Companies in almost every industry are facing the heat of declining customer loyalty. Be it e-commerce, travel or banking, customers have become ever-more demanding and want everything at their fingertips. Ever wondered why companies try to retain customers by offering deep discounts on their services? Because of the simple fact that the customer retention cost is far lower than the customer acquisition cost. But for banks and financial services industry, discounts are not that prevalent given that the market is regulated and works under a lot of constraints. So, how should banks and financial institutions ensure that they are able to acquire customers at reasonable cost and retain them, and provide them good experience. The answer is ‘banking analytics’.

Banking analytics seems to be a promising solution in such a case.

The financial services industry is one of the industries which has embraced analytics across the entire customer lifecycle. Right from identifying products for a customer to designing promotional campaigns, data science has been adopted at each customer touchpoint. From underwriting to delinquency, financial institutions are adopting data science and analytics across different functions. In this post, we have highlighted the top 10 analytics projects used by the financial services industry to attract and retain customers. You may try your hands on any one of these projects.

Lead prioritization:

Lead prioritization is used where ticket value is considerably high and resources to sell are limited. In the BFSI sector, the ticket may refer to a high-value loan (home/auto/personal/corporate/etc.) or a policy or an investment scheme. Accordingly, customers can be segmented into different categories based on their likelihood to buy the product. Lead prioritization helps in allocating limited resources to potential leads with a higher chance of a conversion. The more technical term for this is ‘customer propensity modelling’.

Customer lifetime value (CLV):

Customer lifetime value is a value that the customer is likely to provide to the bank in his entire relationship with the bank. This helps the bank in evaluating the investment and effort that the bank should put in to acquire and retain the customers. Lead prioritization and CLV estimation serve a similar purpose of identifying top leads and customers, respectively.

Predicting the life event of a customer:

Based on a customer’s bank statement or credit/debit card statement, one can predict future life event of a customer. Since the banks already have this data for customers, data procurement will not be a task for the bank. Based on the predicted life event, relevant products can be offered to customers. An important point to note here is that the tagging of transactions will play an important role in determining the accuracy of the model. Additionally, in the case of salary accounts of customers, looking at the salary trend of the customer can provide information about a recent promotion or a job change.

RFM modelling:

This is a customer segmentation technique based on three parameters – recency, frequency and monetary. Recency refers to how recently the customer has purchased a product; frequency refers to the frequency of purchases by the customer; while monetary refers to the money value of purchases by the customer. Customers in each segment would usually possess similar attributes and can be served with similar kind of promotional offers.

Cross-selling/up-selling:

Cross-selling/up-selling refers to selling additional products to existing customers. These products could be different from the ones that the customer has already bought or top-up of existing products. An example of cross-selling for a bank is offering a credit card to a customer having a savings account with the bank; while, an example of up-selling is increasing (topping-up) the insurance cover of the policy-holder. Again, in this case, if you can predict the current and future life event of the customer, the probability of cross-selling the right product increases considerably.

Learn about Sales & Marketing Analytics – Top Analytics Projects in Retail Industry

Next best offer:

Next best offer is used for improving promotional efficiency and increase conversion rates at different customer touch points. Based on a customer’s historical behaviour, the company can send targeted promotions to which the customer is likely to respond. This is another method of cross-selling based on a recommendation engine. Again, having details of customer’s current and expected life events can improve the likelihood of offering the right product.

Sentiment Analysis:

Carrying out a sentiment analysis on social media posts can provide detailed insights to customer feedback. Not just social media, sentiment analysis can be carried out on support emails, app reviews or any other customer touchpoint. Furthermore, this can be extended to carry out a lot of other analysis such as emotion analysis, key negative/positive themes, topic modelling, etc.

Delinquency prediction:

NPAs – Non-performing Assets: this term has become very common in the banking world because of rising defaults in recent years. An account is considered NPA if it defaults on its payment schedule. Default (or delinquency) prediction is one of the most common and useful use cases in the industry. Companies, almost on a daily basis, are trying to improve their models to predict delinquent customers. This helps banks to control the revenue (principal + interest, both) loss against an issued loan.

Do you know restaurant industry is using analytics to gain a competitive advantage? Read Restaurant Analytics – Top Analytics Project Ideas to know details.

Predicting the probability of a customer to renew insurance (or any other) policies:

This is very useful for insurance companies. Since the insurance companies don’t have access to customer’s banking history, achieving accuracy becomes challenging only with internal data points. The model for this can be enriched by adding external data points related to the customer’s current life stage. If policy issuer is the same as the bank where the customer holds his account, the prediction model can be enhanced with customer’s banking data.

Churn Analysis:

The technical term in banking parlance for this is ‘balance transfer.’ Balance transfer refers to transferring of live loan account by a customer from one bank to another, mostly because of better interest rates or improved service. Churn analysis provides knowledge about customers who are likely to transfer the balance to other banks. Subsequently, as a remediation measure, the bank can take corrective measures such as building a strong relationship with customers or offering them other products at discounted prices to prevent customer churn.

BFSI sector provides a plethora of other customer analytics projects that you can take up.

Have you worked on any of these projects or maybe, a different one? We would love to hear your views and approach to carrying out these projects. Don’t forget to share your experience with us at info@aimonks.com.