DISCOVER
®MERCHANT PREDICTOR MODEL
A High-Level View of Merchant Attrition
It’s a well-known axiom of business that it costs a lot more to engage a new customer than to retain an existing one, and the merchant acquiring business is no exception. Acquiring new merchants and replacing lost merchants is costly in terms of both time and money. Reducing attrition increases acquirers’ profitability.
The Real Cost of Attrition to Acquirers and ISOs
Total merchant attrition in the United States isestimated to be 20% annually, of which 16% switch to a different acquirer and 4% go out of business.1 The annual cost of attrition to
U.S. acquirers is $2.33 billion.1 Almost
$1 billion is spent by acquirers to replace the merchants who left in the prior year, draining valuable resources.1
How Acquirers Currently Predict and Combat Attrition
Discover® recently commissioned a study on U.S. merchant attrition by Aite Group. This research
revealed that although 80% of the acquirers studied have attrition programs, only 20% have proactive solutions (i.e., predictive databases), and many reach out to a merchant only after receiving a closure call.1 This reactive approach brings back less than one-fourth of merchants,1
who are by then well into their decision to switch to another acquirer.
“Prevention is better than a cure. The most successful strategy for retaining
valuable merchant customers is to detect potential attrition and take effective
action before it occurs. Keeping a profitable customer is always more
beneficial to your bottom line than finding, recruiting and boarding a
new customer.”
— ETA Best Practices: Merchant Retention, June 2010 White PaperToday’s merchant retention tools are limited. Only 20% of acquirers in the Aite study had invested in an internal database to keep track of their merchants’ behaviors.1 The lack of an
effective retention product means acquirers are limited to responding to known triggers in an effort to curb attrition.
$1.40 Billion — Lost Revenue
+ $0.93 Billion — Replacement Costs
= $2.33 Billion — Total Cost1
The Aite study showed the need for a more efficient means of predicting and responding to merchant behavior that is likely to precede attrition. All of the acquirers in the study noted attrition as an “important” or “extremely important” key performance indicator in their business, and agreed that more than 25% of their attrition is preventable.1
“Predictive analytics uses algorithms
to find patterns in data that might
predict similar outcomes in the future.
A common example of predictive
analytics is to find a model that will
predict which customers are likely to churn.”
— The Forrester Wave:™ Big Data Predictive Analytics Solutions, Q1 2013
Acquirers are more likely to retain merchants when they make an effort to keep track of
An Overview of Predictive Modeling
Predictive modeling is a process used to anticipate future behavior. Predictive models are the result of data-mining technology that analyzes past performance plus current data in order to forecast a customer’s future behavior. To accomplish this, data is collected from relevant sources, a statistical model is formulated, predictions are made and the model is refreshed regularly as additional data becomes available.
Predictive modeling is commonly used in traditional direct mail campaigns and information technology. Spam filtering systems, for example, utilize predictive modeling to determine the probability that a specific message is spam.
Predictive models are used for many different purposes in a wide variety of industries. In the payments industry,
predictive fraud detection models identify data patterns related to customer performance. Fraud detection models often perform calculations in real time during payment transactions, to evaluate potential risks of a transaction and guide the merchant’s decision to accept or reject a transaction.
“Predictive analytics enables firms to reduce risks, make intelligent decisions,
and create differentiated, more personal customer experiences.”
— The Forrester Wave:™ Big Data Predictive Analytics Solutions, Q1 2013
The Value of Predictive Models in Merchant Attrition
Discover
®Merchant Predictor Model — A Proactive Approach
to Merchant Retention
At Discover, we’re always looking for new and better ways to help acquirers retain merchants and slow attrition. The large number of merchants that switch acquirers and the cost associated with new merchant acquisition provided the genesis for a predictive model to help acquirers identify merchants at risk of leaving. Late last year, Discover Network launched the Discover®
Merchant Predictor Model, an innovative patent-pending product that enables acquirers to see key changes in a merchant’s behavior that indicate they might be likely to switch to a
different acquirer.
Based on a merchant’s behavior, the Discover Merchant Predictor Model calculates a
predictive score that helps an acquirer prioritize retention or account relationship efforts in order to combat attrition. Through this innovative product and partnerships with acquirers, Discover plans to bring about a game-changing solution for acquirers.
“Merchant acquirers participate in a very competitive market, where high levels of account churn have become the norm. Discover has harnessed some of the new computing and analytical paradigms that are coming out of the Big Data space to try and tackle acquirers’ attrition problems in a very interesting and unprecedented way. Discover Network’s growing acceptance infrastructure supports enough activity to present a representative sample in support of the predictive model used in the analytics solution. As time goes on and as acquirers utilize these analytics, predictive models can be refined for even more robust analytics.” — David Fish, Senior Analyst, Mercator Advisory Group
Benefits of Predictive Modeling in Merchant Attrition
According to the Aite study, very few acquirers use predictive factors in their merchant retention efforts.1 But those who do are performing better, and their attrition numbers are lower than other
acquirers.1 Throughout the business spectrum, companies that have applied predictive modeling
have realized significant benefits.1 Innovative analytic products enable businesses of all sizes to
meet the challenge of acquiring customers and increasing loyalty.
Predictive modeling is a valuable tool for acquirers because it provides insight that allows better targeting of products and services in a relevant and timely manner. It enables decision-makers to take into account factors that might otherwise be unavailable, and increases the likelihood of attaining expected results. As a decision support tool, predictive modeling gives acquirers a way to partner intuition with data, resulting in a more informed strategy.
Higher customer retention
Increased customer satisfaction
Maximized revenue growth
and operational efficiencies
By identifying and quickly addressing signs of attrition.
By targeting customers with individualized and appropriate solutions.
Benefits of Using the Discover Merchant Predictor Model
Although other predictive models in the payments marketplace use general information, Discover has access to merchant information not available to other organizations. By using the Discover Merchant Predictor Model, your courtesy calls to clients will be targeted to merchants most likely to attrite, and your time can be spent on merchants with a higher profit level who are showing signs of impending attrition.
In contrast to the reactive methods most acquirers currently employ for merchant retention, the Discover Merchant Predictor Model offers a proactive approach based on statistical data mining techniques. It gives you the option of placing a courtesy call directly to a merchant who seems likely to churn, or applying other specific retention techniques in an attempt to reverse the merchant’s decision.
The advantages gained from the predictive model outweigh its cost. Reviewing merchant records randomly takes a long time and doesn’t pinpoint merchants who are statistically ready to churn. The time and resources spent on phone calls to merchants can be reduced by changing
your retention strategy and utilizing the Discover Merchant Predictor Model.
Most of the acquirers in the Aite study reacted favorably to the Discover Merchant Predictor Model, which offers a proactive way to spot merchants who are likely to switch.
“Out of all my merchants, who do I call first?”
The Discover Merchant Predictor Model answers this question. It mathematically predicts which of your merchants are most likely to change acquirers. The model enables you to better understand your merchants, and thereby develop a more effective retention strategy. The output of the model provides a rank order of the most critical accounts to guide you on the right course of action. Once you can see themodel output for all your merchants, you can more easily determine who to call first.
