Company-level Ideal Customer Profiles (ICPs) are standard, but top reps should define their own personal ICP. This helps them filter prospects and avoid closing deals that, despite high commissions, will inevitably lead to churn, support issues, and reputational damage down the line.

Traditional ICP scores reflect who *you* want to sell to (e.g., wallet size), which is useless for reps. Instead, sort your entire market based on the quantifiable size of their pain (e.g., projected fines). This gives reps a clear, actionable, and customer-centric reason for outreach.

Stop defining your Ideal Customer Profile with abstract firmographics. Instead, feed context from your best closed-won deals into an AI and ask it to find public data that signaled their specific pain *before* they engaged you. This reverse-engineers a truly effective, data-driven targeting model.

Executive teams often create an ICP based on a 'wishlist' of big logos. The most accurate ICP is actually found by analyzing your first-party CRM data. Examining patterns across both close-won and close-lost deals reveals surprising truths about which customer segments are actually the best fit for your solution.

Ditch the aspirational "Ideal Client Profile," which represents a rare, perfect-world scenario. Instead, build a "Target Client Profile" that defines which customers will perceive the most meaningful value from your offering. This provides a realistic, operational benchmark for qualifying leads.

At the $300k revenue stage with one salesperson, defining a precise Ideal Customer Profile isn't just for targeting. It's a survival mechanism to focus limited resources, prevent churn, and ensure every sales effort contributes to scalable growth, rather than creating future service burdens that consume your only salesperson.

Many businesses fail by creating an offer and then searching for a customer. The correct sequence is to first deeply understand and select your ideal customer segment. Only then can you reverse-engineer an offer that resonates perfectly.

Don't just target the same job titles as your best customers. Dig deeper into the buyer's professional history (e.g., a COO with a 20-year sales background). This backstory is often the true indicator of an ideal fit, allowing for more precise and effective targeting.

Instead of theorizing about their Ideal Customer Profile, Assembled's first GTM hire created a list of all existing customers. By looking for patterns and creating groups based on traits like tech stack (Zendesk), agent count (20-200), and channel complexity, a data-driven, highly specific ICP emerged organically.