Data
Enrichment
Adding missing company/person attributes to improve execution quality.
Definition
Enrichment appends details like role, seniority, company data, and contact context so teams can personalize better and qualify faster.
Why It Matters
Poor data quality slows execution and weakens targeting.
Practical Interpretation
Treat this as a data-governance and confidence problem before using it for targeting. Enrichment should be connected to specific owners and review moments so decisions are repeatable.
How It Shows Up in Laserreach
Company and person enrichment endpoints support account and committee context building.
Laserreach Context
Where it lives: Maintained across data-source configs, enrichment jobs, identity resolution, and scoring inputs.
Execution impact: Company and person enrichment endpoints support account and committee context building.
Operator review question: Would the team make the same decision if source confidence were visible?
Implementation Checklist
- Track source provenance for every high-impact signal.
- Set freshness rules and stale-data cutoffs.
- Define dedupe and confidence thresholds.
Metrics to Track
- Signal freshness and stale ratio
- Match accuracy and enrichment completion rate
- False-positive rate in account routing
Common Pitfalls
- Mixing sources without confidence weighting
- Trusting enrichment output without validation
- Ignoring source-level drift over time
External References
Further reading from external sources for industry context and definitions.