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Using behavioral analysis to extend the life of cardholders while reducing costs
Access to credit remains problematic for many people as restrictions resulting from the 2008 financial crisis continue to have their impact. Consequently, prepaid cards have become increasingly important alternatives, especially for smaller purchases that would otherwise have been made with cash.
However, the highly competitive prepaid card market means that margins are tight – even small administrative cost savings will have a big impact on the bottom line when multiplied across large numbers of circulating cards. Similarly, any extension to a prepaid card’s lifespan, or any additional transactions that can be encouraged, can be the difference between profit and loss.
That’s why it’s so important to understand and be able to predict cardholder behavior.
Consumer spending behavior follows patterns, and prepaid cards are no different. Therefore, it follows that if banks and retailers can identify spending patterns, they can also predict behavior. This would allow them to pre-empt a consumer and take automated actions that could save money or increase card life and usage.
The challenge is to develop mechanisms that use deep historical data analysis to confidently predict those behavioral patterns. All financial institutions and processors gather large volumes of data about their cardholders – their usage patterns, how and where and when they spend their money, peak periods within the month and more – but what institutions do with such information varies.
The critical element is the quality and completeness of the source data. If individual financial institutions do not maintain the depth of historical data or are unable to isolate the critical data points to develop the trends, they should seek out vendors and outsourcers for their larger-scale repositories of transaction data and advanced analytics tools.
From there, financial institutions and processors can determine how best to connect with consumers.
In the current market, banks and retailers are lucky if a general purpose reloadable prepaid card is actively used for more than six months, and consumers rarely use them for more than 20 transactions. All too soon, then, the relationship is over and the card becomes obsolete. Even worse, the significant investment made in acquiring that customer, issuing a card and servicing the transactions is lost.
By analyzing payment and usage patterns, however, banks and retailers can predict typical patterns of behavior and trigger actions. For example, if a prepaid card is operational for five months and it has a low balance that hasn’t been reloaded in the last 30 days, then there is a strong likelihood the card is on the way to the trashcan. An automated offer through the app or a direct SMS text could pre-emptively address the situation and prolong the relationship, however. Likewise, a boost to the cardholder’s loyalty points or a small bonus for reloading the card – $10 for loading $100, or a coupon to a store or restaurant they visit regularly, for instance – could entice the cardholder to reengage.
The aim is to try to get the prepaid card to be front and center again. If a bank or retailer can extend a relationship from six to nine months, or increase the total number of transactions from 20 to 30, this makes a huge difference to the overall prepaid program revenue and saves significant investment in customer acquisition.
In the prepaid world, call centers represent one of the highest costs of a program. While the same could be said of other card products, the low margins on prepaid cards make the problem particularly acute – a single call might be enough to nullify any profit to be made on a card. If such a call could be eliminated by pre-emptive communication, profitability doesn’t have to suffer. When and why are people calling? Often it is just a simple confirmation; current balance, transaction history, value load confirmation, etc. By analyzing card usage, banks can see, for example, that during the first days of each month, there is a spike in calls to request the current balance when salaries are paid. A pre-emptive push of the balance, triggered on the salary deposit could possibly help. Similarly, triggers can be activated on check deposits, reload confirmations, change to balance up or down, loyalty point balances, etc. A simple ping to the phone as an alert could save a support call. If banks see a card balance has hit or nears zero, or see a refund/chargeback on the card, they can expect a call. Again, an automated trigger notification could potentially avert the call. The numbers may be small in isolation, but going from 1,000 to 950 calls per day through intelligent use of automated triggers results in significant savings to the bottom line.
Predictive behavioral analysis based on large historical data sets is still an evolving technology, particularly within the payments space. However, its ability to provide behavioral analysis and proactive insight into customers already shows promise for making prepaid more profitable.
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