Article
Why CRM data quality issues persist after enrichment and what to do about it
CRM Data Quality Issues: Why Enrichment Isn't Enough
You invested in data enrichment tools. You connected them to your CRM. Records updated, fields filled in, and for a moment it looked like the problem was solved. Then the duplicate records came back. Routing errors returned. Scoring models started firing on stale signals again.
This is not a vendor failure. It is a structural one. Enrichment addresses what a record contains. It does not address how records are created, matched, merged, or maintained across your entire GTM system. Those are separate problems, and confusing them is exactly why CRM data quality issues persist even after enrichment tools are in place.
If you manage CRM data for a revenue team, this is the breakdown worth understanding.
The four structural causes of persistent CRM data quality issues
Latest Articles
Article
What great buying team data unlocks across GTM
Your market does not buy as a list of leads. It buys through groups of people with different roles, priorities, and timing. If your systems still treat demand as a single-contact problem, your GTM motion breaks early.
That is where buying team activation changes the model. When you work from complete, current buying team data, you see the people behind account activity, the roles they play, and the signals that show movement. You stop guessing who matters. You start executing against the full buying decision.
This shift matters more now because buyers do most of their evaluation before sales enters the process. According to 6sense, 81% of B2B buyers pick a preferred vendor before speaking with sales. If you do not identify and engage the full buying team early, you lose ground before the first conversation.
For RevOps, demand gen, and sales ops leaders, better buying team data creates a practical advantage. It improves targeting, routing, orchestration, measurement, and audience activation across the revenue stack. It also makesCustom Audiences and Third-Party Data more useful because both depend on identity accuracy and role-level context.
Article
Lookalikes: How to clone your best customers into a prospect list
You already know which accounts closed fast, expanded early, and never needed hand-holding through the deal. Those accounts are data. Most reps leave that data sitting in their CRM and go back to cold lists. Sidekick turns your best customers into a repeatable prospecting strategy by helping you find lookalike companies that match the same profile, in seconds.
This guide walks you through how to use the lookalike feature inside Sidekick, why it works, and how to build a prospect list that actually converts.
Article
How to Score Prospect Fit at the Rep Level, Not Just the Account Level
Your CRM says the account is a fit. The firmographics line up. Revenue, industry, headcount, tech stack. Everything checks out on paper. So you spend two weeks working it. You send sequences, leave voicemails, and chase down contacts across the org chart. Then the deal stalls before it starts. Nobody on the buying side had budget authority. Nobody matched your ICP at the contact level. The account scored well. The people inside it did not.
This is where most sales teams lose hours they never get back. Account-level scoring tells you where to look. Prospect fit scoring tells you who to talk to when you get there. Without both, you prospect blind half the time.
