BD-Sales Pairing Optimization

Project Overview

The goal of this project was to optimize lead routing from Business Development (BD) representatives to Sales representatives by identifying which pairings work most effectively together. In many organizations, leads are assigned randomly or distributed evenly across the sales team, but this approach ignores the reality that not all BD-Sales combinations perform equally. Some pairings naturally excel together while others struggle, and understanding these patterns can dramatically improve conversion rates and revenue outcomes.

To address this challenge, I conducted a comprehensive analysis of 2,200+ opportunities across 18 BD reps and 23 Sales reps, resulting in 413 unique pairing combinations. The methodology began by defining four key performance metrics that capture different aspects of sales effectiveness: Win Rate (conversion success), Early Death Rate (qualification quality), Stale Pipeline Rate (deal momentum), and Average Deal Size (revenue impact). Each metric provides unique insights into pairing performance across the entire sales cycle.

The critical methodological decision was establishing BD-specific baselines rather than comparing all pairs to a single company-wide average. This approach recognizes that different BDs pass leads of varying quality and difficulty - for example, Enterprise BDs naturally have lower win rates than SMB BDs due to deal complexity. By calculating each BD's average performance across all their sales rep partnerships, I created fair baselines that control for these inherent differences. Each BD-Sales pair was then evaluated using percentage deviation from their BD's baseline, which normalizes performance relative to lead difficulty and makes scores truly comparable.

To ensure statistical reliability, I implemented a confidence multiplier based on sample size - pairs with 7+ opportunities received full confidence, while those with 3-6 opportunities received partial weighting, and pairs with fewer than 3 opportunities were excluded entirely. The four metrics were weighted equally at 25% each and combined with the confidence multiplier to produce a final performance score for every pairing. These scores were used at two levels: first, BD-specific rankings identified the top 5 and bottom 5 sales reps for each BD's leads (operational routing decisions), and second, population-wide percentiles classified overall pairing quality across the company (strategic context). The analysis revealed that optimized routing to each BD's top-performing sales reps could generate $600K+ in additional annual recurring revenue - a 50% increase with zero implementation cost, simply by matching leads more intelligently with the right sales talent.