Identifying the Predictive Factors Behind B2B Sales Performance

Client Snapshot:

A mid-sized B2B services company was experiencing inconsistent win rates across its pipeline. Leadership lacked a data-driven method for identifying what separated won deals from lost ones , and whether performance differences across the team reflected skill or territory difficulty.

Challenge:

Sales qualification was based on experience and intuition. Reps had strong instincts, but assessments varied widely and were difficult to standardize. Pipeline data told leadership what was happening , but not why.

This created uncertainty around which factors most predicted outcomes, whether low performers were truly underperforming or simply assigned harder deals, and where to draw qualification gates to avoid pursuing deals unlikely to close.

Our Approach:

We analyzed the company's historical closed deals , wins and losses , across a standardized set of qualification factors using machine learning and statistical significance testing.

The framework identified the specific factors most predictive of deal outcomes, established data-validated qualification thresholds, and evaluated individual rep performance adjusted for deal difficulty, all delivered through a browser-based tool requiring no installation.

Results:

Just a few of the qualification factors accounted for a disproportionate share of outcome variance. Deals meeting target values on key features closed at markedly higher rates.

The fairness-adjusted analysis also revealed that one apparently underperforming rep was actually exceeding expectations relative to deal difficulty, changing the coaching conversation entirely.

Leadership restructured pipeline reviews around the identified factors and established a quarterly reanalysis cadence to track improvement.

The Takeaway:

Structured, statistically rigorous analysis of sales qualification data moves organizations from intuition-based pipeline management to evidence-based decision-making; improving win rates, focusing coaching, and ensuring performance evaluation accounts for the full context behind each rep's results.