Your total addressable market isn't a slide in a pitch deck. It's the living list of every company that could buy your product — scored by how likely they are to buy right now.
Most teams define their ICP once, pull a static export from Apollo, and work that same list until it goes stale. By the time they refresh it, half the companies have changed, new ones have entered the market, and the buying signals that made some leads hot three months ago have expired.
A TAM that updates itself is worth more than a TAM that's perfect on day one. Claude builds your initial TAM from Apollo, enriches it with LinkedIn activity and firmographic data, layers in real buying signals, scores every company, and refreshes the whole thing weekly so your sales team always works from a current, prioritized list.
How Claude Helps You Build This
Instead of manually pulling Apollo exports, cross-referencing LinkedIn, and guessing which companies are in-market, Claude runs the full TAM pipeline. It sources companies matching your ICP, enriches them with real data, scores by signal strength, and re-runs weekly so the list never goes stale.
To do this, Claude uses six skills:
- apollo-lead-finder — pulls companies and contacts matching your ICP criteria from Apollo's database.
- linkedin-profile-post-scraper — enriches each company with founder and exec LinkedIn activity, thought leadership presence, and public visibility.
- linkedin-post-research — identifies companies where decision-makers are actively posting about the problem you solve.
- job-posting-intent — flags companies hiring for roles that signal they need your product.
- funding-signal-monitor — identifies companies that recently raised funding and are allocating budget.
- lead-qualification — scores every company by ICP fit plus signal density, separating "fits your criteria" from "fits your criteria and is buying right now."
You define your ICP once. Claude builds and maintains the TAM.
The Workflow
Step 1: Define your ICP as structured filtering criteria
Tell Claude who your ideal customer is. Claude translates your description into filtering parameters that work across Apollo and LinkedIn:
- Job titles and seniority levels of the buyer
- Company size ranges (headcount and revenue)
- Industries, verticals, and sub-segments
- Technology stack signals (tools they use that are adjacent to yours)
- Geographic filters
This isn't a persona doc that sits in a slide deck. It's a set of machine-readable filters that Claude uses to query Apollo and enrich from LinkedIn. Define it once — Claude uses it every time the TAM refreshes.
Step 2: Build the initial TAM from Apollo
Using the apollo-lead-finder skill, Claude pulls every company matching your ICP from Apollo's database. This is the raw TAM — the full universe of companies that fit your criteria.
For each company, Claude captures:
- Company name, domain, size, and industry
- Key contacts with email addresses
- Tech stack data where available
- Firmographic signals (growth rate, funding history)
A typical pull surfaces 1,000–10,000 companies depending on how narrow your ICP is. This is the foundation — everything else enriches and scores it.
Step 3: Enrich with LinkedIn data
Using the linkedin-profile-post-scraper and linkedin-post-research skills, Claude enriches each company with data Apollo doesn't have:
- Are founders or execs actively posting about the problem you solve?
- What topics are they engaging with on LinkedIn?
- How visible and active is their leadership team?
- Are they participating in conversations relevant to your product category?
LinkedIn enrichment separates companies that passively fit your ICP from companies where decision-makers are actively thinking about the problem. A company that matches your filters AND has a VP posting about the exact challenge you solve is a fundamentally different lead than one that just matches the filters.
Step 4: Layer in buying signals
Using the job-posting-intent and funding-signal-monitor skills, Claude checks every company in the TAM for active buying signals:
- Hiring signals — posting jobs for roles your product replaces or supports
- Funding signals — recently raised a round, actively allocating budget
- Growth signals — headcount increasing, new office openings, product launches
These signals are time-sensitive. A company that raised a Series B last week is in a different buying mode than one that raised six months ago. Claude timestamps every signal so you know how fresh each one is.
Step 5: Score and tier the entire TAM
Using the lead-qualification skill, Claude scores every company across three dimensions:
- ICP fit — how closely they match your criteria
- Signal strength — how many active buying signals, how recent
- Engagement level — LinkedIn activity related to your problem space
The output is a tiered TAM:
- Tier 1 — strong ICP match + active signals + engaged leadership. Outreach immediately.
- Tier 2 — strong ICP match, no active signals yet. Monitor weekly.
- Tier 3 — partial match. Hold until signals appear.
Step 6: Set up weekly TAM refresh
Claude re-runs the entire pipeline weekly:
- New companies matching your ICP get added from Apollo
- Signal data gets updated across all existing companies
- Companies that raised funding or started hiring move up in tiers
- Stale signals expire and companies move down
- Duplicates get caught by the contact cache
Your TAM is never more than a week old. Sales always works from a current, scored list — not a three-month-old export. One team that switched from static exports to a continuously updated TAM reported assembling 340 qualified leads in 48 hours that would have taken three weeks manually.
What You Walk Away With
After running this workflow, you have:
- A complete TAM — every company matching your ICP, pulled from Apollo and enriched with LinkedIn data
- Buying signal overlay — hiring, funding, and growth signals layered on top of firmographic fit
- Scored and tiered companies — ranked by ICP fit + signal strength + engagement
- Decision-maker contacts — emails and LinkedIn profiles for the buyers at each company
- A weekly refresh system — TAM updates automatically so the list never goes stale
Why This Matters
A static TAM decays the moment you export it. People change roles, companies raise funding, new startups enter your market, and buying signals expire. The difference between a team working a fresh, scored TAM and a team working a three-month-old spreadsheet is the difference between pipeline that converts and pipeline that wastes everyone's time.
This workflow gives you a TAM that's always current. Every company scored, every signal timestamped, every week refreshed.
Build your TAM with Gooseworks