How data analytics convert insurance prospects to customers

There is untapped potential in the data that P&C insurers acquire as part of the submission, quoting and underwriting process.

Growth is one of the most important KPIs for P&C insurers. Increase in new customers, a number of policies in force, and the gross written premium will showcase a direct reflection. (Photo: Elnur Amikishiyev/Adobe Stock)

Customer acquisition cost (CAC) is one of the major expenses for any enterprise, including insurance carriers. The average expense of an organization to acquire a new customer is five times more than to retain an existing customer.

Today’s insurance carriers must focus on customer retention as well as acquisition. However, while about 44% of organizations dedicate themselves to the acquisition, only 18% pay attention to retention initiatives.

The cost of acquisition is justified, as it is essential to convert a prospect into a customer. Data and analytics have a crucial role to play in boosting the success of this conversion. A new, modernized approach is needed so that these insights can be leveraged for driving customer acquisition and therefore boost the bind ratio for P&C insurers. The result is an increased close ratio and reduced customer acquisition costs.

The benefit of customer insights

Quoting and underwriting are the key phases within the policy life cycle to acquire a new customer. The average close ratio across the P&C insurance industry is 55%, which means 45% of prospects were missed during the process. Our primary focus is on this 45% of prospects who were shopping for insurance and left the shop without buying the product.

P&C insurance customer base is typically segmented into four categories:

The potential lies in performing a deep dive on the data that P&C insurers already acquired as part of the submission, quoting, and underwriting process. Insights gained from this data can be leveraged in converting current and future prospects into existing customers. Artificial intelligence (AI) and machine learning (ML) can be heavily leveraged to achieve this.

Customer acquisition insights

AI and ML technologies, when applied expertly, can deliver many business benefits by refining, automating and streamlining various processes. An AI-powered solution can take an insurer’s existing data and perform various competitive analyses, assign probability scores, and even offer recommendations:

One recommended approach derives insights as a service without impacting the current underwriting and marketing process.

Underwriting insights for each customer can be made available to agents for review and follow-up actions. Insights and the recommendations provided by our solution can be integrated into the workflow and tracked towards closure.

In the beginning, this step will function as a post-quote analytics solution. Eventually, this can become a real-time, service-based call to obtain insights and recommendations.

The neural network-based model will reside in the enterprise infrastructure. Insights can be available in the portal or can be integrated with existing data analytics tools or platforms.

This approach causes minimal disruption of existing infrastructure and hardly requires any additional infrastructure. A simple, loosely coupled interface can be built between the Policy Underwriting system and the model to enable agents to have access to customer insights for each quote.

Gopal Swaminathan (gopal.swaminathan@saamaanalytics.com) is senior director of Client Success and leads the Insurance Practice at Saama Analytics Inc. This article was first published in the Saama Analytics blog and is republished here with permission.

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