The Company

An AI-driven voice and messaging platform used by 22,000+ businesses worldwide. Sales and support teams use it for smarter conversations through call intelligence, CRM integrations, and automated workflows.

Their product had global reach. Their data did not.

The Situation

Three regional sales teams (US, EMEA, APAC) were each building their own prospecting processes independently. The US team defined ideal customers one way. London defined them differently. Singapore had a third interpretation.

Six data tools were in play across the organization: ZoomInfo, Apollo, Salesforce, HubSpot, Lusha, Cognism. None of them talked to each other. A "Tier 1 account" in one region would not even qualify as Tier 3 in another.

The VP of Global Sales had expansion targets that required scaling outbound across all three regions simultaneously. But the RevOps team was spending most of their time cleaning up data conflicts, fixing duplicate records, and manually reconciling lists.

Contact records decayed within months. Reps would pull a list on Monday and find 40% of the emails bounced by Thursday. Two reps from different regions would sometimes contact the same global account in the same week, creating brand confusion and wasted effort.

“We had more data tools than we knew what to do with, and somehow less usable data than when we started.”

What Corela Did:

Establishing common ground (Month 1)

No automation. No workflows. Just alignment.

We brought RevOps leads from each region together and built a field dictionary from scratch. Every data point got a definition, a source hierarchy, a format standard, and an owner. ICP tier criteria. Industry classification codes. Company size brackets. Intent signal thresholds.

This foundational work was slower than anyone wanted, but it prevented every problem that comes from automating on top of inconsistent definitions.

Constructing the enrichment system (Months 2-3)

With shared definitions locked, we designed Clay workflows using waterfall enrichment: each account passes through multiple data providers (ZoomInfo first, Apollo as fallback, Cognism as third layer). 25+ fields populated automatically per record.

Everything synced to Salesforce with strict governance: territory assignments locked after validation, ICP tiers immutable once confirmed, automatic duplicate detection on every batch import.

All workflows were built and tested in a sandbox environment. The RevOps team QA’d every output before anything touched production data.

Training through doing (Months 3-6)

We ran weekly working sessions where regional reps sat with us inside Clay and Salesforce. Not slide presentations. They learned by editing live workflows, pulling their own filtered lists, and seeing data refresh in real time.

SOPs were embedded directly in the tools where they would be used, not buried in shared drives.

By month four, RevOps was running TAM refreshes independently. By month six, they were onboarding new hires to the system without any GenFlows involvement.

What Changed:

• 4,800+ net-new accounts added and enriched across all regions with standardized data

• 25+ data points per account: employee count, tech stack, ICP tier, territory assignment, intent signals

• One self-refreshing TAM replaced six disconnected spreadsheets and lists

• Regional teams launched outbound from day one with pre-warmed inboxes and territory-segmented lists

• Churn recovery workflow activated, flagging at-risk customers using enrichment signals before renewal conversations

RevOps transformed from a reactive support function into a strategic growth driver. Regional leads stopped maintaining shadow spreadsheets and started trusting the unified system.

What We Learned Together

Defining terms before building workflows saved more time than any automation. Building in sandbox first prevented mistakes that would have rippled across hundreds of reps globally. Regional differences in legal entity names, language conventions, and local data formats broke enrichment logic wherever they were not explicitly accounted for. And adoption came from co-building: teams that helped design the system trusted it immediately.