Turning a Decaying CRM into a Self-Maintaining Sales Foundation
See how Tapistro helped a global manufacturer eliminate CRM data decay with continuous enrichment, automated field updates, and dynamic lead scoring.
100%
Pre-campaign data sprints eliminated
50x
Faster account research with AI agents
A CRM that looked populated, and was quietly going stale
CRM data decay is one of the most damaging problems in B2B sales, and one of the least visible. There is no alert when a contact changes jobs or a company restructures. The record stays, the data looks populated, and the campaigns keep going out.
This global industrial manufacturing enterprise had invested heavily in GTM automation, but the data underneath it was quietly degrading. Key fields were missing across large portions of the database, contact details had gone stale, and lead scores were set once during onboarding and never updated, so sales had no reliable signal for who was actually in-market.
The workaround was a recurring manual sprint. Before every major campaign, RevOps pulled lists, cross-referenced sources, and filled gaps by hand. It was slow, it never fully worked, and it consumed the team's bandwidth every time, while outbound kept going out on stale data.
Not a one-time fix, but an always-on enrichment layer
Tapistro deployed a continuous enrichment layer across the full CRM, running in the background rather than as a one-time cleanup project.
Chain the data sources
Rather than relying on a single provider, Tapistro chained Explorium, Wiza, NeverBounce, and Tap AI Agents in sequence, each filling gaps the previous one missed.
Populate missing fields
Job title, direct phone, LinkedIn, company size, and industry classification were populated continuously as records changed, without anyone touching a spreadsheet.
Score leads dynamically
Lead scores updated as new signals arrived, such as hiring activity, funding events, and website engagement, reflecting who is in-market now rather than six months ago.
Keep one profile per account
New signals were layered onto existing records instead of creating separate entries, so each account was enriched once and kept current.
What changed once the data stayed current
- Pre-campaign data sprints eliminated entirely, so campaigns launch without manual prep cycles
- SDRs work complete, accurate records, with no chasing missing contacts and no outreach to people who left
- Lead scores reflect live buying signals, so sales prioritises accounts at the moment of highest readiness
- RevOps bandwidth freed from data maintenance and redirected to orchestration and pipeline work
- The GTM automation they had already built finally worked as intended, because the foundation it ran on was reliable
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