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.

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.

Your pipeline problem is not a demand problem. It is a data problem.

Most revenue teams treat their CRM as a system of record. They build campaigns, scoring models, routing rules, and forecasts on top of it. They assume the data inside reflects reality. It does not.

CRM data degrades at a rate of roughly 30% per year, according to MarketingProfs. Job titles shift. Companies merge. Contacts leave. Records go stale. Meanwhile, new signals emerge across channels that never reach the CRM at all.

This decay sits beneath the surface. It does not announce itself. It shows up as missed targets, low conversion rates, wasted spend, and frustrated sellers. By the time the symptoms are visible, the damage is already compounding.

This eBook identifies seven specific signs that your CRM data is undermining pipeline generation and deal velocity. Each sign maps to a structural failure in how GTM data is captured, maintained, connected, or activated. And each one points to a common root cause: your data layer was not designed for the speed and complexity your revenue engine now demands.

If even three of these signs look familiar, your GTM architecture needs attention.