Case Studies Optimizing Agent Incentives with APT Test & Learn

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	Optimizing Agent Incentives with APT Test & Learn

Optimizing Agent Incentives with APT Test & Learn

Technology Category	Analytics & Modeling - Predictive Analytics Analytics & Modeling - Real Time Analytics
Applicable Industries	Finance & Insurance
Applicable Functions	Sales & Marketing Business Operation
Services	Data Science Services System Integration
Challenge	A leading insurance provider wanted to optimize the incentive portfolio it offered to independent agencies but had difficulty measuring the true impact of each incentive. The challenge was to accurately isolate the incremental benefit of different incentive programs on agency performance. The provider needed to understand which incentives worked best for different types of agencies and how to target them effectively to maximize returns. Read More
About Customer	The customer is a leading insurance provider that works with a network of independent agencies. The company is large, with over 1,000 employees, and operates primarily in the United States. The insurance provider aims to enhance its agency compensation strategies to drive performance and reward incremental growth. By leveraging advanced analytics and data-driven decision-making, the company seeks to optimize its incentive programs to achieve better results and higher efficiency. Read More
Solution	Using APT’s Test & Learn software, the insurance provider was able to test and compare the performance of different incentive programs. The software allowed the provider to identify the optimal incentive type and frequency for each agent or agency type. It also helped determine areas where incentives could be communicated through lower-cost, digital channels. By designing tests for new incentives and discontinuing unprofitable ones, the provider could validate the effectiveness of its strategies. The solution also included creating a process to remind high-potential agents about incentives predicted to drive the greatest incremental policies. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Analytics & Modeling - Real Time Analytics

Applicable Industries

Finance & Insurance

Applicable Functions

Sales & Marketing

Business Operation

Services

Data Science Services

System Integration

Challenge

A leading insurance provider wanted to optimize the incentive portfolio it offered to independent agencies but had difficulty measuring the true impact of each incentive. The challenge was to accurately isolate the incremental benefit of different incentive programs on agency performance. The provider needed to understand which incentives worked best for different types of agencies and how to target them effectively to maximize returns.

About Customer

The customer is a leading insurance provider that works with a network of independent agencies. The company is large, with over 1,000 employees, and operates primarily in the United States. The insurance provider aims to enhance its agency compensation strategies to drive performance and reward incremental growth. By leveraging advanced analytics and data-driven decision-making, the company seeks to optimize its incentive programs to achieve better results and higher efficiency.

Solution

Using APT’s Test & Learn software, the insurance provider was able to test and compare the performance of different incentive programs. The software allowed the provider to identify the optimal incentive type and frequency for each agent or agency type. It also helped determine areas where incentives could be communicated through lower-cost, digital channels. By designing tests for new incentives and discontinuing unprofitable ones, the provider could validate the effectiveness of its strategies. The solution also included creating a process to remind high-potential agents about incentives predicted to drive the greatest incremental policies.

Impact #1	The insurance provider was able to identify the optimal incentive type and frequency for each agent or agency type.
Impact #2	The provider determined areas where incentives could be communicated through lower-cost, digital channels.
Impact #3	The company worked with agents who had not responded profitably to existing incentives to identify feasible alternatives.

Impact #1

The insurance provider was able to identify the optimal incentive type and frequency for each agent or agency type.

Impact #2

The provider determined areas where incentives could be communicated through lower-cost, digital channels.

Impact #3

The company worked with agents who had not responded profitably to existing incentives to identify feasible alternatives.

Benefit #1	Premiums increased by 4.2% vs. control for Incentive A.
Benefit #2	Premiums increased by 6.1% vs. control for Incentive B.
Benefit #3	Incremental premiums for Incentive A were $29.6MM with a $3.65 premium per payout dollar.

Benefit #1

Premiums increased by 4.2% vs. control for Incentive A.

Benefit #2

Premiums increased by 6.1% vs. control for Incentive B.

Benefit #3

Incremental premiums for Incentive A were $29.6MM with a $3.65 premium per payout dollar.

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Overview

Optimizing Agent Incentives with APT Test & Learn

Operational Impact

Quantitative Benefit