The Data Stack for Prospecting

Modern prospecting isn't about calling more people — it's about calling the right people at the right moment with the right message. That requires data. Specifically, it requires understanding what data exists, where to get it, and how to combine it into a reliable signal for who to prioritize today.

The Three Layers of Sales Intelligence

Layer one is firmographic data: company size, industry, revenue, location, tech stack, funding stage. This tells you who fits your ICP. Layer two is contact data: decision-maker names, titles, emails, phone numbers, LinkedIn profiles. This tells you who to reach. Layer three is behavioral and intent data: hiring signals, website visits, content consumption, review site activity, news triggers. This tells you when to reach — and that's where most teams fall short.

Why Timing Is the Lever Most Teams Miss

Two companies can be identical on paper — same size, same industry, same tech stack — and have wildly different propensity to buy. One is actively evaluating solutions right now; the other won't be in market for 18 months. Without intent data, you're calling both with equal urgency and wasting half your effort. With intent data, you prioritize the one that's ready and come back to the other at the right time.

Building Your Data Stack

A practical sales intelligence stack doesn't need to be expensive. At minimum: a firmographic database (ZoomInfo, Apollo, or Clay) for ICP filtering and contact data, plus visitor intelligence (Kopimore) for behavioral intent signals from your own website. These two sources together give you a filtered ICP matched against companies actively researching your solution.

Course Overview

Over 10 lessons you'll build and use a complete prospecting data stack: hiring signals, visitor intelligence, dynamic account lists, prioritization frameworks, marketing coordination, and ROI measurement. By the end you'll have a data-driven prospecting system that consistently surfaces the right accounts at the right time.