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Leveraging Customer Analytics for Effective Upselling in the BFSI Sector

Published by Cubic Author

Posted on 24/07/2024

Introduction to Customer Analytics in BFSI

‘Tooling up’ with customer analytics and effectively using the related techniques will enable firms to analyze customer data, further understand the segments they cater to, and strategically use the data. It also helps financial institutions not only in increasing their customer base but also in adding new services. By better understanding the customer behavior and by also linking product usage to the customer relationships, financial institutions can identify the customer requirements. This, when clubbed with product rationalization, can help in designing effective and targeted marketing campaigns, aside from leveraging the ever-popular cross-sell and up-sell opportunities. Further, customer analytics can help in reducing alliance and tactical partner overlaps. Of late, customer analytics has been garnering enough attention even among offshore financial institutions. Consequently, financial institutions are increasing their spending in technology initiatives around customer analytics.

The financial services industry, especially banking, continues to face tremendous competitive pressures. Several firms are active in various segments with varied strategies. The very nature of the banking business and the ideas of customer relationship management (CRM) further enable these firms to cross-sell and up-sell a wide range of financial products and services to their customers. These concepts further help in retaining these customers. Customers also today expect personalized and efficient services. Banks and other financial institutions have also realized that they have sufficient customer data and it pays in the long run to derive intelligence from the data.

Understanding Upselling and Cross-Selling in the BFSI Sector

The aim of this paper is to design a framework leveraging customer insight for effective upselling in the banking, financial services, and insurance sector, thereby utilizing customer data analytics. Should such a framework be established, it would provide a sustainable approach for the sector to find an effective way to sell products and services to its existing clientele. This would also provide for the continued benefit from customer lifetime value (CLV). By understanding the complexities of different customer groups across the multiple silos, the banking, financial services, and insurance sector can match specific product and service customer desires and expectations from the portfolio of products, which will allow banks, financial service, and insurance companies to have the full scope of capturing customer needs with a wide array of choice. With customer-centric incentive mechanisms, submit accurate client knowledge with client profitable relationship and prescribe a detailed and simpler customized underwriting process for the insurance industry.

In today’s information-led world, the banking, financial services, and insurance (BFSI) sector has access to customer data that was hardly available before. The proliferation of digital channels, including ATM/in-branch transactions, online banking, mobile banking, and smartphone technologies, has created a wealth of valuable customer information that can be utilized to drive more effective customer relationships and support improved marketing and sales initiatives. This customer data, when combined with enterprise data, provides a great opportunity for the banking, financial services, and insurance sector to understand customers closely, provide relevant and real-time information, mete out right pricing, deliver consistent and superior customer service, and customization that would, in turn, lead to more avenues for selling and cross-selling business offerings.

Strategies for Identifying Upsell Opportunities through Customer Analytics

The easiest way to exploit customer lifetime value-driven segmentation is through customer analytics, which uses five-whys based methods to identify touchpoints of each persona-bracket a customer may fall into and to create and optimize customer journeys. Organizations can find insights about what products work best during moments of truth, which channels to use during those moments, and how to adapt an interaction plan with knowledge about customer triggers, preferences, desires, and needs. In contrast to pursuing a one-size-fits-all marketing philosophy, organizations now have the ability and science of contents and timing of their offerings where they make most sense to customers, including those who have not yet fully embraced the digital customer journey. Customer analytics and AI-based technologies can further qualify as the essential customer journey architecture tools that can help in detecting these moments.

Given the range of products and services that BFSI companies such as banks and insurance providers offer, customer analytics comes as a spark that helps in identifying areas of upselling and cross-selling, i.e., the strategy used to sell additional or complementary products and services to the customer. Banks use event-based marketing to grow balances by using information about their customers to market the right product at the right time. One of the biggest upselling opportunities happens when a customer has just received a loan from the bank. The bank can then provide an umbrella of loan protection insurance which will provide relief to the customer in case the primary repayment provider dies during the life of the loan.

Implementing Personalized Offers and Upsell Products

A targeted approach minimizes the risk of customers considering the communications irrelevant. By delivering relevant and timely offers, you increase the chance of the customer feeling the bank understands their needs, is catering to them, and offers products and services of value. To trigger an upsell opportunity, the customer either has to communicate an incipient need (by any number of broad behaviors) or the bank has to attempt to engage the customer in a sales conversation (triggering a decision around “is this the right product for this customer at this time”). Gathering proper intelligence to be able to make these decisions is at the heart of any successful campaign.

To develop offers that meet your customers’ real needs, you first need to understand that need. The more you know about your customers, the more precisely you can target your offers. When it comes to using analytics to drive upsell strategy, it is crucial to harness the power of big data. Banks already sit on rich amounts of data on customers and can use that to better target and understand their customers, driving value not only for the business but also including stopping unnecessary credit exposure, and therefore any associated risk.

Measuring the Effectiveness of Upselling Strategies in BFSI

Implementing an ineffective upselling program may result in adverse consequences such as customer attrition, ineffective resource allocation, and reduced revenue generation. Firms in the financial services sector must continually implement upselling of financial products and services to their customers. Several articles have discussed the common emerging issues of upselling that the banking and financial industry faces, such as customer attrition through poor customer satisfaction, ineffective and inefficient resource allocation in fulfillment of upsell offers, customer dissatisfaction through making irrelevant, inappropriate throughout the customer service exchange process, reduced return on investment from payoff, margin erosion, and redirection of the customer service initiative from pulling to pushing to drive the revenue trajectory of the business. This creates uncertainty in the desirability of upselling through resource allocation and other negative consequences, depending on the outcome realized. Current research in the financial services has mainly been industry-based and relied on policy, principles, and practices and not on empirical customer data. This is one of the initial studies to utilize an effectiveness and customer process measurement framework.

In industries with high customer support touch points, implementing upselling without customer attrition is more challenging. The current study applies a novel framework focusing on effectuating and decision enabling with customer analytics that leverages data visualization for designing and adoption of an effective upselling strategy in corporate banking. Vector space modelling and affinity-based customer clustering methods are used for designing customer analytics with both desk and financial transactional data. Visualization in the form of a customer clamshell of both deposit and loan dimensions is developed using tabular, tracing, time series, and tree maps. The extent to which both potential revenue and realization of prior upselling is measured to check for the existence of customer dissatisfaction in prior upselling offers. This study applies restricted to growth and incremental variables regression to measure if select frequencies of events are more likely to result in higher upselling conversion rates of liabilities from deposits and/or offering of loans.