I went straight to market size and density, which felt right, but I skipped over rep capacity and cost-to-serve for a while and had to backtrack.
Start by clarifying that as a software engineer, you'd approach this as a data-driven optimization problem, not a pure sales decision. Then walk through a framework that segments the market by potential, models coverage needs, and uses data to allocate reps efficiently, while emphasizing collaboration with sales and product teams.
Pro tip: Acknowledge that sales distribution is ultimately a business decision owned by sales leadership, and position yourself as a partner who can build tools and models to inform that decision—this shows cross-functional maturity.
Ask about the goal: maximize revenue, coverage, or efficiency? Understand constraints like budget, headcount, and existing customer distribution.
Divide the country into territories based on potential (e.g., existing customer density, industry clusters, prospect data) and workload (e.g., number of accounts, travel requirements).
Use data to model how many reps are needed per segment, considering factors like sales cycle, average deal size, and rep capacity. Optimize for balanced workload and opportunity.
Present a data-backed proposal to sales leadership, then iterate based on feedback and real-world performance metrics.
As an engineer, suggest building dashboards or algorithms to continuously monitor and adjust distribution as market conditions change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.