MARKET RESEARCH

Enrich Inbound Accounts with BigQuery Firmographics and Score Fit

When a new account row lands in Airtable, joins it against BigQuery public business datasets to attach firmographic attributes.

CategoryMarket Research
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew account record created in AirtableAirtableAirtable
  • ActionResolve firmographics from BigQuery public datasetsGoogle BigQueryBigQuery
  • LogicBranch on match confidence; flag low-confidence rows for review
  • ActionScore segment fit with OpenAI against ICP criteriaOpenAI
  • OutputWrite firmographics and fit score back to AirtableAirtableAirtable

What it does

Turns a bare company name or domain into a fully enriched, fit-scored account. As soon as a new account appears in your Airtable pipeline, the workflow looks it up against BigQuery public datasets (industry classification, employer size bands, geographic distribution) to fill in firmographics, then scores how well the account matches your ideal target segments.

When to use it

When reps or forms create raw account records and you want them enriched and prioritized automatically before anyone touches them. Ideal for keeping a clean, scored pipeline without manual research per account.

How it works

  1. 1An Airtable event triggers whenever a new account record is created.
  2. 2A BigQuery action queries public firmographic tables to resolve the account's NAICS industry, employee-size band, and headquarters region.
  3. 3A logic step checks whether the enrichment returned a confident match; low-confidence rows are flagged for manual review instead of scored.
  4. 4An OpenAI action assigns a 0-100 segment-fit score with a one-line justification based on your ICP criteria.
  5. 5An output step updates the Airtable record with firmographics, the fit score, and the review flag.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AirtableBases, tables, views, automations.
  2. 2
    Connect BigQueryDatasets, queries, schemas.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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