Using Data Analytics to Increase Cross-selling

calculator-385506_1280One of the many advantages of a composite insurer is the company-wide customer base that can be targeted with a range of product offerings.  A customer who purchased a life insurance policy may also be interested in motor or home insurance.

However, it is often difficult for insurance  companies to leverage the potential of cross-selling.  As of 2015, the insurance industry averages 2 products per customer.  Leading insurance companies are prioritising cross-selling, with Aviva announcing a target of 3 products per customer (Aviva currently averages 1.7 per customer).[1]

In comparison, some banks have reached excellent levels of cross-selling.  Wells Fargo is a good example of a company that is successfully capitalising on this opportunity.  In 2012 they averaged 5.9[2] products per customer in retail banking when, to put it in perspective, three years before, USAA was at the top of the cross-selling league with 3.9 products per customer[3].

Wells Fargo has accomplished this by changing the company culture[4] but this is a big task for anyone who wants to replicate that success.  In contrast, some banks have embraced data analytics as a faster, cost-effective means to achieve the same result.

Spanish banks BBVA and Bankinter are great examples of the value that analytics can add to an organisation.

Bankinter has embedded data analytics in their customer relationship management[5].  Revenue increased by more than 18% from 2001 to 2004 with 14% more customers, increasing profit per customer.  The number of products per customer reached 6.1 from a previous level of 5.5.  In addition, customers reported increased loyalty and satisfaction.

Similarly, analytics enabled BBVA to provide 4% more credit to small and medium business with no increase in default rates.  BBVA’s data analytics centre generates actionable insight that is useful not just to the bank but also to retailers.  By sharing this data with retailers, the bank is expecting to indirectly generate more business for the bank[6].

Anyone who has bought something at Amazon has seen data analytics for cross-selling in action and will be familiar with “customers who bought this item also bought…”  That is Amazon’s way to present you with products their data suggests you may also buy.  In fact, Amazon reported in 2006 that 35% of their sales were due to cross-selling.[7]

An insurance company can adopt customer analytics and have similar success by leveraging its customer base and finding opportunities for cross-selling.

Predictive modelling allows an insurer to identify customers likely to switch to a competitor or those at particular stages in their life cycle who are likely to purchase an additional product.  It can also identify the characteristics of those policyholders holding more of the company’s products and detect customers with whom you may form a long-term and profitable relationship.

The more products you sell to individuals, the more profitable those customers are and the more likely they are to remain your customers in the long term.  Data analytics is a cost-effective tool to help increase sales from your existing customer base in a competitive environment where winning market share is increasingly difficult or can only be achieved at the expense of profitability.

If you found this interesting, you may also want to check this presentation Predictive Analytics for Customer Targeting – A Banking Telemarketing Example


Image source: Pixabay

[1] See:

[2] See Forbes magazine:

[3] See American Banker:

[4] See Forbes magazine:



[7] See:


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