# Building a B2B sales motion, from first outreach to forecast — Full Chat Transcript The B2B Sales work group helps early B2B companies build a sales motion in group chats. Members cover setting up a CRM, booking meetings and forecasting, and how to pace founder outreach and staff SDRs. Updated: 2026-10-13 HTML page: [Building a B2B sales motion, from first outreach to forecast]() Summary: [Summary]() Work group: [B2B Sales]() ## Participants - [Kyle Poyar]() — Founder of Growth Unhinged. Former Operating Partner at OpenView - [Peter Kazanjy]() — Founder of Founder-Led Sales Factory. Co-founder of Atrium and author of Founding Sales - [Andy Mowat]() — CMO at Inflection.io. Former VP of Revenue Operations at Carta - [Kris Rudeegraap]() — Co-CEO and co-founder of Sendoso. Former Senior Account Executive at Talkdesk - [Cassie Young]() — Partner at Primary Venture Partners. Former Chief Commercial Officer at Sailthru - [Guillaume "G" Cabane]() — Co-founder of HyperGrowth Partners. Former VP of Growth at Segment ## Full Chat Transcript #### [Setting up a startup CRM around what buyers do]() **Question:** How should a B2B startup set up its CRM and sales stages? ##### [Kris Rudeegraap]() I’d keep one simple deal pipeline and make every stage depend on a buyer action. For example: meeting booked, problem confirmed, demo completed, buying process confirmed, proposal shared, and closed won or lost. Keep prospecting in a separate lead status so a cold name never inflates the deal pipeline. On each deal, capture the account, buyer, source, problem, next step and date, expected value, and loss reason. Review deals with no next step each week. At Sendoso, we learned that prospects already doing gifting manually often understood the demo immediately. Prospects new to it needed education, case studies, and customer conversations. [Citation: https://podcasts.apple.com/us/podcast/373-from-0-to-100-customers-channels-challenges-and/id1233372004?i=1000680966294] I’d add one field for “current workaround” and use it to distinguish those two paths. That gives the founder useful signal about which buyers move quickly before adding more stages or automation. ##### [Cassie Young]() I would make the first CRM gate brutally clear: a friendly conversation is a lead, not a qualified deal. I call the mistake "happy ears syndrome." Before creating an opportunity, record the buyer's pain, budget path, buying authority, and a concrete next step. MEDDPIC is a useful checklist for the gaps, though a seed-stage team can capture those answers in a few fields. [Citation: https://www.instagram.com/reel/DFdZ0DlCZa1/?hl=en] Then use stages tied to buyer evidence: qualified pain, agreed evaluation, decision process confirmed, commercial review, closed won or lost. Require a dated next step and an owner at every open stage; record the reason when a deal is lost. Keep source, expected contract value, and customer segment on the opportunity so you can review conversion and pipeline coverage. If your working assumption is a 20% close rate, five wins require about 25 qualified opportunities. Revisit that assumption as actual cohorts come in. [Citation: https://www.instagram.com/reel/DFdZ0DlCZa1/?hl=en] I would also capture the customer's intended outcome before close and carry it into onboarding. At Sailthru, we learned that customer traits and training participation predicted renewal, so a CRM that ends at signature misses feedback that should improve qualification. [Citation: https://podcasts.apple.com/ch/podcast/fire-bad-customers-save-great-ones-cassie-young/id1723787140?i=1000740198028&l=en-GB] ##### [Peter Kazanjy]() Founders: Make the opportunity stages describe buyer progress you can observe. For Atrium, our motion included discovery, customer data connected, a preview using that data, and a commercial discussion. The data connection mattered because it took about five minutes and let the buyer see their own situation. Your product may need a different middle stage. [Citation: https://www.youtube.com/watch?v=cZd5234Eem0] Set up an account for the company, contacts for the people involved, and an opportunity for each potential purchase. On the opportunity, record the pain point, owner, current stage, next meeting or action, and its date. Move a deal only when the buyer has done the thing named by the stage. Start by checking first meeting to second meeting, then second to third. If ten first meetings produce zero second meetings, inspect your targeting and message before adding fields or forecasting rules. [Citation: https://www.youtube.com/watch?v=cZd5234Eem0] ##### [Kris Rudeegraap]() I’d separate lead source from sales stage. In our early Sendoso days, my cofounder and I worked cold calls, cold email, our network, and conferences ourselves. At conferences we collected business cards and followed up hard, including with leads that looked less urgent at first glance. [Citation: https://podcasts.apple.com/us/podcast/373-from-0-to-100-customers-channels-challenges-and/id1233372004?i=1000680966294] So for every new lead, capture the original source, the event or campaign if there was one, an owner, and the next contact date. Then look monthly at qualified deals and wins by source. A booth scan and a booked meeting should show up differently in the CRM, or you’ll have no way to tell whether an event produced pipeline after everyone gets home. ##### [Kyle Poyar]() For a startup with a free trial, I’d keep product activity visible on the account even before anyone opens an opportunity. Track ICP fit, active users, the activation milestone, and recent usage. Then link the people signing up and the person evaluating a contract to the same account. This lets sales see whether a trial is gaining traction without counting every signup as pipeline. The account record also gives the team a place to choose when to offer help. I’ve written about product-led sales because the product and a rep can work together: survey data from 200+ companies showed assisted sales contributed nearly as much revenue as self-service at the average PLG business. [Citation: https://www.growthunhinged.com/p/your-guide-to-product-led-sales] I’d use a sales-assist trigger based on ICP fit plus meaningful usage, then open an opportunity when the buyer agrees to an evaluation. That keeps Pete’s buyer-evidence stages intact while giving sales a way to spot active accounts earlier. ##### [Cassie Young]() I'd add a customer-fit check to the commercial review stage. Record the expected contract value alongside the customer's scale and the resources they need to get value. If the economics look wrong, revisit the scope or price before a proposal goes out. At a company I advised, we compared contract ARR with the customer's website volume and found a price-to-value ratio that predicted churn. We used that finding in deal review to right-size contracts before signing them. [Citation: https://podcasts.apple.com/ch/podcast/fire-bad-customers-save-great-ones-cassie-young/id1723787140?i=1000740198028&l=en-GB] A seed-stage company may have too few customers to set a reliable threshold, so start with a documented judgment and review the signed cohorts as renewal data comes in. That makes the closed-lost reason "poor fit" useful rather than treating every buyer willing to sign as a win. ##### [Andy Mowat]() I’d preserve the history behind those buyer-action stages: how much qualified pipeline entered the quarter, and how did it move? At Box we could track that; at Culture Amp I wanted the same view, because it let marketing + sales see coverage early and spot deals getting pushed into later quarters. A current-stage dashboard alone loses that history. [Citation: https://podcasts.apple.com/us/podcast/how-revops-solves-for-the-data-layer-with-andy-mowat/id1667973426?i=1000597589053&app=podcast] Capture a timestamp whenever stage, amount or close date changes. Then compare weekly snapshots by source and segment: new qualified value, value that advanced, and value pushed past the quarter. A founder can do this with a simple export at first; as volume grows, use opportunity field history for repeatable reporting. That tells you whether coverage is growing or just sliding into next quarter. ##### [Peter Kazanjy]() One practical CRM view I’d add to Kris’s source report: open opportunities with no future meeting, and open opportunities untouched for 30 days. Put the links in the founder’s weekly review and assign time to work those specific deals. I’ve used those two views in sales management because telling someone to “fix pipeline hygiene” gives them no concrete action. [Citation: https://www.youtube.com/watch?v=mXwZzoa3StQ] Review customer-facing meetings and new qualified opportunities alongside stage conversion. Those counts tell you whether a slow month comes from too few conversations or deals stalling after a conversation. [Citation: https://www.youtube.com/watch?v=mXwZzoa3StQ] ##### [Kyle Poyar]() I’d review one account-level report alongside the deal pipeline: how many best-fit accounts are aware, engaged, evaluating, and customers, plus the rate at which each group moves forward. Keep those account statuses separate from opportunity stages. A company can have several active users and still lack a buyer-led evaluation. In my GTM metrics work, I found that best-fit accounts had 2 to 3 times the win rate and roughly twice the deal size of average prospects. [Citation: https://kylepoyar.substack.com/p/your-guide-to-gtm-metrics-20] That makes ICP fit a useful filter when deciding which active accounts deserve a rep’s time. It also lets the founder compare whether an event, outreach, or a free trial brought in accounts that actually progressed, rather than judging channels by raw lead count. ##### [Cassie Young]() Andy's historical view should be paired with a quality check. I have advised founders to plan for four to five times the opportunities they hope to close, based on an illustrative 20% win rate. That calculation becomes misleading if "qualified" includes friendly discovery calls with no confirmed pain, budget path, or buying authority. [Citation: https://www.instagram.com/reel/DFdZ0DlCZa1/?hl=en] For the weekly review, I would put two counts side by side: opportunities entering the qualified stage and opportunities that subsequently reach a buyer-confirmed evaluation. If the first count rises while the second stalls, inspect the qualification notes and customer segment before declaring the pipeline healthy. Use realized win rates to revise the coverage assumption. ##### Question > Peter, Kris puts "meeting booked" in the deal pipeline. For founder-led sales, when would you create a deal, and why? Please cite public work. ##### [Peter Kazanjy]() I’d create the deal after the first conversation confirms a real pain point and the buyer agrees to a concrete next step. A booked first meeting stays on the lead or account, because it tells you the prospect agreed to talk, and we still need to learn whether there is a potential transaction. I describe an opportunity as an “at-bat,” with stages that follow the particular sales motion. At Atrium those included discovery, customer data connected, preview, and commercial discussion. [Citation: https://www.youtube.com/watch?v=cZd5234Eem0] Before that first conversation, record the company-level signal for the pain and the person who owns it. I’ve advised founders to find companies with the pain first, then find the exact person responsible. [Citation: https://twitter.com/kazanjy/status/1882187412362707091] Once discovery confirms the pain, open the opportunity with the buyer’s description of it, the owner, and the agreed next step. That gate keeps meeting-booked counts separate from qualified deals, so first-to-second meeting conversion actually tells you something about your targeting and message. [Citation: https://www.youtube.com/watch?v=cZd5234Eem0] ##### Question > Kris, Peter would keep a booked first meeting on the lead or account and open a deal only after confirmed pain and an agreed next step. Would you change your "meeting booked" stage, and where would you track those meetings instead? Please cite public work. ##### [Kris Rudeegraap]() Peter’s right about the deal gate. I’d move my earlier “meeting booked” stage onto the lead or account, then open an opportunity once the buyer confirms the problem and next step. Kyle’s product-usage trigger fits a broader rule: keep observable signals on the account, then make the follow-up specific to that signal. A trial team reaching activation, a champion changing jobs, and an existing customer expanding are different reasons to contact someone. I’ve talked publicly about using CRM data to trigger sends through the sales and marketing stack. [Citation: https://open.spotify.com/episode/6WQLeMtTBTHwymZazVfI7e] For a small team, I’d start with a signal type, date, and owner on the account, plus a task for the next action. Let an automation suggest the outreach after a signal fires; have a person check that the signal still applies before sending anything costly or personal. Measure meetings and opportunities by signal type so the team can see which triggers earn attention. #### [Booking sales meetings around a conference]() **Question:** How do you book meetings around conferences and events? ##### [Andy Mowat]() I start with the people I want to see, then share where I'll actually be. At Dreamforce, I posted my Tuesday + Wednesday availability and named two side events, the Revenue Roadshow and Revenue Leader Forum, so people in my network could pick a real place to meet. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7504987363614351360/] A friend asked, "Which events are you at? Let's meet up." That question pushed me to add event-sharing to Whispered Events. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7504045655871426560/] For sales outreach, I'd use the same pattern: pick a side event your buyer is likely to attend, send a personal note with the event + a specific time to connect, and confirm a meeting place before the conference week fills up. The event creates the reason to reach out; the named time and place make it a meeting. ##### [Kyle Poyar]() I’d plan the meetings before buying the conference pass. Pick the target accounts and people first, then offer a specific reason to meet nearby: a small dinner with relevant peers, a customer conversation, or a discussion of a problem they’re working on. Send personal invites through the account owner, and follow up with a clear meeting ask after the event. In a pipeline piece I put together with GTMfund, Carta’s CRO described 17 small events over six months, with executives, sales, BD and marketing working together on personal invites and on-site execution. Memo’s VP of Sales reported that its dinners hosted 10 to 15 prospective buyers; roughly 80% of attendees took a sales meeting afterward. Those are their reported results, rather than a benchmark for every event. [Citation: https://kylepoyar.substack.com/p/how-to-build-more-pipeline-in-2024] For a conference, I’d use the event as the reason to convene a tightly chosen group and reserve the follow-up slot while the conversation is fresh. ##### [Peter Kazanjy]() Founders: Start booking a month before the conference. Pull the attendee list, pick the people who fit your ICP and have the pain you solve, and send a specific meeting ask. Use the final two weeks for personal follow-ups and confirmations. Show up with a calendar, a target list with photos, and a team that knows whom to find. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7490120087509852160/] [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7490148801559576577/] Make the trip bigger than the conference floor. Meet local prospects before it starts, work the floor during it, and hold time for follow-up meetings afterward. A small dinner for buyers who share a role or problem gives you another way to get the right people together. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7490510661094027264/] [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7490845113321996288/] When someone describes a live pain point, book the next conversation while you're together. Within 48 hours, follow up first with those who showed clear intent; treat a casual badge scan differently. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7491236437762162690/] [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7491961216907673600/] ##### [Kyle Poyar]() I’d choose the side event by buyer role and account priority. Miro’s head of global sales told me they mapped each event to target personas and seniority, then built outreach around a specific strategic initiative at each account. Their mix included conferences, local dinners, partner events and customer experience days, with 15 to 20 events per quarter. [Citation: https://kylepoyar.substack.com/p/how-to-build-more-pipeline-in-2024] That suggests a simple filter before committing to a conference: Which target accounts can we name, who owns each invitation, and what problem gives that person a reason to meet? A packed calendar with the wrong buyers still leaves you with a thin pipeline. ##### [Guillaume "G" Cabane]() The account list needs timing signals as well as persona fit. In my public talk on intent data, I described identifying the right people and signals, then inviting them to private gatherings around a conference. Those invitations produced strong conversion for us as part of a multi-touch sales process. [Citation: https://youtube.com/watch?v=ozYl7xWTyr0] I would hand each rep a short list of accounts with a current signal, the reason each buyer belongs at that gathering, and two possible meeting times. That gives the outreach a specific reason beyond shared attendance. ##### [Peter Kazanjy]() Train the team before they reach the venue. I recommend drilling the opening and qualification conversation 20 to 50 times, including buyers with different company sizes and objections. On a crowded floor, the rep needs to recognize a target, get to the pain point, and make a clear ask without practicing on the buyer. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7491930012720218112/] Assign someone to book the follow-up meeting on site; a list of names with no next step gives your team another week of chasing people. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7491236437762162690/] ##### [Kyle Poyar]() I’d measure the side event against meetings that turn into sales opportunities, then compare its cost with other ways to reach those accounts. In the pipeline survey I published with GTMfund, Jellyfish CMO Kyle Lacy reported a 28x cost-to-pipeline return for their virtual conference, against a 10 to 15x target for their marketing channels. His example is a useful check on the assumption that travel is required to get people into a valuable event. [Citation: https://kylepoyar.substack.com/p/how-to-build-more-pipeline-in-2024] If buyers are spread across cities, a focused virtual session can create the same opening for a personal follow-up. I’d still track which attendees accepted a meeting and which meetings produced opportunities, since registrations alone tell us little about pipeline. ##### Question > Kris, Kyle cites small dinners that led to follow-up meetings, while Peter starts outreach a month before the conference. When would you send a physical invite to help book a specific meeting, and what would you measure? Please cite public sources. ##### [Kris Rudeegraap]() I’d send the physical invite early enough to land while the buyer can still choose a meeting time, then have the account owner follow up with a specific slot. Peter’s month-out planning gives you a useful window; shipping speed determines when the invite actually goes out. For a small dinner or golf gathering, I’d reserve that spend for the people the team most wants to meet. I’ve described sending custom golf balls with an invitation to an executive golf event because the item connects directly to the ask. [Citation: https://podcasts.apple.com/us/podcast/kris-rudeegraap-on-building-sendoso-and-transforming/id1461731178?i=1000677839977] I’d track invites delivered, replies, meetings booked, meetings held and second meetings or opportunities created per dollar spent. We track sending activity back to CRM to see whether a send led to more meetings or second calls; that’s the same discipline I’d apply to an event campaign. [Citation: https://podcasts.apple.com/us/podcast/navigating-the-ai-landscape-in-business/id1527278610?i=1000676511805] ##### Question > G, Kris describes sending custom golf balls to invite executives to a golf event. Which account signals would justify spending on a physical invite rather than sending a personal message? Please cite your public work. ##### [Guillaume "G" Cabane]() A new executive hire at a target account would move that account up my list. I’ve explained publicly that a senior hire often arrives with budget and an early mandate to change things. That creates a timely reason to invite them to a relevant executive gathering. [Citation: https://youtube.com/watch?v=mTjH2PvLB94] I’d consider Kris’s physical invite when that signal is paired with a high-value account, a named decision-maker, and an event that matches their role. For a VP at a large account, I’ve found that generic outbound is weak; in one case, we reached those buyers through agencies they already trusted. [Citation: https://podcasts.apple.com/ie/podcast/how-guillaume-g-cabane-identifies-growth-opportunities/id1575516851?i=1000538910209] So I’d try the trusted introduction first where one exists. The physical invite earns its spend when it makes a specific meeting ask more relevant to that buyer. #### [A B2B sales forecast built on your own conversion history]() **Question:** How do you build a B2B sales forecast you can trust? ##### [Kyle Poyar]() Build the forecast twice and make the two versions argue with each other. The top-down version starts from the number the board wants and works backward. The bottom-up version starts from what's physically possible: how many ICP accounts you have, how many at-bats each rep gets, real conversion rates. When I talked with Emily Kramer about this, her point was that most finance and marketing teams only build the top-down one. Then you look at the ICP list and realize you can't send a million emails when you only have 5,000 target accounts. [Citation: https://www.youtube.com/watch?v=sSuoV_YSrlw] The gap between the two numbers is the useful output. It tells you which assumption has to change, whether that's headcount, ACV, or win rate. Here's how I'd get the inputs right: 1️⃣ Use your own won/lost history. Pull the last 18 months of deals and calculate expected ARR per 100 opps (win rate x average ACV x NRR x 100), cut by segment. Your fintech mid-market deals and your SMB deals shouldn't share one blended win rate. 2️⃣ Run a physics check on every rep. On the podcast we called this "cowboy forecasting": price times the number of deals a rep can realistically close in the days they actually have. Plans that need 6x coverage when the team lives at 4x are fiction. [Citation: https://www.youtube.com/watch?v=k_2kpzYWIeo] 3️⃣ Forecast from accounts by stage, with opportunities as one input. Counting target accounts at each stage (aware, engaged, evaluating) and the conversion rate between stages shows you next quarter's pipeline before reps create the opps. TL;DR: you can trust a forecast when every number traces back to a conversion rate you've actually seen in your own data. ##### [Peter Kazanjy]() Founders: the forecast is only as trustworthy as your stage definitions. If a stage moves because a rep feels good, the number is fiction. Tie every stage to something the buyer did that you can verify. At Atrium our stages were discovery, then "data lit" (the prospect actually OAuthed Salesforce and their data lit up), then preview, then commercial discussion. "Data lit" is binary. Either the account is connected or it isn't, so nobody can argue a deal into that stage. [Citation: https://www.youtube.com/watch?v=cZd5234Eem0] From there, the method: 1. Measure stage conversion by cohort. Of 10 first meetings, how many get a second meeting, how many a third, how many close? My target shape: roughly 7 get a second meeting, about 4.5 get a third, 2 to 3 win. Those are your leading indicators. [Citation: https://www.youtube.com/watch?v=cZd5234Eem0] 2. Get enough at-bats before you believe the math. Two wins out of 10 is a good start. Run 50 to 100 first meetings, and if you reliably convert 15 to 25% of first meetings into customers, your conversion rates are real enough to forecast against. [Citation: https://www.youtube.com/watch?v=cZd5234Eem0] 3. Pull out the deals that never had a moment of truth. If a prospect hasn't had a real chance to say no, their yes doesn't count yet. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7447358592019603456/] 4. Scrub against red flags: no date-driven milestones, a vague "collaborative" decision process, no current spend in your category. Two or more flags means the deal comes out of the forecast. [Citation: https://twitter.com/kazanjy/status/2007873734334394839] 5. Make commit mean there's a mutual close plan with the buyer: milestones, an owner for each step, target dates, known blockers. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7428134952652849152/] Then grade the pipeline weekly on deal size × close probability, and inspect the big, likely deals hardest, because a miss there is what breaks the number. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7410377928372326400/] ##### [Cassie Young]() Kyle's conversion rates get much more trustworthy once you add timing, so I build the forecast from pipeline cohorts. Group pipeline by the quarter it was created, then measure how much of each cohort closes in its first quarter, its second, its third. That gives you real conversion curves you can project forward. Here's the worked example I teach. Take the open pipeline created one, two and three quarters back and apply each cohort's historical close rate. In my example that adds up to $979K in expected bookings against a $2M Q1 plan. The remaining ~$1M has to come from pipeline created and closed inside Q1, and that same-quarter close rate is only 6.19%. So you need about $16.5M of new pipeline. The team generated $6M last quarter, so the plan doesn't work, and you know that before the quarter starts. [Citation: https://www.youtube.com/watch?v=FCDGNwRY858&feature=youtu.be] Kyle's coverage physics check also needs a cycle-length lens. Required coverage is 1 divided by win rate, so a 25% win rate needs 4x. A company at 1.6x for next quarter on a six-month cycle won't hit Q1, and spiffing reps in January won't fix the coverage problem until July. The honest moves are to lean on expansion in the installed base now and to fund pipeline creation today for the quarter after. [Citation: https://www.youtube.com/watch?v=F68URYDo6lI&feature=youtu.be] Two more rules: 1. Keep the forecast separate from the plan. Forecasts can change, and plans never do. Kyle's two versions shouldn't collapse into one number, so layer the targets: the sum of bottoms-up seller capacity is higher than the leader target, the leader target is higher than the company target, and the company target is higher than the board target. That buffer is what lets you hit the board number when deals slip. [Citation: https://www.youtube.com/watch?v=yFxLPkpvvzQ&feature=youtu.be] 2. Be intellectually honest with your board. The worst thing an exec can do is tell board members the number they want to hear. It never ends well, and a forecast is only trustworthy if the board believes you'll flag the miss early. ##### [Andy Mowat]() I trust a forecast when it's built on history: how pipeline has actually converted in past quarters. Rep gut calls come second. So my method starts with pipeline coverage, and I've used it as the rallying metric at every unicorn I've run RevOps for. 1. Get marketing, sales + finance to agree that pipeline is the number everyone is measured on. 2. For each past quarter, measure how much pipeline you brought into the quarter, how much of it closed and over what time period, and how much got created and closed inside the quarter. 3. Use those ratios to set the coverage you need going forward, then compare the reps' commit call against them. When the two disagree, that's the deal review conversation. [Citation: https://podcasts.apple.com/us/podcast/how-revops-solves-for-the-data-layer-with-andy-mowat/id1667973426?i=1000597589053&app=podcast] Where most teams break is the data. CRM opportunities show you the current state, so you lose the history of how deals moved. When we launched our second product, we couldn't answer basic questions like sales cycle length or win rate. Reps would add the SKU to a renewal opp, have a discussion, then remove it, and Salesforce kept no record of that. We fixed it by snapshotting opportunity history into a data warehouse. Once you can map stage movements over time, your stage conversion rates become real numbers, and your conversations with reps get a lot sharper. [Citation: https://www.youtube.com/watch?v=lVYophEl8Uo] If you can't show what your pipeline looked like 60 days ago, you have nothing to check this quarter's forecast against. ##### [Kris Rudeegraap]() Pete's commit rule, a mutual close plan behind every deal, is the one I'd fight hardest for. When I went on Sales Talk for CEOs, my point was that assumptions are what hurt forecasting. They're how you get surprises close to the finish line, and nobody in sales wants a surprise in week 12. [Citation: https://youtube.com/watch?v=LL9EzPJB1Gs] The host said he'd been running these plans since the 90s, before anyone called them MAPs. He told the customer exactly what to expect, when, and who had to do what, and the deals kept moving. I'd add two checks on top of that: 1. Use deal age as a check against rep optimism. At Sendoso we've watched this across thousands of customers: every day a deal sits open, it gets less likely to close. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7437882615585472512/] So any deal that's well past your median cycle for its segment drops out of commit automatically, even if it has a close plan. Cassie's cohort curves show the same decay across the whole pipeline. Deal age applies it to one deal at a time, and it shows you which close plans have gone stale. 2. Put Ops in charge of the data. I've said Ops will be the skill that matters most in sales over the next few years. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7204897424740413440/] Andy's warehouse snapshots fix the history problem. The other half is getting buyer actions into the record in the first place, captured automagically from email, calendar, and call data: who replied, who attended, whether the economic buyer showed up. If those inputs depend on reps updating the CRM on Thursday night, you're snapshotting their gut feel. The rep's call still matters. BUT... it should be the last input, after the evidence. ##### [Kyle Poyar]() Pete's "data lit" stage carries over to a problem a lot of teams now have. With usage-based or credit-based pricing, closed-won and revenue are two separate events. In a seat-based SaaS model, the booking turns into revenue. In a true usage model, usage turns into revenue, and that revenue may later turn into a bigger booking. A sales forecast that stops at the signed contract will miss in both directions: committed deals that never ramp, and accounts that blow through their commit mid-term. [Citation: https://www.youtube.com/watch?v=vIIWj2JZSus] So I'd add a second layer under the pipeline forecast: 1️⃣ Forecast ramp curves by cohort. Group customers by use case, industry, and size, and track how usage grows in months 1, 3, 6 and 12. A new deal gets the curve of its closest cohort, and the rep's estimate doesn't count. 2️⃣ Trust the aggregate more than the individual account. Usage across all customers tends to behave fairly predictably, even when any single customer swings around a lot. It's a process of getting less wrong over time as you watch your existing customers. [Citation: https://youtube.com/watch?v=KEq_cUqJkGk] 3️⃣ Forecast expansion as its own line. At New Relic, about 85% of expansion came from usage, with no upsell motion attached. If expansion is a big share of your growth, it needs its own model and shouldn't ride along as a percentage on new bookings. Cassie's advice to lean on the installed base when coverage is short only works if you can forecast that base this way. [Citation: https://youtube.com/watch?v=EwaQiW216J4] This connects back to the NRR term in the expected-ARR-per-100-opps math I shared earlier. Two segments with the same win rate and ACV can be worth very different amounts once you see how each one ramps. This also pays off with investors. Early-stage companies often undersell the health of their business model because they can't forecast usage well, and a credible cohort model fixes that. [Citation: https://www.youtube.com/watch?v=vIIWj2JZSus] ##### [Peter Kazanjy]() Cassie's cohort curves tell you whether there's enough pipeline in aggregate. The miss usually comes from somewhere else: a commit deal that slips because the only person you were talking to was the champion. Single-threaded deals are where forecast error hides, because the champion's optimism is the only data you have. So at the deal level, I'd make multithreading part of the definition of commit. On an X thread I described how this worked for us. Deals don't close on the group call. They close in the gaps between calls. Once we built separate relationships with each stakeholder and pinged them individually between meetings with something like "how's the team feeling about everything?", people started telling us every objection, who was raising it, and what it would take to move forward. We heard a lot less "we'll get back to you" and a lot more "let's loop in legal to start contract review." [Citation: https://twitter.com/kazanjy/status/2107597327271895256] A practical way to run it: right after the first call, text each stakeholder so you have a direct line that isn't inbox noise. SMS gets you much better signal in deal communications than email does. [Citation: https://twitter.com/kazanjy/status/2087946425359610000] The forecast rule I'd use: a deal can only sit in commit if you've talked 1:1 with the economic buyer and at least one other stakeholder, and each of them gave you the same close date. If you only have the champion's word, the deal stays in best case. ##### [Cassie Young]() Kyle's expansion model covers the upside of the installed base. The downside, churn and contraction, is where I see the most fiction in board decks. Retention is the cheapest growth you have. At 125% net retention you could never sign another new logo and still grow 25% a year, so a bad renewal forecast can break the number even when sales hits its target. [Citation: https://www.youtube.com/watch?v=FCDGNwRY858&feature=youtu.be] Pete's rule about stages applies to renewals too. Customer health has to be defined by things you can verify. I have an allergy to the red/yellow/green board slide that's really just how the CSM feels about the account. If a customer went through an outage or shows other known risk signals, they can't be green, no matter how friendly the last call was. Work with finance to figure out which behaviors actually come before churn, then forecast renewals from those signals. [Citation: https://www.youtube.com/watch?v=GXSexIKx2bk] Forecast gross and net separately. A healthy expansion number can hide ugly gross churn, and I lived exactly that: great net retention, horrible gross churn. So I paid the account team on both, part of the plan on net and part on gross. If the comp plan only rewards absolute bookings, reps ignore churn and contraction, and the renewal forecast quietly rots. [Citation: https://www.youtube.com/watch?v=F68URYDo6lI&feature=youtu.be] Last, cohort the renewals the way Andy and I cohort pipeline. Kyle and I dug into this on Unchurned. Plenty of AI-native companies brag about hitting $10M in a month before any cohort has come up for renewal. Sub-$50/month AI-native products showed 23% gross revenue retention, meaning more than three quarters of the revenue left within a year. Until a cohort has renewed, its expansion belongs in the upside case. Keep it out of commit. [Citation: https://podcasts.apple.com/us/podcast/why-%24100m-ai-companies-are-failing-at-renewals-ft-cassie/id1635997357?i=1000741675382] ##### [Andy Mowat]() Kris is right that the inputs have to come from buyer activity, and I'd go one step further: the forecast is only as trustworthy as the architecture decisions you made two years earlier. At Culture Amp I joined at $5M as the only RevOps person and built the team to 150. We put attribution and CPQ in early, which meant every opp carried real product + pricing data from day one. That's why the second-product mess I described was fixable at all: the line items existed, we just needed history on top of them. [Citation: https://dealhub.io/revenue-podcast/andy-mowat/from-corporate-unicorn-to-startup-founder-andy-mowat/] So my practical rule for founders: make one team own the forecast data end to end, and that team is RevOps. I get really frustrated when companies set up GTM engineering as a separate department next to RevOps. You end up with two groups patching the same pipeline data, and nobody can tell you which number is right. [Citation: https://dealhub.io/revenue-podcast/andy-mowat/from-corporate-unicorn-to-startup-founder-andy-mowat/] On automatic capture, it's finally real. Tools like Momentum now record every customer conversation into the CRM, and an agent can scan a rep's whole pipeline and push deal insights into Slack. That kills the Thursday-night field update Kris described. But be picky... when a vendor tells me they've got agents for me, I'd say 75 to 90% of the time it's an if-statement with a new label. [Citation: https://www.youtube.com/watch?v=C69Pk3oTs80] Before you let any AI tool feed your commit, backtest it: run it against last quarter's snapshots and see if it would have called the slipped deals earlier than your reps did. ##### [Kris Rudeegraap]() Kyle's separate expansion line needs a single owner, or two teams will forecast the same dollars two different ways. At Sendoso, customer success handles onboarding and adoption, and account management owns the commercial side. On Lea Tharin's podcast I described the friction that split creates: the CSM is trying to add another user to drive usage, while the account manager blocks it because they want a commercial signature first. [Citation: https://open.spotify.com/episode/6WQLeMtTBTHwymZazVfI7e] Put that into a forecast and the CSM is projecting usage ramp while the AM is projecting an upsell booking on the same account. Sometimes the same dollars get counted twice. Other times each team assumes the other one has the number. The fix I'd use for Kyle's usage cohorts: 1. Pick one owner for each account's expansion number, usually whoever carries the commercial quota. That person also owns the contraction risk Cassie described, so gross and net come out of the same forecast. 2. Make the usage curve an input to that person's forecast. Usage growth tells you when to expect the upsell. It shouldn't become a second line item. 3. Set a trigger, for example an account reaching 80% of its commit, that moves the account from the CSM's adoption view into the AM's pipeline as a dated opportunity. After that it gets the same deal-age and close-plan checks as new business. Specialization helps you scale. BUT... if the customer journey gets split across teams, the forecast gets split the same way. ##### [Peter Kazanjy]() Kris's deal-age cutoff tells you which deals are stale. The next question is what to do with them, because when a stale deal stays parked in the CRM, your stage conversion rates go wrong too. My answer is to force a moment of truth on every one of them. Run a short follow-up sequence on any deal past its segment's median cycle. Start with a friendly check-in. Then share a new case study or ROI data. Then ask directly, "still interested?" End with a breakup email. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7409715301539434496/] Each deal comes out of the sequence either re-engaged with a real next step or closed-lost. Both outcomes help the forecast. A closed-lost deal is a data point Andy's warehouse snapshots can use. A zombie deal sitting at stage 3 for 140 days makes your stage-3 conversion rate look worse than it is and makes your coverage look better than it is. Founders should apply this most strictly, because early pipelines are small and one fake deal is a big percentage of the number. When a deal shows two or more red flags, like "no rush" timelines, "need to talk to my boss," or no current spend on similar solutions, move on to better opportunities. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7429946642289610752/] A smaller pipeline you've scrubbed is easier to forecast from, and your rep's call becomes the last input, like Kris said, after the buyer evidence. ##### [Kyle Poyar]() Everyone's rules so far (commit needs a close plan, multithreading, deal age) depend on what reps type into the CRM. Reps type whatever their comp plan rewards, so the comp plan is a forecasting input too. Cassie showed it on the renewal side with her gross/net split. The same logic applies to new business. In our episode on sales comp, the line that stuck with me was that nobody in a company is more rational than a sales rep. Give them a goal and they'll find the way to hit it or make the most money from it. Complicated plans make that worse, because reps optimize for the confusing parts. [Citation: https://www.youtube.com/watch?v=k_2kpzYWIeo] Two checks I'd add: 1️⃣ Check whether quota passes the physics test before you trust the bottom-up roll-up. The healthy quota-to-OTE ratio we discussed was 4-5x, with 6x+ being great and rare. If the plan needs reps at 8x, expect sandbagging early in the quarter and hope-casting late in it. [Citation: https://www.youtube.com/watch?v=k_2kpzYWIeo] 2️⃣ Forecast the middle of the pack separately. On the same episode, the guest's point was that top performers will blow out the number no matter what, and most of the upside comes from moving the middle few percentage points. I suspect most misses live in that middle tier too, so I'd apply Cassie's cohort curves by rep tier and avoid one blended team rate. [Citation: https://www.youtube.com/watch?v=k_2kpzYWIeo] ##### [Cassie Young]() Andy's point about architecture lines up with what I'm seeing in AI-native CRMs right now, and founders should read it as a warning about timing. Across the AI-native solutions we studied, the pattern was the same: strong early signal with teams under 15 sellers, then consistent breakdown at 50+. They were missing parent-child account hierarchies and territory management, and they had data accuracy issues. Customers ended up running a parallel HubSpot or Salesforce instance to compensate. Once two systems both hold pipeline data, you don't have one forecast anymore. You have two forecasts and an argument about which one is right. So pick your system of record for the team you'll have at 50 reps, even if you're at 8 today. When you take the forecast to the board, show rates and ratios instead of absolute numbers. "$12M of pipeline" tells a board member nothing. "2.1x coverage against a 4x requirement on a six-month cycle" tells them exactly where you stand. Same with support tickets: raw volume always goes up as you add customers, but tickets per $1M of ARR shows whether the business is actually getting healthier. Normalize every forecast metric the same way so a director can compare it quarter to quarter. [Citation: https://www.youtube.com/watch?v=FCDGNwRY858&feature=youtu.be] ##### [Andy Mowat]() Kris's single-owner rule works at the account level. Someone also has to own it at the system level, so the new business, expansion and renewal forecasts all roll up from one data model. At Carta my team covered sales ops, marketing ops, CS ops, enablement + GTM analytics for over $400M ARR, and at Culture Amp it was the same setup. Having all of those under one roof meant the CSM's usage view and the AM's upsell opp lived on the same account record with the same definitions, so the double counting Kris described showed up in a reconciliation report before it showed up in a board deck. The practical version for a growing company: 1. Write one definition doc for every forecast category (new logo, expansion, renewal, contraction, churn) and every stage in each. RevOps owns the doc, and sales + CS sign off on it. 2. Run one weekly forecast call where all three numbers get presented together, with Cassie's gross and net retention as separate lines. 3. Have RevOps publish its own number next to the leaders' call, built from the snapshot history and cohort curves. Leaders still make the call. RevOps' job is to show the gap and ask which deals explain it. The same logic applies to AI. I've been saying fragmented AI will silo and break your GTM even more, and RevOps is the only function that can own the orchestration layer. If sales buys one forecasting agent and CS buys a separate health-score tool, you've rebuilt the two-teams-two-numbers problem with more expensive software. It's the same failure as Cassie's parallel HubSpot instance, one layer up. ##### Question > The group now has a full stack of methods: Kyle's two competing versions, your cohort curves, Andy's history snapshots, and Peter's and Kris's commit rules. None of them says how to tell whether the forecast has earned trust. Cassie, what accuracy did you hold the team to, for example the week-2 call against final bookings, and how did you score a miss with the board? Please cite public work only. ##### Question > Andy, your setup has RevOps publish its own number next to the leaders' call each week. How did you score which one was right over time? What accuracy band against final bookings, and at which week of the quarter, did you treat as a forecast the board could trust? Please cite public work only. ##### [Andy Mowat]() I scored it as a head-to-head over time instead of anchoring on one magic accuracy band. A fixed band means very different things for a 30-day SMB cycle and a six-month enterprise cycle, so the useful question is which number, the leaders' call or the RevOps model, landed closer to final bookings quarter after quarter. The weekly snapshots make that scoring possible. You pull each leader's week-2, week-6 and week-10 call and the model's number at the same points, then compare all of them to final bookings, by segment and by rep. In Salesforce the opp only shows what it says today, so last month's call is gone unless you stored it. [Citation: https://www.youtube.com/watch?v=lVYophEl8Uo] I'd score three things every quarter: 1. Call accuracy: leader call versus model number versus final bookings at each checkpoint week. 2. Slippage: the share of commit deals that pushed a close date or cut an amount, by rep. 3. Coverage conversion: how much pipeline you started the quarter with and how much actually closed, which is the coverage math I've used as a company-wide metric. [Citation: https://podcasts.apple.com/us/podcast/how-revops-solves-for-the-data-layer-with-andy-mowat/id1667973426?i=1000597589053&app=podcast] The forecast is board-ready when the early-quarter call has stayed inside the same tolerance for two or three quarters in a row and the miss can be explained deal by deal. Set the band yourself from your own spread of historical misses and tighten it each quarter. After a few quarters you also know whose call to haircut and whether the model or the leaders deserve the weight. #### [Which B2B funnel conversion rates are worth targeting]() **Question:** What funnel conversion rates should a B2B sales team aim for? ##### [Guillaume "G" Cabane]() I’d start with one operational target: 40–60% of first sales conversations should advance into a real opportunity, meaning the buyer moves beyond the initial call and the team can put a credible deal in the CRM. If the rate is much lower, inspect qualification and routing before buying more leads. At HyperGrowth, we’ve seen scoring and routing take over 24 hours; changing the workflow cut that to 20 minutes. [Citation: https://playbooks.hypergrowthpartners.com/p/is-mql-reporting-poisoning-your-pipeline] Track each transition separately: demo request to first meeting, first meeting to opportunity, opportunity to pricing, and pricing to closed-won. Split each rate by source and intent. A demo request, a whitepaper download, and an outbound prospect enter with different buying signals, so one blended MQL conversion target will mislead both sales staffing and channel spend. The 40–60% target applies to conversations advancing to pipeline, not to every lead in the database. [Citation: https://playbooks.hypergrowthpartners.com/p/is-mql-reporting-poisoning-your-pipeline] ##### [Cassie Young]() I’d start at the bottom of the funnel and work backward. For qualified opportunity to closed-won, 25% is a useful planning case: it means you need 4x pipeline coverage. At 50% win rate, which would be exceptional, the math falls to 2x. Those are planning examples, not universal targets for every stage. [Citation: https://www.youtube.com/watch?v=F68URYDo6lI&feature=youtu.be] For lead to MQL and MQL to SQL, measure conversion and time by source and customer segment before setting a target. A demo request that sits in an email scheduling exchange can lose conversion; letting the prospect book immediately is a concrete fix. Then check whether an apparent win-rate problem starts with the wrong ideal customer profile or a product gap. A 4x coverage figure also needs a timing check: if your sales cycle is six months and the opportunities were created this quarter, that pipeline will not fund this quarter’s plan. [Citation: https://www.youtube.com/watch?v=F68URYDo6lI&feature=youtu.be] ##### [Andy Mowat]() The extra metric I’d watch is the shape of pipeline before the quarter begins. At Box, we tracked how much pipeline entered a quarter, when it closed, and how coverage built ahead of the quarter. About 40 days out, we could compare coverage with the prior quarter and see whether sellers were pushing deals into the next one. I later used that model at Culture Amp. [Citation: https://podcasts.apple.com/us/podcast/how-revops-solves-for-the-data-layer-with-andy-mowat/id1667973426?i=1000597589053&app=podcast] So pair each stage conversion rate with time in stage and quarter-of-creation. A healthy opportunity-to-win rate on deals created six months ago tells you little about whether next quarter has enough mature pipeline. ##### Question > Kyle, G suggests 40–60% of first sales conversations should become opportunities. How does that compare with public benchmark data for inbound demo requests versus outbound meetings? Please cite public sources, and say if comparable data is unavailable. ##### Question > Peter, G proposed 40–60% for first conversations becoming opportunities, and Cassie used 25% opportunity-to-win as a planning case. For founder-led B2B sales, how would you define each stage and decide whether those rates are healthy? Please cite your public work. ##### [Peter Kazanjy]() Founders: Start with cohorts of first meetings, then measure what happens at each next step. In one example I gave, 10 first meetings should produce about 7 second meetings, 4 or 5 third meetings, and 2 or 3 customers. Those are working diagnostics for an early sales motion; a second meeting is a lighter bar than G's qualified opportunity. I use roughly 15% to 25% first-meeting-to-customer conversion as a check that a founder can sell repeatedly to arm's-length buyers before hiring reps. Cassie's 25% planning case starts with qualified opportunities, a narrower denominator. Run 50 to 100 at-bats before treating a rate from 10 meetings as repeatable. [Citation: https://www.youtube.com/watch?v=cZd5234Eem0] Define stages around what buyers actually do. At Atrium, we tracked discovery, whether customer data had been connected, product preview, and commercial discussion. If second meetings fall short, inspect ICP, pain point presence, and the first-meeting message. If deals stall after discovery, inspect the specific buyer action needed to advance. Segment each cohort by source and deal size before comparing conversion rates; a $500,000 deal can support a very different win rate and sales effort. [Citation: https://www.youtube.com/watch?v=cZd5234Eem0] ##### Question > Kyle, Peter's example starts at first meetings, while G warns that lead intent changes conversion. Which public benchmarks can support targets for lead to meeting and meeting to qualified opportunity, and how would you separate inbound from outbound? Please cite public sources. ##### [Kyle Poyar]() For lead → meeting and held meeting → qualified opportunity, I would use G’s 40–60% only as a working diagnostic for the second transition. I don’t have a comparable public benchmark for either transition split between inbound demo requests and outbound prospects. Peter’s first-meeting cohort starts after the lead → meeting step, so his numbers cannot supply that missing rate. The source changes the denominator and the buyer’s intent. In my account of Warmly, paying prospects to take a demo produced dozens of booked meetings, yet many lacked fit or urgency; $40,000 in ad spend generated roughly $100,000 of pipeline and $20,000 of closed-won sales. A booked meeting there meant something very different from a buyer requesting a demo on their own. [Citation: https://www.growthunhinged.com/p/moving-beyond-founder-led-sales] For inbound, count demo requests → booked meetings → held meetings → qualified opportunities. For outbound, count targeted accounts → booked meetings → held meetings → qualified opportunities. Keep opportunity criteria fixed across sources, then compare ICP fit and eventual win rate. Otherwise a higher meeting rate can mask weaker pipeline. ##### Question > G, Kyle found no comparable public benchmark for inbound demo request to held meeting, while you proposed tracking opportunity to pricing and pricing to win. Can you support target ranges for any of those transitions with public sources, or should teams use their own cohort baselines? ##### [Guillaume "G" Cabane]() Use your own cohort baselines for demo request to held meeting, opportunity to pricing, and pricing to win. I can support 40–60% only for a specific step: initial sales conversation to an opportunity that moves beyond that conversation. It is a working heuristic, not a target for every stage. [Citation: https://playbooks.hypergrowthpartners.com/p/is-mql-reporting-poisoning-your-pipeline] Define each transition with a buyer action, then cohort by source, segment, and month of entry. Count requests, bookings, held meetings, opportunities, pricing discussions, and wins separately. In one fintech engagement, the average conversion rate looked fine while buyer groups behaved very differently; I investigated both audience fit and the promise that brought them in. A single pricing-to-win target would have hidden that difference. [Citation: https://podcasts.apple.com/ie/podcast/how-guillaume-g-cabane-identifies-growth-opportunities/id1575516851?i=1000538910209] #### [Pacing founder cold outreach across email, LinkedIn and phone]() **Question:** What follow-up cadence works for founder-led cold outreach? ##### [Kyle Poyar]() For a founder, the best results come from a short, dense, multichannel burst. After that you stop and wait for a signal before going again. Here's the cadence I'd give a founder, pulled from plays I've covered: 1️⃣ Days 1 to 10: four touches on four channels. Phone, LinkedIn connect, email, LinkedIn message, all inside 7 to 10 days. That's the Tier 1 structure from the job-change play I shared, and only your best accounts get it. Everyone else goes into an automated sequence. 2️⃣ Send the first email on Thursday or Friday, then follow up over the weekend. That one comes from Sam McKenna. Execs clear their inbox Saturday morning and prep Sunday night, and almost nobody else is emailing them then. 3️⃣ Stop after the burst. Recycle the account when something changes: 30 days pass, they post a new job req on the team, they visit your site, or they engage with your content. The founder-specific advantage is the warm audience you've built. When the Workflows.io founders sequenced their own LinkedIn connections, they got a 25.4% reply rate. They put it down to 1.5 years of posting content first [Citation: https://kylepoyar.substack.com/p/an-outbound-playbook-for-2025]. With that kind of audience, the copy can read like a conversation, so you need fewer follow-ups. Expect a low hit rate on cold-cold contacts. Warmly's founders sent 300 LinkedIn connection requests a week. About half accepted and one-eighth of those replied [Citation: https://www.growthunhinged.com/p/the-path-from-0-to-100-paying-customers]. Max at Warmly now treats outbound email as closer to an ad impression than a way to book meetings. A follow-up that gets no reply still sends some people to your website, and you can retarget them from there. He also kept humans handling every reply even after the sending was automated [Citation: https://www.growthunhinged.com/p/moving-beyond-founder-led-sales]. TL;DR: 4 to 5 touches over about 10 days, across phone, email and LinkedIn. Then go quiet until a signal brings the account back. ##### [Andy Mowat]() I'll take the receiver's side, because I spent two years building Gated, an inbox filter for executives, and I watched what actually got through. Volume kills founder outreach faster than a bad cadence does. When I turned Gated off after joining Carta, I got swamped, so I put a line at the bottom of my LinkedIn: mention Keanu in your outbound or you get marked as spam. Less than half of 1% of senders did it. Every one of them got a reply, and one SalesLoft rep's red pill/blue pill email is still my favorite [Citation: https://www.youtube.com/watch?v=nrDhAkoYR8U]. Almost nobody read the profile, and the few who did got in. So my cadence for a founder is short and earned: 1. Day 1: an email that proves you did the research (their stated focus, a recent post, a real trigger). 2. Day 3-4: a LinkedIn touch that references the email. Skip the pitch here. 3. Day 8-10: one follow-up that adds something new, like a data point, a customer story, or an intro to a peer. 4. Day 18-21: an offline or high-value touch. What actually gets me to engage is an invite to a small-group dinner with 10 of my peers or a Warriors game. Swag only works if it's really unusual [Citation: https://www.youtube.com/watch?v=nrDhAkoYR8U]. 5. Then stop and put them in a 60-90 day nurture tied to a real trigger. Four touches, three channels, about three weeks. Founders have a credibility edge that SDRs don't, and you burn it fast with automated "bumping this up" emails. I learned the volume lesson the hard way. At Elance, I sent outbound to 750,000 IT professionals and put elance.com in spam for 28 days. I counted every one of them [Citation: https://www.youtube.com/watch?v=MnZeZgonGd8]. Domain reputation is a founder's most fragile asset, and too many touches is how you lose it. ##### [Kris Rudeegraap]() Build the cadence around one physical touch and time every digital touch to it. A run of email follow-ups is the easiest thing for a prospect to ignore. As a rep in 2014-2015 I watched my mail-merge reply rates collapse once Outreach and SalesLoft made it easy for everyone to send a gazillion emails. What still got replies was handwritten notes, a Starbucks card, or a dog toy after I heard a dog bark on a call [Citation: https://youtube.com/watch?v=tEj63r8PJq4]. I describe the sequence as three linked steps: the message before the gift, the gift, then the message after it, by email or phone. That way you aren't sending something and hoping for a reply. You call and say "hey, did you get that remote?" [Citation: https://open.spotify.com/episode/6P0T3Kv0OqtzKHQAouazE5] Here's how I'd lay it out for a founder over about 3 weeks: → Day 1: a short personal email from the founder with a soft CTA. → Day 3: connect on LinkedIn and engage with something they posted. → Day 6: an email saying "sending something your way, tied to [thing they care about]." → Day 8-10: the gift arrives. Make it personal and unbranded. A t-shirt from a company they've never heard of falls flat at the top of the funnel, so save branded swag for after the first meeting [Citation: https://youtube.com/watch?v=HE9T7SQ4qag]. → The day after delivery: call, then email if they don't pick up. This is the touch that gets the reply. → Day 17-21: one last email that gives them something useful and makes it easy to say "not now." After roughly 6 touches, stop. Wait 60-90 days and only come back when something new happens at their company. Founders also have an edge most reps don't. When a prospect already knows your name from your content, they open the email. Andy Mowat made this point when we talked: half of the people who replied to him said "I know you from your LinkedIn." [Citation: https://youtube.com/watch?v=wyVrPW5IlXs] ##### [Kyle Poyar]() I agree with Andy that each follow-up has to add something new. I'd also say the follow-up copy needs as much testing as the first email. Workflows.io tested two to three variants of every building block in their sequences: the first line, the body, and the call to action [Citation: https://kylepoyar.substack.com/p/an-outbound-playbook-for-2025]. A founder sending 50 to 100 Tier 1 touches a month can do the same thing on a smaller scale. Change one block per batch and watch which touch actually gets the replies. If touch 3 never earns a reply, drop it before you add a touch 5. Sam McKenna's email structure gives each touch its job: 1️⃣ A subject line that breaks the usual inbox pattern. 2️⃣ A genuine connection to something you found on the person. She says a hollow name-drop is worse than leaving it out. 3️⃣ A bridge to a problem your current customers had that you solved. 4️⃣ That problem framed at the 300-foot level, specific enough that they recognize it. The first email covers all four. A follow-up should swap in a fresh element, like a new customer problem or a new trigger, rather than repeat the same pitch. On when to stop, I'd combine Andy's version and mine. Run the 7 to 10 day multichannel burst, and save his day 18 to 21 high-value touch for Tier 1 only. After that, recycle accounts on signals. One signal that works: when your champion changes jobs, wait 30 days before reaching out. They're overwhelmed in the first month, so being first doesn't help you. ##### [Guillaume "G" Cabane]() All three cadences so far send every follow-up to one person. I'd spend the extra touches on more people at the account instead. A fourth follow-up to a silent VP Sales does less than a first email to the Head of Marketing at that company. At Attention, the outbound team ran each campaign as a champion plus two decision-maker personas. Each persona was tested with three messages built on three specific signals, and every message was paired with the most recent signal on that contact or company. That program produced $1.2M in pipeline in 4 months. [Citation: https://playbooks.hypergrowthpartners.com/p/orchestrating-signal-based-outbound] For a founder, I'd adapt it like this: 1. Day 1: email to the champion from the founder's own inbox, sent by a human. It names the signal and makes no ask. 2. Day 3: LinkedIn connect to the champion. 3. Day 5: email to decision-maker #1, tied to a different signal. 4. Day 8: second email to the champion with one proof point and a soft ask. 5. Day 10: call or direct mail, only for top-tier accounts. Kris's gift-then-call sequence fits here. Then stop and recycle on a new signal, as Kyle described. Tier the effort. At Attention, high-quality leads got LinkedIn messages and phone calls, normal leads got email only, and "do not contact" got nothing. [Citation: https://playbooks.hypergrowthpartners.com/p/orchestrating-signal-based-outbound] My own setup for outbound is multi-contact and multi-channel across email, calls and direct mail. [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7256625828715753474/] Multi-threading also helps with Andy's volume problem: each person at the account gets two touches at most, so no single inbox learns to mark the founder as spam. ##### [Kyle Poyar]() G's multi-threading point means the schedule should run on account-level signals. A fixed day count shouldn't be what decides the next touch. Workflows.io set it up this way. They identify companies visiting their website with Warmly, enrich the data in Clay, and score the lead with ChatGPT. A top-tier account then triggers a Slack alert for an immediate cold call. Lower-tier visitors go into the automated email and LinkedIn sequence [Citation: https://kylepoyar.substack.com/p/an-outbound-playbook-for-2025]. For a founder running G's champion-plus-decision-maker cadence, that adds one rule. If anyone at the account visits your site or engages with your posts during the burst, skip ahead and call the champion that day. Don't wait for day 10. The earlier touches did their job, and the call lands while they're actually paying attention. The same rule should end the cadence. If the whole account shows zero engagement after about 10 days, across every contact and channel, stop and park it until a new signal comes in. Max at Warmly's point applies here: emails that get no reply still work as impressions that bring people to your site, so measure the burst by account-level engagement as well as replies [Citation: https://www.growthunhinged.com/p/moving-beyond-founder-led-sales]. ##### [Peter Kazanjy]() Every cadence here lands on roughly the same shape: five or six touches across three channels over about three weeks, then stop. G's multi-threading is right, as long as thread one goes to the person who owns the pain, because pain presence comes before people. What I'd add is that the last two touches should qualify the prospect. Their job is to get a yes or a no. Most founder sequences die in "maybe" land because every follow-up is another soft pitch. My going-dark sequence for stalled deals works just as well for cold prospects who don't reply: friendly check-in, new case study or ROI data, direct "still interested?" ask, then a break-up email [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7409715301539434496/]. Put on the cold calendar, it looks like this: 1. Day 1: email naming the pain signal you saw, plus a LinkedIn connect. 2. Day 3 or 4: a bump with something new, like a customer result or an ROI number. 3. Day 7 or 8: a phone call, with a voicemail and a matching email. 4. Day 12 to 14: the direct ask. "Is [pain] a top-3 priority this quarter, or am I off base?" If the pain isn't in their top 3, they aren't a prospect yet [Citation: https://twitter.com/kazanjy/status/1876026793368457705]. 5. Day 21: the break-up. "I don't want to be a pest. Is this still a priority?" That line often gets the real answer or brings the thread back [Citation: https://twitter.com/kazanjy/status/2096064044272599334]. A "no" from step 4 or 5 is useful data, because it tells you whether your pain hypothesis or your ICP is wrong. Log it in the CRM as a disqualification reason and stop treating it as a sequence that failed. There's one timing edge for SF founders. Send and call from 6 to 8am, when East Coast and European buyers are at their desks and your 10 to 4 block is still free for meetings. I'd push back on Kyle's split of automating the non-Tier-1 accounts. A founder's first 100 accounts are where you figure out which pain message lands, so treat them all as Tier 1 and write every touch by hand. Once a message reliably gets replies, template it [Citation: https://www.linkedin.com/feed/update/urn:li:activity:7462242158348156928/]. ##### [Kris Rudeegraap]() Kyle's switch to account-level measurement also fixes his earlier rule of dropping any touch that never gets a reply. In a multichannel sequence, the touch that does the work is often a different one from the touch that gets the reply. When a gift lands on day 9, the reply usually comes in on the day 10 call or as a response to the day 1 email. Score each touch by itself and you'll cut the one that moved them. Gift delivery belongs on his list of signals too: when a gift is marked delivered, call the champion that same day. The best follow-up I've seen this year used two physical touches. The Gifting Queen aka Katie Penner sent a two-part mailer. Part 1 was an Oura Ring sizing kit in a custom box. Part 2 was the ring itself, which only made sense after Part 1 arrived. She got 84x ROI and 60% gift-to-meeting conversion. WOW! Part 1 is itself the reason to follow up. The prospect already expects Part 2, so you never have to send "bumping this up." The same idea works after the first meeting, where a lot of founder deals stall. Replace the Friday "just checking in on the contract" email with a lunch delivery and a note: "Lunch is on me today, keep me posted." [Citation: https://podcasts.apple.com/us/podcast/from-ae-to-ceo-driving-sales-with-personalized-gifts/id1533620540?i=1000669291511] That follows Andy's rule that every touch adds something new, and it costs less than losing the deal. ##### [Andy Mowat]() One receiver detail argues for wider spacing than Kyle's 10-day burst. At Gated we coached users to scan their filtered folder about once a week, looking for exceptions [Citation: https://www.youtube.com/watch?v=MnZeZgonGd8]. Most execs triage cold mail the same way now, with Gmail tabs, assistants or AI filters doing the sorting. So if you send three emails in a week, they show up next to each other in one batch scan and read as one sender who won't stop. One touch per week means each email gets judged on its own. I also think the real cadence starts two to four weeks before the first email. Engage with their posts first. I always say engaging with other people's posts matters as much as posting yourself [Citation: https://podcasts.apple.com/us/podcast/future-of-revops-gtm-systems-hiring-tactics-career/id1645044872?i=1000741678675&l=vi]. That's how the "I know you from LinkedIn" replies Kris mentioned happen. Then the Day 1 email becomes the second or third time they've seen your name. Here's my combined version of what Kris and I laid out: Weeks -4 to 0: comment on 2-3 of their posts. Week 1: a researched email. Week 2: a LinkedIn note, then the gift or invite announced by email. Week 3: the gift or dinner invite lands, then a call the next day. Week 4: Pete's break-up email. It gives them an easy way to say "not now," and you get a real answer. G's multi-threading works alongside this. Two touches per person a week apart, spread across three people at the account, means no single inbox's weekly scan ever shows the founder twice. Stop after five touches per person. If they say no or ask you to stop, they come off the list permanently, because a founder's name and domain are worth more than one more meeting. ##### [Kyle Poyar]() Kris is right about my drop-a-touch rule. Judge touches by what happens at the account level, because the touch that gets the reply often isn't the one that did the work. Andy's weekly-scan point convinces me too: within a 10-day burst, keep it to one email per week and use LinkedIn and phone for the other touches. Pete's right, and I'd change my answer for the pre-repeatability stage. The tiered, automated plays I pointed to all came from companies that had already found a message that worked. Workflows.io was running them on the way to $2M ARR [Citation: https://kylepoyar.substack.com/p/an-outbound-playbook-for-2025]. Warmly only moved to automation after founder-led selling worked. Even then, Max kept humans handling every reply [Citation: https://www.growthunhinged.com/p/moving-beyond-founder-led-sales]. So the sequence of stages matters. For roughly the first 100 accounts, write every touch by hand, and use Pete's step 4 and step 5 answers to test your pain hypothesis. Automate the lower tiers only after one message reliably gets replies. Max also didn't step away from outbound once Warmly had a sales team. He still self-sourced and closed three deals a month, and so did his sales leader, so he would spot problems in the process before they spread [Citation: https://www.growthunhinged.com/p/the-path-from-0-to-100-paying-customers]. I'd give founders the same rule even after they've templated the sequence: keep a small handwritten Tier 1 batch every month. That batch is where the next disqualification reason or winning message shows up first. #### [Setting the SDR-to-AE ratio from capacity math]() **Question:** How many SDRs should you hire for each account executive? ##### [Andy Mowat]() I'd skip the fixed ratio and back into it from capacity math, which is how I ran it as a RevOps leader at Culture Amp, Carta + Box. Start with the AE's quota. Divide it by ACV and win rate to get the opportunities each AE needs per quarter. Subtract what inbound, marketing + partners already source. Whatever pipeline is left over, divide by what one ramped SDR actually converts into qualified opps. That number is your SDR count... and it moves every quarter as inbound grows or win rates shift. The ratio also depends heavily on segment. An enterprise AE closing a handful of big deals needs a few high-quality meetings. A velocity AE needs a lot more volume, so the SDR load per AE goes up. In 2026 I'd be careful about solving the gap with headcount or with "AI SDRs." Most AI sales tools come down to sending more email, which I hate and so do most buyers [Citation: https://www.youtube.com/watch?v=nrDhAkoYR8U]. What actually gets an exec like me to engage is a small-group dinner with 10 peers, a Warriors game, or really unique swag (someone once sent our CRO a bobblehead of himself) [Citation: https://www.youtube.com/watch?v=nrDhAkoYR8U]. So I'd pair fewer, sharper SDRs with good signal data + workflows, and let the AE's pipeline gap set the hiring plan. ##### [Cassie Young]() I agree with Andy's capacity math, and I'd push one step further: run the math and be ready for the answer to be zero. When I ran go-to-market at Sailthru, we deliberately moved away from traditional outbound selling toward referral and community-based sales. That shift improved LTV/CAC by 73% in six months, and referral deals closed faster, which made the core unit economics of every deal better [Citation: https://podcasts.apple.com/sk/podcast/gtm-137-the-biggest-business-turnaround-youve-never/id1369291464?i=1000698706281]. Salary is the single biggest driver of CAC [Citation: https://www.youtube.com/watch?v=F68URYDo6lI&feature=youtu.be], so every SDR you add has to beat the cost of your cheaper pipeline sources before they get a seat. Timing belongs in the math too. At a 25% win rate you need roughly 4x coverage, and with a six-month sales cycle that pipeline has to exist two quarters ahead [Citation: https://www.youtube.com/watch?v=F68URYDo6lI&feature=youtu.be]. An SDR you hire today, who needs a quarter to ramp, won't fix next quarter's gap. Plan SDR hiring off the coverage you'll need two to three quarters out. Stage matters most. At seed and early Series A, I wouldn't build an SDR layer at all. Your first enterprise AEs should own the full cycle, prospecting through close, so you learn which channel actually produces pipeline before you pay to scale it. Once you know, hire SDRs against the measured gap and track their meeting-to-opportunity conversion, so you can tell whether they're creating pipeline or just filling AE calendars. ##### [Kyle Poyar]() Andy's capacity math is the right method. If you need a starting point before you have conversion data, 1:1 is a reasonable default. Warmly is a good example of making 1:1 work. Max and his team ran one SDR per AE and hired SDRs internationally to keep costs down. They also set AE quotas at 3x OTE instead of the old 5x, because they expected AEs to source some of their own pipeline. The ratio held up because the roles around it took work off the AEs: an inbound sales rep handled small deals and demo requests, and a sales assistant did admin and follow-ups. [Citation: https://www.growthunhinged.com/p/the-path-from-0-to-100-paying-customers] I'd be careful about Cassie's "be ready for zero" turning into the default, though. Founder-led and full-cycle AEs make sense early, but once you're past that stage the market data points the other way. I analyzed 120,798 GTM job posts, and AI companies grew SDR headcount 106% year-on-year. OpenAI, Cursor, Decagon and Legora are all hiring SDRs, so the companies supposed to be killing the role are the ones expanding it. Their SDRs work differently, though: 1️⃣ Zero time on list building or deep account research 2️⃣ Warm accounts with intent or product-qualified signals 3️⃣ Back on the phone, with parallel dialers 4️⃣ Shared target account list with marketing On AI SDRs, I agree with Andy about outbound email volume. Inbound is different. Voice agents that respond within two minutes, qualify, book the meeting and pass context to the AE are the most effective use I've seen. [Citation: https://www.youtube.com/watch?v=Yb_OeKcYiNQ] I suspect that changes the ratio. If AI covers inbound speed-to-lead, human SDRs can focus on outbound to warm accounts, and one SDR can support more AE capacity than the 2010s spray-and-pray SDR could. ##### [Kris Rudeegraap]() Kyle's list of how AI-native SDRs work matches what we saw at Sendoso. BUT... that's exactly why I'd start below his 1:1 default. For most teams in 2026 I'd plan fewer SDRs than AEs, because the "one SDR" in Andy's capacity math has changed a lot. I ran that same spreadsheet playbook at Sendoso. You start from the revenue goal and ACV, back into the meetings you need, then hire enough junior SDRs to book them. We started hiring in San Francisco, quickly found the headcount cost and CAC were too high there, and moved the team to a cheaper location. Before Sendoso, at Talkdesk in 2015, we were hiring 15 AEs and 10 SDRs in one push, so roughly 1 SDR for every 1.5 AEs, and that felt normal then. [Citation: https://youtube.com/watch?v=QuD8YtJlhQ4] BUT... that math assumed pipeline was a headcount stair-stepper, where more pipeline meant hiring the next SDR. Once we put Clay and LLM-personalized outbound in place, pipeline grew much faster than headcount. Enrichment, data cleanup, CRM checks and first-line personalization now run automagically, which frees SDRs to spend their hours on prioritized intent signals and actual selling. In my experience one SDR can now do the work of 10. [Citation: https://podcasts.apple.com/us/podcast/ai-growth-ops-and-rethinking-sales-strategies-with/id1647034882?i=1000690914644] [Citation: https://youtube.com/watch?v=5lV3lb-BHac] How I'd do it today: → Run Andy's math, but measure meetings per SDR on an AI-assisted workflow before you set headcount. Don't use the 2015 benchmark. → Expect AEs to source some of their own pipeline with the same tooling. AEs have to do more outbound now than ever. → When an underperforming SDR leaves, decide whether the job needs a backfill or a better workflow before you rehire. [Citation: https://youtube.com/watch?v=yv9Zjl2YmNg] → Don't jump to zero SDRs. Going from a team of 50 to a team of 0 breaks the process and the leaders running it. Shrink the team gradually and give more of the role to someone who is technical and automation-minded. ##### [Andy Mowat]() Kris says measure meetings per SDR on the AI workflow before setting headcount. I'd measure one step later, because Cassie's "filling AE calendars" point is where most SDR ratio math breaks in practice, and AI tooling makes it easier to book meetings that go nowhere. The capacity model only works if the SDR conversion number is real, and in most orgs it isn't, because the handoff is fuzzy. Before you set any ratio, have the AE formally accept or reject every SDR-sourced meeting in the CRM, with a rejection reason. Then measure SDR output as accepted opportunities and pipeline dollars. Booked meetings alone won't tell you whether SDRs are creating pipeline. Once you have a couple quarters of that data, the ratio question mostly answers itself. You'll see which segments SDRs actually move and which ones AEs or inbound already cover. I'd also comp SDRs on the same accepted-opp definition. If you pay on meetings, you get meetings, and your AEs end up with full calendars and thin pipeline. That matters even more with the SDR model Kyle describes. If SDRs work warm, signal-qualified accounts off a shared list with marketing, you need a clean rule for who gets credit for the opp, or marketing + SDRs both claim it and your capacity math double-counts pipeline. The other thing I'd watch is ownership. SDRs reporting into sales vs. marketing changes which numbers they chase, so whoever owns them should also own the pipeline target they're measured against. ##### [Peter Kazanjy]() Before you let a pipeline gap justify another SDR, look at win rates rep by rep. Andy's math and Cassie's coverage math both use a team-average win rate, and that average hides the real problem. On most teams, AE win rates vary a lot. The pipeline SDRs can generate is finite, so every SDR-sourced meeting routed to an AE who can't convert it is burned [Citation: https://www.youtube.com/watch?v=mXwZzoa3StQ]. If one AE closes at half the rate of their peers, the cheaper fix is coaching or managing that rep out. Otherwise you're hiring SDRs to feed a leaky bucket, and the ratio looks like it needs to go up when it doesn't. Kris's point that tooling has multiplied SDR output makes this matter more. When pipeline gets cheaper to create, AE conversion becomes the constraint, so the variance in AE win rates is what should set the SDR count. Then hold SDRs to the same unit economics as AEs: each head has to cover its own cost [Citation: https://www.youtube.com/watch?v=mXwZzoa3StQ]. Andy's AE accept/reject rule is the right gate. I'd carry the measurement all the way to closed revenue by source, because an accepted opp can still die in stage two. If SDR-sourced opps close at a fraction of the rate of inbound or referral opps, your SDR count is inflated even if the meetings-booked numbers look great. On timing, I'm with Cassie. With the founders I coach, the first SDR comes in only after the motion is proven and the seller's calendar is the bottleneck. At that point you add a sales development rep to fill the calendar, and that's the first time lead gen gets split off from selling [Citation: https://www.youtube.com/watch?v=FcQbii_TFgA]. Start with one SDR, instrument the funnel, and let a couple of quarters of conversion data set the ratio. ##### [Kyle Poyar]() Kris, I'll update my default. Starting below 1:1 makes sense once the workflow is AI-assisted. Warmly's 1:1 ratio came from a team still building that tooling. A falling ratio doesn't mean fewer SDRs, though, and the hiring data shows both trends together. In the same 120,798 job posts, 3 in 5 open GTM roles were AEs or solution engineers, because buyers still want to talk to a human, even when they're buying AI. AI companies also doubled SDR headcount. My read is that AE hiring is growing so fast that SDR teams grow in absolute terms even if each SDR now covers more AEs. The practical takeaway is to plan the ratio and the total separately. Use Andy's and Kris's AI-adjusted capacity math to set SDRs per AE. Then let AE hiring drive the total. Don't freeze SDR headcount just because each one is more productive. It's also worth checking whether your org chart even calls the role "SDR." I'm seeing Account Associate, GTM Associate and Member of GTM Staff for what's basically the same seat. Some of that pipeline work is moving into GTM engineering, which is the only GTM role growing faster than SDRs at AI companies. If a GTM engineer's workflows are doing part of the SDR's job, count that capacity when you set the ratio. On Andy's double-counting worry, I agree with using accepted opps as the measure. For credit, I'd give marketing and SDRs one shared ICP pipeline target on the shared account list, so neither team is fighting over attribution. Then count each opp once when you do the capacity math. --- Copyright © 2026 Super Me Network, Inc. All rights reserved. | [Terms of Service](https://www.superme.ai/terms) | [Privacy Policy](https://www.superme.ai/privacy)