We identified and extracted relevant data elements from the client’s database and checked its accuracy. Following approaches were used:
An exhaustive list of factors was identified, and hypotheses were formulated.
Each hypothesis was tested, and exploratory analysis was performed on them. This helped in analyzing certain patterns in the sales data which eventually helped to forecast sales.
The team designed a model using Python which assimilated dynamic fields to input quantity thresholds/rebates and estimated incentive pay-outs and revenue.
Key metrics were identified, and a Power BI dashboard was developed to track the performance of the program.
We monitored the performance of the incentive program on a fixed cadence and suggested course corrections as needed.
Following were the outcomes after designing the incentive program model:
Due to time tracking of the incentive program performance, the learning group were able to take proactive course correction measures.
There was a significant sales lift.
Customer satisfaction was increased.
Existing skill gaps were gradually reduced around emerging technologies in the market.
