In today’s highly competitive marketplace satisfying customers has never been more challenging. They are extremely demanding and insist on being treated as an individual with specific needs. They want to be made to feel that the offering is personally addressed to them.
To understand their motivations and behavior, banks have over the period invested in different tools which gave them some information, but not exactly the one the customer expected. Banks needed to address issues like, how can they ensure long-term loyalty from its high-value customers? How can they attract and retain different types of customers and what additional product to sell? What rewards to target at its profitable customer?
The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes.
Having the relevant data helped banks to target the right offers at the right time and make changes as and when required throughout the customer lifecycle. It all finally boiled down to data and its effective usage.
Enter predictive analytics—an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. The result is more effective customer relationship management strategies, including advertising and marketing campaigns; customer buying habits, up sell and cross-sell initiatives; and long-term customer loyalty, retention, customer screening and rewards programs.
Customer buying habits
Studying the customer usage and targeting the right product is a task which banks independently and in conjunction with retailers are trying. By employing predictive analytics, banks can quickly isolate different customer segments and replace a “one-size-fits-all” campaign with individualized, highly relevant messages tailored to each customer’s profile, resulting in a higher response rate. This helps banks in targeting the right product for the right customer. For example, a sale of high end gadgets can be rightly targeted at a person who has shown a trend of buying gadgets over a period of time and will be happy to receive timely and rewarding information. These messages can be used using different marketing channels like e-mail, call centre, direct mail, net banking, mobile banking etc. It could also help in predicting the needs of consumers and accordingly work out a portfolio of products which could result in an easy buy in.
Cross Sell
In a cross sell scenario banks that offers multiple products, an analysis of existing customer behavior can lead to efficient cross sell of products. The successful cross seller needs to know what specific products it should offer to whom and may be predict the outcome. This could result in more effective cross sell and thereby directly leads to higher profitability per customer and strengthening of the customer relationship.
In an age where getting a profitable customer is tough and banks are still evolving the right strategies to increase the share in the customer’s wallet, cross selling another product to an existing customer definitely helps. These customers will tend to be loyal to the institution and also recommend others thereby also reducing the cost of acquisition.
Predictive analytics can help analyze customers’ spending, usage and other behavior, and help cross-sell the right product at the right time by building models with customer getting a set of scores, which represent the likelihood of the customer wanting to purchase another product.
Customer retention
Customer retention is another major area where banks need to concentrate more and there by minimize customer attrition. Customer attrition needs to be minimized and loyal customer need to be rewarded. Banks tend to be reactive to customer attrition and many a times it is too late to retain a customer.
Predictive analytics can help banks identify whether existing customers are keen to switch to other banks and what could be the trigger for such an action.
As banks tend to have a large customer base, very often they tend to loose track of their profitable customers. It is easier to get newer customers but an old customer is always profitable and better reference. By applying predictive analytics and a frequent examination of a customer’s past service usage, service performance, spending and other behavior patterns, predictive models can determine the likelihood of a customer wanting to terminate service sometime in the near future.
Predictive analytics can help banks identify whether existing customers are keen to switch to other banks and what could be the trigger for such an action. An intervention at this stage with lucrative offers and proper assessment of what has gone wrong and what to do can increase the chance of retaining the customer.
Enhanced Customer screening
With the advent of advanced analytical applications, banks are now able to enhance customer screening. Banks based on certain parameters (e.g. better analysis of credit worthiness, proactive monitoring of loan portfolios and enhanced screening of customers) have been able to track customer usage by monitoring in which place a customer normally shops and also what is the billing amount month on month incurred by a customer.
Similarly by tracking the amounts spent by the customers, banks can set a pattern on the monthly spends. If it increases for a certain month then call back is done to find out why it has happened and based on the feedback take preventive measures by monitoring such accounts closely and in turn help the collection systems in banks to vigorously pursue and ensure timely recovery of the dues and thereby minimize defaults of cases.
Thus, the data from customer screening acts as valued inputs for predictive analytics. For instance, banks can monitor the frequent cash withdrawals in credit cards, delayed payments and using predictive analytical tools come up with a reasonable estimate whether the customer is about to default or not. This is on the risk mitigation side. Whilst in the case of increasing business, the same data could help in predicting the next requirement of the customer and suitably positioning a bank product for such requirement
Drive growth, manage risk
In conclusion, predictive analytics can help banks keeping a tab on its customers who in turn could help in expanding the business or minimize losses as the case may be. With the amount of competing services available, banks need to focus efforts on customer retention which could be achieved by maintaining continuous consumer satisfaction. In such a competitive scenario, consumer loyalty needs to be rewarded and customer attrition needs to be minimized.
Banks have proactively started keeping tab on their customers by enhancing their systems and technology which will identify the customer behavior and expand bank’s business while at the same time also help in reducing frauds, delinquency and enhance customer satisfaction.
Disclosure: The author is employee of TCS. The opinions expressed herein are my own and do not reflect those of the company.
I am trying to draft a Doctorate Research proposal. My preferred area is Data Analytics in Banking. I am thinking of developing a predictive model on the performance on the performance of commercial banks in Nigeria. Do you think it is something that has available strong literature base? Is there any way we can link up for your assistance?