MAXIMIZING EFFICIENCY: A CASE STUDY IN USING BUSINESS ANALYTICS TO ANALYZE SALES STAFF GEOGRAPHICAL TRACKING

Introduction

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 monitoring and tracking of sales staff involves using data analytics to analyze data on sales staff performance and location in order to identify patterns and trends. This can help to optimize resource allocation, inform training and development strategies, and improve 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 most essential employees in any company are the sales staff. They are primarily responsible for generating new business, retaining existing clients, and building the company’s reputation in the market. Therefore, it is crucial for any company to regularly monitor the activities of its on-field sales staff to track their productivity and performance.

In this regard, the primary concern of the company was that it was not able to gauge and monitor the whereabouts of the on-field sales staff to determine whether the sales staff spent time working efficiently conducting meetings or were busy during office time on other work.

The BI team was commissioned to utilize the data points to develop a monitoring mechanism for tracking the sales staff.

Available Data Points

The following key data points were available for analysis captured in the company’s Customer Relationship Management (CRM) system and network provider’s system.

The company offered paid mobile phone SIMs to its sales staff to maintain customer relationships. Therefore, it was able to obtain hourly location-based data of sales staff from the network provider.

Unique Identifiers:

  • Sales Staff Emp ID

Employee Details:

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

Professional Experience Details:

  • Tenure, Experience, Industry

Meeting Details:

  • Meeting ID, Meeting Date,
  • Meeting Details

Remuneration Details:

  • Salary, Commission

Performance:

  • Sales Performance

Location:

  • Tower Based Hourly Locations


Solutions

The BI team used the published data sources scheduled for daily 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). The team also utilized data obtained from core HR system and APIs from network provider. 

Using the available data, the BI team developed a customized rule-based solution designed to measure and compare sales staff’s efficiency, attendance and tracking status. It was an interactive tool that allowed sales agents’ scoring based on defined rules and assumptions and highlighted sales agents who were not fully utilizing their time.

Model Rules and Logic

The model followed the below-mentioned rules and logic:

a)  Tracking – Absolute:

  • The model identified individuals as movers and couchers for both the meeting count and visits count.
  • Movers were those with Meetings or Visits < 3
  • Couchers were those with Meetings or Visits > 5

b) Tracking – Relative

  • The staff was classified by comparison of the number of meetings entered in CRM and the number of visits tracked.
  • If the number of CRM meetings was less than the tracking-based meeting count (acceptable deviation of 20%) then the case was classified as “Dummy Visits.”
  • If the number of CRM meetings was greater than the tracking-based meeting count (acceptable deviation of 20%) then the case was classified as “Dummy Entries.”
  • Otherwise, the case was classified as “No Difference.”

c)     Efficiency

  • At minimum it was expected that each Sales Staff should conduct three meetings in a day.
  • Efficiency measured whether staff met this daily target.
  • Staff was marked as Achieved if the target was met on either CRM/Tracker and Not Achieved otherwise.

d)    Performance

  • The model identified individuals with retail revenue achievement above 80%, i.e., rated Good and above.

Model Scoring

a)    Efficiency

  • The model assigned the score of “0” if the location of the Sales Staff matched his recorded residential address or office/branch address.
  • The model assumed that the sales staff was engaged in a meeting if any location other than the Home or Office was identified at a given time and was given a score of “1”.

b)    Tracking

  • The model matched the locations with recorded addresses consecutively at an interval of 1 hour, if the exact location other than home/office was identified for two consecutive hours then no additional score was awarded.
  • The model analyzed the activity of each Sales Staff during working hours and highlighted an individual if he/she was found to be at a particular location (other than the office/home) for more than three hours. If this pattern was repeated for more than a week the case was marked as a red flag.

Data Analysis for Key Insights

Based on the analysis of data using the implemented model, the following sales staff cases were identified;

  • Sales staff with significant deviations between the tracked locations and meeting as per CRM were highlighted
  • Sales staff who were found to be present at a unknown location persistently for more than 3 hours were highlighted
  • Based on the highlighted cases further investigations were carried out and it was found either the staff was engaged doing parallel jobs or were driving uber.
  • However, there was also a considerable number of employees who exerted greater effort but were unable to close sales; these employees received particular mentoring and training.

Such cases were further analyzed along with other factors like sales staff performance, productivity, and revenue-to-cost ratio for finalization. The insights were then shared in a formal report with key stakeholders in the company to take timely action.

Results

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

  • The revenue contribution from the retail sales team increased
  • The productivity of the sales staff was enhanced as compared to the previous year
  • Regular monitoring and tracking mechanism resulted in increased visibility and transparency of sales staff performance which helped in fair decision making at the time of appraisals
  • Outperformers were identified and rewarded
  • Disciplinary action was taken against underperforming employees

Key Highlights of the Analysis