Account Mapping Techniques

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 👇

Industry-based ABM v.s. Use Case-Based ABM. If your product has multiple use cases, stop targeting industry verticals, and start targeting clusters. VERTICAL-BASED ABM Most ABM programs create vertical-based list, messaging and content. But if those accounts have different use-cases and challenges, you risk ending with content and messaging that's either:  - Irrelevant to the majority of accounts you target - Generic, “common denominator” messaging that doesn’t differentiate you and doesn’t address the highest priority need CLUSTER-BASED ABM Cluster: group of companies with a similar challenge/use-case, despite their tier or vertical. With cluster targeting you can: - Cut through the generic noise with messaging, content and case studies that address the highest priority need that's top of their mind - Differentiate in a way that matters to them (by showing how you're supporting that use-case better than competitors) - Focus on accounts with a high probability to become a sales opportunity instead of just chasing vertical Tier 1 accounts EXAMPLE As an ABM lead for Hubspot, I might create an ABM program for B2B SaaS companies. After all, they all need sales and marketing tools. It’s also helpful to have industry related assets, case studies and messaging. But different SaaS companies have different buying triggers: - Switching from a legacy system, because they lack functionality and integrations - Companies that want to start ABM but their CRM/marketing platform don’t support it - Startups that outgrew their separate siloed tools You can already see how each of these use-cases deserves completely different messaging. --- Once you've defined the clusters, the next step is mapping accounts to them. Here is how. 1. Export all your won deals and segment them by use-case 2. Group all accounts by use-case and compare revenue metrics - Revenue - Average deal size  - Sales cycle length - Lifetime value  - # of successful case studies You'll be able to make an unbiased decision which clusters have a higher priority. 3. Select a top cluster aligned with your goals and run deal analysis  - Top 5 clients by LTV/ACV - Top 5 fastest deals - Recent 5 lost deals 4. Extract patterns - Firmographics - Technographics - Buying committee structure - Won and lost reasons - Signals and account insights that signal the specific use-case 5. Develop a cluster ICP with qualification & disqualification criteria --- Now you can build a highly targeted list of accounts that are more likely to convert into pipeline and be a good fit, and align your program with their high priority needs. We recently published results and a breakdown of four programs where a switch to cluster-based ABM generated $7 million in pipeline:  https://lnkd.in/dZWKNAbC

According to Gartner, the average B2B deal now involves 11 decision makers - up from 5 just a few years ago. Yet most sales teams fail to accurately map their buying committees. This costs deals. Here's what I've learned from scaling sales teams about mapping stakeholders effectively: 1. Start early. Map the full committee during discovery, not when you're trying to close. You uncover hidden influencers and gatekeepers who can accelerate - or stall - your dea;. Our data shows deals with early mapping close 2.3x faster. 2. Go beyond titles. Document each stakeholder's: - Personal objectives - Key concerns - Reporting structure - Level of influence - Communication preferences 3. Track shifting dynamics. If you’re only building a relationship with your champion, you’re one reorg away from losing the deal. 57% of buying committees change composition during the sales cycle. Review and update your map bi-weekly. 4. Quantify impact. For each stakeholder, identify: - Budget ownership - Veto power - Implementation involvement - ROI expectations 5. Build champion coalitions. Connect supporters across departments. Deals with 3+ active champions are 43% more likely to close. 6. Spot risks early. If a key decision-maker is silent, you know where to focus your energy. Companies we work with that implement robust stakeholder mapping see: - Higher win rates - Faster sales cycles - Larger deal sizes Don't treat buying committee mapping as a checkbox exercise. Make it your competitive advantage. High performing sales teams aren’t always the ones with the best product. They’re the ones who know the room - and speak to everyone in it. Want to dive deeper into effective stakeholder mapping strategies? Let's connect.

🎯 Enterprise Sellers: Do You Know the Who Behind the Deal? 🎯 In enterprise sales, winning isn’t just about having the right solution—it’s about navigating the purchasing committee. Most enterprise deals involve 6-10 decision-makers, each with different priorities, concerns, and influence. If you’re not mapping out the committee and aligning your relationships to them, you’re leaving your success up to chance. Side note... there are way more people influencing behind the scenes. ✅ Why mapping matters: Identify the real influencers. It’s not always the title that matters—it’s the person driving the internal conversation. Understand competing priorities. Finance cares about cost, IT cares about integration, and operations care about efficiency. Knowing who cares about what helps you tailor your message. Prevent deal roadblocks. Missing a key stakeholder means risking a veto late in the game. ✅ How to map relationships: 1️⃣ Start with your network: Use tools like LinkedIn and CRM data to see who you, your colleagues, customers, or partners know at the account. Warm connections can accelerate access and build trust faster. 2️⃣ Ask early and often: During discovery, ask your champion who’s involved in the decision process. Confirm and expand this map over time. 3️⃣ Leverage partnerships: Industry connections or mutual customers can help bridge gaps to hard-to-reach stakeholders. 4️⃣ Tailor your engagement: Once you’ve mapped the committee, personalize your outreach to each stakeholder’s role and priorities. Speak their language, not your product’s features. 💡 Pro tip: Deals get stuck when you’re talking to one person. Deals move when you’re influencing the entire committee. Mapping the purchasing committee and aligning relationships isn’t just a nice-to-have—it’s a must-have in enterprise sales. If your team isn’t doing this, you’re flying blind in a complex decision process. Are your sellers equipped to connect the dots? #EnterpriseSales #RelationshipMapping #PurchasingCommittee #SalesLeadership #PipelineAcceleration #Numentum

Our 2-person marketing team generates ~£1M in pipeline every month. That only works because our account engine is built before the campaign starts. We follow this order: 1/ First, we build the account universe Before we run ads, we score the ICP and map the TAM using multiple sources: company size, stack, ABM maturity, and existing signals. If that layer is weak, Tier 1 just becomes a list of logos. A good logo does not automatically make an account Tier 1. 2/ Then, we qualify the account before reaching sales By the time a rep sees the account, we want the research, CRM sync, tiering and contact sourcing already done. The rep should not have to work out whether the account is 1:1, 1:few, or 1:many after the signal fires. That route should already exist. 3/ We use intent to change the motion A page visit from a junior person is useful context, but it should not trigger a full 1:1 play. Multiple stakeholders engaging, repeat visits, event activity, or a live opportunity are different. That is when the account can move from light coverage to a more intentional motion. If the signal sits in a dashboard for three days, the motion is already late. 4/ We run the tier play that matches the account Our average deal is $50-60k, and some have reached £500k. If we gave every account the £500k treatment, the motion would bankrupt itself. So we adapt the tier play to each of them: → 1:1 gets LinkedIn ABM ads, personalized microsites, and sales plugin outreach when the account earns it. → 1:few gets segment-level campaigns (ads, microsites, event invites, pre/post-event touches). → 1:many stays covered until the signal changes. 5/ We give sales the next step inside their workflow They are fed with real-time insights, account digests, and contact-level assets they can act on inside the workflow they already use. What we usually see within our customers: 20x increase in account coverage and millions in pipeline influenced within 6 months. That only worked because the account logic was built before the campaigns started. That’s how we build our engine; now you know how to build yours. I’d use this map before asking the team to scale the motion.

Not all red accounts are created equal. And treating them like they are? That’s where teams go wrong. This came up in a session I had with a CS leader last week. We were rethinking how to structure their account health reviews, not just for the team, but for exec conversations too. We asked a simple question: What if you looked at “at-risk” accounts through a second lens - their expansion potential? So we built a 2x2 matrix: 🟢  Health Score (Low ↔ High) 💰 Expansion Potential (Low ↔ High) And then we mapped the accounts. What we found surprised the team:  ➡️ Some red accounts were worth exec-level attention. ➡️ Others? Still red, but not worth over-investing. ➡️ Some green accounts looked great, but had no upside. The takeaway? You don’t treat every red account the same. You prioritize based on risk and reward. Here’s how we used the framework to drive sharper conversations: 📍 High Risk + High Expansion: Blockers are costing us revenue. What can we do to turn this around? Do we need exec involvement? 📍 Low Risk + High Expansion:  What’s the plan to accelerate growth? How are we nurturing that potential? 📍 Low Risk + Low Expansion:  Are we over-servicing this account? Can we streamline? 📍 High Risk + Low Expansion: What’s the containment plan? How do we minimize disruption? This kind of thinking is baked into the REACH Framework™ I share with my clients. It’s not just about reacting to churn. It’s about triaging strategically and aligning CS, Sales, execs, and other cross-functional teams on where to spend time. Below is the 2x2 visual we created. Let me know if you want to talk about how you can action it - I'd be happy to chat! 💬 How are you prioritizing red accounts today? Would this framework help your team? #CustomerSuccess #CSLeadership #REACHFramework #ChurnPrevention #ExpansionRevenue #AccountManagement #PostSaleStrategy #ChiefCustomerOfficer #Growth

I've built ABM programs at every stage, from startup to enterprise. Attribution looks different at each. Here's what works when starting from zero: My northstar: account-level revenue influence If you’re building ABM from the ground up, here’s a channel view: LinkedIn Ads: Fibbler captures impression and engagement data at the account level. → Match ad exposure to CRM accounts →Overlay with stage progression + dates → Compare velocity: exposed accounts vs. dark accounts Status: Trackable at the account level LinkedIn Organic: Fibbler (again) shows which target accounts are engaging with your content - not just vanity metrics. → Filter by your target account list → Use engagement spikes as intent signals before outreach → Correlates directly with outbound reply rates when timed right Status: Not easy, can be trackable with CRM discipline LinkedIn Outreach: HeyReach logs replies and meetings at the contact level.  Push contact level activity to CRM and roll up the Account →Tag every touch with account ID on sync → Map: sequence step → reply → meeting → opportunity → You're tracking one contact - full buying committee view requires CRM discipline Status: Not easy, can be trackable with CRM discipline 1:1 Email: your MAP (e.g. Instantly.ai) tracks replies and meetings. → Sync to CRM via native integration/n8n → Aggregate all contacts from one account under a single account record → Map: reply → meeting booked → stage change → Open tracking is unreliable post-Apple MPP - replies and meetings are your only real signal Status: Helpful, can be trackable with CRM discipline That (kinda) worked in the 2023-2025 era. The tools being disconnected was the problem. But now there's a layer that sits above all of them - that pulls the attribution story together. Before you layer AI on top of it - get the CRM structure right first. That means: → Every touch tagged with an account ID on sync → Contacts rolling up to a single account record → Stage progression dates logged cleanly Once that's clean, Claude connected to your GTM stack via MCP actually works. Because you're not asking it to fix messy data - you're asking it to connect clean data into a story. Then query it: → Which target accounts had the most LinkedIn exposure this month? → Which of those moved pipeline stages? → Which channels touched them before they moved? Result: a ‘connected’ story from account identification through to sales. The tools aren't the bottleneck anymore. The discipline around how you structure the data is. Where's your biggest attribution gap right now: the data structure or the tooling? Comment below.

When organizations have thousands of accounts in their CRM, determining which ones the sales team should prioritize becomes critical. "Starting with the letter A" isn't a strategy. Manual account research and scoring isn't feasible either - a few minutes per account, it would require quarters of dedicated work. More importantly, this kind of repetitive research just binds time that the sales team can spend on higher-impact activities like being creative to get into accounts, at times completely even without automation or spray and pray. This is where 2025-level account scoring becomes a valuable tool. Not the basic "employee size + industry + funding" scoring that's commonly available. Clay enables building completely custom data sets that align with an organization's unique account evaluation criteria. The system (I build on top of Clay) can incorporate any signal that helps identify high-value prospects - even when that data isn't available in standard databases. Here's a real example: For one client, identifying whether companies primarily served residential or commercial customers, along with their specific service offerings, was crucial. This data doesn't exist in any database - it requires actually understanding how each company describes their business. The solution was custom AI agents that: → Read through company websites → Interpret varied descriptions of services → Map unstructured language to standardized categories → Feed clean, structured data back to the CRM and scoring Now the sales team can instantly see account priorities and relevant details, focusing their time on meaningful conversations rather than repetitive research. There's a bunch of data transformation and custom API calls built in to send the data from web research into the CRM as individual data that can be searched for and changed with just a click. No black-box scoring. For the Clay builders interested in implementation details, I've included a technical walkthrough below showing how to handle mapping unstructured company descriptions to standardized verticals and market focus. #GTMEngineering #RevOps

Raw financial data doesn't know where it belong in your models. This is why a key requirement of FP&A models, whether in Excel or a 3rd party tool, is account mapping. I use tables. Accounting systems like Quickbooks, Xero, Business Central, and NetSuite code transactions one way. Excel models and FP&A software expect something else. Why? Because a financial modeler set it up the way he wanted it. We have two choices of how to deal with it: 1. Bridge with mapping tables 2. Deal with a manual mess The idea is that you create a structured lookup, that I call mapping tables. All of these are dynamic Excel tables that have codes, names, classes, and other tags. It technically doesn't matter where these tables are located, because Excel knows the data by name, not by cell locations. But it's always a good idea to keep them all in a single repository. The example you see here is for a fictional not-for-profit. I've created mapping tables where entity codes map to business units. Fund codes map to revenue types and restrictions. Program codes map to functional categories. And account numbers in the COA map to the P&L. Any data can then pushed into the model quickly and easily. Every month, data flows automatically into the right places without manual reclassifications or hunting through the trial balance. If anything in the model needs to be updated, that is easily done in the mapping tables. I'm not going to get into a VLOOKUP/HLOOKUP battle here, but you can use whatever lookup techniques that suit you. This is a key feature that FP&A tools are built on. But it can also be replicated easily in Excel. A well-structured mapping table is one of the less glamorous parts of the FP&A models, but it's also one of the most important.