Among businesses, banks have shown the highest adoption of analytics. Sophisticated customer analytics in the Indian banking industry is helping banks reduce their exposure, cut down on customer acquisition costs and extract better profitability from existing customers.

One sign that customer analytics is rapidly being used is the growing number of banks establishing integral customer analytics cells. And it is not just restricted to MNC banks like Citibank and Standard Chartered who are emulating the best practices of their parents abroad but also home-grown banks like ICICI Bank. The latest bank to introduce analytics is the private sector HDFC Bank, which has set up its own customer intelligence unit.

One of the oldest areas in which banks have been using analytics with substantial gains is credit scoring. Statistical credit score-cards serve up as a better alternative to the traditional judgmental methods of appraising risk when a bank makes a decision whether to sanction a customer loan or issue a credit card or not.

Earlier, banks would invest heavily in hiring credit appraisers to scrutinise each loan application and the supporting documents to determine creditworthiness. Risk Scorecards combine historical loan default data with the demographic and transaction details to arrive at a risk score for an applicant.

Statistical techniques are applied to data on existing customers to generate equations that can accurately distinguish good customers (customers who repay on time) from bad customers (defaulters or delinquents). This equation or scorecard is used to score new applicants.

The advantage with statistical scorecards is that they lend themselves to automation. From the consumer's point of view, this ensures quick turnaround time in the evaluation process as well as total consistency and elimination of bias, which may be present in human analysis.

One reason for the growing usage of analytics techniques is the computerisation of bank databases and the way the data is now being organised. Most major banks have made crucial investments in data warehouse, which marks a shift away from the traditional information storage in data-silos.

Another instance of analytics in banking and where results are apparent almost instantaneously is cross-selling. Banks are leveraging their existing databases of customers more judiciously to rope in customers for lending products like credit cards and loans. Since banks are sitting on a wealth of information like liability transaction, which sets the base for response models predicting their response to another marketing offer.

The statistical techniques throw up interesting triggers about the customer setting the stage for life-cycle based marketing or event-based marketing. Already, close to 70 per cent of credit cards portfolios of most banks are sourced through cross-sell from their own bank account customers.

Unlike the West where the banks have the advantage of working with credit bureau data, Indian banks are handicapped by the lack of such bureaus in India and have to rely solely on their own proprietary data. Another hindrance to some cutting edge work in India is the lack of analytical manpower and expertise within banks. Most banks that have only lately joined the bandwagon realise the importance of analytics but are constrained by lack of any repository of knowledge within the bank.

One crucial fallout of analytics-based marketing campaigns is the tremendous cost savings accomplished by the bank by restricting its soliciting efforts to the customers who are predicted to be active rather than widening its efforts onto the entire customer base and incurring huge costs there. This departure from carpet bombing also brings to table a hit rate which is at times 50 per cent better than a randomly generated contact base. Roughly translated, it means a bank can get one-and-a-half times more eventual customers to a particular offer while actually contacting a much narrower customer base.

Nowhere else is the effect of analytics-based marketing more apparent than in credit cards companies where analytics have become a way of life. In a fiercely competitive battle for wallet share, where an average credit-cardholder holds three or four credit cards (and free credit cards have become the norm) getting credit card customers to spend on your credit card and ensuring that they stick to your credit card when the annual renewal approaches, becomes a daunting task for every credit card marketing manager. Analytical solutions step in here for almost every stage in the customer's life cycle choosing the right people for such specialized products as Balance Transfer, instalment-based repayments and cash-based products.

Segmentation strategies that help a portfolio manager to know smaller cohesive groups sitting within his larger customer-base, understanding their transaction patterns and hence pre-empting his requirements goes a long way towards customising campaigns, offers linked to campaigns and even the tone of the communication directed towards the customers. Banks are increasingly tailor-making campaigns and communication material for segments within the customer-base.

*The author is vice president, Fractal Analytics.