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Acquisition Cost Allocation at Progressive Insurance
Pfeifer, Phillip E.; Potter, Benjamin Case M-0785 / Published October 25, 2010 / 11 pages.
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Product Overview

Omar Parvaiz, Progressive Insurance field product manager in charge of Tennessee and South Carolina, must analyze the results of a yearlong test of a new pricing algorithm he developed. The new algorithm allocated marketing costs to policies at a variable rate so the allocated marketing costs (and corresponding price in the cost-plus pricing system) increased with the other costs (primarily expected loss costs) associated with providing auto insurance to the policy holder. In contrast to the variable-cost allocation method was the traditional flat method in which each policy was assigned a fixed amount of marketing cost. The change to the variable-allocation scheme would make Progressive's pricing more competitive for higher-priced policies and less competitive for lower-priced policies. Tennessee was chosen as the experimental group, and other states in the region served as the control. Parvaiz must now figure out if the new pricing algorithm is better than the old one.

  • Overview

    Omar Parvaiz, Progressive Insurance field product manager in charge of Tennessee and South Carolina, must analyze the results of a yearlong test of a new pricing algorithm he developed. The new algorithm allocated marketing costs to policies at a variable rate so the allocated marketing costs (and corresponding price in the cost-plus pricing system) increased with the other costs (primarily expected loss costs) associated with providing auto insurance to the policy holder. In contrast to the variable-cost allocation method was the traditional flat method in which each policy was assigned a fixed amount of marketing cost. The change to the variable-allocation scheme would make Progressive's pricing more competitive for higher-priced policies and less competitive for lower-priced policies. Tennessee was chosen as the experimental group, and other states in the region served as the control. Parvaiz must now figure out if the new pricing algorithm is better than the old one.

  • Learning Objectives