Traditionally, banks in India have focused on the corporate sector, so individual customers have always been of lower priority. However, with the huge defaults on loans to corporate borrowers, (see Corporate Borrowers Owe Over Rs 5000 Crore Each to Five PSBs), banks have burnt their fingers, and are feeling the need to increase lending to individual customers. In this sector, the challenge that banks face is to identify good and bad potential borrowers.
At the same time, consumers in developing countries have an increasing appetite for banking products such as loans of various types and credit cards, so makes perfect business sense for banks to focus on individual customers. A recent survey shows that 85% of customers in India buy at least one new product each year. However, consumers also have more choice today than ever before. 35% of new business goes to a competitor, and not to the customers’ current primary bank
With new customers becoming scarce, it makes sense to win the loyalty of existing customers, so that the business going to competitors decreases, or at least, doesn’t increase. Winning new customers, limiting attrition and seizing opportunities to cross-sell all start with a bank’s ability to earn loyalty.
There are many factors that affect loyalty, including the product and service offered, sales and marketing efforts, service experience, brand and media. However, one of the most important and effective ways to build loyalty is by leveraging insightful and meaningful customer segmentation methods.
I believe we need to go beyond profiling individuals, and looking at the Household View. In a family oriented society such as India, it becomes a key component of segmentation. This household view aggregates information for all customers residing at the same address and enables far more sophisticated segmentation analysis by considering the client’s financial needs in a broader context.
Using an ‘agile’ approach – starting with the available data, and following an iterative process of segmentation, we can start building the unified household database and base segmentation. This is then supported by clustering techniques to segment. The segmentation definition gets refined based on what is succeeding in an iterative manner. In this way, we can build an ongoing model of predicting, such as predicting who would be good borrowers.
We then see that each customer segment has different priorities and expectations from the bank, and lifetime value for the bank. We can see and predict the propensity towards specific products depending on whether the household is a single adult or a childless couple or a young family or an empty nester or a mature adult.
While data unification is not new to banks, so far they have only seen it as a compliance requirement, and neglected its power to get the right customers.
This analysis and the insights gained can enable banks to offer the most suitable products and services to the household. This will go towards building a deeper relationship with these customer households and generating loyalty.