As a credit card provider, you’ve got less than a six-month window to win your customer over. If your customer doesn’t use your credit card within the first six months, they probably never will. The trouble is that it can take some providers six months just to figure out there’s a problem, leading to higher levels of customer attrition (churn rates).
However, credit card providers can significantly boost customer retention and increase customer value by using an AI-assisted analytics platform like OpenText™ Magellan™. Powering loyalty in credit cards is key to not only generating an additional source of revenue each time a transaction is made, but also helps to reinforce and reward a customer’s loyalty to the provider, ensuring priority of the card and helping to keep customers loyal in the future.
The great thing about my job is that you often get called into meetings just as the project is being delivered, so you get to tell the customer just how well it’s gone. For example, I recently met with a large bank that had been concerned about churn rates with its credit cards. Using AI-assisted analytics, we were quickly able to help the bank identify tens of thousands of accounts at risk of attrition. Using a simple four-step process, we brought together all information to identify which cards were active or inactive and which were at risk, then develop a 360-degree view of the customer for each card at risk.
Four steps to apply AI-assisted analytics to reduce customer churn
Not only were we able to accurately highlight which cards were at risk, we were also able to identify patterns and trends in the customer data to help predict the type of communication and promotional activity that would turn those accounts back into loyal customers. A successful retention campaign for the bank represents tens of millions of dollars.
Customer churn: Are Financial Services companies listening?
Our client knew that it’s easier and more cost-effective to build business with an existing customer than it is with a new customer. Yet the customer churn rate within financial services is extremely poor. In 2017, one in five U.S. customers left their provider, making financial services the third worst industry for customer attrition. The figures are even worse when you look at credit cards. According to JD Power’s Hong Kong credit card satisfaction survey, 25% of cardholders are thinking of switching service.
To compound issues, in Asia, customers are beginning to cut down the number of cards they have. Their choice is based on the level of service they receive in terms of the benefits and rewards on offer. Anthony Chiam, service industry practice leader at J.D. Power, said, “With nearly 25% of cardholders considering switching from their primary card issuer—either for a better rewards program or better benefits—it is important that issuers consider their long-term engagement strategies to minimize customer attrition.”
Identifying the good customers
To engage effectively with customers means really knowing them , using all the data you can muster to work out exactly what they want and how you can tailor your service to their needs. The challenge is that a large credit card provider is dealing with millions of customers and tens of millions of transactions each month. You can’t wait for the electronic document team to pull all the data together and run off a report–this can represent months of lost time when looking to tackle attrition.
Detect Patterns….why did it happen? Profile the best customers
Of course, not all customers are good customers. A credit card provider needs to be able to distinguish between good and bad customers, i.e. “profitable vs. unprofitable/delinquent.” For instance, companies often think that the more time a customer contacts the call center, the more likely they are to be a bad customer. This may not be the case. Instead, the content of the last contact is likely to be more revealing.
The OpenText Magellan platform allows for call center logs to be automatically converted to text to enable sentiment analysis that builds a picture not just of the customer’s interactions with the company but also their perceptions and opinions on the company and its products and services.
Reducing churn through advanced analytics
You need to be able to work with all the information that you have on a customer–from enterprise applications, social channels, transactional information and call center interactions–to identify the attributes that make someone a good customer for you, create accurate profiles of the customers at risk you want to retain, segment those customers based on buying behaviors and preferences, and tailor their promotions and rewards accordingly.
Preparing and executing advanced analytics to optimize customer retention
An AI-assisted analytics platform such as Magellan goes through five distinct stages (see diagram) to allow you to clearly differentiate a loyal customer from one exhibiting the potential for churn. You can begin to see why some accounts have become inactive and, where there’s value in retaining the customer, you can predict which marketing and promotional activities have the greatest chance of converting them back into active, profitable business.
To find out more about how OpenText can help reduce churn for credit card issuers and other financial service providers, click here.