Sales Performance & Customer Profiling Analytics

The objective was to replace intuition-led recruitment assumptions with a data-backed profile of the characteristics associated with stronger sales performance. By linking employee attributes with observed results, the analysis aimed to sharpen candidate shortlisting, focus training investment and give HR and sales leadership a repeatable basis for testing whether the desired profile continued to predict performance.

Leadership faced a recurring workforce decision with direct implications for revenue, turnover and training cost: what profile should the organization prioritise when hiring frontline sales staff? The answer required more than comparing average performance. The analysis needed to isolate relevant characteristics, test them across performance bands, account for differences within the sales population, and translate statistically meaningful patterns into practical recruitment criteria.

• Focused on Relationship Managers — the entry sales role contributing 53% of revenue and 83% of the salesforce.
• Broke monthly performance into bands and tested traits for statistical significance.
• Ran univariate and multivariate analysis (highlight tables, Sankey charts), separately for males and females.

• Female staff averaged ~81% revenue achievement — about 10% higher than males.
• The 26–30 age group performed best, averaging ~83% achievement.
• Master’s-degree holders averaged ~80% vs ~73% for Bachelor’s.
• Staff with 1–3 years of banking/insurance experience averaged ~93% achievement.

• HR, Business Development and Sales jointly revised candidate shortlisting criteria to match the profile.
• Established monitoring criteria to review candidate performance periodically and test the strategy.