Sales Planning

Unpopular take: Sales reps should never do their own list building. The best way to scale a GTM motion is to have one central target account list with every relevant company in the TAM. And everything works backwards from it: - Outbound - ABM ads - Targeted campaigns But I understand why teams don’t have this important GTM asset. Prior to AI, a proper TAM map was a massive undertaking that would’ve required actual data science. So most resorted to exporting a few ZoomInfo lists and calling it a day. Now, it’s way more accessible. Here’s how we map out TAMs in 9 steps (the 2026 way): 1️⃣ Start in the CRM • Enrich and analyze your closed won accounts to look for commonalities. • Get qualitative input from your GTM teams on what makes a great account. 2️⃣ Build your ICP model • Based on three sets of data: firmographics, technographics and account-fit signals. • Backtest your against closed-won. 95% should qualify if your model is right. 3️⃣ Diversified list-building • Pull broad lists from 2+ sources (e.g. databases like Apollo.io / Ocean.io, or scraping tools like Apify / Serper). • Don’t overly rely on filters (e.g. many software companies are not under the software development industry). 4️⃣ Data cleanup • Merge your separate lists into one source of truth. • Find missing data points (e.g. domains) and validate every domain is operational (free). 5️⃣ Initial qualification • Design a research agent to do initial binary qualification based on product/industry fit (e.g. is this actually an eCommerce brand?) • Use lower cost models like 4o-mini or Haiku given scale. 6️⃣ Deep enrichment • Order your enrichments so that the ones most likely to disqualify companies, are positioned first. • For qualified accounts, layer in custom research points relevant to your GTM. 7️⃣ Score every account that survives • Score every account into: Tier 1, Tier 2, Tier 3 • Avoid intent signals in your scoring, as this is purely account fit. 8️⃣ Stakeholder mapping • Analyze the titles typically involved in your deals and split them into: decision maker, champion, and influencers. • Source those titles at every company that qualifies. 9️⃣ Push it back to your CRM • Load companies, contacts, and custom properties into your CRM, depending on how big your TAM is (for large TAMs, just upload Tier 1s) • Turn the TAM map workflow into an automated CRM enrichment flow, so every record in your CRM gets the same custom research. --- The end result is a much easier time for reps. They don’t need to build ad hoc lists and if they want to do outbound. They simply build their segments from the CRM/data store. Allowing 90%+ of the effort to be spent on activity. Full guide below 👇

Every GTM miss I’ve seen starts the same way: A TAM slide that looks great in the board deck… but breaks the moment it hits the field. We’ve heard it dozens of times from early-stage founders: “Our TAM is $20B, we can sell to all SaaS companies, 50–500 employees.” In reality: silence, long cycles, and deals that never quite get to commit. Here’s how we fix that at Leadle, so that the “wishful ICP” becomes a list of buyers who actually pay. How we reconcile TAM with reality:  1. Start with the ‘wishful ICP’. We write it down clearly: industry, size, roles, use case. This is the story you want to be true. 2. Pull reality from the CRM. We look at the last 12–18 months of deals in CRM - wins, losses, no-decisions. Who actually paid? Who never moved? It usually doesn’t match the story above. 3. Enrich everything. Every account and contact goes through Clay - funding stage, team size, hiring, tech stack, geography. Now the “slide TAM” becomes an actual account list with context. 4. Score segments by what matters. Not all ICPs are equal. We measure: → Meetings per 100 accounts → Win rate → Deal size → Cycle length The top segments become “go-to” cohorts. The rest? We drop them. 5. Run plays in cohorts. Each cohort gets its own brief: who we’re targeting, the triggers to watch, and the opener that’s resonating. The scored accounts are synced back into Pipedrive → Clay signals → Smartlead/HeyReach workflows. That way, the team isn’t just blasting - they’re connecting at the right moment. ✅What changes when you do this → TAM shrinks (good), focus sharpens (better), pipeline moves (best). → Messaging stops being generic. because you’re speaking from real patterns you’ve seen. → Planning gets clearer: you know which 500 accounts to work this month and why. Every time we see GTM go sideways, it’s rarely because of the channel. The problem is higher up, at the strategy level. When you fix your TAM, you have a >50% chance of fixing your GTM.

I used AI agents to solve market segmentation in a niche industry. The sales team at my new company was struggling with one main thing: Not having a reliable signal that a company was relevant for them or not. Can't blame them... finding sales signals in the energy space is, well, not a common thing. Tools like Clay, ZoomInfo, and Apollo are great for general B2B signals. But when you need signals that are deeply vertical, like: → Who's developing renewable projects in Europe? → What’s their role in those projects? → Where are their assets, and how large are they? ...there’s no out-of-the-box data provider that solves that. So I built something internally to help us fix that gap. Using Clay, I set up a multi-agent research workflow that: • Scrapes company websites + sitemap data • Uses agents (Claygent, EXA, Perplexity) to extract project-level info • Distills it into structured columns - tech type, project size, role, geography • Aggregates and verifies multiple research sources And with the results, we can now segment companies into our TAM / SAM / ICP The outcome powers: → Sales prospecting → Market expansion → Campaign planning No more asking AI "Is this company a good fit?' Instead: AI focuses on gathering data → we control of market segmentation: → TAM → SAM → ICP The principles apply in GTM Engineering: → Use AI to gather intel, not make judgments → Pick the few signals that will definitively segment your market → Build deterministic filters, not subjective scores Your market segmentation is your competitive advantage. Don't outsource the logic. Hope it's useful for other GTM people building in verticals with niche signals✌️

During our time at Spendesk, we went from 5% to 15% meeting booked rate without hiring a single SDR, thanks to a signal-based selling framework. Here's how this framework works: Step 1: TAM building You stop building lists. Signals automatically feed new ICP-fit accounts into your TAM in real time. A company raises a Series B, a new VP Sales joins a firm that matches your ICP. That account enters your system automatically. Step 2: Enrichment The moment a new account enters your TAM, an AI research agent fills every context gap automatically. Contact info, tech stack, company size, and recent news. The rep gets a fully loaded account record without touching a single tab. Step 3: Sales Readiness Scoring A single signal doesn't tell you much. The system stacks signals across every account in your TAM to build a Sales Readiness Score. A new hire triggered the account entering the system but an agent keeps monitoring: funding activity, job postings, website visits, intent data. High readiness goes into the rep's book of business. Medium readiness goes to the AI SDR. Step 4: Automated action The system routes the right play to the right person without manual input. Rep gets a Slack alert with full context. CRM is already updated. All they have to do is show up. Reps only touch accounts that are already ready. We ran this at Spendesk across 100+ reps. Meeting booked rate went from 5% to 15%. This is a different model entirely. ↓

Your team has the same objection handling training. Half hit quota. Half miss by 40 percent. The difference is not people skills. It is process discipline. One group runs the system. One group wings it. Here are the 3 places where revenue actually dies: 1.) The Connect Problem (Not the Close Problem) Sales leaders think they have a closing problem. They spend 10k per rep on objection handling and executive presence training. But here's the math: Your rep makes 100 dials. 70 hit switchboards because the list is unverified. 30 ring through. 20 go to voicemail. 10 connect. 3 book meetings. You're training reps to close 3 meetings. The real problem is you only got 10 connects. Connect rate is the choke-point. Better data coverage is the only lever that matters. Set quarterly TAM refresh targets. Demand verified mobile and direct dial coverage on every list. Install bounce-rate SLAs. Most teams lose at the top of the funnel, not the bottom. 2.) The Hours Problem (Not the Motivation Problem) Your rep works 50 hours a week. 30 of those hours are not selling. Research on accounts. Enrichment. CRM data entry. Meeting prep. You're spending money on motivation and coaching when the real issue is structural waste. AI removes this work. Account research gets automated. Data enrichment runs in the background. First-pass summaries get drafted. This reclaims 20 hours a week. But here's the gate: human review before anything touches a buyer. No auto-send. No AI-written objection responses going out raw. The goal is more selling hours, not worse emails. 3.) The Visibility Problem (Not the Accountability Problem) You run forecast reviews. You ask reps why deals slipped. You think you have an accountability problem. You actually have a visibility problem. You don't know where conversion breaks. Start tracking stage-to-stage yield: dials to connects to meetings to opps to wins. Then backsolve the required inputs. 10 wins at 20 percent close rate means 50 opps. 50 opps at 30 percent meeting conversion means 167 meetings. 167 meetings at 10 percent dial-to-meeting rate means 1,670 dials. Now you know the number. Now you know where it breaks. Fix the stage that's failing, not the rep's attitude. People skills matter when you're on the phone. But most reps never get on the phone enough because the process is broken. You're teaching parallel parking to someone who doesn't know how to start the car. Fix the math first. Train the skills second.

There's a specific moment in high-value agency sales that changes everything. It's when the prospect realizes you understand their market better than they do. TAM analysis creates that moment. Before touching any campaigns, you present a complete breakdown of their addressable market. You show them: The total universe of reachable companies that fit their ICP. How those companies segment into tiers, which 20% deserves 80% of focus, and why. - Verified contact availability rates per segment. - A 6-12-month penetration roadmap showing exactly how you'll work through their market systematically. - Conservative outcome projections based on realistic conversion benchmarks. This takes about 45 minutes to build. But it repositions the entire relationship. You position yourself by demonstrating the market intelligence they don't have. The work you deliver after this doesn't change. Same cold email infrastructure. Same copywriting process. Same campaign execution. But the client isn't buying execution anymore. - They're buying certainty in an uncertain market. - They're buying someone who's done the research they haven't. - They're buying strategic positioning backed by data. Why do prospects informed by TAM analysis close at higher rates? Because of what the framework proves. - That you're a strategic partner, not a service provider. - That you think in systems and data, not just tactics. - That you've done the work before they've signed the contract. Strategic partners command different rates than execution vendors. The difference isn't in what you deliver. It's in what you demonstrate before delivery begins. Same work. Different frame. Different outcome.

A GTM leader showed me a photo from her January offsite last week.   A whiteboard. Green sticky notes. A TAM number circled twice in red.   Then she said something I haven't stopped thinking about.   "Every deal we are closing this quarter? Not one of them was on that board."   She hadn't planned badly.   Her market had simply moved on.   B2B data decays faster than most planning cycles.  Companies change priorities.  Champions switch jobs.  Budgets shift.  Buying signals appear and disappear.   Your TAM isn't static. Your GTM plan shouldn't be either.   Instead of running another quarterly planning session, here's what she changed.   1. Start with your wins.   She looked at her last 20 closed won deals and asked one question:   What was true 90 days before they entered the pipeline?   Funding round. Hiring surge. A new executive. A major technology change.   Those became her buying signals.   2. Score your TAM against those signals every month.   Nothing fancy.   Crunchbase alerts. LinkedIn saved searches. One scoring column in the spreadsheet they already used.   3. Attach context to every account.   Persona journeys, recent changes, trigger events.   The knowledge stays with the account instead of getting buried in a slide deck.   4. Replace quarterly planning with a weekly re-rank.   30 minutes.   Review the top 50 accounts.   Adjust priorities.   Keep the plan alive instead of rebuilding it every quarter.   5. Measure coverage, not activity.   Ask one question:   What percentage of your TAM was actually worked last quarter?   Not contacted once.   Actually researched, prioritized, and pursued.   The first number is usually uncomfortable.   That's exactly why it matters.   She still has that whiteboard photo.   It's no longer a GTM plan.   It's a reminder that markets change faster than planning cycles.   The best GTM teams don't build better plans.   They build systems that adapt.   P.S. I am thinking about starting a newsletter where I break down practical GTM systems like this with templates you can use immediately. If that's something you'd read, let me know in the comments.

If I were the first sales leader at a Series A startup in 2026, here's the 5-step plan I'd follow in the first 90 days:   Step 1: Build out your entire TAM   Stop operating on a "on-demand lead model" where you're constantly scrambling for more leads. Get every single account you could potentially target into one database.   You need to confidently say: "Here's how many accounts exist. Here's how many reps we need in which geographies and verticals over the next 12-24 months."   Step 2: Create a scoring model based on historical data   Enrich all your existing closed-won and closed-loss deals and do the analysis.   Companies think they serve "all of home services." But when we actually dig into the data, we find that commercial landscapers convert at 3x the rate of plumbers.   So make sure you're marrying your TAM with your actual performance data to understand where you can win. Then go chase the low-hanging fruit.   Step 3: Divide territories the right way   Here's the mistake: You have 4 reps, so you split your entire TAM across 4 territories.   No.   We learned this from Nathaniel, the Chief Sales Officer at Podium who scaled them from $1M to $200M+. His catchphrase: "Good fences build good neighbors."   You want right-sized territories from day one. Even if you don't have all those reps yet.   Because here's what happens otherwise: Every time a new rep joins, everyone's territory shrinks. They were used to calling anyone and everyone. Now they can't.   Also – a rep with 500 focused accounts will outperform a rep with 5,000 accounts almost every time. They won't just send generic emails to 5,000 people. They'll actually do the work on the 500.   Step 4: Build 5-10 trigger-based playbooks   Don't just give reps accounts. Give them ammunition. What are the key signals that indicate a company is ready to buy?   Examples: • They open a second location • They cross 200 Google reviews • You sign one of their competitors Then build specific outreach playbooks for each trigger. "When X happens, here's the exact message to send."   Step 5: Hire and train against this system   Now you actually have a machine. You're not guessing. You're not hoping. You have a system that tells reps exactly who to target, when to reach out, and what to say.   That's how you go from "first sales leader figuring it out" to "we have a repeatable, scalable motion" in 90 days.

Most people think TAM building is just “pull a list.” But if you want accuracy and full coverage, the process takes a lot more work. Here’s how we built a complete market from scratch for a recent client: 1. Start with the customer base Before touching any filters, I pulled every current customer they have and reverse-engineered the patterns. I wanted to find out how their ICP actually shows up on LinkedIn (and other data platforms). So we looked at: - Their LinkedIn industries - Typical employee-size ranges - Common keywords in their company descriptions That gave us a baseline ICP profile rooted in data... not assumptions. 2. Use ZoomInfo to find every company that matches those filters Once we knew what the ICP looks like, we used ZoomInfo to find every company on LinkedIn that matched those attributes. 3. Run a qualification prompt (and refine it… a lot) We built a prompt that looks at each company’s website and decides if it’s truly ICP-fit. It wasn’t perfect at first (it rarely is). We ran it on the first 100, spot-checked for edge cases, tweaked, re-ran… Then repeated that cycle 5+ times until the output is bulletproof. 4. Filter out fake “US-based” companies Everyone in outbound knows how many companies list themselves as US-based but are actually 90% overseas. So we ran a second prompt that calculates what % of employees are actually in the US. Threshold: 75%+ US-based. DM me if you want that prompt. 5. Validate TAM coverage by mapping back to customers Once the list was built, we checked how many of the client’s real customers appear in it. If a customer didn’t show up, we dug in: - Was it an industry mismatch? - Missing keyword? - Or something in the prompt logic? We kept adjusting until every customer appeared, unless they weren’t on LinkedIn at all (more on that later) 6. Segment the accounts for messaging With the TAM locked in, we ran a final research prompt to segment each account into the buckets we’d use for outbound: - Niche - Size - Technology - Operating model - And any other traits that would change messaging angles 7. Fill in the gaps when customers aren’t on LinkedIn Not every market lives entirely on LinkedIn. So if a client’s customer base extends beyond what LinkedIn captures, we pull data from places like ZoomInfo or run custom scrapers to round out the full TAM. #sales #outbound #coldemail

Most B2B companies are guessing at their market opportunity (Here's how to map it systematically) Your TAM is the foundation for every strategic decision you make. Yet most businesses approach market analysis with gut feelings instead of systematic frameworks. The cost of getting this wrong: Wasted resources targeting the wrong segments, missed revenue opportunities, and strategic decisions based on flawed assumptions. This TAM mapping process transforms how you identify, evaluate, and capture market opportunities: Start With Clear Analysis Goals Before diving into data, define what you're actually trying to understand: → Market size assessment for realistic revenue projections → Revenue potential analysis for investment decisions → Strategic planning for resource allocation The 5-Step Market Evaluation Framework 1. Demographics Analysis - Who are your potential customers by company size, industry, role? 2. Geographic Segmentation - Where are these prospects located and how does location affect buying behavior? 3. Behavioral Patterns - How do they currently solve the problem you address? 4. Competition Assessment - Who else is serving this market and where are the gaps? 5. Growth Potential - Is this market expanding, contracting, or stable? Market analysis isn't a one-time project. You need to create a continuous optimization loop: → Monitor performance against projections → Refine segments based on actual conversion data → Update analysis as market conditions change → Make strategic adjustments based on new insights Why this matters: Companies that systematically map their TAM make better targeting decisions, allocate resources more effectively, and identify expansion opportunities their competitors miss. The difference between companies that scale predictably and those that plateau? Systematic market understanding versus guesswork. Your market analysis should drive every go-to-market decision you make. How systematically are you analyzing your total addressable market?