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. As we know, customer acquisition cost (CAC) is one of the major expenses for any enterprise. The average expense of an organization to acquire a new customer is five times more than to retain an existing customer. Enterprises need to focus on 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 potential customer into a customer. Data & 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; hence, boosting the bind ratio for P&C insurers, resulting in increased close ratio and reduced customer acquisition cost.
Boosting Customer Acquisition for P&C Insurers with 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:
- Price deterministic
- Customer satisfaction-oriented
- Loyalty based Auto-renewal
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 with Artificial Intelligence and Machine Learning
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:
Competitor Comparison: A comparative premium analysis of the company’s top 3 competitors.
Customer Lifetime Value (CLV): The dollar value representing the total worth of doing business with a particular customer.
Bind Probability: It is a score indicating the probability of converting a given customer through the recommendation and package pricing.
Renewable Probability: The measure of the probability that a customer will Renew the policy after the first term if Acquired.
Cross/Up-Sell Propensity: A score derived on the basis of Customer Data and Similarity Analytics.
Customer Segmentation: Used to predict the trends of the customer segment and provide actionable insights to the agent/campaign marketing.
Package Recommendation: A package deal, bundled with other products (Home, Umbrella, Motorcycle, etc.) is offered, based on the information provided, or made available, and the auto insurance quote.
I recommend an approach that 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 solution will function as a Post Quote Analytics solution, and eventually, can become a real-time, service-based call by an online application/agent to obtain insights/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.