CRM & RevOps teams

CRM hydration

Backfill incomplete account records with structured contact data, automatically.

The problem

Why this is hard by hand.

CRMs rot. Marketing uploads a CSV of company names, reps create accounts with half the fields blank, and over months you accumulate thousands of records missing websites, phone numbers, or any usable email. That gap quietly throttles every downstream play - routing, scoring, sequencing - because the data they depend on is not there.

biz collect turns hydration into a repeatable pipeline. For each thin record you run a search by business name, category, and location, attach the structured contact data that comes back, and write it to the account. The same fields come back every time, so your field mapping is built once and stays stable.

The workflow

How biz collect solves it.

Identify thin records
Query your CRM for accounts missing a website, phone, or email. These are the records worth hydrating; skipping complete ones keeps credit spend efficient.
Search by name and location
For each thin record, POST to /v1/search using the business name as a keyword plus its known city to scope the match. You get a job_id back immediately.
Poll for the result
Poll GET /v1/jobs/:id until the job completes. Polling is free, so a large backfill does not multiply credit cost while jobs run.
Match and map the fields
Match the returned business to the record (by name and address), then map website, phone, deduped emails, social profiles, and hours onto your CRM fields. The schema is stable, so this mapping is written once.
Write back and log
Update the account in Salesforce or HubSpot. On Pro and Business plans, usage logging lets you audit which workflow spent which credits; Business adds CSV export for a reviewable handoff.

Fields & endpoints

What you actually use.

POST /v1/search

Run one search per thin record using the business name and known city as inputs.

website, phone, email(s)

The core fields that turn a half-empty account into a contactable one.

address, opening hours

Help you confirm you matched the right business before writing back.

Usage logging

On Pro and Business, audit credit spend by workflow so backfills stay accountable.

CSV export

On Business, export results for a reviewable handoff to ops or for manual QA.

Example

Illustrative: backfilling a stale account list

Imagine a RevOps team inherits 3,000 accounts where only the company name and city are reliably filled. They build a pipeline that, for each account, searches biz collect by name and city, polls the job, and matches the returned business by name and address.

Matched records get their website, phone, and deduped website emails written back to HubSpot, and the run is tagged in usage logging so finance can see exactly what the backfill cost. Records with no confident match are flagged for manual review rather than overwritten. This is an illustrative pattern; match quality depends on how identifiable each business is publicly.

Who it's for

RevOps and sales ops teams responsible for CRM data quality.
Marketing teams that import company lists and need them enriched.
Anyone tired of reps skipping accounts because the contact fields are blank.

Build it

Everything you need to ship this workflow:

CRM hydration

Frequently asked questions.

How do I hydrate a CRM with biz collect?

For each incomplete record, POST a search using the business name and known city, poll the job, match the returned business by name and address, then map website, phone, and deduped emails onto your CRM fields and write back.

Can I write results back to Salesforce or HubSpot?

biz collect returns structured JSON (and CSV on the Business plan), so any pipeline or automation tool can map those fields to Salesforce, HubSpot, or another CRM. biz collect provides the data; your integration writes it back.

How do I keep credit spend predictable on a big backfill?

Only search records that are actually missing data, rely on free polling for async jobs, and use the daily spend limit on your plan as a guardrail. On Pro and Business, usage logging shows spend per workflow.

How do I avoid overwriting good data with a bad match?

Match the returned business to your record by name and address before writing, and flag low-confidence matches for manual review instead of overwriting. The address and hours fields help confirm you matched the right business.

Is there an audit trail for compliance?

On Pro and Business plans, usage logging records API activity and credit spend, and Business adds CSV export. biz collect collects only publicly available business data under a Swiss, revFADP/GDPR/CCPA-aware posture.

Still have questions? Talk to us.