Nathan Starr

With no established GTM playbook, the starting point was defining an initial ideal customer profile (ICP), positioning, messaging, and approach to acquisition. I helped build that foundation, including the channel tests, tool selection, qualification, and GTM systems needed to put it into practice.

Prospect conversations, conversion data, wins and losses, and customer behavior shaped the next decisions. Later, I led a broader ICP and vertical strategy reset using new-business performance, product engagement, retention, expansion, and market opportunity to sharpen targeting, messaging, and sales execution.

With the initial GTM foundation in place, the next challenge was figuring out which acquisition motions could create repeatable pipeline. The work expanded across outbound, paid acquisition through LinkedIn, Google, and Capterra, and later SEO and AI-search visibility.

The emphasis increasingly moved toward economics rather than activity: connecting channel spend to customer fit and downstream conversion, understanding where additional investment was justified, and changing tactics when the evidence did not support them.

Along the way, I scaled Sales Development from 1 to 10+ reps, with responsibility for hiring, onboarding, playbooks, coaching, workflows, and measurement.

Inbound qualified-opportunity volume grew approximately 8× in under 12 months during an earlier phase of that acquisition build.

As the company scaled, the work increasingly moved upstream from individual acquisition programs to how the business planned and managed revenue. Working across Finance and GTM, I translated targets into capacity and pipeline requirements, built forecasting and pacing frameworks, improved visibility across new business and customer growth, and helped leadership understand where performance was diverging from plan.

Sales process, qualification, deal stages, and review routines evolved alongside the planning model so the operating system reflected how the business actually needed to run.

As AI capabilities matured, the question was less whether the GTM team should use AI and more where it could improve the way the team worked. I led efforts around AI-enabled workflows, automation, and API integrations, alongside broader evaluation of the GTM technology stack.

The goal was practical: identify where technology could improve prioritization, execution, or operating leverage rather than adding tools for their own sake.