The Key to Success: A Case Study in Using Business Analytics to Profile and Train Sales Employees


Industry: Financial Services  

Company: Asset Management Company

Company Description

The company is one of the leading Asset Management Companies of Pakistan managing USD 800 million in investment solutions like open-ended mutual funds, pension funds, and investment advisory portfolios.

Business Objective

The business objective of identifying the optimal sales employee profile involves using data analytics to analyze data on employee performance and customer interactions in order to identify key characteristics and skills that contribute to successful sales performance. This can help to inform the selection and training of sales employees, resulting in improved sales performance and customer satisfaction. This can ultimately drive business growth and success by maximizing the efficiency and effectiveness of the sales team.

Business Challenge

The objective of this analysis was to uncover valuable insights into one of the company’s key business questions, which was “What is the right salesperson profile for hiring?”

The question had a pivotal role in the company’s overall strategic objective of growth in terms of both revenue generation and sales force management.

Available Data Points

The following key salesperson information data points were available for analysis captured in the HR and Performance and Commission systems at the time of salesperson onboarding and for monthly performance-based commission disbursements respectively:

Unique Identifiers:

  • Sales Staff Emp ID

Employee Details:

  • Emp Name
  • Designation, Grade
  • Gender, Age, Qualification

Professional Experience Details:

  • Experience, Industry


  • Sales Performance

Data Analysis for Key Insights

The BI team used the published data sources scheduled for monthly update compiling data from multiple database tables accessible for big data analysis using data warehousing (MS SQL Server) and analytical tools (Tableau Server and Tableau Desktop).

A)   Analysis Approach

The following approach was followed for extracting key insights from the data:

1. Selection of Designation:

The starting position in the sales force hierarchy (Relationship Managers) was selected for analysis as the initiating point. This designation contributed 53% to the total revenue and constituted 83% of the salesforce.

2. Analysis of Performance Bands:

Monthly performance figures were utilized for the analysis. The sales staff performance was broken down into defined performance percentage bands. From this, high-performance bands were focused for gauging prominent and statistically significant traits of good performers.

3. Selection of Key Traits:

The bigger question was broken down into smaller ones, and the following attributes of a high performer were determined:

  • Which gender performs better?
  • Which salesperson age group has higher performance?
  • What is the ideal education level, experience and background of a high-performing salesperson?

4. Univariate Analysis:

In the univariate analysis, each key trait was analyzed against revenue performance bands.

5. Multivariate Analysis:

In the multivariate analysis, relationships and patterns were analyzed amongst multiple key attributes. Highlight tables and advanced visualizations, including Sankey Charts, were utilized to identify patterns and concentrations to gain valuable insights. This analysis was conducted for males & females to identify their optimal profiles.

6. Analysis Limitations:

As the analysis matured and key insights were uncovered, improvement areas in key attributes data were identified, and measures to minimize deviations from the optimal profile were implemented.

B)   Key Insights

In-depth analysis of data revealed the following insights:

High Performing Gender

  • Females had 10% higher average revenue achievement than males, about 81%
  • 47% of female sales staff achieved more than 50% of their revenue target

Preferred Age Group

  • 26 yrs. -30 yrs. old sales staff had an average revenue achievement of about 83%
  • 44% of the sales staff in this age group achieve more than 50% of their revenue target

Preferred Education Level

  • Sales staff with an education level of Masters had an average revenue achievement of about 80% while Bachelors had 73%
  • 45% of the sales staff with a degree achieve 50% of their revenue targets.

Preferred Experience Level

  • Sales staff with 1-3 years of experience in the banking or insurance industry had an average revenue achievement of about 93%
  • 52% of the sales staff with the above experience achieved 50% of their revenue targets

The key insights from the analysis were shared in a formal report with relevant stakeholders with infographics for traits and detailed profiles for male and female salespersons.


Based on the above insights, Human Resource Department in collaboration with the Business Development Unit and Sales Team devised a hiring strategy that involved taking the following actions: 

  • The candidate shortlisting criteria were updated to select the candidates based on the revised strategy
  • The monitoring criteria were devised to review the periodic performance of candidates for assessing the effectiveness of the revised strategy


As a result of this activity, the company was able to achieve the following outcomes:

  • Sales team performance over the next year improved and staff turnover reduced from 40% to 30%
  • Due to low turnover, the staff training cost and loss due to nonperforming employees reduced

Key Highlights of the Analysis