Importance of prediction of income of customers to banks

By Priya Chetty on September 10, 2011
Image by Freepic

In the current scenario, banks & other financial services have millions of customers using their services like ATM, debit cards, internet banking, etc. as the customer base multiplies in numbers, there is a need for latest technology, better staff, lower interest rates, more efficiency in services.

Distinguishing a long term customer

This result in a pressing need for a constructive model to predict which customer would more likely last long. The average life time value and their incomes, demographic and psycho-graphic needs, among others. CRM here plays an effective role in increasing the competence of the system, thereby improving retention in many ways:

  • It paves way for predictive modeling of easy identification of sustained customers, why some customers leave and what should they do about it. It also helps classify customers into long-term clients and short-term patrons by predicting the value of each customer.
  • It enables banks to achieve a new platform of personalization in services and marketing approaches that seed loyalty.
  • It enhances customer interaction which fosters satisfaction and reliance among customers, because of consistent sharing of information among all.

Why predict your customer?

There is a growing need for prediction and evaluation of customer income and portfolio in order to assess their value and the average lifetime of the customer. For example, the professional, wealthy, childless couple may possess a portfolio that signifies fashionable possessions, social status and acceptance, charity, decision making equality and long-term retirement comfort. For a couple with adolescent children, other factor would hold priority like opportunity and education horizons for the children, insurance benefits and protection of the wealth at their death, utilization of wealthy possessions.

With the aging life cycle and spectrum of portfolio-and the associated risk- a number of portfolio assets like liquid, fixed, high risk, debt-based, low risk, credit based varies accordingly. Prediction of income and value hence, gives the banks a design of preferences of these customers at a given point of time. Thereby enabling them to offer the right product at the right stage with good relations.  If the expenses of a customer fall short of his funds accessible with a debit card, it may be the right time to offer him credit based solutions. The advantage for financial services firms is their individual segmentation of customer’s data that facilitates analysis to categorize the optimum combination of outcome of risks and assets overtime.  Incidentally, no other industry enjoys the liberty or skills and experience to access and evaluates each individual’s financial goals and habits, expenditure and income, segment profiles of risk and behavior, scope to carve offerings and form linkages between them. However, the disadvantage is that they often fail to recognize or utilize the value of outcomes for the client.