December 12, 2023

Redefining a retailer’s sales incentives plan through an analytical perspective

How can sales incentives be leveraged to boost sales, customer satisfaction, and employee retention

Redefining a retailer’s sales incentives plan through an analytical perspective

At a glance

Challenge

With increasingly demanding customers and the rise of omnichannel trends, sales force teams face new challenges critical to retail performance. Our task was to revamp an outdated incentive plan lacking analytical insights. The plan included various components—individual, collective, quantitative, and qualitative—but lacked visibility into their impact on team behavior and performance. This case study demonstrates how analytics can optimize incentives for thousands of sales employees.

Solution

To design an effective sales incentives plan, it is essential to understand how each component impacts both sales and customer satisfaction, a key driver of retailer performance. An advanced statistical regression model was created, revealing strong relationships between incentive plan components and employee attributes, and their influence on retailer outcomes. Using the model’s results, a simulation tool was developed, enabling the retailer’s team to predict the impact of various plans on sales growth and associated costs, including total incentives and satisfaction scores.

Results

The retailer’s sales incentives plan was redefined by eliminating ineffective components, adjusting others, and adding new elements to address customer satisfaction and omnichannel sales. This led to an expected 25% increase in investment, a 1.5% rise in revenue, and up to a 35% reduction in employee turnover. The methodology and simulation tool provided enable the retailer’s team to efficiently iterate on future adjustments and evaluate the impacts of proposed changes.

Challenge

With increasingly demanding customers and the rise of omnichannel trends, sales force teams face new challenges critical to retail performance. Our task was to revamp an outdated incentive plan lacking analytical insights. The plan included various components—individual, collective, quantitative, and qualitative—but lacked visibility into their impact on team behavior and performance. This case study demonstrates how analytics can optimize incentives for thousands of sales employees.

Approach

Solution

To design an effective sales incentives plan, it is essential to understand how each component impacts both sales and customer satisfaction, a key driver of retailer performance. An advanced statistical regression model was created, revealing strong relationships between incentive plan components and employee attributes, and their influence on retailer outcomes. Using the model’s results, a simulation tool was developed, enabling the retailer’s team to predict the impact of various plans on sales growth and associated costs, including total incentives and satisfaction scores.

Results

The retailer’s sales incentives plan was redefined by eliminating ineffective components, adjusting others, and adding new elements to address customer satisfaction and omnichannel sales. This led to an expected 25% increase in investment, a 1.5% rise in revenue, and up to a 35% reduction in employee turnover. The methodology and simulation tool provided enable the retailer’s team to efficiently iterate on future adjustments and evaluate the impacts of proposed changes.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

With increasingly demanding customers and a growing omnichannel trend, sales force teams face new challenges, performing a crucial role in the overall performance of retail companies. Having an incentive plan defined years before and lacking analytical background, the challenge was to support our customers performing an end-to-end plan revamping.

The plan possessed innumerous components, ranging from individual to collective, quantitative, and qualitative. However, there was little visibility about the impact of each component in the sales force team behavior and performance.

The present case study aims at demonstrating how analytics can help to leverage incentives for thousands of sales employees.

To design an optimal sales incentives plan, it is necessary to understand how each plan component influences not only sales but also customer satisfaction, a crucial driver on the retailer’s performance.

An advanced statistical regression model was created, unveiling strong relationships between the different incentive plan components (individual/collective, quantitative/qualitative, etc.), the employees’ attributes (tenure, training, etc.), and the retailer’s main results.

Subsequently, using the core results and coefficients from the previous model, a simulation tool was developed, allowing the retailer’s team to acknowledge and foresee the impact of distinct potential plans in sales increase and expected overall costs (total incentives costs, satisfaction score costs, etc.).

Our AI-generated summary

Our AI-generated summary

The retailer’s sales incentives plan was thoroughly redefined through the elimination of ineffective components, calibration of others, or the addition of new elements, thus addressing new dimensions, such as customer satisfaction and omnichannel sales.

The project gave our customer a clearer view of the importance of incentives, thus leading to an expected reinforcement of the current investment by 25%, with a corresponding increase of 1.5% in revenue, and an up to 35% decrease in employee turnover.

The delivered methodology and simulation tool allow the retailer’s team to quickly iterate on future adjustments, thus understanding the end-to-end impacts of the proposed changes.

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