Cross-sell Propensity Model To Boost Sales Of Add-On Insurance Products
Data Analytics InsurTech

Cross-sell Propensity Model To Boost Sales Of Add-On Insurance Products

By Dipak Singh May 20, 2021 - 4,670 views

Are you facing trouble in cross-selling your add-on insurance products? Indeed, it is not a rare challenge that the insurance industry is facing. For the last five years, reports suggested that insurance companies fail to achieve a high conversion rate as they fail to identify customers who are most likely to purchase the product.

It is often found that insurance companies, despite having ten products, end up acquiring a customer base for the top two products. It becomes a concerning situation when the rest of the eight products fail to achieve the break-even point. Sustainable revenue growth cannot be achieved if an insurance company fails to increase existing customer “share-of-wallet”. 

Advancement of Propensity Model

In light of the dripping conversion rate, insurance companies are leveraging propensity modelling for precision marketing. The underlying success of cross-selling products to your existing customer base is by offering them a relevant product. In this process, insurance companies aim to raise customer value. 

Propensity modelling is leveraged to identify the future behaviour of the customer based on past data. We can also define it as a statistical scorecard used to identify the customer segment who will most likely respond to the offer. 

Let us dig in more to understand the functioning of propensity modelling in the insurance industry. While you were offering motor insurance to your customer, you would also like to offer a few more add on products such as a “zero-depreciation” cover; passenger protects cover, engine and gearbox cover and many more. Now, if you randomly select a customer group and aggressively offer these multiple products, there is a high chance that your effort goes in vain. 

In these scenarios, propensity modelling comes handy. In propensity modelling, due to its mathematical approach to conclude and predict future customer behaviour, it has been proved to be highly efficient in identifying the right customer group for direct marketing, over here insurers are also trying to achieve growth in upselling and cross-sell of their products. 

Mechanism of Propensity Model

In the propensity model, approaching individual customers are substituted with customer segment with similar behaviour. With a statistical model, AI runs through the complex mathematical data and maps the customer with identical behaviour. In this way, it forms a group of the customer of similar liking. 

When insurers approach these customer segments by offering them the relevant products, the propensity of buying the product increases and insurers achieve high conversion rates. The propensity model deals with a high volume of customer data and a machine learning model that helps them predict with high accuracy. Therefore, the propensity in the insurance industry works with customer demographic data, their transactional data, psychographic and personality information. 

What to note when looking for the right propensity model?

The true effectiveness of the propensity model can be achieved if it can be advanced with the newer data, can generate more significant predicted outcomes and deploy in a structured manner. Here is the list of the characteristics of the propensity model when we are looking for it to deploy in our insurance industry. 

Look for its scalability

A propensity model must be scalable. In such unprecedented times, customers are coping with the deadly virus, and resources in the insurance industry are limited. It is a waste if the offers are made randomly. Thus, the propensity model must be scalable as it should generate huge volumes of predicted outcomes, enhancing precision marketing.

Look for a structured framework

When we talk about generating a huge volume of the predicted outcome, we also have to consider that it should be understandable, actionable and measurable. An outcome that fails to give actionable insight makes the framework weak. For the insurance industry to map down the customer segment backed with the data must also help insurers to understand which products should be pitched into the clients. 

How can it be advantageous for the Insurance Industry?

When we talk about the business impact that the propensity model can bring to the insurance industry, we have to take note of the following:

Increase Customer Life Time Value

Customer lifetime value is the expected relationship with the customer in the future, and micro segmenting customers and deploying cross-sell campaigns from the propensity model can increase it. 

Increased accuracy in identifying potential targets in cross-selling

With cross-sell propensity model, insurers get an accurate picture of the customer preference. Analytic companies can deploy a decision tree model powered with AI, helping deliver transparent pictures to the insurers through a comprehensive dashboard. This unearths the powerful insight for a better direct targeting campaign.

Deploy Propensity Model to Cross-sell right product to the right customer

In the insurance industry, the risk is vast for both the insurer and insured. Understanding the true value of insurance is cloaked under various risk. As customers worry about complex underwriting process and at the same time, insurers worry about low penetration of lined products. With the propensity model, insurers generate propensity score for customers, which helps in reducing wastage of resources through relevant marketing to the relevant customers for the relevant product. 

We have seen advancement in analytics and how it has been helping the insurance companies amidst the challenging time. Here are few listed business impacts we have noted among our clients after deploying cross-sell propensity model:

  • A comprehensive data platform has helped in getting easy access to insightful customer data, thus enhancing the effectiveness of cross-selling and up-sell.
  • As the conversion rate increases, the rejection rate decreases; therefore, the cost is optimised as cost per conversion drops. 
  • It becomes easier to achieve analytics maturity as now insurance companies are breaking the data silos and getting an actionable insight through data-driven strategy.
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