The banking and financial services sector, which generates data from every interaction with its customers, now wants to give a technological edge to refine this and improve the customer engagement and experience. Companies in the sector are increasingly embracing sophisticated data analytical tools to decode such voluminous data to identify the most valuable customers, timely objectives (acquisition or retention) and optimal ways to reach out (direct marketing and channel strategy), thereby enhancing the quality of services offered throughout a customer’s lifecycle. Having recognized the golden opportunity lying in a customer’s digital footprints, the sector is also vetting even a customer’s online purchases and social media posts to create a superior experience. Let’s look at the valuable role data analytics can play in a customer’s life cycle.

Improves Customer Experience & Efficiency

From the acquisition stage, knowing and engaging with a customer is a major challenge for the workforce. Data analytics helps an institution integrate huge voluminous data from disparate and new sources to enable a top down view of customers. The technology allows the creation of a unique outlook for each customer, incorporating the entire relationship with the organization, inclusive of the risk scores and metrics, profitability, the ability and propensity to pay and the underlying lifetime value of a customer. Most of such platforms now allow addition of data from newer sources that may help supplement the interpretation of a customer by an organisation.

New Sourcing Mechanisms

With analytics, one can pick up key inputs from customer spending habits, using the history of their transactional data across all channels, lifestyle information (car and student loan payments or ATM usage), market indicators such as payment patterns on websites, digital web footprints and interests such as sports, activities on social networks and portfolio metrics. Though complex and sophisticated, such additional information points can refine or drastically alter an organization’s perspective of a customer, influencing its decisions.

Enabling Intuitive Interactions

Improving customer experience leads to real and quantifiable value for companies only if it is linked to a broader strategy - from pricing a product to delivering it. The evaluation of customer experience is complex for varying expectations and the relative value of each segment to a customer. Data analytics enables an intuitive and personalised interaction, with timely advice that adds value to a

customer-organisation relationship (key to get new accounts, more wallet share and reduce customer attrition) and a competent service delivery across multiple channels which are critical to enrich customer experiences. Here, an impeccable linkage between the digital front-end experience and the back-end is a vital.

While analytics can help reduce delinquency, its role is far more critical to manage the collection of delinquent accounts or recovery of charged-off loans

Identifying More Valuable Customers

A thorough profiling would demarcate the potential of a customer to become more valuable as well as to create a more meaningful service segmentation. Further, if it can be analysed in conjunction with those of the credit bureaus, it could become more actionable in terms of credit disbursals and so on. Analytics can cut down bad debts by knowing and managing the overall exposure, more consequential take-ups of relevant offers and delegating more resources to the highest prospective customer besides providing a holistic customer communication.

Enhanced Customer Intelligence

Data analytics is ideal for managing key information inflow from customers without being intrusive; for example, an automatic extension of credit limits for good customers (based not only on risk, but the overall projected lifetime value of the individual) can be authorised, using the overall conduct so far. This could lead to a higher customer satisfaction. Similarly, a customer-level segmentation enlightens if the products – an organisation has at its disposal - are good enough to serve their needs or if it needs to offer alternative products to improve engagement and profitability.

Lower Customer Attrition

Analytics can also lower customer attrition through account-level incentives such as rewards, limit increases, etc.) besides reviving dormant accounts that may have potential untapped value. A major opportunity to enhance average revenue per customer comes from cross-sale or up sale opportunities. Data analytics can reveal varying or progressive needs while balancing existing credit and risk exposure across accounts. This can fine-tune the marketing campaigns. Another advantage is the creation and optimisation of loyalty programmes - which if executed well - can maximize returns going forward.

Active Credit Risk Scoring, Delinquency

When it comes to debt management, analytics provides dynamic customer reviews to detect early signs of increasing risk thus helping organisations take active steps to avoid delinquencies. In fact, credit risk scoring is one of the most critical aspects that witnesses the application of analytics. Besides specifying risks of lending to a customer, credit scores point at whether, how much and at what rate to grant credit. It takes into account the need to increase or decrease an existing credit limit, adding a dimension of behavioural scoring.

While analytics can help reduce delinquency, its role is far more critical to manage the collection of delinquent accounts or recovery of charged-off loans. It can easily enable a collector to prioritize the collection accounts to maximize the productivity – something a human intervention would find hard to achieve. It all begins at the application level where fraud needs to be detected and managed by using the right solution. Fraudulent applications have been on the rise forcing the financial services sector to raise their guard. Analytics can sieve microscopically for genuine customers before weeding out potentially deceitful applicants, without much loss of time.

The successful names in the banking and financial services space have always excelled in their customer relationships. They have been equally rewarded with better opportunities for cross-sell and lower attrition for their optimal use of the big data and the learning derived from it. Data analytics has just added much sharpness to their mission - how to excel at the customer interface.