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1 minute read
The Hidden Cost of Data Enrichment Failures
**With last week's blow up news about Apollo and Seamless banned from LinkedIn, I had to weigh in on the costs of data enrichment.**
Data enrichment failures waste 23% of marketing budgets, according to Forrester's B2B Data Management Report. Most failures stem from poor implementation rather than tool selection.
Common Failure Points:
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Inconsistent Data Validation – Lack of uniform data validation leads to errors and duplicates.
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Poor Matching Criteria – Ineffective matching results in lost opportunities.
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Lack of Maintenance Protocols – Without regular updates, enriched data loses value over time.
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Over-Enrichment – Too much irrelevant data clutters systems rather than improving insights.
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Insufficient Quality Checks – Without audits, errors remain undetected and impact decision-making.
SiriusDecisions' Marketing Operations Study shows that effective enrichment increases lead quality by 67%. However, Gartner's Data Quality Survey finds that only 24% of organizations have successful enrichment programs.
How to Fix Your Strategy:
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Define Minimum Data Requirements – Establish baseline data fields needed for enrichment.
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Implement Validation Rules – Automate checks to ensure data accuracy.
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Schedule Regular Refreshes – Keep data updated to maintain relevance.
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Monitor Match Rates – Track how effectively new data integrates with existing records.
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Measure ROI – Continuously assess the impact of enrichment on marketing performance.
HubSpot's State of Marketing Data reveals that companies with systematic enrichment see 41% higher conversion rates. Additionally, ZoomInfo's B2B Database Research shows that proper enrichment reduces data decay by 66%.
Final Thoughts
Data enrichment should enhance, not hinder, marketing performance. A structured approach with clear validation rules, ongoing monitoring, and strategic updates ensures long-term success.
Need help optimizing your data enrichment strategy?
Schedule a consultation: https://meetings.hubspot.com/dataopsgroup/gtclient
Related Reading: The True Cost of Bad Data
