Customer story · Data Quality & Enrichment

The scoring model wasn't broken — 90,000 contacts had no industry

A PE-backed financial-services platform blamed its lead-scoring model for skewed results. The audit found the cause underneath: tens of thousands of contacts with no industry or company association, plus mis-tagged segments. LeanScale fixed the data, and the deduplication process around it.

ProofWhat happened on a real engagement.
Fintech & Financial ServicesSector
Enterprise / PE-backedStage
6–12 monthsDuration
2Min read

Anonymized. The company is described by sector and stage only — no customer is named, and quotes are attributed by role.

#The challenge

Lead scores were skewing in ways the model design did not explain. An audit traced the cause upstream: roughly 90,000 contacts had no industry value and roughly 80,000 had no associated company record, so fit scoring had almost nothing to score against. Thousands more contacts were tagged to the wrong segment entirely. Separately, a bulk deduplication run was merging records in ways that would have quietly corrupted reporting, and a rep departure was about to leave thousands of in-flight contacts without an owner.

#The approach

Root-cause the skew instead of re-tuning around it

Traced skewed scoring to missing firmographic data rather than model design — roughly 90,000 contacts with no industry and roughly 80,000 with no associated company — and reported the cause rather than adjusting weights to hide the symptom.

Segment correction at the source

Fixed roughly 1,100 mis-tagged segment contacts as the upstream cause of the skew, and cleaned segments containing firms outside the approved list.

Auditing a merge run before it reached reporting

Audited a bulk deduplication run of roughly 2,100 merges and caught around 90 problematic merges — including roughly 60 discarded qualified leads — before they corrupted the reporting they fed.

Email validation across the database

Ran an email-validation audit to separate deliverable contacts from decayed ones, so list size stopped flattering the engagement numbers.

Ownership continuity through a rep departure

Managed a rep departure end to end — roughly 5,500 contacts reassigned across departed owners and 14 workflows updated — so no in-flight leads went cold during the handoff.

A taxonomy so the gaps do not reopen

Scoping a company-hierarchy and ICP taxonomy so firmographic data has a structure to hang on going forward, rather than being re-cleaned every year.

#Outcomes

Root cause found rather than papered over

The scoring skew was traced to roughly 90,000 contacts with no industry and roughly 80,000 with no associated company, and to roughly 1,100 mis-tagged segment contacts upstream of it.

~90 bad merges caught before reporting

An audit of roughly 2,100 bulk merges caught around 90 problematic merges, including roughly 60 discarded qualified leads, before they reached the dashboards.

No leads dropped through a rep exit

Roughly 5,500 contacts were reassigned across departed owners and 14 workflows updated during the transition.

In their words

What the customer said

“I know that you guys often help us with really like tactical implementation work, especially on the data side with [our data lead]. After hearing from the C suite this morning, like, I need help with the total roadmap.”
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