Cross-selling Strategies and Data-driven Analytics the Key to Driving Business Growth in the Financial Sector
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Machine learning tools and Artificial Intelligence can complement traditional cross-sell models by using more data sources to come up with insights to determine future customer behaviour and ascertaining the best channels to tap them
Companies are increasingly focusing on increasing customer wallet share as a key strategy to drive business growth. This marks a shift from the earlier trend to widen market share by acquiring new customers. For generations, the business expansion strategies of companies have centred on increasing their customer base. However, in the contemporary technology-driven corporate ecosystem, businesses are largely focused on enhancing value propositions for existing customers through effective cross-selling strategies.
Big Data Revolution
The digital revolution in the financial services industry has bolstered the pace in cross-selling of products. Corporates in the financial services sector are rapidly shifting from opinion surveys to behavioural data, in an effort to better analyze and predict the future behaviour of customers and evaluate potential prospects. This trend is becoming largely popular with the advent of data-driven technologies and the increasing availability of “big data”, particularly digital behaviour data. Customer assessment is done through tracking of their purchases and customers are profiled on the basis of their credit history.
Engaging the Customers
The benefits of customer engagement can be leveraged to drive cross-selling of products and reduce the cost of acquisition. It is estimated that onboarding new customers are likely to cost 8-10 times more than selling related products to existing clients. Moreover, cross-selling also has the potential to boost customer retention exponentially. A deeper connect on a personal level ensures superior quality of customers and is crucial to a greater understanding of their behavioural traits. A customer begetting another customer acts as a self-sustaining proposition for boosting business growth and development and is key to expanding the market for the products of a company.
With company operations becoming increasingly customer-centric in the financial sector, the Net Promoter Score (NPS) is emerging as a vital parameter for assessing customer loyalty to a brand. To put it simply, the higher the number of customers recommending the use of a particular product, the higher is the NPS. The financial services industry is placing a high onus on NPS which is a key metric of gauging customer engagement and brand value and is the best measure to anchor a company’s customer experience management. Given that there exists a clear correlation between NPS and the sales and profitability of a financial institution, the importance of the NPS measurement in the banking and financial services industry cannot be overlooked. In most cases, it is observed that higher the NPS, better is the top line and bottom line of the institution. Measurement of the NPS on a regular basis provides the organization with an accurate idea of the number of loyal customers. Improvement in the score forms an integral part of boosting the market positioning of a product.
New business models should deploy data-based analytics to effectively assess customer buying preferences. Machine learning tools and Artificial Intelligence can complement traditional cross-sell models by using more data sources to come up with insights to determine future customer behaviour and ascertaining the best channels to tap them.
Building effective cross-selling strategies are proving to be a key challenge area for financial institutions. It is definitely not going to become easy as customer expectations are quickly changing and digital disruptors are entering the market with continuous experimentation in new digital sales tools.