Optimizing Agent Incentives with APT Test & Learn
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- APT Test & Learn
Tech Stack
- Data Analytics
- Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Customer Satisfaction
- Productivity Improvements
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
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
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