By Patti Murphy
It's an axiom of business that good data leads to good decisions. The challenge comes in understanding how to interpret that data and integrate it into a business's operations. "It's not just about deciphering data," said Basant Singh, global head of products and merchant business unit at ACI Worldwide. "You have to be able to look back on historical data and trends and come up with prescriptions. You need to use that along with present-day data and information on changing customer behaviors."
For example, the COVID-19 pandemic fundamentally changed consumer behavior. Prior to COVID, most consumers shopped at brick-and-mortar establishments and used cash or cards to pay for purchases; today ecommerce and mobile ecommerce are the preferred shopping venues for larger numbers of consumers. Consumers are also embracing new payment methods, like mobile wallets and buy now, pay later (BNPL) schemes. Then there is the role of social media, which can shape customer behaviors. It also offers new ways to learn about and influence customer behavior.
All of these touchpoints are digital and generate electronic repositories of information. That information in turn can be mined to predict trends, like the propensity of a customer to remain loyal.
ACI has addressed the challenge of better understanding data through a combination of descriptive and predictive analytics, integrated with its omnicommerce platform that optimizes customer conversion.
The company also uses what Singh described as a "data lake" fed by multiple streams of information, including data from its 700 acquiring partners and 80,000 merchant clients, as well as connected devices like inventory scanners, smartphones and other payment devices. "We're not just deciphering data. We're looking back on historical trends and coming up with prescriptions," Singh said.
Case in point: ACI's analysis suggests that when a consumer uses a mobile wallet, there is a 37 percent increase in the number of transactions they make. Among customers who use mobile wallets and BNPL, the increase is 43 percent.
Analytics can even be used to identify problems before they affect customers, for example, finding and remediating erroneous payments before customers complain. "We're sitting on a large set of data," Singh said. "We've built up our analysis to monetize that data and create value for clients." And that helps clients sell more, which in turn boosts customer loyalty and revenue potential for ACI and its partners, he explained.
The technological underpinnings of what companies like ACI do are not new. They are similar to the tools card issuers use to predict accounts that are likely to close, for example. "The technology has been around for a while; we're just finding different use cases for it," said Tim Sloane, vice president of payments innovation at Mercator Advisory Group. "Even Amazon does it."
As anyone who has shopped on Amazon knows the company's recommendation engine plays an enormous role in driving sales. By collecting data from individual customer preferences and purchases, the company can create profiles and extrapolate those to find other people with similar preferences and make purchase recommendations.
The same type of predictive analytics can be applied to merchant acquiring. The key is being able to access as much information from as many sources as possible. And there is no dearth of available information, Sloane noted. Merchants have a lot of data about customers, the items they purchase and how and when. Acquirers have payments data from all the merchants they serve. Networks and processors have information gleaned from acquirers and transactions. And banks have all kinds of information on customers.
Predictive analytics can be used to predict just about anything: weather patterns, customer shopping habits and merchant churn among them. It uses statistical algorithms, combined with internal and external data and paired with machine learning (a type of artificial intelligence), to glean insights that can forecast future actions. The more data sources that are available the more accurate predictions can be. This means tapping into outside sources. "If you're only using the data you have access to, you're probably missing out on things," Sloane said.
Sebastian Builes, CEO at Arcum, agreed. Arcum created a predictive algorithm that can be used to identify merchants at risk of leaving their acquirers. Builes said Arcum's algorithm can identify at-risk merchants up to 12 months before they defect with better than 90 percent accuracy.
Customer attrition is a challenge for any business, but it can be a real buzz kill in merchant acquiring. "Churn is something this industry is always chasing," said Johnny Stevning, vice president of business intelligence at Fiserv's CardConnect.
In fact, it's not uncommon for an ISO to lose 20 percent of its merchant accounts each year. Goldman Sachs Equity Research estimated that the merchant acquiring industry loses $2 billion a year to merchant attrition and spends an additional $1 billion a year acquiring new merchants to replace those that have defected.
"Very few industries would tolerate attrition rates of 20 percent, or more. But many ISOs and agents in our industry have come to accept this as a reality that cannot be changed," said James Shepherd, president of CCSales Pro. "The truth is that we can change this reality, and many ISOs have figured this out and are seeing significant reductions in attrition."
It's all about being proactive, rather than reactive, Builes said. Most ISOs, if they address churn at all, do so by tracking the general characteristics of merchants who leave, or they may have customer service reps call to inquire why they left and perhaps offer an enticement to come back. With predictive analytics, ISOs can identify merchants before they leave, understand why they are dissatisfied and take steps to remedy the problem. "You get an opportunity to heal the relationship," Builes pointed out.
Arcum uses predictive analytics to help ISOs address churn. But Arcum is not alone. CardConnect has taken a similar approach to identifying the problem. "We've been working on our churn model for two years," Stevning said.
Using a machine learning algorithm, CardConnect creates churn scores for each merchant in a portfolio, accessible via an ISO management tool it calls copilot. "They use this [copilot] every day, and can access the churn scores there," said Kyle Aceto, CardConnect director of business development. Stevning added, "It gives our partners the opportunity to go in and take action."
Each merchant account gets analyzed and assigned a score of between one and 100. "It's not just transaction data that we analyze," Stevning said. "We also look at how engaged merchants are with our products and customer service tickets as well. The closer that score gets to 100, the more likely the account is to churn."
It's akin to a credit score, only with churn scores the lower the number the better, he added.
A score of 50 or above suggests the merchant account may be at risk, alerting the ISO that they need to do something to improve the relationship, Stevning said. Aceto agreed, adding, "We use this tool to educate our partners, and to offer solutions that can help improve the relationship."
The results have been impressive. Aceto said that ISOs who use CardConnect's churn scores are two times more likely to retain a client than they would were they to use traditional merchant retention strategies. Churn scores are not just a merchant retention tool, either. "We also use them to evaluate portfolios for purchase," Stevning said.
Builes believes the future of merchant retention is in leveraging data—internal as well as external data points—along with predictive analytics to identify at-risk merchants before they make any moves to leave, then taking proactive steps, like personalized solutions that solve their specific pain points.
Builes identified five primary reasons why merchants leave an acquirer. They are:
"With the right information, you can be cross-selling a merchant before they walk out the door," said Builes. Retention tactics can range from providing a better POS to offering stickier programs like cash discounting; from adding new payment methods to helping create an online presence.
Builes said his analysis suggests merchants offering NFC payments are 5 percent less likely to leave than those without, and that merchants with online stores are 2 percent less likely to churn.
Based in Atlanta, Arcum was born from work Builes did helping newspapers identify subscriber churn. When he stumbled upon the payments industry, he said he was struck by the rich quality of available data. Soon, he was analyzing over 200,000 unique merchants across five different payment companies to create an algorithm that could predict merchant churn. It took about two years to research and perfect the algorithm. "This is a complex problem," he said.
When Arcum works with an ISO, it collects data on 250 variables for each merchant account. This includes such data points as when the merchant account was opened, SIC code, selling agent, physical locations, processing volumes and values, and fees paid. That data then is enhanced with macro data, like merchant ZIP codes and inflation and unemployment rates unique to those ZIP codes. In all, Arcum adds about 50 variables to the mix, Builes said.
Builes said Arcum can identify merchants most at risk of leaving in three, six or 12 months. It can even determine which agents are most likely to contribute to churn. But not everyone is keen on the idea of churn models. "Many of the people who come to us are younger executives," Builes noted. "These people have identified a problem and are willing to try something new to solve it."
"Acquirers are very slow to change; they are very slow to innovate," Singh said. "But the industry is changing drastically." With the rise of connected devices and growth in digital payments, the analytics available to acquirers and their partners only increase, Singh suggested.
As for those who ignore the power of data analytics, they risk getting left behind the churn curve. 
Patti Murphy is senior editor at The Green Sheet and co-host of the Merchant Sales Podcast. Follow her on Twitter @GS_PayMaven.
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