How to run a TAM analysis (with a company count API)

A TAM analysis is a count of the companies you could sell to, multiplied by what each is worth. The count is the hard part. This guide gets it from a free API call instead of a guess.

TL;DR

• Count target companies with GET /v1/companies/count, then multiply by your average contract value (ACV).
• It takes about 20 minutes and spends 0 records. The optional sample pull in Step 4 spends 25.
• Narrow in three layers: TAM (all fits), SAM (the part you can serve), SOM (the part you can win this year).

Prerequisites

• A LeadOcean API key in $LEADOCEAN_API_KEY. The Free plan works: 1,000 records for life, no card, 5 requests a second.
• curl and jq. A spreadsheet for the multiplication.
• An ideal customer profile: industry, headcount band, country. One sentence is enough.
• Your average ACV. Use closed-won data, not a list price.

Step 1: Write the profile as filters

Turn the sentence into filter values before you count anything. Enum values are exact strings, so resolve them first.

curl -s "https://api.leadocean.io/v1/enums/industry?q=software" \
  -H "x-api-key: $LEADOCEAN_API_KEY" | jq '.data.values[:5]'

Our worked example: US software companies with 51 to 500 employees. That is industry: Computer Software, hqCountry: US, employeeRange: 51-200, 201-500. Lookups are free.

Step 2: Count companies (TAM)

Call the company count endpoint with the same filters you would send to company search. It is free and returns an exact total with breakdowns.

curl -s -G "https://api.leadocean.io/v1/companies/count" \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  --data-urlencode "industry=Computer Software" \
  --data-urlencode "hqCountry=US" \
  --data-urlencode "employeeRange=51-200,201-500" | jq '{total: .data.total}'

The same call is the MCP tool leadocean_count_companies, so an agent can do it in chat. Do not use the people count for this: it counts people, not companies. Array filters go in the query string as one comma-separated value, such as employeeRange=51-200,201-500 (LeadOcean OpenAPI, October 2026).

Step 3: Narrow to SAM and SOM

SAM is the slice you can actually serve today. Add hasMailableContacts: true to keep only companies with at least one person you can email.

curl -s -G "https://api.leadocean.io/v1/companies/count" \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  --data-urlencode "industry=Computer Software" \
  --data-urlencode "hqCountry=US" \
  --data-urlencode "employeeRange=51-200,201-500" \
  --data-urlencode "hasMailableContacts=true" | jq '{total: .data.total}'

SOM is a judgment, not a query. Take your SAM and apply the share you can win in 12 months, based on your pipeline capacity. Write that number down with its assumption.

Add a buyer count too. POST /v1/people/search with count=true as a query parameter and limit=1 is free. It returns meta.total (capped at 100,000) and meta.distinctCompanies.

curl -s -X POST "https://api.leadocean.io/v1/people/search?count=true&limit=1" \
  -H "x-api-key: $LEADOCEAN_API_KEY" -H "content-type: application/json" \
  -d '{"hqCountry":["US"],"industry":["Computer Software"],
       "employeeRange":["51-200","201-500"],
       "jobLevel":["VP","C-Team"],
       "jobFunction":["Sales & Business Development"]}' | jq '.meta'

Step 4: Multiply and sanity-check

Revenue TAM is company count times ACV. Use the SAM count for the serviceable figure.

TAM (companies)   728
SAM (mailable)    476
ACV               $24,000   (your number, not ours)
TAM revenue       728 x $24,000 = $17,472,000
SAM revenue       476 x $24,000 = $11,424,000

Then sanity-check with a small pull. Search 25 companies and read them. If half are not your buyer, your filters are wrong, and the count is wrong with them.

curl -s -X POST "https://api.leadocean.io/v1/companies/search" \
  -H "x-api-key: $LEADOCEAN_API_KEY" -H "content-type: application/json" \
  -d '{"hqCountry":["US"],"industry":["Computer Software"],
       "employeeRange":["51-200","201-500"],"limit":25}'

That call spends 25 records. One search pages to 10,000 rows (meta.depthCapped tells you), so narrow the filters rather than walking the index.

Step 5: Cut the TAM by region and sector

The count response also breaks the total down by industry, headcount band, HQ country, HQ state and NAICS. Use it to find where the market is concentrated, then decide which slice to enter first.

curl -s -G "https://api.leadocean.io/v1/companies/count" \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  --data-urlencode "industry=Computer Software" \
  --data-urlencode "hqCountry=US" \
  --data-urlencode "employeeRange=51-200,201-500" | jq '.data.facets.hq_state[:4]'

In our example California holds 204 of 728 companies, about 28%. A team that covers one state first sizes its SOM from that row, not from the national total. Re-run the count per slice. Each call is free, so ten slices cost nothing.

Write down every filter next to every number. A TAM figure without its filters cannot be reproduced next quarter, and nobody can challenge it.

What you get

Real output from the free MCP tools on 2026-10-21. These are company counts, not mailable-person counts.

The 728 split into 460 at 51-200 and 268 at 201-500. The largest HQ states were California (204), Massachusetts (51), New York (51) and Texas (44).

The people count covered VP and C-Team in Sales & Business Development at those companies. It returned 145 people across 66 companies, counting only verified, catch_all_valid or catch_all emails. With no email condition it returned 681. Treat 145 as mailable buyers, not everyone in the role.

Response shape from the API reference for a company search row, placeholder values:

{
  "success": true,
  "data": [{ "company_data": { "company_name": "Acme", "company_domain": "acme.com" } }],
  "meta": { "count": 25, "limit": 25, "credits": 25, "total": 728, "nextCursor": "<cursor>" }
}

Troubleshooting

FAQ