Auto Insurance Business Model Diagnosis

Big data helps to optimize an auto insurance comparison tool

About This Project

Overview

Our client, a leading global banking group was launching an auto insurance comparison tool in one of its key Latin American markets. They were seeking to know how to optimize the business model to build an ideal customer base.

Our advanced analytics team and consultants worked hand in hand to analyze the product and existing customer database, extracting insights to help the client develop a model that attract customers with higher loyalty while generating true value and competitive advantages for the group and its insurer partners.

Challenges

The client asked Altair to advise on the following issues:

  1. How does the auto insurance comparison tool business model capability compare to industry benchmarks?
  2. Given its business strategy and current market trend, where should the tool improve to acquire more quality clients? How to improve conversion rate from quoting to issuance to make the tool profitable for the group?
  3. How could the tool add true value to both our insurer partners and the customers?
  4. How should the new strategies be implemented?
  5. What governance and process should it follow to effectively and efficiently manage the system database?

Altair’s Solution

Altair created a 3-stage plan to addressing challenges and issues, applying big data as key analytical muscle.

Stage 1: Business model evaluation and opportunity identification

Our consultants incorporated the existing data gathered by the client and the latest insurance market to gather preliminary insights for the projects. We discovered a few key problems in the business model:

  • Although the country’s auto insurance market is growing, volume of quoting and issuance through our tool is decreasing
  • The acquisition and retention of certain customer segments (mainly women and young people) is not satisfactory
  • The relatively low penetration and conversion rates of the country indicate growth potential in the future; however, it also requires further customer education

Keeping the key issues in mind, Altair came up with a few opportunities for improvement: capitalizing the increase in auto insurance market, optimization of product positioning and customer segmentation, increase cover of services and conversion rate, among others.

 

Stage 2: Data cleaning and analysis

After we identified the opportunities, we want to further prioritize the problems to tackle. With the help of our advanced analytics team, we started data cleaning in the following area: customer profiling and segmentation, quoting, issuance, renovation and cancellation.

An analytical tool was developed to project the financial and operational impact of all the proposed improvements. With the insights gathered from the study, we finally pinpointed four priorities in two areas:

  • Existing client base: increase premium, develop a full-scale telemarketing retention program, use new system to track client activities more closely
  • New clients: revise marketing strategy, focusing on the less developed customer segments

 

Stage 3: Further improvements

Applying big data analytics is a long-term undertaking, which is why Altair wasn’t satisfied with the model based on historical data but developed a 2.0 plan for the client, incorporating marketing trends and scenario planning to predict the changes in the market and carry out fine tuning on a regular basis. Further partner and client data cleaning procedures were laid out and value creation strategies identified.

Outcome

The client was pleased with the diagnostic structure designed by Altair and moved on to implementation stage right away. In the next part of the project, with actual data coming in, Altair will simultaneously start fine tuning of the current model and further identify opportunities to address and risks to mitigate.

Country

Mexico

Date
Category
Analytics, Banking and Financial Services, Mobility
Tags
Predictive Modelling, Pricing

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