The Client and The Setting
The client is one of India’s largest life insurance companies. They sell their policies through a mix of channels – agents, bancassurance and direct channels. Salespersons and agents are compensated by a combination of fixed and variable pay, and periodic “contests”.
Client Mandate
Historically, there is not much science that has gone into the structuring of the incentive schemes and contests. Can you look at the data and help us answer the following questions?
1, How do we divide the budget between fixed payments, variable payments and contests?
2. What kind of incentive schemes can we use to drive different kinds of behaviour from the salespeople?
3. We want to cut the budget for variable compensation from next year. How can we do so while not affecting sales adversely?
Problem Solving
The main challenge in this assignment was to figure out the impact of contests and other incentive schemes in the face of messy data. There was the seasonality effect. There had been a regime change in the industry a few years prior.
What came as a blessing was that the client had tried out a large number of permutations of contests and incentive schemes in the preceding few years. Moreover, the schemes did not always overlap across the different distribution channels.
Making use of these quirks and using some clever data analysis, patterns became clear about the efficacy of different kinds of incentive schemes. From then on, it became straightforward to develop a correlation between the sort of scheme in play and its impact on overall sales.
The final recommendations answered all the questions that the client had posed, and gave out a clear roadmap on how to structure the scheme going forward.
Apart from data analysis, this assignment also involved the heavy use of game theory and behavioural science while designing the frameworks.
Result and Outcomes
The end result of the assignment was a comprehensive framework for incentive design and structuring. The recommendations were accepted by the company in full, as the data analysis clearly showed that cutting budgets would not result in any adverse impact on sales, thus allaying any fears that the sales team might have.
In terms of impact, the year when the recommendations were implemented coincided with an external event that ended up having a severe adverse impact on the industry. Hence, it was not possible to do an apples-to-apples comparison and measure the precise impact of the recommendations.