies.)

• On the fence? Subscribe before The Pantry goes live to lock in the current rate.

Subscribe now

For Paid Members

Scorecard: The meeting-quality diagnosis

The full meeting-quality diagnosis scorecard with if-yes / if-no branches. Including what to fix.

Layer 1: Scope the problem before you fix anything

Read more

Free: The ABM That Starts With People Report

Most ABM targets accounts. But accounts don’t buy, people do. This report breaks down how to build ABM around the decision-makers who actually influence the deal.

Get the free report

An SDR leader I chatted with in Q1 had a problem:

He’d hired four BDRs the same week. Five weeks in, three had booked meetings. The fourth had booked zero.

Scaling SaaS org. Low patience. ROI watched closely. He wanted to let the rep go.

If you’ve led an outbound team, you’ve been there. The number is short. One rep is dragging it. Leadership wants to know what you’re doing. Firing the rep is the obvious answer. It also tells you the bar is high.

Before you pull the trigger, look at the data.

In 2025, 78% of B2B sellers missed quota (Ebsta). Right now, 44% of SDRs and 57% of AEs are missing their number (RepVue, May 2026, role-level).

Two different lenses, same direction.

Here is what most leaders do when a rep misses quota.

They look at the number. They check how long the rep has been ramped. They compare to the rest of the team. The rep is behind. The ramp window is closed. The rest of the team is on track. Decision made. PIP, then exit.

Shaunt Voskanian, CRO at Figma, said it on 20VC:

His point: quota is a lagging indicator. It’s downstream of territory, ICP, messaging, enablement, comp design, and a dozen other things you control. Pinning the miss on the rep is the cheapest read of the data you have.

It also protects the leader more than the team. Firing the rep closes the loop. Nobody asks if the territory was carved fairly. Nobody audits the ramp. Nobody pulls the call recordings to see if the booking reps are running a different play.

HBR has a name for this: the Set-Up-to-Fail Syndrome. Manzoni and Barsoux’s research found managers default to blaming the employee even when their own behavior, attention, and resourcing are the cause.

So diagnose first. Five checks, in order. Three on the rep. Two on you.

Here’s how it runs:

The Five-Check Diagnostic

Run them in order. Don’t skip ahead. Each check rules out a cause before you spend time and money on the next one.

Check 1: Effort

Pull the activity:

• Calls dialed

• Emails sent, LinkedIn touches

• Accounts worked.

• Prep for discovery calls.

Compare to the reps who are booking.

If the volume isn’t there, you have a motivation problem. Talk to the rep. Find out what’s going on. Sometimes it’s personal. Sometimes the rep checked out two months ago and you missed it. Either way, this is the cleanest call on the list. If effort doesn’t come back in two weeks, exit.

If the volume is there but results aren’t, move to Check 2.

Check 2: Knowledge

Read five cold emails. Listen to a discovery recording.

• Does the rep know the product?

• Can they articulate the problem you solve in the buyer’s words?

• Do they know the top three objections and have a response for each?

• Can they map a use case to a persona without thinking about it?

If no, you have a knowledge gap. That’s a 2-week fix. Onboarding refresh, shadowing top reps, certification on objection handling. If knowledge is there, move to Check 3.

Check 3: Skills

Knowledge is what the rep knows. Skills are what they execute under pressure. Pitching cleanly with a script in front of them is not the same as handling a buyer who interrupts in the first 15 seconds.

Listen for: opener delivery, qualifying questions, objection handling, control of the call, next-step ask. Compare to your top performer’s calls on similar accounts.

If the rep knows what to do but can’t execute it live, you have a skill gap. That’s a 4-to-8-week fix with deliberate practice. Role-plays, call reviews, live coaching on skill reps, not deal reviews.

If the rep is executing the skills, move to Check 4. This is where most diagnoses stop. Don’t stop here.

Check 4: Coaching transfer

This is the first check on you.

Pull your coaching notes from the last six weeks. What did you tell the rep to change? Did it show up in the next call? The one after?

Training transfer research is brutal on this. Across 89 studies, what reps actually apply from coaching depends on three things: the rep’s cognitive ability and conscientiousness, the rep’s own motivation, and the support they get from you and their peers after the session. If two of those three are weak, coaching doesn’t stick.

Two failure modes here. One: you’re giving feedback but not following up, so the rep hears five contradicting notes and applies none of them. Two: you’re “coaching” but it’s deal review dressed up. Frank Cespedes’s HBR data says most managers can’t tell the difference.

If the rep is coachable and you’ve been running deal review, the fix is on your side, not theirs.

Check 5: System

This is the second check on you, and the one most leaders skip.

If the rep is putting in the effort, has the knowledge, has the skills, and is applying coaching, and the number still isn’t there, the system is broken. Walk it backwards:

• Territory. Did this rep get the same account quality as the others? Run the ICP fit score on each book. If one rep is working a B-list, that’s not a rep problem.

• Data. Is the contact data on this rep’s accounts as good as the others? Bad data quietly tanks conversion before the rep ever opens their mouth.

• ICP and messaging. Sometimes one rep was handed the messaging deck that worked six months ago and the others figured out the new one in the Slack channel.

• Quota design. Aggressive quota set in a market-push environment, no safety net. Shaunt’s frame: that’s a quota design miss, not a rep miss.

• Enablement and process. Are the booking reps using an asset, a list source, or a workflow this rep doesn’t know exists?

The system check takes a week of your time. Most leaders skip it because it implicates them. That’s why you run it.

If the system is broken, firing the rep doesn’t fix it. The next hire walks into the same trap.

The Decision

After the five checks, you’re in one of three places.

One. Effort is missing, or coaching isn’t sticking and the rep isn’t coachable. Exit. Fast.

Two. Knowledge or skill gap, rep is coachable, effort is there. Give it 30 to 60 days of focused work on the specific gap. Not generic coaching. The gap you diagnosed in Check 2 or 3. If the leading indicators move, keep going. If they don’t, exit.

Three. The rep is doing the work and the system is broken. Fix the system. Keep the rep. This is the call most leaders won’t make because it means going to their boss and saying “the miss isn’t the rep, it’s how we built the territory, the data, or the play.” That conversation is harder than firing someone, and it’s the one that compounds.

Fix the system once, the next four hires ramp faster.

Back to the SDR leader from the intro.

He ran the checks. Effort was there. Knowledge was there. Skills were there. Coaching was landing. The rep was working the same play as the others.

So he gave it more time instead of pulling the plug.

By Feb 27, the rep hit two of three meeting targets. One of those meetings turned into €30K of pipeline. By the end of the quarter, the rep had generated €120K of pipeline across four SQLs. That was more than the rest of the 11-rep BDR team combined.

If he’d fired in week five on instinct under leadership pressure, that pipeline doesn’t exist. The leader doesn’t look like he hired well. The team loses a quarter of its number. The next hire walks into the same broken-ramp setup and the cycle runs again.

One last thing.

If you only remember one line from this:

quota miss is a symptom, not a diagnosis.

Firing feels like the cheap, decisive move. It isn’t. You eat ramp time, severance, recruiter fees, lost pipeline, and the team’s read on whether you have their back. Hiring well is expensive whether the next rep works out or not.

The five checks cost a week of your time. They tell you which reps to keep, which to coach, and which part of the system to fix before the next hire walks into it.

One more loop to close. If the rep does exit, run a hiring post-mortem.

• Did they have the right soft skills for the role and the stage of the company?

• Did your interview process actually surface those skills, or did you find out three months in?

• Feed every answer back into the next scorecard, the next reference call, the next interview.

That’s how you stop running the same diagnosis on the next rep.

Run the checks before the next exit conversation. If you’ve already had it this quarter, run them on the next rep before the pattern repeats.

See you in the next newsletter.

Elric

🔒 For Paid Members: Team Outbound Planner

The 5-check diagnostic above runs on one rep. The Team Outbound Planner runs the same logic on the whole team and tells you the two levers to pull this quarter.

It’s the same Root Cause Analysis I use with clients, packaged as a Google Sheet. Fill in your numbers, see where the team is leaking, get the two highest-ROI fixes.

P.S. The Pantry (the new product I’m building) will include an AI version of this. Same diagnostic, runs on your own data, no spreadsheet. More on that soon.

Grab the Google sheet here:

Read more

Free: The ABM That Starts With People Report

Most ABM targets accounts. But accounts don’t buy, people do. This Influ2 report breaks down how to build ABM around the decision-makers who actually influence the deal.

Get the free report

Most teams research accounts without a system.

They open a list, tab through LinkedIn and the careers page, copy a few facts into a doc, then start typing the email. Twenty reps means twenty different processes, and nothing a manager can coach against.

I run the same two steps on every account, with one framework underneath. Here’s how it works.

5 mistakes I see with account research

Mistake #1: no research step at all.

Most reps go straight from a list to a sequence. Skip Miller calls this out in Outbounding (my full breakdown here): research is part of the process, not optional. You don’t skip it any more than you skip writing the email.

Mistake #2: only researching the contact.

“Find 3 things to personalize.” Most contacts aren’t active online. Even when they are, none of it connects to why their company might need your solution.

Company data is always there. News, hiring patterns, funding, tech stack, leadership changes, team structure.

Research the company first. People second.

Mistake #3: no framework, no documented process.

If you’re running 20 SDRs, each one researches differently right now. Nothing to coach against. Nothing to QA. No standard for onboarding new reps.

You can’t improve what you can’t see.

Mistake #4: doing all of this manually in 2026.

AI pulls funding rounds, leadership changes, hiring patterns, and press coverage in seconds. If your reps are still tabbing through LinkedIn, Crunchbase, and the careers page by hand, you’re paying SDR salaries for work an AI does in 1-2 min.

Mistake #5: running prompts in chat instead of building a workflow.

One-off ChatGPT prompts don’t scale. Every rep writes their own version, outputs land in random docs, nothing is reusable. A workflow runs the same prompt the same way every time, lands the output in the same place, and lets the next rep inherit the system instead of rebuilding it.

These mistakes stack up when you scale. One rep wastes their own time. Twenty reps waste pipeline. And you can’t coach against twenty-five different research methods, so quality stays uneven and ramp takes longer than it should.

The Account Research System

The Account Research System sits between two things you’ve already done: defined your ICP and selected your target accounts. It comes before “what to say.”

Two steps:

• Step 1: Research. Gather the facts.

• Step 2: Reason. Turn facts into hypotheses.

One framework underneath both: SPICED.

One output that feeds every channel: cold email, cold calls, LinkedIn, and the discovery call if they take the meeting.

Why SPICED?

I picked SPICED (by Winning by Design) because it’s buyer-centric. Situation, Pain, Impact, Critical Event, Decision. Every letter is about the prospect’s business, not the rep’s process.

Compare that to BANT: Budget, Authority, Need, Timeline. BANT is rep-centric. It’s the rep’s qualification checklist. You can’t pre-research any of it. Budget and timeline only come up once you’re already talking. BANT works for inbound calls where the prospect raised their hand. It doesn’t work for outbound, where you have to earn the meeting first.

SPICED is different. Most of it can be reasoned from what’s already public.

What’s online: funding rounds, leadership changes, hiring patterns, tech stack, GTM motion, press, customer logos. That’s enough to build the Situation and infer the Critical Event.

What’s not online: which pain is highest priority right now, what it’s actually costing them, who internally owns the decision. That’s the gap. You close it by stacking the public facts into a hypothesis, then testing the hypothesis on the call.

That’s the whole reason this works before the meeting is booked. SPICED forces you to reason about the buyer’s business, and most of the inputs are already sitting in public sources.

That public/not-public divide gives you a natural two-step split:

• Step 1 = S (Situation). Pure facts.

• Step 2 = P-I-CE-D. Reasoning on top of those facts.

Step 1 is what AI does well. Step 2 is what the framework forces AI to do that it wouldn’t do on its own.

Step 1. Research: the S in SPICED

To make it simple, let's focus on a net-new account. No prior conversations. No CRM notes. Just what you can find online.

Pure fact-gathering. The job here is data. Save the interpretation for Step 2.

• Employee count and growth trajectory

• Tech stack: CRM, sales tools, marketing automation

• Funding stage and recent rounds

• Leadership changes: new CRO, CMO, VP Sales

• Hiring patterns: 5 AE openings, 0 SDR postings

• GTM motion: PLG, sales-led, or channel

• Customer base and market positioning

• Account mapping: who’s on the team, their backgrounds, what tools they’ve used before. If there’s a CRO and 2 reps, the CRO is your go-to person. (First part of D, Decision in SPICED.)

One caveat. Not all of this data lives in the same place.

Some is easy to find with AI: funding rounds, job postings, leadership changes, press coverage. An AI model pulls that in seconds.

Other data isn’t available via web search. Headcount growth over the past 12 months. Sales team size compared to 24 months ago. For that, you need enrichment tools: Clay for headcount and team changes over time, BuiltWith for tech stack.

This is the input for every step after this, including who to contact.

Example: Replit researched through TrackRec’s lens

If you’ve been reading the past few newsletters, you’ve seen me return to TrackRec → Replit. TrackRec, a company that helps organizations hire sales talent.

Without a system, a rep finds: Replit raised $400M, they’re hiring 12 roles. They send:

“Congrats on the raise! We help companies hire sales talent. Open to a 15-min call?”

Congrats, then pitch. No connection between the two. The rep found the facts but never connected them.

Here’s what the Account Research System produced instead.

Target account: Replit. Researched through the lens of TrackRec, a company that helps organizations hire sales talent.

Perplexity output for this step:

P.S. Based on my recent benchmark, for this step I recommend Perplexity or GPT-5.2 Thinking (as of April/May 2026).

Step 2. Reason: the P-I-CE-D in SPICED

Step 1 gave you the Situation. The facts.

Step 2 is where you reason through them using the rest of SPICED: Pain, Impact, Critical Event, Decision.

A pile of facts without a framework leaves you guessing. SPICED organizes those facts into hypotheses about why this company might need you.

This is the move I call layering flavors.

Step 1 gives you the raw ingredients. Step 2 stacks them so each one changes the meaning of the next. A funding round alone is just a fact. A funding round plus 12 open roles plus a new CRO plus no recruiting leader is a different dish entirely.

Now they’re hypotheses.

Pain.

Look for facts that imply a constraint: roles that stay open, leaders who scale through methods that don’t scale at the next stage, capacity missing where growth requires it. The pain isn’t in any single fact. It’s in how the facts together describe the bottleneck.

Impact.

The framework asks: what does this constraint cost them, and on what clock? Revenue at risk per month. Hiring milestones missed against a board commitment. Time burned during a leader’s first 90 days. Impact is always a number times a time horizon.

Example for Replit:

Critical Event (identified from Situation).

Leadership changes are critical events. Funding rounds are critical events. Multiple openings going up at once is a critical event. These create the time pressure that makes “later” stop being an option for the buyer.

Example for Replit:

Decision (hypothesized from the org map).

You already mapped the account in Step 1. Now you reason through it.

Who’s the economic buyer? Who would feel the pain most? Look at a leader’s first hires. Anyone they brought in from a company that uses your product, or a competitor’s, is a champion hypothesis.

Company size gives you process clues. 50 employees means the CEO is in every deal. 500 means department heads. 2,000 means procurement.

Example for Replit:

Blockers:

Two steps. Four connected hypotheses on top of the Situation.

Outreach Angles

From this research, three angles surface: the hiring-philosophy angle (passion vs. motion at 200 hires), the recruiting-capability angle (the gap, without attacking the team they’ve built), and the geography + ramp angle (SLC SDRs and NYC enterprise AEs running simultaneously). Each one opens a different conversation. None of them lead with “congrats on the raise.”

You can use this info on the phone, in a cold email, or on LinkedIn. And for your discovery calls when you book the first meeting.

That’s the Account Research System. If you want to start using it, try with 10 accounts to control the outputs, then scale with Clay, n8n, or any other orchestration tool.

Hope this is helpful.

Talk soon,

Elric

P.S. If you need help setting this up in your outbound systems, let me know.

For Paid Members - Your Account Research System

Paid members get the complete system: 3 prompts that personalizes this entire framework to YOUR company.

You can use this inside your orchestration app (Clay, Claude Code, etc).

The Prompts:

Prompt 1: Company Context Builder. Give it your website, ICP, conversations with customers, or G2 page. It builds your company context: ICP, value props, how buyers solve this today, critical events that create urgency. This is the lens for everything else.

Prompt 2: Account Research Generator. Paste your company context. It generates a customized research prompt with your competitors, buyer titles, and signals hardcoded in. Save it. For every target account, just add the company name and run.

Prompt 3: Reasoning Generator. Paste your company context. It generates a customized hypothesis prompt with your pain points, proof points, and outreach angles baked in. Save it. For every target account, just paste the research and run.

Prompt 1: Company Context Builder

Copy everything between the lines below.

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This episode was originally recorded in French for Cognism’s Prospect podcast with Laetitia Fall. The English audio you’re hearing is an AI-translated dub generated with ElevenLabs. Original French version.

Listen on YouTube, Spotify, Apple Podcasts, and Snipd.

We discuss:

• Why outbound isn’t dead, but pipeline got roughly 10x more expensive (1% conversion in 2015 to needing 1,000 emails per opportunity in 2023, source: Winning By Design)

• Why global SDR headcount grew over 20% in a year while public layoffs dominated the LinkedIn feed

• The Owner.com benchmark: 40 appointments per BDR per month, 25% close rate, $70-80K sourced revenue per BDR per month

• When to hire experienced SDRs vs juniors, and the 12-meetings-per-month threshold that signals your playbook is ready

• Why ICP work beats tool selection (Tier 1 closes at 25%, Tier 2 at 5%, you need 5x the volume to compensate)

• Snowflake’s 140 ICP data points and why 10-15 is the realistic target for most teams

• Multichannel orchestration when only 30% of your prospects are reachable on LinkedIn

• The data-quality test most teams skip: coverage rate per persona, per market

• AI in outbound (January 2026): what actually works, why most LinkedIn AI messages fail, and the foundation AI needs (market, CRM, message principles, signals)

Referenced:

• Cognism’s original French episode

• Host: Laetitia Fall

  • LinkedIn

  • YouTube

• Sources cited in episode: Winning By Design, Insight Partners (Jeremy Donovan’s portfolio data), Owner.com BDR benchmarks, Snowflake ICP methodology

My biggest takeaways from this conversation

The State of Outbound in 2026

Read more

[Live Webinar] Why GTM Orchestration Breaks Without Contact-Level Execution

Every contact in a buying committee takes a different path. Most ABM nurtures them identically. Devin Reed (ex-Gong) joins Influ2 to break down where orchestration fails and how contact-level execution fixes it. May 6, 10 AM CST.

Save your spot

This newsletter is also available in video and audio version.

You can listen on YouTube, Spotify, Apple Podcasts, and Snipd.

A few weeks ago I saw a post from the CRO of Vanta.

Someone sent her a Quake rocket launcher replica, a reference to her time as a professional Quake player. She posted about it. She took the meeting.

Then a similar post: the CMO at Incident.io got a signed vinyl of their favorite album of all time. Same reaction. Same outcome.

Both worked for the same reason. The research is the system. The gift is the output.

I went down a rabbit hole figuring out how to turn this into a repeatable system for a GTM team running ABM on tier-1 accounts.

Here’s the math. If your top accounts are worth $200k–$1M in ARR, a $300 gift is cheaper than the cold email sequence you’d run instead. You’re not adding to the budget. You’re moving acquisition spend from one channel to a better one.

Cold outreach is harder every quarter (not dead, harder). Generic gifting is the same problem in a new channel, a branded notebook is just another ignored touch. One of my SDR team already sent bottles of champagne or tequila to celebrate promotions and new jobs. Better than nothing. Not personal enough to break through on your top accounts.

The whole system scales with Clay and Claude Code (or any similar tool) once you’ve tested it by hand on 10–20 accounts. Then run it on your top 200 with your GTM team.

Prompts at the bottom.

The 2-step play

Step 1: Find the clue + score the prospect. A specific personal detail the prospect has publicly declared about themselves.

Each prospect lands in one of four buckets:

• Identity: they declared a past identity (”ex-pro Quake player,” “former touring drummer”)

• Passion: they declared a specific, narrow obsession (”favorite album of all time,” “seen Radiohead 7 times”)

• Milestone: no clue, but a recent trigger in the last 90 days (funding, promotion, book launch)

• No gift: nothing strong. Revisit in 90 days. Expect this 70% of the time.

Step 2: Turn the clue into a gift + note. Specific to the clue, not the broader category.

If Step 1 fails, Step 2 doesn’t happen. Most of your prospects won’t have a strong enough clue. That’s the filter working. And realistically, not all prospects will share specific clues that you can use

P.S. I’m not talking about personal stuff from Facebook or Instagram. I mean public business stuff.

Step 1 - The clue

Not every personal detail qualifies. There’s a specificity ladder.

Only Passion and Signature clues qualify.

The rule: focus on the statement, not the source. A Signature clue in a LinkedIn About section is as valid as one on a 2-hour podcast. What matters is specificity and that the prospect said it themselves.

Look for Signature language in their own words: “favorite of all time,” “changed my life,” “in a past life I was...,” “I grew up doing X.” Most common in podcasts, long-form posts, and keynote intros.

Where to look:

• LinkedIn (About section, posts, articles)

• Podcast appearances and interviews

• Conference talks, YouTube keynotes

• Their own blog, Substack, Medium

• Published articles, book forewords

• Company page “About” and “Founding story”

Here’s the output when I ran this on Kyle Norton (CRO of Owner.com):

Interesting thing:

When I was testing the prompts, Perplexity was the one returning the best output. Just to let you know, last time I tested, Perplexity and ChatGPT were both equally finding stuff. This time, ChatGPT failed, so just for FYI. This a good reminder to always keep testing the AI models + benchmarking them.

The decision

Run the research on your prospects of your top accounts. For each one, the output is a tier.

Step 2 - The gift + the note

Once you have the clue, the gift has to map to the specific niche, not the broader category.

What to send for each bucket:

Identity gift: an artifact from the past identity. Quake CRO got a rocket launcher replica. A former touring drummer gets a signed pressing of the album they toured behind. A former chef gets the cookbook from their old kitchen. The rule: it has to be the specific artifact, not the category. Not “a guitar pick”, the pick from the show.

Passion gift: a high-signal item inside the niche they named.

• “Huge Radiohead fan” → the limited Kid A Mnesia box.

• “Deep into pour-over coffee” → a Lance Hedrick-recommended grinder.

Milestone gift: tied to the trigger.

• New Series B → a framed print of their funding announcement.

• Promotion to CRO → a signed copy of The Qualified Sales Leader.

• Book launch → buy 10 copies, send to their team with a note.

No gift: don’t force it.

Here are some recommendations I got for Kyle:

Personalized Solid Oak Martial Arts Belt Display & Trophy Rack on Etsy:

1. Go to https://www.etsy.com/listing/667908227/martial-arts-belt-display-personalized.

2. Select ‘Oak’ finish and your preferred number of belt slots (1-slot is cleanest for a single black belt display).

3. In the ‘Personalization’ field enter: Top plaque line 1: ‘Slow is Smooth. Smooth is Fast.’ | Line 2: ‘Kyle Norton’ | Line 3: ‘Black Belt — OpenMat MMA’.

4. Add to cart. Production + shipping is approximately 7–14 days (seller ships from the US with free shipping).”

The note is the other half.

Here’s the actual handwritten note sent with the Quake replica to Vanta's CRO:

Two things make this note work:

First, the note stacks two references. “Professional gamer” is the surface clue. “Killcreek” is her old gamer handle, and the Vanity Fair quote about the rocket launcher is the flex. That’s the detail that proves the note wasn’t AI-written or outsourced.

Second, the pitch is one sentence with proof. Two recognizable customer logos (Ashby, Amplitude) + one hard number (25% convert).

Here’s the template you can use behind every note:

1. Research proof - the surface clue, in the prospect’s own language

2. The rabbit hole - a deeper detail that required real effort to find

3. Gift bridge - one line connecting research to why you’re sending this

4. Pitch + proof- one sentence on what you do, with logos or a number

5. Soft ask - specific, low-friction (”chat next week?” not “would love to connect”)

6. Name + direct email - not a calendar link

~60–90 words. Handwritten on a real card.

Here's an example for Kyle:

And that’s it!

Hope this is helpful.

Talk soon,

Elric

The 3 prompts

Three prompts:

PROMPT 1: Research & Classify

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Here’s everything I published in Q1 at Outbound Kitchen, plus what’s coming in Q2.

This newsletter is also available in video and audio. Watch or listen on YouTube, Spotify, Apple Podcasts, or Snipd.

What we cooked in Q1

March 2026

• Podcast - OK28: How ElevenLabs is Scaling Outbound From 5% to 46% With Human SDRs Jonathan Chemouny (GTM at ElevenLabs) on rebuilding SDR motion and moving reply rates from 5% to 46%.

• Podcast - Best AI for Account Research? I Tested 7 AI Models
  March 2026 benchmark. Seven models, same account research test. Which one wins and where each breaks.

February 2026

• Newsletter — I tracked 232 outbound teams. Here’s what I found.
  47% of the 232 outbound teams I tracked grew their SDR headcount in 2025.

• Podcast — OK26: 5 AI Cold Call Training Scenarios Every Outbound SDR Team Should Run
  Troy Johnson (SDR Enablement at Jumpcloud) on the 5 AI roleplay scenarios.

• Newsletter — ClickUp built THIS before scaling dials
  The call prep system behind 3x pipeline and 150 dials/day.

• Newsletter — 9 ways I use Claude Code for outbound
  Call prep, tradeshow workflows, campaign building. The 9 workflows I run before touching Clay.

January 2026

• Podcast — OK25: How to Build a Profitable Outbound SDR Team
  Jay Glenn on SDR P&L math, headcount decisions, and why the payback window is now 9 months.

• Newsletter — How to prove outbound is working (to your CEO)
  The exec dashboard that answers the questions your CEO is actually asking.

• Podcast — OK24: How DoorDash Scaled Outbound from $291M to $8.6B
  Diane Ring (ex-DoorDash) on full-cycle field reps, city-level ownership, and scaling to $8.6B without SDRs.

• Newsletter — How to Divide Your Outbound Market Into Territories (7-Step Guide)
  TAM → SAM → ICP → territories. Seven steps for splitting a market cleanly.

• Podcast — OK23: How to Build SDR Enablement from Scratch
  Troy Johnson on when to hire your first enablement lead and the 3 things every outbound playbook needs.

• Newsletter — 100 emails → 1 meeting (2015). Now it’s 1,000+
  Cost per opportunity is at an all-time high. What actually moves reply rates in 2026.

• Podcast — OK22: How to Cold Call for Higher Connect Rates
  Jason Bay (CEO, Outbound Squad) on higher connect rates, better conversations, and meetings that show up.

What’s coming in Q2

The Pantry (Launch: May 20th)

I’m converting the paid newsletter into The Pantry. Building it now. I’ve been saying “soon” for months. Should have shipped it in Q1. Finally committing.

When I recorded the video yesterday, I had not finalized the launch yet. I worked on it this morning, and the date will be May 20.

How it splits:

• Free newsletter: keeps running. Free advice, frameworks, playbook walkthroughs.

• Paid (Pantry): the full system. Systems, prompts, files, Claude Code setups (agents, etc), AI workflows. Everything I build with customers or my previous full-time roles, turned into something you can run yourself.

What’s already built and landing inside:

I’ve shipped a lot of internal tools for customers recently using Claude Code. Templatized versions land in The Pantry so you can run them on your own data.

Three worth calling out:

• Tradeshow attendee scrapers: workflows for pulling attendee lists from the apps and sites where they actually live.

• Account-based outbound campaign system: the one I didn’t plan to build, and the one I’m most impressed with right now. Account list → contact discovery → outbound message, with a feedback loop baked in. Works across cold email, cold calling, and LinkedIn outreach. Built for sales teams running real multi-channel motions, not solo reps.

• Rep call-shadowing system: records call transcripts with reps, then turns them into recommendations. Flags where time is being lost, what could be done differently, and where reps can add more value. Built to sharpen team workflows.

The campaign system isn’t ready yet. I’ll share progress.

Pricing when Pantry launches: $79/month or $499/year.

To everyone who supported the paid newsletter until now: thank you. You made this possible. That’s why your current rate stays, a small way to say thanks.

• Already a paid subscriber? Nothing changes as long as you stay subscribed. (If you cancel and come back later, the new rate applies.)

• On the fence? Subscribe before The Pantry goes live to lock in the current rate.

Subscribe now

Q2 podcast lineup

• Growth Marketing director of Gorgias

• Co-founder & CRO of Reachdesk

• Growth Outbound working with a team of 20+ rep outbound team and building their own internal sales app

Systems and newsletters in the pipeline

• Account research system

• How to scale outbound teams in 2026

• How I use Clay for outbound

• Account-based gifting system

• Cold calling system

• How Snowflake uses AI for outbound

• Event system: how to maximize tradeshow output as a sales team

• How to build AI outbound systems for your team

• Trend watch: top outbound teams building their own apps for SDRs and AEs

Help I’m still looking for

Finalizing my US mobile phone number benchmark for 2026. I have datasets from Clay data providers (Upcell, Leadmagic, etc), Wiza, Apollo.io, Lusha, and LeadIQ. Still need ZoomInfo (normally should be good) and Cognism.

If you work at a lead gen agency or in RevOps and have access to either, reply to this email. Will pay for this.

Happy to be back!

Let’s get cooking,

Elric

Listen on YouTube, Spotify, Apple Podcasts, and Snipd.

Jonathan Chemouny is GTM at ElevenLabs, where he scaled outbound from 5% to 46% of pipeline in one year with a small distributed team. He previously built SDR teams at Intercom and Lattice. His team runs with no job titles, no standups, and no activity metrics, operating on async Slack and trust-based autonomy.

Listen on YouTube, Spotify, Apple Podcasts, and Snipd.

This episode was first published on outbound.kitchen.

We discuss:

• Why an AI-native company at 95% inbound decided to hire human SDRs instead of automating everything

• How ElevenLabs defines outbound -- pure cold vs. PLG-assisted -- and why the distinction changes the entire playbook

• Why finance and VC backgrounds outperform career SDRs, and the 4-round hiring process that screens for AI fluency and pressure tolerance

• The operating system: no standups, no pipeline reviews, async Slack (”Slack militaire”), and AI agents replacing management overhead

• SDRs running demos, flying to meet prospects, and attending events solo -- one rep skipped President’s Club for CCW Berlin and returned with 60+ SQOs

• The AI SDR handling multilingual inbound qualification (78% of decisions require no human intervention)

• WhatsApp as a prospecting channel in Europe and why LinkedIn is outperforming email

Referenced:

• ElevenLabs VP of Sales, Carles Reina: Inside ElevenLabs $330M ARR Sales Machine & The 20x Sales Comp Plan Reps Must Hit (20x Base-to-quota model)

• Jonathan Chemouny on LinkedIn: linkedin.com/in/chemouny/

• ElevenLabs: elevenlabs.io

My biggest takeaways from this conversation:

Read more

Subscribe now

By the way, if you’d rather watch or listen to this newsletter, you can find it on YouTube, Spotify, Snipd or Apple Podcasts.

Six months ago, I tested 6 AI models for account research.

GPT-5 Thinking took the crown.

Since then, the landscape has shifted again:

• OpenAI: ChatGPT 5.2 Thinking

• Anthropic: Claude Opus 4.6

• Google: Gemini 3 Pro

• xAI: Grok 4.2 (Beta)

Time for round 3.

What I’m testing today: 7 models:

• ChatGPT 5.2 Thinking (NEW)

• Claude Opus 4.6 (NEW)

• Gemini 3 Pro (NEW - The 2.5 model hallucinated last time)

• Grok 4.2 Beta (NEW)

• Perplexity Sonar Deep Research (returning)

• Grok 4 (returning)

• Argon by Clay (NEW - first time testing a Clay-native model)

Why I keep doing this:

Two things keep changing: new models are released, and existing models keep improving. The best tool for account research six months ago might not be the best one today. I don’t assume, I test.

1. Each model is great for specific use cases.

2. I test the model before I scale it with Clay, Cargo or n8n.

3. Account research is where model quality matters most. Your message quality and discovery call prep depend on the quality of your research.

1. Today’s Test: TrackRec + Replit

TrackRek:

Today I’m testing this for a company called TrackRec. They help companies hire sales talent faster, verifies quota attainment, ranks candidates on data, cuts time-to-hire.

If you’re a rep at TrackRec, you need to know: Is this company hiring salespeople? Who’s the decision-maker? What’s their employer brand? What tools are they using? Is there a reason to reach out right now?

Replit:

The company I’m testing on: Replit. $2.8M revenue to $150M+ ARR. $250M raise in September. 10+ open sales roles. CRO was on a podcast talking about hiring through his network.

I gave all 7 models the same prompt. 12 specific instructions. Same company, same prompt. Here’s what they found.

Disclaimer

These are my results. One company, one prompt, one test. Your results might be different. The point isn’t “use Perplexity.” The point is: here’s a framework for testing which model works best for YOUR account research.

Important

All models were tested in the web app, except Argon which was tested directly in Clay. Web app vs API vs Clay will give different results, different token limits, different output formats. Next time I will add API testing to the benchmark.ons.

2. What I Changed vs the last test

Last time: 3 criteria (web access, reasoning, additional insights).

This time: 6 weighted categories. Added Accuracy, Business Relevance, and Research Usability. Business Relevance asks: did it find what the BUYER needs? Research Usability: can a rep actually act on this?

Added an instruction completion table.

The prompt asks 12 specific things. Did you do what I asked? Yes or no. Perplexity did 10/12. Gemini did 1/12.

3. The Scores: Top 3 Deep, Bottom 4 Compressed

1. Perplexity Sonar — 9.2/10

Found the most. VP of SDR no one else found. Found both funding rounds. Found the CRO’s podcast and pulled quotes from it. One false claim: said RepVue had no profile when 5 other models found it. Best researcher, with one blind spot.

2. GPT 5.2 Thinking — 7.5/10

Zero false claims. 24 citations. Everything it said was true. But missed the January funding round, missed the podcast, missed 7 of 10 tech stack tools. Nothing wrong, but nothing unique. The safest model. Not the deepest.

3. Grok 4.2 Beta — 7.2/10

Fastest model, 56 seconds. Best RepVue depth, found sub-ratings no one else pulled. Honest about uncertainty, hedged on the $400M round instead of stating it as fact. But only found 1 tech stack tool.

Scoreboard (March 2026)

Bottom 4

• Grok 4 (6.1/10) — Found the $400M round but generic reasoning. Reliable, not insightful.

• Claude Opus 4.6 (5.6/10) — Best reasoning in the test. Worst champion identification. Missed the CRO despite checking 11 sources. Use for analysis, not finding people.

• Argon (Clay) (5.3/10) — Found the CRO and the podcast from just 4 pages. Most efficient model. But too thin for standalone research.

• Gemini 3 Pro (4.2/10) — Worst researcher. 1/12 instructions completed.

4. What I Found When I Verified Everything

After scoring, I manually verified every claim the models disagreed on. Six disputed facts. Checked LinkedIn, the articles, Replit’s website.

Zero hallucinations.

No model made stuff up. Last time, Gemini fabricated two executives. In this one, nobody invented anyone. That’s progress.

Three false claims

Claude said “no visible CRO.” Here he is. Ghazi Masood, CRO at Replit. Five other models found him.

Perplexity said “no RepVue profile.” Here it is. Overall rating 4.7, quota attainment 73%. Four models found it.

Only Perplexity found him. Nate Robbins, VP of Sales Development at Replit.

REPORTED vs. VERIFIED.

Press says the $400M round happened. Replit’s own website only shows $250M. Should your SDR reference funding the company hasn’t announced? Grok 4.2 hedged — said “follow-on talks.” That might be the most honest answer in the whole test.

Verification Summary

Zero hallucinations. 3 false claims total.

5. How I’d Actually Use These

Perplexity or GPT for the research, and if you want to go 1 layer deeper use Claude to analyse the findings.

• If you’re running 500 accounts: GPT is the safest. Zero false claims. You trade depth for safety.

• If you’re researching 10 high-value accounts: Perplexity. Spot-check the “not found” answers.

6. Where You Can Actually Use Each Model

Every model was tested in its web app (or Clay for the Argon Model). If you want to run these at scale in Clay or via API, here’s what’s available:

The scores in this test are web app scores. If you run GPT 5.2, Claude, or Gemini through Clay’s native integration, the output might be constrained by Clay’s token budget. Perplexity and Grok 4 are available directly in Clay. Grok 4.2 is beta and web-app only, you can’t use it in Clay yet. Claygent IS Clay’s model (Argon), so that score is exactly what you’d get.

In the next benchmark, I’ll test the models in the web app vs. Clay vs. API to measure the gap.

Here’s the prompt, If you want to get the prompt I used:

P.S. I’m also building a prompt builder for paid members so you can create your own account research prompt customized to your product.

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• Instant access to 40+ paid newsletters, including:

  • 6 AI prompts to find and reach accounts 10x faster

  • The Guide to Building an Enterprise Prospecting System

  • [Starter Kit] Step 1: Build your ICP + Persona Creation v1

• My Outbound Chef Kit: 130+ resources (20+ email templates, cold call scripts, AI prompts, top tools, and more). Value: $1,000+

And the best part? You can try it free for 7 days. Cancel anytime.

But I think you’ll love it!

👉 Start your free trial today.

If you only read LinkedIn, you’d think outbound is dead.

AI is replacing SDRs. Cold calling is dead. Email is dead. The SDR role is dead.

If you stopped there, you’d think outbound is in trouble.

Last year, I started tracking top teams to see what they’re actually doing. I also looked at global SDR numbers by country and company. I’ve been tracking 232 companies since March 2025.

I cross-referenced my research with data from Insight Partners, The Bridge Group, SaaStr, RepVue, and PeerSignal.

Here’s what the data shows for 2025:

• Global SDR headcount increased by 12%.

• 47% of the 232 companies grew their SDR teams in 2025.

• Top GTM teams like Rippling, Ramp, Vanta, and Owner.com expanded their outbound teams.

• AI companies are investing in SDR teams (OpenAI, Anthropic, ElevenLabs, Clay).

• Traditional industries, not just tech, are investing in SDRs too: logistics, banking, paint, marketing agencies, and more.

1. The market split into three tiers.

Global SDR headcount:

• 170,000 in March 2025.

• 190,000 by December, 2025.

That’s 20,000 new SDR roles in nine months.

And the hiring isn’t slowing down.

In December 2025, there were 11,259 open SDR roles globally, across 4,944. companies. By February 2026, that number hit 14,135 postings across 6,248 companies.

By Country (top 10)

The US holds 37% of global SDRs, but the growth is happening everywhere. Mexico grew 20%. South Africa grew 33%. Ireland grew 33%. The Philippines has 5,000 SDRs and grew 11%.

I have 213 companies with full March-to-December 2025 data. Here’s what I found:

Here’s what I found:

47% of companies grew their SDR teams. 12% cut. And 41% did neither, they added a head here, lost one there, averaging +1.5 SDRs per company across the full year.

And 41% did neither, they added a head here, lost one there, averaging +1.5 SDRs per company across the full year.

Aggregate Stats

• Net SDRs added across the 213 companies: +2,772

• Growers added +3,171 headcount (avg +32 per company)

• Decliners lost -532 headcount (avg -20 per company)

• Flat band: +133 across 87 companies (effectively flat)

• Median company grew +8.9% (+8 heads)

• Mean growth was +19.8% (pulled up by explosive growers)

The market is splitting into movers vs. the stuck middle.

By Broad Category

By Funding Status

54% of VC-backed companies meaningfully grew, but 13% meaningfully declined. No other funding category has this spread.

Biggest Growth Stories (Absolute Gains, March-Dec 2025)

The movers:

Cybersecurity went all-in. ThreatLocker (+60%), KnowBe4 (+52%), CrowdStrike (+12%). Six out of eight cyber companies in my data grew SDR teams. The security buyer is hard to reach, outbound is the play.

• CrowdStrike 280 (+12%)

• Fortinet 141 (+11.9%)

• Palo Alto Networks 113 (-1.7%),

• SentinelOne 113 (+27%)

• ThreatLocker 83 (+59.6%)

• Zscaler 42

• Rapid7 80 (+23.1%)

Traditional industries

Another thing I learned from this research: Traditional industries are also investing in SDR teams, not just SaaS companies anymore.

• HDFC Bank in India has 446 SDRs.

• Sherwin-Williams, a paint company, has 220.

• POOLCORP, which sells pool supplies, has 156.

• Labatt Breweries grew its SDR team by 67%.

• Accenture: 320 SDRs

• TP (Teleperformance), BPO/Outsourcing: 241 SDRs

The outsourcing market: 92% of SDR agencies in my dataset grew. Mid-tier agencies grew 20-49%. Companies want outbound, many just don’t want to build it in-house.

SaaStr and Emergence Capital surveyed 560+ companies and found 36% of B2B software companies cut SDR headcount in 2025. SDRs had the highest reduction rate among all sales roles.

Big Tech: Flat, Not Pulling Back

They’re maintaining, which for companies this size means they’ve decided the current headcount is roughly right. Amazon (AWS) is the exception worth watching.

Why the growth? My hypothesis:

More companies entering more markets means more sellers competing for the same buyers. Inbound alone can’t sustain growth when your category has 47 competitors instead of 12. Outbound is how companies create demand when demand doesn’t come to them.

If outbound were dying, the numbers would be going the other direction.

2. AI companies are hiring SDRs

39% of top AI companies with open GTM roles are actively hiring SDRs, data from Adam Schoenfeld at PeerSignal.

OpenAI is building its first SDR organization. Anthropic is hiring an 8-12 person BDR team. Clay, LangChain, ElevenLabs, and CoreWeave are all building SDR functions from scratch.

These are the companies building the AI tools that are supposedly replacing SDRs. And they are hiring SDRs.

There are currently 2x more Enterprise AE roles than SDR roles posted at AI companies, which means SDR hiring is coming, because those AEs need pipeline.

If AI were going to replace the SDR role, the AI companies would know first. Instead, they are investing in it.

3. What the winners built (and what the cutters got wrong).

Insight Partners surveyed 150 companies: top performers were 1.6x more likely to expand SDR teams. Only 2.9% of the best GTM teams eliminated their SDR function -- versus 9.8% of the broader market.

What I found in my dataset is similar to the data from Insight Partners.

The best GTM teams are investing more in SDRs.

• Rippling: 300 to 360+ SDRs (+20%)

• Ramp: 100 to 140+ (+40%)

• Vanta: 64 to 82 (+28%)

• Gong: 61 to 84 (+37%)

• Owner.com: 20 to 30 (+50%)

Since 2024, I study the best outbound teams:

Snowflake. Ramp. ClickUp. Owner.com. Rippling.

They don’t have a silver bullet. They invest heavily across the board:

• Systems: data infrastructure. AI and automation.

• Enablement.

• Talent: Hiring the right people.

Think of it like a Michelin-star kitchen. Each person is specialized. Each part of the system is optimized. They succeed because everything works together.

That’s what top outbound teams look like right now. Most companies are still looking for the one tactic that will fix everything. It doesn’t exist.

And the output tells the story.

• Owner.com and Ramp generate 3–4× more pipeline per rep than their competitors.

• ClickUp has shared publicly that they generate 3× more pipeline with the same number of SDRs.

• Verkada built a system where their MDRs (SDRs) are 4–6× more productive than traditional SDRs.

What’s the difference?

Productivity.

They built systems that let their team spend maximum time on high-quality prospecting, not preparing to prospect. If your reps are spending time on manual research, finding and cleaning data, writing individual emails from scratch, toggling between five tools, you’re burning budget on low-value work.

Top teams build backend systems that push data into tools reps already use.

AI

The best teams are using AI:

• Snowflake’s 15x’d their reply rate using AI to write outbound messages. They iterated 140+ times to get their prompts right.

• Gorgias cut 200 sequences down to 10 modular sequences and uses prompts to write targeted emails at scale.

• Ramp had a fully automated AI outbound motion and killed it a few months ago,they hit a ceiling. Now they’re investing more in the team, not automated outbound.

But here’s what most companies miss: most bought ChatGPT seats and told their teams to use it manually. That’s just adding another tool to the stack.

The real unlock: build AI workflows that run in the background and push outputs directly into your CRM or sales engagement platform, where your team actually works.

The cutters were cutting bad outbound.

Companies that cut treated SDR as a cost line. Hired bodies. Measured activity. Called it outbound. Companies that expanded treated SDR as a system. Invested in enablement. Measured outcomes. When the cost experiment got expensive, they cut. When the system produced returns, they scaled.

Most only had headcount.

Salesloft, a sales engagement platform, now has zero SDRs. They cut their SDR team. They’re PE-backed and focused more on efficiency than growth.

Outreach and Apollo also cut their SDR teams in 2024/2025 and in 2025 started rebuilding them from scratch.

I know three companies that tried cutting SDR teams entirely to replace them with AI. One early-stage company eliminated the team in 2024. By 2025, they hired a Head of BDR and eight BDRs on the floor. Two years without outbound pipeline, then a rebuild from scratch, because AI couldn’t replace the conversations that generate pipeline.

The other two, both around 1,000 employees, are in the same cycle. Cut. Trying to automate.

The SDR role is harder. That doesn’t mean it’s dead.

The role is evolving.

• Manager spans are shrinking, 6.4 reps per frontline lead, down from 8. That’s more coaching time per rep, not less.

• Ramp time hit 3.0 months, the fastest ever, down from 5.7 in 2020.

• Tenure rebounded to 1.9 years from a low of 1.4 in 2022. 97% of orgs now advertise advancement tracks.

GTM engineer

The GTM Engineer role is forming around it.

In November 2024, Brendan Short counted roughly 200 GTM Engineers globally, technical operators who build the infrastructure around SDRs (data pipelines, enrichment workflows, tool integrations) and other GTM teams.

He asked in one of his newsletters: The Rise of the GTM Engineer.

“Will this number be higher or lower this time next year? My bet: it will be much, much higher.”

Indeed, 15 months later:

• 2,125 people hold the title on LinkedIn.

• 67% got it in the last 12 months.

• One in four works at an agency.

4. The economics changed. The bar is higher.

The cost of outbound is rising from every direction.

Winning by Design documented a 10x conversion decline over 8 years: in 2015, 100 cold emails generated one opportunity. By 2018, it took 200. By 2023, it takes 1,000+. The volume required per opportunity has grown tenfold. And it keeps climbing.

Insight Partners advises 500+ B2B SaaS companies. Jeremy Donovan, their EVP of Sales, puts the breakeven at $40-50K ACV: below that, human outbound doesn’t pencil out. That threshold keeps rising. Companies with $20K ACV are redeploying SDR budget into inbound and channel partners.

The exception is extreme velocity, Owner.com runs 8-10K ACV with a 2-call, 6-day sales cycle and generates $70K/month per BDR in closed-won.

But that’s the exception, not the model.

SDR salaries

Base was flat at $55K for three years, then jumped to $60K in 2026. OTE stepped up from $80K to $85K in 2025 and held. Top performer ceiling peaked in 2024 ($131K) and has slightly declined since.

More attempts per opportunity. Higher cost per SDR (2015 → 2026). The economics of outbound have fundamentally shifted.

And yet the best teams are beating those economics. Bridge Group found pipeline per SDR up 26%, from $3M to $3.78M annually. Phone-centric teams generate 1.4x more qualified conversations than email-first teams. Insight Partners’ portfolio data puts it even starker: 70% of meetings are booked by phone. Not email. Not LinkedIn.

The gap between the top and the average is widening, not because of talent, but because of infrastructure & enablement.

You can’t outspend bad systems. You have to build better ones.

What this means for you

Someone posts “SDRs are dead” and it gets 500 likes. A survey says 36% of SaaS companies cut SDR teams. A vendor promises their AI tool can replace your whole team. And suddenly you’re in a board meeting defending the existence of your SDR org.

It depends who you’re learning from.

If you’re taking strategy cues from people who say outbound doesn’t work, you’ll make decisions based on that narrative. If you look at what Rippling, Ramp, Owner.com, and ClickUp are actually doing, building, expanding, investing in systems, the picture is different.

Being bad at outbound doesn’t mean outbound doesn’t work. It means you were bad at it.

Outbound works. Even in 2026.

And in competitive markets, it does something inbound can’t, it manufactures timing. While your competitors wait for hand-raisers, outbound lets you reach the right companies before anyone else knows they’re in-market.

Audit your system. Data infrastructure. AI workflows. Training and enablement. The right people. The winners aren’t doing one of these well, they’re doing all of them. If your system has gaps, fix those before adding headcount.

In March, I’ll share the full dataset (SDR headcount, salaries, GTM Engineer role, and more).

Until next time!

Elric

Sources:

• Original research via LinkedIn Sales Navigator (190K SDR headcount, 232 companies, 119 countries, March-December 2025).

• 2025 SDR Metrics and Trends Report: Bridge Group (SDR metrics, 351 companies)

• SaaStr/Emergence Capital (SDR team cuts, 560+ companies)

• Adam Schoenfeld/PeerSignal (AI company hiring)

• RepVue (SDR compensation)

• Winning by Design/Revenue Architecture (conversion trends)

• Jeremy Donovan/Insight Partners (Revenue Leadership Podcast E61)

As soon as you’re ready, here’s how I can help:

1. Outbound Consulting Call: Need quick help? Book a 40-min session here.

2. Outbound Private Chef:

   1. Part-time, I’ll build, optimize efficiency, and execute your outbound systems.

   2. Coaching/Advising: get tailored advice to fix your strategy, boost efficiency, and drive results.

3. Interested? Reply to this email or hit me up at bonjour@elriclegloire.com

Listen on YouTube, Spotify, Apple Podcasts, and Snipd.

Troy Johnson is the SDR Enablement Program Manager at JumpCloud, where he runs the SDR Enablement program for the Americas. His team books 80-90% of meetings through cold calls. He has over eight years in sales development, including six years managing SDR teams. This is his second appearance on the show, Part 1 covered SDR enablement foundations, and Part 2 dives into how they use AI to make reps more confident on the phone.

Listen on YouTube, Spotify, Apple Podcasts, and Snipd.

This episode was first published on outbound.kitchen.

We discuss:

• How JumpCloud uses AI role-play to certify SDRs on cold calls during onboarding, 100+ practice scenarios before going live

• Running a March Madness-style qualification competition to drive AI adoption and peer coaching

• The monthly rhythm for rolling out updated cold call talk tracks with live practice and manager demos

• Using Gong’s theme spotter to identify the most common phone objections and build targeted AI drills

• Building an SDR-to-AE progression program with AI call scenarios aligned to AE managers

• Why managers should practice AI role-play themselves to lead by example

• Tools used: Hyperbound, Orum, Gong, and ChatGPT Voice Mode as a free alternative

Referenced:

• Hyperbound: hyperbound.ai

• Orum: orum.com

• Troy Johnson on LinkedIn

• Gong

• ChatGPT

My biggest takeaways from this conversation:

Read more

• Instant access to 40+ paid newsletters, including:

  • 6 AI prompts to find and reach accounts 10x faster

  • The Guide to Building an Enterprise Prospecting System

  • [Starter Kit] Step 1: Build your ICP + Persona Creation v1

• My Outbound Chef Kit: 130+ resources (20+ email templates, cold call scripts, AI prompts, top tools, and more). Value: $1,000+

And the best part? You can try it free for 7 days. Cancel anytime.

But I think you’ll love it!

👉 Start your free trial today.

The live sessions are back, first one in 12 months.

The podcast format has changed. No more talking-head conversations. We build live, show screens, walk through real examples.

First session: 5 AI Role-Play Use Cases for Outbound Teams and Cold Calling. Part 2 with Troy Johnson.

Built for GTM leaders developing cold calling skills at scale.

Register here.

You’ve seen this pattern before. Ramp, Owner.com, and now ClickUp.

Outbound teams producing outsized results.

Not just through headcount. Through systems.

This week, Kyle Coleman posted this. Same team size. 3x the pipeline.

In the post, he called out four systems driving the results:

1. XDR activity went from ~35 to ~150 calls per day through infrastructure changes

2. Automated signal monitoring that pushes intel into rep workflows programmatically

3. Every tool and process audited for friction, with XDRs co-authoring every change

4. AI embedded at every layer, context stitching, workflow automation, feedback loops

I already broke down ClickUp’s outbound strategy in a previous issue:

How ClickUp Scaled to $300M With 2 Outbound Teams

Since then, ClickUp’s Growth Ops team shared one of their recipes.

Today we focus on one specific system, the AI-powered call prep that turns a parallel dialer into a context machine. It’s the specific workflow under #4 that directly enables #1. When you use a parallel dialer like Nooks and go from 35 to 150 calls a day, you need a way to maintain quality at that volume. The AI-powered call prep is what makes that possible, it turns every dial into a context-loaded conversation.

Andy Cabasso (Growth Ops Manager) broke down the system architecture, what failed, and the one thing that made it stick.

P.S. key Results from Clickup:

• 11x LTV lift when customers move from self-serve to sales-assisted​

• 3x CAC reduction (50% from culture/team alignment, 50% from incrementality testing)​

• $1M pipeline/month from their inbound AI-SDR in the first month of piloting​ (via Retool)

• $300M+ ARR announced September 2025

• Same team size. 3x pipeline.

• Top SDRs booking 200+ meetings in a quarter.

The Recipe

ClickUp uses Nooks, a parallel dialer that lets reps dial six prospects simultaneously. When someone picks up, you have zero seconds to prepare. Before this system, reps had Salesforce, LinkedIn, Pocus, Outreach, and multiple dashboards open. Scrambling for context mid-conversation.

The Growth Ops team built a system that killed the scramble.

Here’s the exact architecture.

The system runs as an automated workflow in Retool. You could build something similar in Workato, n8n, Make, or Zapier.

Step 1: The trigger. Every time a prospect enters a sequence in their sales engagement platform, that platform sends a webhook to Retool with the prospect details. That webhook starts the workflow.

Step 2: Data pull. A set of SOQL and SQL queries pulls data from their data warehouse tables. They bring in CRM data about the contact and account, recent logged sales activities, details on the current email sequence the prospect is in, and product usage highlights: free vs. paid, signup date, key events.

Step 3: Enrichment. If there are gaps on the contact or company, an enrichment flow runs to update and fill in missing fields.

Step 4: Three parallel AI queries. Full context loaded. Three AI models fire at the same time:

• Query 1 (Perplexity): Looks up the company online and finds recent news relevant to ClickUp’s offer. Summarizes the context and how it ties into their pitch.

• Query 2 (OpenAI GPT) Summarizes the company context, the prospect’s activity history, and their product usage.

• Query 3 (Gemini): Uses everything they know about the prospect plus internal data to develop a full talk track for the rep.

Step 5: CRM write-back. A POST webhook writes these outputs into custom fields on the prospect record in their CRM. Those fields sync to their dialer.

The result: Prospect picks up. Rep already has background, activity summary, and a talk track on screen.

The rep sounds like they’ve been studying the account for 20 minutes. “Hi, I noticed your team has been using ClickUp for project management but hasn’t activated the automations, curious if that’s intentional.” That’s the difference between a hang-up and a conversation.

No training deck does that. Live context does.

That’s the recipe. Not more dials. Better conversations on every dial.

What Broke

The workflow worked on day one. The AI outputs were unusable.

Three things kept breaking:

1. Bad data queries from the warehouse.Wrong fields, stale records, incomplete data, everything downstream breaks. Andy Cabasso’s advice: "if the AI output is wrong, check your SOQL and SQL first. The data is usually the problem”.

2. Poor prompt engineering. Even with clean data, poorly constructed prompts produce generic or misleading outputs. If an AI output looks off, start by improving the prompt, tightening the instructions, and giving clearer examples.

3. Misalignment between what reps needed and what the system delivered. This one is the least obvious. The system might be technically sound, clean data, good prompts, but if it delivers information the rep doesn’t actually need in the moment, it’s noise.

Here’s what that looks like in practice:

“A prompt might tell a rep the prospect is on a free plan when they’re actually a paid customer. That’s a credibility-killer. And in a cold call, there’s no recovery from getting basic facts wrong.”

One wrong output and the rep never opens it again.

They tested everything. Different models for different jobs:

• Gemini performed best for structured, rule-based tasks

• GPT handled creative prompts and flexible phrasing

• Claude had decent real-time performance, but still lagged

• Perplexity was essential for up-to-date web context

No single model does everything well. If you’re running one model for every task, you’re getting mediocre output on most of them.

What Actually Made It Work

Their SDRs told ops when the outputs were wrong. Every time.

• The talk track was off → they said so.

• The data was stale → they flagged it.

• Format wasn’t useful → they pushed back.

Without that loop, the system would’ve looked good in a demo and died in production.

Most companies never get here. They invest in the architecture, the workflows, the integrations, the AI models. They ship something that looks impressive in a demo. And then reps quietly go back to their old way within a week.

The feedback loop itself is simple, a Slack channel or a weekly review. The hard part is reps who actually say “this output is wrong” and ops teams who actually fix it.

ClickUp got this right because they started with the culture before they scaled the technology. One of their SDRs flagged the original problem and then helped shape every iteration of the solution.

Talk tracks got more accurate. Reps stopped checking Salesforce manually. Call conversion improved. Each iteration compounded because the people using the tool were the same people shaping it.

The Takeaway

This is one system. ClickUp’s 3x didn’t come from the AI call prep alone. The first breakdown covers the rest of their outbound strategy. Read my first deep dive here.

Three questions to audit your own AI implementation:

1. Is the data correct? Audit your SOQL/SQL queries before blaming the model.

2. Are the prompts specific enough? Vague instructions produce generic results. Tighten the input, tighten the output.

3. Do the people using it have a voice in improving it? If reps can’t flag bad output and see it get fixed, they’ll stop using it.

ClickUp took reps from ~35 calls a day to ~150, with a top performer booking 200 meetings in a quarter. We’re still finding the ceiling on what a rep can do when the systems keep getting better.

Follow Kyle Coleman (Global VP Marketing, ClickUp) and Andy Cabasso (Growth Ops Manager) for the source material.

Until next time, build systems, not shortcuts.

Elric

What’s coming in February:

• Benchmark for U.S. mobile phone numbers (the first of several)

• 2025 outbound benchmark and recap

• How I use Clay

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And the best part? You can try it free for 7 days. Cancel anytime.

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👉 Start your free trial today.

As soon as you’re ready, here’s how I can help:

1. Outbound Consulting Call: Need quick help? Book a 40-min session here.

2. Outbound Private Chef:

   1. Part-time, I’ll build, optimize efficiency, and execute your outbound strategy.

   2. Coaching/Advising: get tailored advice to fix your strategy, boost efficiency, and drive results.

3. Interested? Reply to this email or hit me up at bonjour@elriclegloire.com

• Instant access to 40+ paid newsletters, including:

  • 6 AI prompts to find and reach accounts 10x faster

  • The Guide to Building an Enterprise Prospecting System

  • [Starter Kit] Step 1: Build your ICP + Persona Creation v1

• My Outbound Chef Kit: 130+ resources (20+ email templates, cold call scripts, AI prompts, top tools, and more). Value: $1,000+

And the best part? You can try it free for 7 days. Cancel anytime.

But I think you’ll love it!

👉 Start your free trial today.

I’ve been using Claude Code since December. It hasn’t replaced Clay, but it’s changed how I use Clay.

Claude Code now comes before Clay in my workflow. It’s become my prep layer, the place where I do the thinking work before Clay does the enrichment and sequencing.

Here are 9 ways I use Claude Code:

1. Call prep that shows value (Before and during the call)

2. Tradeshow attendee workflow

3. Account-Based Campaign Builder

4. ICP/TAM analysis

5. Account list building

6. Merging and cleaning CSVs before Clay

7. Web scraping

8. For one client: a custom CRM lookup app because their CRM’s API was too limited for what I needed

9. Building a knowledge layer from customer conversations

For anyone who hasn’t used it:

Claude Code is Anthropic’s command-line tool that lets you give Claude tasks and it executes them, writing scripts, pulling APIs, scraping websites, processing files, building workflows. Think of it like having a technical co-pilot that can actually do things on your computer, not just answer questions.

It’s built for developers. I’m not a developer. But it turns out you don’t need to be one to get serious value out of it for outbound.

The problem with AI tools for outbound is the same problem with Notion. If someone hands you Notion and says “go,” you’re staring at a blank page. You need to see specific examples of how other people use it before it clicks. That’s what this newsletter is, the specific ways I’m using Claude Code for outbound right now, so you can see what’s possible and steal the ideas that make sense for you.

How I Use Claude Code for Outbound (On Top of Clay)

1. Call prep that shows value (Before and during the call)

I recently listened to Jeanne DeWitt Grosser, COO of Vercel (and former CBO at Stripe), talk about treating Go-To-Market (GTM) like a product, obsessing over the customer experience at every touchpoint. The line that stuck with me:

“Make every sales interaction great, whether customers buy or not.”

That made me rethink how I show up to prospect calls. I started asking myself: what am I actually bringing to this call? Because if the answer is just a list of qualifying questions, I’m not giving the prospect any reason to care.

So I started building things for them before the call even happens. Not just to show value, to educate them on how they should be thinking about their market:

• Here’s how you should think about your TAM.

• Here are companies showing specific pain signals online right now that could be great accounts for you.

• Here’s what the opportunity actually looks like.

And then I hand them a sample of what I’ve already done for them, so whether we end up working together or not, they walk away with something useful.

I’ve trained a Claude Code workflow on SPICED and Gap Selling, the same frameworks I use with clients. When I give it an account, it looks for:

• Situations that signal pain

• Problems I can tie to my services

• Critical events that might accelerate a deal

• Gaps between current state and where they want to be

And then I use that research to build actual deliverables I can share on the call:

• TAM analysis: for one of their markets, showing where the opportunity is

• In-market accounts: companies showing specific pain signals online that could be strong prospects for them, with the reasons why

• Account mapping: showing business units within a target account

• Account-Based Campaign Examples

The key decision I made here was to build these as visual dashboards instead of just handing over CSVs. I could give them a spreadsheet and let them scroll through rows. But when you show someone a visual breakdown of their market, where the opportunity is, which accounts are showing pain, how the segments break down, it lands completely differently. They can actually see what the opportunity looks like for their business. A CSV is data. A visual is a story.

Last week, I sent a message to a prospect before our call:

“Hey, I prepped a TAM analysis for the market you mentioned. Super excited to walk through it.”

During the call, they were already engaged. Because I’d already given them something useful before we even started talking.

Here's an example for the TAM analysis:

I think applying the idea of GTM as a product experience to outbound means exactly this: when someone gets on a call with you, they should get value whether they buy or not. Claude Code is what’s letting me do that without spending half my day on building this.

2. Tradeshow attendee workflow

This one’s still a work in progress, but it’s already saving hours of manual work.

Here’s what my old process looked like:

1. Go through the tradeshow website manually

2. Study the website to figure out what scraper I need (Apify, etc.)

3. Scrape the information

4. Bring it into Clay to cross-reference against the CRM

5. Look up contacts in another Clay table

6. Build a separate table to find contact info for people who aren’t in the CRM yet

7. Package the output and send it to my customer

That was multiple Clay tables, multiple tools, and a lot of manual decision-making at each step.

Now, depending on what the tradeshow gives you access to, there are a few ways I handle this with Claude Code:

Scenario 1: Tradeshow website (sponsors, speakers, exhibitors)

The most common one. I give Claude Code the tradeshow URL. It scrapes exhibitors, sponsors and speakers, figures out for each whether it’s a person or a company, checks if they fit my client’s ICP, looks them up in the CRM, finds the right contacts, and pulls contact info for anyone missing.

Scenario 2: Web app with full attendee list

Some tradeshows give you access to the attendee list through a web app. Sometimes Claude Code can scrape it directly via Apify or other tools. Other times I download the HTML and feed it to Claude Code so it can analyze the full list. Either way, it handles the enrichment from there.

Scenario 3: Mobile app only (what’s next)

Some tradeshows lock the attendee list behind a mobile app with no web version. Jordan Crawford recently showed a clever workaround for this: screen share from your phone to your computer, then use Claude Code to take screenshots and extract the information. I’m not doing this yet, but that’s my next step when a client needs it.

In all three scenarios, Claude Code handles what used to be steps 1 through 6 of my old process. The output is a CSV ready for Clay, which then pushes to the CRM. I send my client a Google Sheet with everyone prioritized — who to talk to first, who’s already a customer, who’s net new.

What used to take hours now takes under two. And I’m not done, the next version will include photos of each person and a talk track on why they’re worth talking to, so the team knows who to find on the floor. I’ve seen Jordan Crawford do something similar, and I want to take it further by pulling CRM context into that view.

3. Account-Based Campaign Builder

This is the one I’m most excited about, and the one I’m still actively building.

Right now, when I build a campaign for a client in Clay, it takes a long time. I’m creating multiple tables, clicking through enrichment steps, mapping contacts to accounts manually, writing messages one pattern at a time. The setup work before a single email goes out is significant.

Here’s what I’m building with Claude Code instead:

I start with two CSVs. One is the list of companies I want to prospect. The other is the contacts at those companies. I feed both to Claude Code, and from there, it connects the dots.

It maps which contacts belong to which companies. It looks at each account and finds the angles, who at that company could be a name-drop, who’s a recent hire, what’s happening at the company that creates an opening. It does the account research for each company on the list: what they do, what pain signals they’re showing, what’s relevant to my client’s value prop.

Then it pulls from my library of POV, talk tracks, cold emails and LinkedIn messages, templates I’ve built and refined across clients, and matches the right message to each situation and each contact. A recent hire gets a different message than a VP who’s been there three years. A company that just raised funding gets a different angle than one that just laid off their SDR team.

The output can go a few different directions depending on what I need:

• A CSV ready to go with the full emails or messages written out, plus a POV talk track for the reps so they know why each person is getting that message

• Or even pushing this directly to the systems where the rep are spending their time

• A prompt built specifically for Clay, so I can import it directly and Clay handles the enrichment and sequencing from there

Instead of building multiple Clay tables and configuring each one, I’m doing the thinking and the mapping in Claude Code first, and Clay becomes the execution layer.

I’m still building this out. The workflow isn’t fully systematized yet, right now I’m doing pieces of it across different sessions. But I can see where it’s going. When it’s done, I should be able to take a campaign idea, feed it two CSVs, and get back a fully researched, fully written campaign ready to load into Clay or hand to a rep.

I think this could be a game changer, not just for my own business, but for anyone running account-based outbound. The end goal is building this into a system that anyone can plug into. That’s what I’m working toward.

What Else I’m Running Through Claude Code Weekly

• ICP/TAM analysis: for clients and for my own prospect research

• Account list building

• Merging and cleaning CSVs before Clay

• Web scraping (or building Apify scrapers for harder sites)

• For one client: a custom CRM lookup app because their CRM’s API was too limited for what I needed

• Building a knowledge layer from customer conversations, I’m storing all my conversations with customers and prospects (call notes, pain points, objections, what they reacted to) and layering additional research on top (company data, market context, competitive landscape). Every conversation feeds it. Right now I’m using Clarify as my CRM and working on connecting it to Claude Code via MCP so this flows in automatically. The more context Claude Code has, the better everything else gets, call prep, account research, campaign setup. And when you store enough conversations across enough prospects, you start spotting patterns that sharpen your outbound messaging and targeting.

Why I use Claude Code over other AI tools?

It’s faster than Clay for certain tasks.

I love Clay. But for some things, merging CSVs, quick data analysis, prepping account lists, doing lookups, Claude Code is just faster. In Clay, you need to connect your accounts, create a table, set up your columns, configure your enrichments. That setup time adds up. In Claude Code, I describe what I need and it runs. For the stuff that needs to go into Clay, I prep it in Claude Code first so the Clay table is cleaner and simpler.

It connects to your stack.

Claude Code can connect directly to APIs and MCPs, Lemlist, EXA, Apify, client CRMs, so it can pull data, run lookups, and build lists on its own. That said, I only use it for reading right now. I don’t let Claude Code write to CRMs. I don’t trust it to not break something yet. Clay handles the write layer, which gives me a checkpoint before anything hits a client’s system.

It compounds.

Most AI tools are one-shot. You ask, you get an answer, you start over. With Claude Code, the workflows I build for one client, call prep, tradeshow scraping, ICP analysis, I reuse and improve for the next one. The frameworks I’ve trained it on (SPICED, Gap Selling) carry forward. Each client engagement makes the system better for the next client, even though I’m not sharing any data between them. The skills and the knowledge compound over time.

I’m just getting started.

I’ve been doing this since December. Most of what I showed you is V1 or V2. I can already see where this is going, and it’s way bigger than what I’m doing today.

I’m not saying any of this is perfect. The tradeshow workflow is still V2. The campaign builder is still coming together. But that’s the point, I’m building in public, and this is where I am.

The shift from “prepping to qualify” to “prepping to educate and show value” has changed how prospects show up to my calls. And Claude Code is what’s making it possible.

Elric

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  • 6 AI Prompts for Researching Accounts & Cold Outreaching Them (10x Faster)

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• My Outbound Chef Kit: 130+ resources: 20+ email templates, cold call scripts, AI prompts, top outbound tools, and more, everything the best outbound teams use. Worth $1,000+

And the best part? You can try it free for 7 days. Cancel anytime.

But I think you’ll love it!

👉 Start your free trial today.

As soon as you’re ready, here’s how I can help:

1. Outbound Consulting Call: Need quick help? Book a 40-min session here.

2. Outbound Private Chef:

   1. Part-time, I’ll build, optimize efficiency, and execute your outbound strategy.

   2. Coaching/Advising: get tailored advice to fix your strategy, boost efficiency, and drive results.

3. Interested? Reply to this email or hit me up at bonjour@elriclegloire.com

The podcast format has changed. No more talking-head conversations. We build live, show screens, walk through real examples.

First session: 5 AI Role-Play Use Cases for Outbound Teams and Cold Calling. Part 2 with Troy Johnson.

Built for GTM leaders developing cold calling skills at scale.

Register here

New podcast episode is up!

Listen on YouTube, Spotify, Apple Podcasts, and Snipd.

Jay Glenn is the founder of Jay Glenn Agency, where he helps B2B companies build financially sustainable SDR programs. After witnessing mass SDR layoffs in 2022, Jay developed a suite of P&L calculators that have become essential tools for SDR leaders proving ROI to their executives.

Listen on YouTube, Spotify, Apple Podcasts, and Snipd.

Grab the SDR P&L Calculator.

We discuss:

• Why 2022 was the wake-up call for SDR financial accountability

• The SDR P&L Calculator — walkthrough of inputs, outputs, and the 7.91x return benchmark

• The $50K ACV threshold for sustainable SDR models (and why lower ACVs fail)

• Customer lifecycle as the most underrated profit lever for SDR teams

• When to hire SDRs vs. when to invest in product and full-cycle AEs

• The Headcount Gap Calculator for modeling scale decisions

• Why inbound and outbound need separate conversion rate assumptions

• How to present SDR financials to your board (with or without good news)

Referenced:

• Jay Glenn on LinkedIn: ⁠https://www.linkedin.com/in/jayglenn/⁠

• Jay Glenn Agency: ⁠https://www.jayglenn.co/⁠

• RepVue (SDR compensation data): repvue.com

• Clay: clay.com

• TitanX (phone deliverability): titanx.com

My biggest takeaways from this conversation:

Read more

• Instant access to 40+ paid newsletters, including:

  • 6 AI prompts to find and reach accounts 10x faster

  • The Guide to Building an Enterprise Prospecting System

  • [Starter Kit] Step 1: Build your ICP + Persona Creation v1

• My Outbound Chef Kit: 130+ resources (20+ email templates, cold call scripts, AI prompts, top tools, and more). Value: $1,000+

And the best part? You can try it free for 7 days. Cancel anytime.

But I think you’ll love it!

👉 Start your free trial today.

A GTM leader asked me about this recently:

“CEOs don’t understand true lead sources and the number of touches needed to convert a lead. They need visibility into SDR/BDR work.”

Most companies have one revenue dashboard. Outbound, demo requests, PLG, partners, all mixed together.

Leadership sees “pipeline” and “revenue.” But they have no visibility into what outbound specifically contributes.

This creates problems:

You can’t prove outbound is working, even when it is. Outbound gets compared to inbound unfairly (more on this later). Budget conversations turn into “outbound isn’t producing.” And when cuts come, outbound goes first.

Most executives don’t understand what outbound actually takes. They see inbound demos converting at 40% and outbound converting at 20%, and they ask: “Why is outbound underperforming?”

It’s not underperforming. They just don’t have the context.

The solution: a dedicated outbound section in your exec reporting. Organized by pipeline source. One that shows what outbound contributes, what it takes, and whether it’s working.

Here’s how to build it.

P.S. This is for execs and board meetings, so I’m not covering what front-line managers or reps should track and use.

PART 1: Why Outbound Needs Its Own View

1.1 The Visibility Problem

Outbound is buried.

When everything lives in one revenue dashboard, you can’t see what outbound specifically contributes. You can’t compare outbound to outbound standards - only to inbound, which isn’t fair. And budget decisions get made without understanding the motion.

The first question your exec dashboard needs to answer:

“What percentage of our pipeline - and revenue - comes from outbound?”

If you can’t answer this, you don’t have visibility.

Here’s what separation looks like:

Now you can see what each motion contributes - pipeline AND revenue - and have informed conversations about resources.

1.2 The Education Problem - CEOs Don’t Understand the Work

That’s the real problem. They see the numbers but don’t understand what it takes to produce them.

Track your own productivity metrics:

• Activities per account to activate

• Activities per meeting booked

• Activated accounts per opportunity

These aren't industry benchmarks to hit — they're YOUR baselines to track and improve. One company might need 80 activities per meeting, another might need 1000. The number matters less than knowing your number and improving it over time.

Once you know your numbers, the CEO understands:

• Outbound is not “send some emails and get meetings”

• There’s a predictable amount of work behind each conversion

• More pipeline = more resources (or better efficiency)

How outbound differs from inbound:

The 2x Rule for Setting Expectations

From my observation working at different companies - a simple mental model for executives:

If your inbound demo win rate is 40%, expect outbound at ~20%. If your inbound sales cycle is 60 days, expect outbound at ~120 days.

This is backed by data. HockeyStack analyzed 50+ B2B SaaS companies and found:

• Outbound conversion: 17% vs Inbound: 28%

• Outbound sales cycle: 82 days vs Inbound: 54 days

Give executives this mental model BEFORE they see your outbound numbers. It stops the “why is outbound underperforming?” question before it starts.

Part 2: The Framework: How to Structure Outbound Reporting

2.1 The Account Funnel

For mid-market and enterprise, use accounts, not leads.

Why? You’re working multiple contacts per account. Lead-based reporting doesn’t capture the full picture. Account-based shows the true conversion from target to won.

(Note: For SMB/high-volume, lead-based may still work.)

The Account Funnel Stages:

The key distinction:

• “Engaged” means they showed intent (passive).

• “Activated” means we did the work (active). An account isn’t activated until your SDRs have hit the minimum prospecting threshold, not just one touch.

The key question this funnel answers for execs:

“If I add 100 accounts to our outbound funnel, how much pipeline will I create?”

Build YOUR conversion rate from your own data over time.

2.2 Adding Quality to the Funnel

Counting isn’t enough. “10 meetings booked” doesn’t tell you if they’re the RIGHT meetings.

Add two quality dimensions:

Account Tier (ICP fit):

• Tier 1: Perfect fit, highest priority

• Tier 2: Good fit

• Tier 3: Acceptable fit

Contact Seniority (Above/Below the line):

• Above the line: VP+, decision-makers

• Below the line: Managers, individual contributors

Example dashboard view:

“12 meetings with Tier 1 accounts” tells a very different story than “45 meetings.”

2.3 Standardize Your Definitions

Without standardized definitions, you’ll spend board meetings arguing about what the numbers mean instead of what to do about them.

Define these before you build:

Winning by Design puts it well: “Measuring related sales metrics against different points creates confusion. You must measure against the same point.”

If you’re comparing outbound to inbound, compare at the same stage.

Action: Get alignment on definitions. Write them down. Share with the team. Do this BEFORE building any dashboard.

Part 3: The Dashboard - What to Actually Show Execs

3.1 The Metrics That Matter

Winning by Design breaks metrics into three types:

3.2 The Exec Outbound Dashboard

Execs need to answer 4 questions:

1. Is outbound contributing? (% of pipeline AND revenue)

2. Is it efficient? (Conversion rates, cost per opp, CAC)

3. Is it improving? (Velocity trend)

4. Are we on track? (Progress vs targets)

What to show:

A) Revenue & Pipeline Contribution (The headline)

• Revenue from outbound-sourced deals ($) + % of total revenue

• Pipeline generated from outbound ($) + % of total pipeline

• Comparison to other sources

• Progress vs target

B) Funnel Performance

• Accounts at each stage

• Conversion rates between stages

• Progress vs target for key stages (meetings, qualified opps)

• Trend over 4-5 periods

C) Quality Indicators

• Meetings/Pipeline by account tier

• Decision-maker involvement rate (% above the line)

• Win rate on outbound-sourced deals

• Average deal size

D) Velocity

• Pipeline velocity (current vs previous period)

• Trend over time

E) Productivity & Cost Metrics

Activity efficiency:

• Activities per meeting booked

• Activities per opportunity created

Cost efficiency:

3.2 What NOT to Show Execs

Keep these for manager dashboards:

• Activity counts (calls, emails, LinkedIn touches)

• Individual rep performance

• Open rates, click rates

• Daily/weekly metrics

Execs need outcomes and efficiency, not activity details.

3.3 Two Phases: Validate, Then Scale

Outbound isn’t a switch you flip. It’s a motion you build in stages.

Phase 1: Validate (0-18 months)

The goal isn’t revenue yet. The goal is proving outbound can work for your business.

Why 9-18 months? If you’re hiring new reps: one quarter to ramp, one quarter to build pipeline, one quarter to close. If your sales cycle is 90+ days, you won’t have reliable conversion data for at least two quarters. This is normal.

What to report:

• Accounts engaged and activated

• Meetings booked (with quality breakdown)

• Early pipeline created

• Learnings: what’s working, what’s not

What NOT to report:

• Revenue (it’s $0, and that’s expected)

• Win rates (you don’t have enough data)

• ROI (meaningless without conversions)

How to message it:

“We’re in validation mode. Our goal is to build a repeatable playbook. Here’s what we’re learning about our market, and here’s the quality of meetings we’re booking. In [X] months, we’ll have conversion data we can trust.”

You’re done with Phase 1 when you can answer: “If I add 100 accounts, how many opportunities will I get?”

Phase 2: Scale (18+ months)

Now you have data. Now you can talk about efficiency.

What to report:

• Revenue and pipeline contribution

• Conversion rates at each stage

• Pipeline velocity

• Cost per opportunity, Outbound CAC

• Progress vs. targets

How to message it:

“Outbound is contributing X% of pipeline and Y% of revenue. Velocity is improving. Here’s our path to [target].”

The mistake most teams make:

They try to report Phase 2 metrics during Phase 1. Revenue is $0, so they look like they’re failing. Execs lose confidence. Budget gets cut before the motion has time to work.

Don’t let this happen. Set expectations early: “We’re in validation mode. Here’s our timeline to Phase 2.”

Back to the original question:

“CEOs don’t understand true lead sources and the number of touches needed to convert a lead. They need visibility into outbound work.”

With this framework, they will.

They’ll see what outbound contributes - pipeline AND revenue by source. They’ll understand what it takes - 100+ activities per meeting, the 2x rule for expectations. And they’ll know whether it’s working - conversion rates, velocity, cost per opportunity.

No more mixed funnels. No more unfair comparisons to inbound. No more “why isn’t outbound producing?”

Paid subscribers: I’m working on a Dashboard Recipe, the definitions, formulas, and requirements spec you can hand to your RevOps team to build this in whatever system you use. Keep an eye on your inbox.

Thinking about upgrading to the paid newsletter? Here’s what you’ll get:

• Instant access to 40+ paid newsletters, including:

  • 6 AI Prompts for Researching Accounts & Cold Outreaching Them (10x Faster)

  • The Guide to Building an Enterprise Prospecting System

  • [Starter Kit] Step 1: Build your ICP v1 + Persona Creation v1

• My Outbound Chef Kit: 130+ resources: 20+ email templates, cold call scripts, AI prompts, top outbound tools, and more, everything the best outbound teams use. Worth $1,000+

And the best part? You can try it free for 7 days. Cancel anytime.

But I think you’ll love it!

👉 Start your free trial today.

As soon as you’re ready, here’s how I can help:

1. Outbound Consulting Call: Need quick help? Book a 40-min session here.

2. Outbound Private Chef:

   1. Part-time, I’ll build, optimize efficiency, and execute your outbound strategy.

   2. Coaching/Advising: get tailored advice to fix your strategy, boost efficiency, and drive results.

3. Interested? Reply to this email or hit me up at bonjour@elriclegloire.com

Diane Ring led B2B strategy and operations at DoorDash from 2018 to 2023, joining when the company had ~500 employees and $291M in revenue, and leaving post-IPO with 15,000+ employees and $8.6B in revenue. She was on the leadership team for Merchant Strategy & Operations, where she helped scale the sales team from ~200 reps to thousands across North America and international markets. Most recently, she was Sr. Director of Sales Ops at TouchBistro, leading strategy and ops for a 100+ person GTM organization.

Listen on YouTube, Spotify, Apple Podcasts, and Snipd.

We discuss:

1. Why DoorDash used full-cycle field reps with no SDRs — and the 4 reasons handoffs killed velocity

2. The General Manager model: how city-level ownership created a “general manager of revenue” mindset

3. The 3 pillars for scaling outbound infrastructure: data, tooling, and culture

4. Account scoring when your data isn’t perfect: why “just start somewhere” beats waiting for accuracy

5. How to adapt outbound playbooks for international expansion (and why US vendors often fail in Canada)

6. AI coaching in practice: real-time monologue alerts, post-call scorecards, and AI roleplay bots for onboarding

7. Building rep-centric infrastructure: minimizing CRM fields and reducing tool sprawl

Connect with Diane:

⁠https://www.linkedin.com/in/diane-ring/

Referenced:

• Trellus (AI dialer + coaching): https://www.trellus.ai/

• Gong: https://www.gong.io/

• Outreach: https://www.outreach.io/

• Momentum: https://www.momentum.io/

My biggest takeaways from this conversation:

Read more

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I got this question from a GTM leader in December:

“I’m reading your newsletter. A topic I find very interesting (and I don’t find elsewhere) is how to divide the SAM into territories for each rep. Is this something you plan to cover soon?”

This is the question nobody answers well. Not TAM for investors. TAM/SAM/ICP for territory planning.

Here’s the step-by-step process:

P.S. Before we start: TAM, SAM, and ICP are not fixed. They evolve as your product and GTM evolve.

• New integrations → SAM grows (more companies can buy)

• New use cases → ICP expands (new segments become ideal)

• New markets → TAM grows (new geographies, new verticals)

• New GTM resources → SAM grows (add French speakers → France becomes addressable)

Snowflake started selling only to ad tech and gaming. That was their entire ICP, maybe <1,000 companies. Now they sell to dozens of industries.

This isn’t a one-time exercise. Revisit it quarterly as your product and team grow.

Why This Matters

When companies plan their sales team, they use TAM instead of ICP.

“We have 100,000 companies in our market.”

But if your ICP accounts convert at 25-30% and everything else converts at 1% or 0%, you don’t have 100,000 accounts. You have a much smaller number that will actually buy.

What happened at Chili Piper:

We overestimated the market. TAM looked huge—all B2B companies that book meetings.

Reality: ~11,000 accounts globally fit our ICP.

At 600 accounts per SDR (3-month contact cadence), that’s a maximum of 18 SDRs worldwide.

We over-hired. Gave reps “enough accounts.” But many had near-zero probability of converting. Productivity dropped. Eventually, we laid off part of the team.

If we had done this exercise before hiring, we would have known the real capacity of our market.

The 7-Step Process

1. Define your TAM → Total market size

2. Filter to SAM → Companies that COULD buy

3. Discover your ICP → Companies you SHOULD target

4. Define Anti-ICP → Accounts to exclude

5. Score all accounts → Every account has a tier

6. Calculate capacity → How many reps your market supports

7. Divide by equal potential → Fair territories

Step 1: Define Your TAM (Total Addressable Market)

What: Count the total number of companies in your market.

Output: A number. The upper bound.

This is your starting point. The broadest definition of who could theoretically be a customer.

Examples:

• Chili Piper: All B2B companies that book meetings with prospects

• Rippling: All employers (any company with employees)

• Snowflake: All companies that store and analyze data

• Wiz: All companies using cloud infrastructure

• Owner.com: All restaurants

TAM is mostly useful for investors and market sizing. It’s not useful for territory planning on its own.

Step 2: Filter to SAM (serviceable addressable market)

What: Filter TAM to companies that COULD buy based on product requirements.

Output: A smaller list of companies that fit your product.

SAM filters for:

• Right integrations (CRM, tech stack)

• Right geography

• Right use case fit

Examples:

• Chili Piper

  • TAM: All B2B companies booking meetings

  • SAM filter: Uses Salesforce or HubSpot

  • SAM: B2B companies on supported CRMs

• Rippling

  • TAM: All employers

  • SAM filter: Supported geos, 50+ employees

  • SAM: Mid-market employers in US

• Snowflake

  • TAM: All companies with data

  • SAM filter: Already on cloud (AWS/GCP/Azure)

  • SAM: Cloud-adopted companies

• Wiz

  • TAM: All companies using cloud

  • SAM filter: Meaningful cloud footprint

  • SAM: Companies with real cloud infrastructure

• Owner.com

  • TAM: All restaurants in US

  • SAM filter: 1-10 locations, offers delivery

  • SAM: Independent restaurants with delivery

Step 3: Discover Your ICP

What: Identify the common traits of your BEST customers.

Output: A list of detectable signals that predict success.

The Definition Most People Get Wrong

Mark Roberge:

“A great ICP is based on LTV, not lowest CAC, and that’s where a lot of people get that message wrong.”

Broken intuition #1: ICP = where our inbound demand is That’s not necessarily who you built the product for.

Broken intuition #2: ICP = where we have the highest close rates Only true if they stick around.

The correct frame: ICP = companies where you solve a significant problem.

When you solve a real problem, customers renew, expand, and refer. That shows up as high LTV. LTV is the signal—not the definition.

How to Discover Your ICP

• Quality data: Customer conversations (Gong), interviews, CS calls → “Why did you buy? What problem did we solve?”

• Quantity data: External sources at scale (Clay, LinkedIn) → Validate patterns across your customer base

ICP Must Be Detectable

If you can’t find the signal in external data, you can’t build target lists at scale.

• Chili Piper ICP traits: SDR + AE model, high inbound volume, routing complexity Where to detect: LinkedIn (SDR headcount), job postings (demand gen, RevOps)

• Rippling ICP traits: Distributed workforce, tool sprawl, HR/IT/Finance Ops exists Where to detect: LinkedIn (multi-location, ops roles), job postings

• Snowflake ICP traits: Data team exists, building analytics/ML/AI Where to detect: LinkedIn (data engineer titles), job postings (dbt, Fivetran)

• Wiz ICP traits: Security team exists, compliance pressure Where to detect: LinkedIn (security roles), job postings (SOC2, compliance)

• Owner.com ICP traits: High delivery volume, DoorDash/UberEats dependency Where to detect: Google Maps, Yelp, Uber Eats/DoorDash presence

Step 4: Define Anti-ICP

What: Identify accounts with near-zero probability of converting.

Output: A “do not contact” list.

These are companies where CAC doesn’t make sense and same for LTV. If you give these accounts to reps, it doesn’t matter how good they are, these accounts will never close.

Examples:

Chili Piper:

Anti-ICP: Not using Salesforce/HubSpot CRM

Why: Product doesn’t integrate

Rippling Anti-ICP: <30 employees, contractor-heavy, locked into PEO Why: Too small, no pain, switching cost too high

Snowflake Anti-ICP: No data team, Excel-only, on-prem only Why: No buyer, no use case

Wiz Anti-ICP: No cloud footprint, no security team Why: No attack surface, no one to sell to

Owner.com Anti-ICP: No delivery, fine dining, large franchises Why: No margin pain, corporate procurement

At Chili Piper, if we had scored all accounts upfront, we would have seen some had 0-1% win rates. We paid reps to work accounts that were never going to convert.

Step 5: Score All Accounts

What: Assign a tier to every account based on ICP fit.

Output: A ranked list of all accounts.

Why Score?

Snowflake’s data:

• Tier A accounts: 25% had an open opportunity

• Tier B-E accounts: 5% had an open opportunity

• Tier A converts 5x better

If you don’t score, you don’t know how much real potential you have.

If you don’t score, you don’t know how much real potential you have. You might give reps accounts with 0% probability of closing.

How to Score

Use a simple 2-layer system:

• Layer 1: Fit Score (0-100) — How well does this account match your best customers?

• Layer 2: Value Prediction — What’s the potential deal size?

I covered the full scoring framework here

→ Quick Start: Build Account Scoring for Outbound

Step 6: Calculate Capacity

What: Figure out how many reps your market can support.

Output: A maximum headcount number.

The Formula

ICP Account Count ÷ Accounts per Rep = Max Reps

The Contact Frequency Rule

Accounts should only be contacted every 3 months. Touch them more often and reply rates tank.

This means:

• If an SDR works 200 accounts/month

• And accounts should only be contacted every 3 months

• Each SDR needs 600 accounts minimum in their territory

The Chili Piper Example

• ICP count: ~11,000 accounts globally

• Contact cadence: Quarterly (3 months)

• Accounts per SDR: 600

• Max SDRs: 18 worldwide

If you hire 30 SDRs, you’re burning through accounts twice as fast. Reps run out of fresh accounts. Productivity drops.

The Key Insight: Optimize Before You Hire

Before adding headcount, ask: “Have we maxed out our current team’s productivity?”

Doubling productivity is cheaper and faster than doubling headcount.

I broke down the full capacity planning framework here

→ Here’s how to plan your 2026 outbound team (the right way)

Step 7: Divide by Equal Potential

What: Create territories with equal potential, not equal account count.

Output: Fair territories where every rep has the same opportunity.

Start with Geography

Don’t over-complicate this. Divide by geography first—where are your ICP accounts?

If you have:

• 10,000 ICP accounts in the US

• 1,000 ICP accounts in Europe

You know your US team will be ~10x the size of your Europe team. That’s your starting point.

Vertical specialization (by industry) comes later, when you have the GTM team maturity. Companies like Snowflake can dedicate reps to financial services or healthcare. But that requires scale and enough accounts per vertical to justify it.

How to Divide

It depends on what data you have:

Early (no historical data) What you have: ICP definition but no win rate data How to divide: Equal ICP accounts per rep

Some traction What you have: Win rate data by tier How to divide: Equal Tier A accounts per rep, then balance the Tier B/C mix

Mature What you have: Deep historical data How to divide: Use conversion rates to balance expected pipeline per territory

Early stage: Make sure each rep has roughly equal ICP accounts.

With historical data: Make sure each rep has equal Tier A accounts (your highest converters), then balance the rest so no one is overloaded with low-probability accounts.

Quota Should Reflect Territory Maturity

If a rep is working a territory with proven data that it’s working, their quota should be higher than someone starting a new territory from scratch.

Equal potential ≠ equal quota. A mature territory with momentum should carry a higher target.

The Mechanics Depend on Your GTM

The exact process for territory assignment depends on your model—inside vs. field, segment splits, language requirements, existing customer relationships.

But the principle stays the same: balance by potential, not by count.

Where to Get the Data

• Horizontal B2B SaaS: LinkedIn Sales Navigator + Clay → Headcount by function, company size

• Restaurants: Datalane → Restaurant-specific data, delivery status

• Ecommerce: Storeleads → Online stores, platforms

• Benefits/Brokers: Benefitflow → Employer benefits data

• Enrichment at scale: Clay → Combine sources, validate signals

Quick Reference: All 5 Companies

Chili Piper

• TAM: B2B companies booking meetings

• SAM: Uses Salesforce/HubSpot

• ICP: SDR+AE, high inbound, routing complexity

• Anti-ICP: No CRM integration

• Data: LinkedIn, Clay, BuiltWith

Rippling

• TAM: All employers

• SAM: Supported geos, 50+

• ICP: Tool sprawl, distributed, Ops roles

• Anti-ICP: <30 employees, PEO locked

• Data: LinkedIn

Snowflake

• TAM: Companies with data

• SAM: Cloud + analytics needs

• ICP: Data team, building AI/ML

• Anti-ICP: No data team, Excel-only

• Data: BuiltWith, HG Insights

Wiz

• TAM: Companies with cloud

• SAM: Cloud + security pressure

• ICP: Regulated, security team, compliance

• Anti-ICP: No cloud, no security

• Data: BuiltWith, HG Insights

Owner.com

• TAM: All restaurants

• SAM: 1-10 locations, delivery

• ICP: High delivery, marketplace dependency

• Anti-ICP: No delivery, fine dining

• Data: Datalane, Google Maps

The Takeaway

Your TAM doesn’t build pipeline. Your ICP does.

The 7 steps:

1. Define TAM — Total market (for investors)

2. Filter to SAM — Companies that COULD buy (product fit)

3. Discover ICP — Companies you SHOULD target (LTV signals)

4. Define Anti-ICP — Accounts to exclude (0% probability)

5. Score all accounts — Tier every account by ICP fit

6. Calculate capacity — How many reps your market supports

7. Divide by equal potential — Start with geography, balance by ICP accounts or Tier A accounts

Which step is the hardest for you right now?

1. Defining ICP vs SAM

2. Finding the right data

3. Scoring accounts

4. Calculating capacity

5. Dividing territories fairly

Hit reply and tell me, I’ll cover the most requested topic in a future issue.

Elric

🔒 Paid Subscriber Bonus: The Territory Planning Toolkit”

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Troy Johnson is the SDR Enablement Program Manager at JumpCloud, where he supports three outbound and inbound teams across the Americas. After six years as an SDR manager, he transitioned into enablement and ramped himself up by interviewing 15+ enablement leaders externally and using AI to find patterns across the conversations. He now runs a bi-weekly peer group with other SDR enablement managers to share best practices.

Listen on YouTube, Spotify, Apple Podcasts, and Snipd.

We discuss:

1. What SDR enablement actually is, the difference between a “head coach” and a “skills coach”

2. How Troy ramped himself up in 90 days by interviewing 15 enablement leaders and using AI to find patterns

3. The three essential components every SDR playbook needs from day one

4. How to split your time between content, coaching, and training (the 30/30/30 framework)

5. When to hire your first SDR enablement person — team size, motions, and complexity signals

6. The ideal onboarding structure: 2-3 weeks of foundations + “Prospecting Bootcamp” through day 60

7. Using AI to accelerate business acumen — the underrated use case most teams miss

Where to find Troy:

On LinkedIn: ⁠https://www.linkedin.com/in/techsalestroy/

My biggest takeaways from this conversation:

The Head Coach vs. Skills Coach Model

Read more

• Instant access to 40+ paid newsletters, including:

  • 6 AI prompts to find and reach accounts 10x faster

  • The Guide to Building an Enterprise Prospecting System

  • [Starter Kit] Step 1: Build your ICP + Persona Creation v1

• My Outbound Chef Kit: 130+ resources (20+ email templates, cold call scripts, AI prompts, top tools, and more). Value: $1,000+

And the best part? You can try it free for 7 days. Cancel anytime.

But I think you’ll love it!

👉 Start your free trial today.

“AI is replacing SDRs.”

I keep seeing this on LinkedIn.

So I started tracking the data myself.

Based on my tracking via LinkedIn Sales Navigator:

• March 2025: 170,000 SDRs globally

• December 2025: 190,000 SDRs globally

That’s +20,000 SDRs. +12% growth in 9 months.

AI isn’t replacing SDRs. But something else is happening that most GTM leaders are missing completely.

The Reality Is More Nuanced

I’ve talked to companies that fully replaced their SDR team with AI.

One of them fired their 3 BDRs the day after we spoke. They wanted to do everything with AI. No humans. Full automation.

12 months later, they hired a new Head of BDRs and are rebuilding the team internally.

AI is making productive SDRs more valuable, and unproductive ones redundant.

That word, productive, is what this newsletter is about.

The Economics Are Broken

Let me show you something.

Conversion rates have collapsed:

• 2015: 100 cold emails → 1 opportunity

• 2018: 200 attempts → 1 opportunity

• 2023: 1,000+ attempts → 1 opportunity

That’s a 10x decline in 8 years.

(Source: Book: Revenue Architecture by Jacco van der Kooij, CEO of Winning by Design)

Why the collapse?

It’s saturation. The same thing that happened to digital ads is now happening to outbound:

• More SaaS companies, more providers

• More sales teams sending more volume

• Inboxes are flooded

• Most messages aren’t relevant

• Buyers are overwhelmed and tuning out

Meanwhile, SDR salaries rose from $44K (2015) to $80K (2024) to $85K today—and now they’ve plateaued.

Here’s the math that should worry you:

Salaries doubled. Conversion rates collapsed 10x. That means your cost per opportunity has increased dramatically, even though salaries have finally stopped rising.

You’re paying more per output than ever before.

Here’s the forcing function:

If conversion rates keep declining, and you can’t reduce headcount costs, and you’re running the same playbook, something has to change.

You can’t out-hire this problem. You can’t out-train it.

There’s only one lever left: Productivity.

The Formula Most Leaders Miss

Most GTM leaders get two out of three things right.

Here’s what I’ve learned working with outbound teams over the past 14 months:

Outbound Results = Talent × Productivity × Enablement

Not addition. Multiplication.

Most leaders invest heavily in two of these:

• Talent: Hire more SDRs. Hire experienced reps. Build out the team.

• Enablement:Train them. Coach them. Run call reviews. Improve messaging.

Then they step back and say: “Go figure it out.”

It doesn’t work.

Why? Because they’re missing the multiplier: Productivity.

Let me be specific about what I mean by productivity.

It’s not about working harder or spending more hours on the phone. It’s about maximizing the time your team spends on actual prospecting—not on manual tasks that can be automated today.

In 2022, if you wanted to enrich data automatically, you needed technical people to build it. Today? Tools do it for you. AI can extract intelligence from calls, update your CRM, research accounts, all in the background. What wasn’t possible three years ago is now table stakes.

But productivity also means focusing on the right accounts—the ones with the highest chance to convert, at the right time, with the right information. Because if you’re sending generic messages to the wrong companies, it doesn’t matter how many hours your reps work.

Productivity is all of this working together.

Here’s what I saw across 15 companies I worked with in 2025:

• They pick one data provider and call it done

• They buy one tool and expect magic

• They let SDRs figure out the rest on their own

• Then they wonder why results aren’t coming

They invest in talent. They invest in enablement. But they underinvest in the systems that make talent and enablement actually work.

If Productivity is low, you can multiply Talent and Enablement all day. The result stays flat.

Why Productivity Gets Ignored

Here’s what I think is happening.

In 2020, maybe even 2024, you could get away with ignoring productivity. Hire people. Train them. Let them grind. Results showed up eventually.

But the environment has changed:

• Conversion rates are down 10x

• Salaries doubled while output collapsed

• Cost per opportunity is at an all-time high

• Saturation is increasing

• Buyers are harder to reach and quicker to ignore

The old playbook, hire more, train, doesn’t scale anymore. It’s hitting a wall.

Here’s what’s possible when you get productivity right:

If a rep is booking 10 meetings a month with the wrong systems, they should be booking 20 with the right systems.

That’s not a stretch. That’s table stakes.

Top companies like Owner.com and Ramp are getting 3-4x their competitors’ results. And they’re not doing it by hiring 3-4x the headcount.

They’re maximizing productivity per rep before hiring more.

What Productivity Actually Means

This is where most leaders get confused.

Productivity is NOT:

• The number of tools you’ve deployed

• The automation percentage you can report to the board

Productivity IS:

Contacting the right accounts.

Tier 1 accounts convert at 5x the rate of lower tiers. If your reps are spraying across 40,000 accounts instead of focusing on the right 1,000, you’re burning capacity.

Having the right data.

Coverage. Quality. Accurate phone numbers. If 40% of your numbers don’t work, your “productive” calling time is actually 60% productive.

Knowing the context.

Your system should know who’s already familiar with your brand—who’s visited your website, who’s a former champion now at a new company, who’s shown intent. These signals should feed prioritization automatically, not require reps to dig for them.

Removing manual work.

Research, data cleanup, message writing, this should happen in the backend, not eat up rep time.

Maximizing revenue-generating activities.

If an SDR works 40 hours a week, how many of those hours are spent actually prospecting vs. preparing to prospect?

Here’s a simple benchmark:

Your reps will spend ~20% of their time on internal tasks (meetings, 1:1s, admin). That’s unavoidable. The question is what happens with the other 80%. Most teams I see are at 40-50% actual prospecting time. The rest goes to list building, data cleanup, manual research. Best-in-class teams hit 70-80% prospecting time. That gap, from 50% to 80%, is nearly doubling productive capacity with the same headcount.

And this compounds. When your reps ARE prospecting, are they targeting the right accounts? Time on prospecting × ICP quality = actual output. Fix one without the other and you’re still leaving capacity on the table.

One question cuts through the noise: Does this tool add clicks or remove them?

The Backend vs. Frontend Test

Here’s a simple framework you can use immediately.

When you’re evaluating a tool, ask: Is this for the backend or the frontend?

Frontend tools = tools reps interact with directly.

• They add clicks

• They require training

• They eat into prospecting time

Backend tools = tools that run automatically.

• AI workflows

• Data enrichment

• Automation that feeds outputs to where reps already work

Most companies buy frontend tools and call it “investing in productivity.”

They’re wrong.

Real productivity investment means building backend infrastructure so your reps can focus on selling:

• AI workflows that do account research automatically

• Data enrichment that runs on a schedule, not on-demand by reps

• Automation that routes the right accounts to the right reps with the right context, already in their CRM

Your reps shouldn’t spend time cleaning data.They should be prospectors.

This Is Complex. That’s The Point.

I’m not going to pretend this is easy.

Building real productivity infrastructure, data systems, backend automation, signal routing, is complex. It requires thinking through a lot of moving pieces that most teams try to do manually.

And here’s the thing: there’s no single tool you can buy to solve this. Each company is different. Some companies, like Pigment, have built their own custom systems from scratch.

Should you build or buy? That’s one of the main questions I’ll be exploring in this newsletter throughout the year. Because it’s not a simple answer, and getting it wrong is expensive.

But that complexity is exactly why it’s a competitive advantage.

Most teams won’t do this work. They’ll keep hiring, keep training, and keep wondering why results aren’t scaling.

The ones who invest in productivity will compound.

What This Means for Your Team

If productivity is the multiplier, here’s what changes:

Maximize output before hiring.

Top outbound teams aren’t scaling headcount first. They’re getting more from each rep before adding more reps. That’s the model.

Bring in Ops earlier.

Most companies wait too long to invest in operations and data roles. If productivity is the multiplier, you need people building the systems, not just people working in them.

Measure what matters.

Stop tracking:

• Number of AI tools deployed

• Automation percentage

• Cost savings

Start tracking:

• Time reps spend on revenue-generating activities

• Conversion rates by account tier

• Data enrichment rate and accuracy

If you can’t measure productivity, you can’t improve it.

The Transformation

Here’s what this looks like in practice.

Before (most teams today):

• Reps spend 40+ hours/week, but only 15-20 hours actually prospecting

• 30-40% of phone numbers don’t work

• Every rep sources their own data, does their own research

• Results: 10 meetings/month

• Cost per meeting: high + increasing

• Response: “Hire more reps”

After (productivity-focused teams):

• Reps spend 40 hours/week, 30+ hours actually prospecting

• Data is validated, enriched, and routed automatically

• Research happens in the backend before reps touch accounts

• Results: 20-40 meetings/month, consistent or improving

• Cost per meeting: low + stable or decreasing

• Response: “Maximize before we scale”

Same headcount. 2-4x the output.

That’s what investing in productivity looks like.

Two Questions to Ask Yourself

Before you hire another SDR or schedule another training session, ask:

1. What high-value activities should your reps focus on exclusively?

2. What’s currently distracting them that AI/tools/data should handle?

If you can’t answer these clearly, you have a productivity problem.

And no amount of hiring or enablement will fix it.

Why I’m Writing This

This isn’t just about what I’m focused on in 2026.

I’m seeing this as a common challenge across almost every company I talk to. GTM leaders are stuck, conversion rates are declining, cost per opportunity is rising, and the old playbook isn’t working. But most don’t know where to start fixing it.

Productivity is complex to solve. There’s no single tool, no quick fix. It requires rethinking how your systems work together.

That’s exactly why I’m excited to dive deep into this topic this year.

Whether it’s the newsletter, the podcast, or the work I do with customers—productivity is the thread that ties it all together. I’ve been working on this with customers throughout 2025, and I’m going all-in on it in 2026.

This newsletter is just the beginning. More to come.

If you want to explore what this looks like for your team, reply to this email. Let’s talk.

Elric

Coming in January based on your questions:

• TAM vs SAM vs ICP (updated version) → Next week

• Outbound dashboards and reporting for exec/board meetings

• How I use Clay as my outbound orchestration layer

Also posting in January:

• Best mobile data provider in North America for 2025

• Outbound benchmarks for 2025

The podcast is back too. If you missed the first episode of the year, listen here.

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