Incentive plan for pharma distribution agents network

8+
Secondary research.
4+
Machine learning algorithms.
4
Data analytics.
2
PLS analysts.

Client

A leading multinational Pharmaceutical Manufacturer.

Opportunity

The client wanted to optimize their distributor agent incentive plans for recommending prescription drugs to medical practitioners. They used to manually consolidate their payout amounts on new businesses earned, sales representative bonuses etc. However, the client wanted more control on the costs to create a balance between the costs and sales incentives to the distribution team so that they would recommend the client’s products over other products available in the market, for a particular sales territory.

The client also had many different ongoing sales incentive plans and wanted to gain a deeper understanding of which programs were working and which weren’t.

SOLUTION

SG Analytics helped gather and consolidate information from different sources including the client's distributor relationship management system. The data helped to assess each sales agent distributor in regards to performance indicators including revenue earned to paid commission fees, etc. Next, SG Analytics helped aggregate this data into sections such as top 10 percentile performers, top 20 percentile etc. This helped in gaining “more incremental value” from focusing on a single demographic for modeling a selection of incentive plans, for a particular geography.

Using sophisticated Machine Learning algorithms, the data was populated and the outputs such as the following were established:
  • Exact percentage commission that should be given to top performers (Top 20 % Performers) to encourage them to sell the client’s product over competitors?
  • Plans that would be effective in increasing sales and persuade the bottom rung (Bottom 20%) of distributors to sell the client’s products more.
  • Impact forecasts on distributor productivity for changes in the compensation plans.
  • Identification of rewards and recognition programs that overcompensated or undercompensated distributors based on their performances.

In addition to crunching historical compensation data, SG Analytics’ incentive planning model also allowed the client to simulate the financial impact of incentives and compensation plans. For example, a user could change a two-week incentive plan for junior sales reps into a year-long incentive strategy for major distributors and test how such a tweak would influence sales and productivity levels compared to previous months.

R
Python
SalesForce
DOMO

VALUE DELIVERED

►
1
SG Analytics enabled the client to identify incentive programs that overcompensated / undercompensated distribution agents based on performance metrics.
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2
The client leveraged SG Analytics' solution to increase the productivity of its distributor agent network significantly.