Machine Learning-Based Risk Prediction in Pre-Sales Engagements for Tech Solutions

Based on a pre-sales tech project customer churn analysis of 5,500 interactions, we identified three important drivers of churn: proposal presentation, participation in the solution showcase, and C-level stakeholder involvement. Machine learning models (XGBoost, Random Forest, Support Vector Machines) correctly predicted churn at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 6. 5 \%}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 2. 3 \%}$</tex>. Demonstration of power and stakeholder engagement quality were associated with successful conversions. By combining measures of engagement, stakeholder profiles, and competitive strength, a predictive model emerged to tackle pre-sales churn risk. The work complements literature that has focused on postpurchase churn, and it gives sales organizations superior resource allocation, earlier risk detection, and better conversion. The work demonstrates pre-sales behavior can be correctly modeled and produces actionable recommendations to optimize prospect retention strategies in complex tech selling.