bankingciooutlook

Customer Segmentation Driven by Data Analytics: Bringing Bunch full of Banking Benefits

By Banking CIO Outlook | Friday, August 23, 2019

Banks and other financial institutions are making efforts to propagate the right services to the customers because personalized service offering requires a deep understanding of customer preferences through intensive data analytics.  

FREMONT, CA: Changes in the financial world have shifted the motives of the banking paradigm along with evolving customer requirements. As a result, customization has emerged as a vital factor for financial institutions, especially for commercial banks. With this, they started clustering their customers to meet the varied needs of them all while optimizing market share, value, and profits. This grouping process is known as segmentation and is applied in different methods by financial organizations. Once armed with the knowledge of the most engaged, profitable, and ideal customer lifestyle segments firms can model based on them to build data-driven personas. Intending to tap new market segments banks are encouraging segmentation efforts increasingly.

Banks get valuable insights for lifestyle segmentation based on consumer actions. Past behavior of a customer is an indicator of future behavior. Although it can never ideally guarantee the future, it can help make informed assumptions for the financial institutions. Market research alone can never answer, but more in-depth analysis of customer preference can help. It is highly effective for determining appropriate marketing strategies for each target market. The most accessible approach to segmenting the market for banks are looking at customer behavior and the progression through the customer relationship life cycle. Customers are a valuable source of information, so financial institutions are collecting data that lets them identify customers and how they behave. This may vary depending on the customer profile. A customer's age, gender, income, and any other details matter a lot. The segmentation of banking customers can fall into the following categories.  

• New-Customers

This segment primarily consists of consumers who are customers of competitive banks and younger customers who are new in the banking relationship. Analyzing the data of this segment can reveal that they are interested in special offers and deals or currently dissatisfied with their existing provider. This group can be targeted for savings programs in an attempt to win their long-term loyalty.

• High-Value customers

High-Value customers have larger deposits and loans with the financial institution. Their overall profit contribution is highly attractive to banks. Analyzing their details can reveal that, as their profit contribution is highly attractive to banks and are more costly to retain in terms of additional serviced and special offers.

• Medium-Value customers

Medium-value customers are potent to grow high-value customers as they have a significant financial business to conduct. With special offers or preferences, banks can diversify accounts of these segments quickly.

Understanding the lifestyle patterns and purchasing behavior of these segment through data analytics can bring more business to the banks. The more a bank is aware of their customers' needs, it is easier for them to identify opportunities to sell them new financial services and target them with relevant offers. Analyzing data on the existing customers makes it easier to find new ones. The above segmentation of customers into various classes customers are offered with appropriate financial services. As customers purchase banking services and other offerings in relation to their financial independence, a clear division of each customer segments can contribute a lot for the financial institutions. Also, looking after customers also helps build customer loyalty and selling more to existing customers is far more cost-effective and finding new customers.

Customer segmentation for baking is becoming exponentially better and more efficient with the help of big data. If financial institutions have an improved understanding of the differences and nuances of their different customer segments, they can rethink their marketing strategy. To welcome more targeted, cost-effective approach and a higher return on investment, firms who have already implemented the tactics have ended up rethinking their strategy and improving overall marketing performance, being themselves the eyes of their customers.

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