Writing and customer stories on Salesforce and Dynamics 365 data quality — deduplication, validation, routing, and what breaks when data drifts.
Salesforce ships AI across sales, service, and marketing — Einstein Analytics, Copilot for Sales, GPT-drafted emails, call summaries. Every one of them runs on the CRM data underneath it.
Deduplication, validation, verification, enrichment, and standardization are the five practices that make CRM data reliable enough for AI to act on.
Cloudingo and Plauti both remove Salesforce duplicates, but they fit different orgs. Compare native processing, matching depth, automation, and AI-assisted matching to find the better fit.
Seamless.AI's Salesforce RevOps team eliminated duplicate contacts, accounts, and cases with Plauti, cutting a five-minute contact merge down to 30 seconds.
Four strategies keep Salesforce data reliable: validation, regular cleansing and deduplication, governance policies, and user training on entry standards.
AI changes how Salesforce data quality gets managed: active learning for deduplication, NLP-based fuzzy matching, predictive cleanup, and automated workflows.
Five root causes explain most of the bad data in a CRM: entry errors, incomplete records, duplicates, outdated information, and no shared data standards.
Five practices improve CRM data quality in practice: cleansing and deduplication, profiling and auditing, governance, master data management, and defined metrics.
Poor data quality costs businesses hundreds of billions of dollars a year. This chapter covers five concrete ways clean CRM data pays for itself, from the customer journey to revenue.
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