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Tackling Low Completion Rates?A Compare.com Conundrum (A)
Venkatesan, Rajkumar; Craddock, Jenny; Brodie, Kyle Case M-0947 / Published September 8, 2017 / 17 pages.
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Product Overview

In 2016 Andrew Rose, CEO of Compare.com, an online price comparison website for car insurance shoppers, faced a troubling problem. Completion rates for the site's detailed online questionnaire were at an alarming low. Site visitors, increasingly accessing the site on mobile devices, were proving they did not have the time or incentive to answer the site's requisite questions and thus were dropping off the site before purchasing policies from the site's partner insurance carriers. As Rose and his management team struggled to lift the completion rates, they narrowed their options to three potential solutions. A task for Kyle Brodie, a summer intern, was to design and run an experiment that could yield valuable insights from an estimates display, including which customer groups, if any, responded best to the estimates, and where the estimates should be included in the questionnaire. In the A case of the series, readers are faced with selecting the best solution for lifting Compare.com's completion rate. In the B case, readers must design an experiment to test the selected option, and decide on the location and content of an estimate display test. In the C case, readers are presented with the design implemented by Brodie and a summary of the results of that experiment. They must then figure out the implications of those results for Compare.com. This case was originally written for an MBA marketing class examining Marketing Analytics. It would also be suitable for similar classes in undergraduate, Executive MBA, and Executive Education programs.


Learning Objectives

Introduce students to the steps involved in building an effective purchase funnel; Reveal successful approaches to A/B testing and how they relate to a company's overall marketing strategy; Demonstrate that design of tests requires understanding the trade-offs faced by management and the nuances of the customer journey; Learn to design an experiment and draw conclusions from test results.

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  • Overview

    In 2016 Andrew Rose, CEO of Compare.com, an online price comparison website for car insurance shoppers, faced a troubling problem. Completion rates for the site's detailed online questionnaire were at an alarming low. Site visitors, increasingly accessing the site on mobile devices, were proving they did not have the time or incentive to answer the site's requisite questions and thus were dropping off the site before purchasing policies from the site's partner insurance carriers. As Rose and his management team struggled to lift the completion rates, they narrowed their options to three potential solutions. A task for Kyle Brodie, a summer intern, was to design and run an experiment that could yield valuable insights from an estimates display, including which customer groups, if any, responded best to the estimates, and where the estimates should be included in the questionnaire. In the A case of the series, readers are faced with selecting the best solution for lifting Compare.com's completion rate. In the B case, readers must design an experiment to test the selected option, and decide on the location and content of an estimate display test. In the C case, readers are presented with the design implemented by Brodie and a summary of the results of that experiment. They must then figure out the implications of those results for Compare.com. This case was originally written for an MBA marketing class examining Marketing Analytics. It would also be suitable for similar classes in undergraduate, Executive MBA, and Executive Education programs.

  • Learning Objectives

    Learning Objectives

    Introduce students to the steps involved in building an effective purchase funnel; Reveal successful approaches to A/B testing and how they relate to a company's overall marketing strategy; Demonstrate that design of tests requires understanding the trade-offs faced by management and the nuances of the customer journey; Learn to design an experiment and draw conclusions from test results.