MARKET RESEARCH
TAM Sizer from BigQuery Public Census Data by Segment
On a schedule, queries BigQuery public Census and business-pattern datasets to size the total addressable market for each of your target segments.
How it runs
The automated pipeline, trigger to output.
- TriggerQuarterly planning schedule fires
- ActionQuery Census County Business Patterns in BigQueryBigQuery
- LogicMap NAICS/geo buckets to target segments; drop sub-floor segments
- ActionApply ACV and penetration assumptions to compute TAM/SAM/SOMOpenAI
- OutputWrite ranked sizing table to Notion databaseNotion
What it does
Produces a defensible TAM (total addressable market) estimate for every segment you sell into. It pulls firmographic counts and revenue proxies from BigQuery public datasets (US Census County Business Patterns, ZIP Business Patterns), multiplies by your average contract value assumptions, and publishes a ranked sizing table to Notion that your GTM team can act on.
When to use it
When you need board-ready or planning-cycle TAM numbers grounded in real public data rather than top-down guesses. Run it quarterly to keep segment sizing fresh as you add or redefine target segments.
How it works
- 1A scheduled trigger fires at the start of your planning cadence (e.g. quarterly).
- 2A BigQuery action runs a parameterized SQL query against County Business Patterns, returning establishment counts and employment by NAICS code and geography.
- 3A logic step maps each NAICS/geo bucket to your defined segments and filters out segments below a minimum-count floor.
- 4An OpenAI action applies your ACV and penetration assumptions to convert counts into dollar TAM, SAM, and SOM bands with a short rationale per segment.
- 5An output step writes the ranked sizing table to a Notion database, one row per segment.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect NotionPages, databases, comments.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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