Cleaning a CRM without fixing what created the mess just resets the clock. We fix both — so the database stays clean after we leave.
Salesforce and HubSpot. Tool-agnostic by design — the same discipline runs through native CRM tooling, a dedicated data-quality platform, or both.
Most teams treat dirty data as a housekeeping chore — a scrub that happens when a report looks wrong or a campaign misfires. That framing is why the mess always comes back.
The failure rarely looks like a data problem. It looks like a sales problem, a marketing problem, or a forecast nobody trusts.
This is the shared vocabulary we use with your team throughout the engagement. Numbers below are a specimen audit — 60,000 records across Contacts, Accounts, Leads, and Deals.
Classifying this way separates noise from risk. "Incomplete" may be enrichment work; "business validation" may be 47 deals distorting your forecast right now.
The sequence is deliberate. Skipping a phase is the most common reason a cleanup decays.
This is the scorecard we hand back in Phase 1 and re-run in Phase 4. Find your own numbers on it — the band you land in is the priority order.
Bands are drawn from JayOh data engagements and segmented by ARR band in the real deliverable. We show the model so you can argue with it.
Two inputs do the work: the demand you already pay for that can't be routed, and the hours your team spends rebuilding reports it doesn't trust.
Recovered revenue plus reclaimed reporting hours, at a 90% reduction in data errors and 5.8 hours per person per week returned — both measured on JayOh data engagements.
Pressure-test this with usCleaning one side while the other keeps pushing dirty records back is a common and expensive failure. We remediate and install guardrails on both systems and the integration between them, so the two stay clean together.
in data errors across the cleaned environment
issue resolution once clean data and monitoring were in place
saved per person from automated, trustworthy reporting
methodology across 40+ CRM and marketing-automation migrations
in forecast accuracy on clean, business-validated pipeline
Start with the scan. You'll get the seven-category baseline and the band you land in — before anything changes.
We engineer growth systems. JayOh builds the RevOps architecture, GTM execution and data infrastructure that turn messy revenue operations into predictable pipeline for B2B SaaS and PE-backed teams.
