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DT, January 2017

The Big Payoff in Big Data
January 1, 2017

By Peter Lucas

Acquirers are starting to leverage the power of massive—and diverse—pools of information. The challenge lies in unlocking big data’s full potential.

Big data has been a hot topic for years as companies look for innovative ways to become more competitive, boost customer retention, and raise their bottom line. Now merchant acquirers, which are under constant pricing and customer-retention pressure, are taking a keen interest in it, too.

For acquirers, big data represents an opportunity to supplement their internal transaction data with external data, such as social media postings, to find insights that can boost merchant transaction volume, keep merchants in the fold longer, and increase merchants’ lifetime value, all while holding the line on transaction pricing.

The challenge facing acquirers is whether they have the resources to identify the relationships among the vast amounts of information that makes up big data and tease out insights that can differentiate their business. That’s a tall order, since the concept of big data in general is still new to acquirers. Even acquirers pushing the envelope on data analytics will admit that a lot of trial and error goes on with big-data strategies.

At the root level, big data is every piece of digitally generated information collected about a company’s customers, brand, operations, and outside influences, such as economic data, that can affect its business. Gathering and storing all that information creates enormous sets of structured data that can be easily searched within a database, such as transaction data. But it also generates unstructured data from such sources as blogs, social-media postings, or customer-generated reviews that lack any organized structure.

No Slam Dunk

Acquirers are struggling to get their arms around big data, and no wonder. The sheer amount of data is exploding. By 2020, annual data production is forecast to reach 40 zettabytes, up from 2.8 zettabytes in 2012, according to research firm IDC. A zettabyte is one sextillion, or 10 to the 21st power, bytes of data.

Managing such a huge amount of information is not a part-time job. For acquirers to put big data to use, payment experts agree they must either build a dedicated in-house team or farm the task out to a third-party specialist.

“Acquirers that want to get ahead of the big-data curve have to invest in it,” says Jared Drieling, business intelligence manager for The Strawhecker Group, an Omaha, Neb.-based payments consultancy. “The investment is so great that only a handful of acquirers are proactively pursuing it.”

Not surprisingly, the acquirers leading the charge into big data are among the largest in the business. First Data Corp., for example, opened a Palo Alto, Calif., office in 2013 as part of its big-data strategy. Since opening the office, the company has grown the number of employees in Silicon Valley to 100, up from the original three.

Locating an office in the heart of the country’s most renowned tech sector gives First Data a presence smack dab in the middle of a huge pool of data analysts, which aides their recruiting efforts, says Drieling, a former First Data executive who worked on SpendTrend, one of the acquirer’s big-data products.

But even with a local office, recruiting that talent is no slam dunk. “Pulling analysts away from the tech sector is tough,” Drieling says. “Acquirers that want that talent have to be willing to chase it.”

SpendTrend analyzes transactions coming through First Data’s network to suss out consumer spending patterns in the United States. On a macro-level, the data helps merchants do better market forecasting and make better business decisions. It also helps them compare their sales to competing merchants.

On a micro level, merchants can use the data to identify popular products and times of the day consumers most actively purchase, and see how such external variables as the weather impact sales.

In addition to weather patterns, First Data uses such external data as merchant-location and consumer-demographics data by zip code. This information can help acquirers develop strategies to help merchants grow their business by identifying new locations with strong consumer spending to which they can expand, says Sandeep Garg, head and general manager for information and analytics at First Data.

“We can break down spending data on a local level to a 20- or 10-mile radius,” Garg says.

Another product to emerge from First Data’s big-data strategy is Clover Insights, formerly known as Insightics, which provides merchants with information that can improve operational efficiency. “These kinds of data analytics are a value-added component that help with merchant retention,” Garg says.

Lifetime Value

Vantiv Inc. is another big acquirer that has invested in building an in-house analytics staff. The Cincinnati-based company has a 20-person team that crunches data to predict merchant attrition rates and develop retention strategies.

Vantiv made its foray into big data through a third-party analytics firm. Once its statistical models proved effective at both identifying merchants likely to jump ship and developing strategies to keep them in the fold, Vantiv pulled the program in-house, says Scott DeAngelo, head of core product, revenue management and data science for Vantiv.

The company has since expanded its use of big data to help with merchant acquisition by identifying fast-growing and highly profitable merchant categories. Once a prospect has been identified, Vantiv builds models that project the lifetime value of the merchant to determine how to competitively price its services without sacrificing too much margin to land the customer.

“We will run multiple scenarios on how raising or lowering pricing will affect a merchant’s long-term value to identify a pricing position,” DeAngelo says. “The last thing any acquirer wants is to lower prices to acquire or retain a customer when they don’t have to.”

Vantiv supplements its internal data with information from third-party sources such as Dun & Bradstreet Inc. that pull in consumer reviews of a merchant, including comments on Facebook and other social-media channels, to understand the merchant’s reputation among consumers. Meanwhile, company press releases help Vantiv’s team grasp the merchant’s business strategy and potential sales volume.

‘Cash Is King’

Scraping information off social-media sites can help acquirers develop marketing strategies to boost customer loyalty for the merchant and grow sales. Restaurants, for example, are frequently reviewed online by consumers. Parsing the data within those reviews can help acquirers develop offers that incent customers to return based on their comments as part of a loyalty program, says Ian Stuttard, head of product and innovation, North America, for Alpharetta, Ga.-based acquirer Elavon Inc., a unit of U.S. Bancorp.

“The more insights into customer behavior an acquirer has, the better positioned it is to approach a prospective merchant client,” Stuttard says.

Helping merchants lower transaction costs is another way acquirers are putting big data to work. Elavon, which works with third-party analytics firms to develop big-data strategies, will highlight higher-cost transactions for merchants, such as when a card number was manually entered. Additional information can include the store locations where the transactions took place and time of day the transactions occurred.

Such information can help a merchant determine whether staff at a specific store or during a certain shift needs more training on operating a POS terminal, Stuttard says.

Acquirers can even use big-data analytics to improve a merchant’s cash flow by getting them paid sooner. Routing a debit transaction through a network that has a late cutoff time for same-day settlement, for example, can boost a merchant’s daily operating revenues.

“For quick-service restaurants, same-day clearing for transactions taking place late in the day can be more important than running the transaction through a lower-cost network, because for them cash is king,” Vantiv’s DeAngelo says. “Looking for those kinds of relationships within big data can make a merchant’s operations more efficient.”

Data for Security

While most acquirers focus on using big data to attract, retain, and increase merchant transaction volume, more-advanced users are putting it to work to lower the risk of fraud and reduce false positives that cost a merchant sales.

First Data’s advanced fraud intelligence solution plumbs the depths of the dark Web, which criminals use to exchange intelligence about the latest fraud scams. This information helps acquirers develop strategies to detect e-commerce fraud, says Steve Petrevski, senior vice president and general manager, security and fraud solutions, at First Data.

“The dark Web can provide clues as to what criminals know about how a merchant sets thresholds for what will trigger a fraud review for a particular transaction, such as purchasing multiple gift cards in $50 denominations,” Petrevski says. “That information helps merchants adjust their fraud-detection strategies.”

At the same time, gathering data about card issuer’s approval practices can help acquirers reduce false declines. For example, consider data about an issuer with a high denial rate for recurring transactions made by cardholders between 12 a.m. and 3 a.m., such as a monthly subscription to a coffee merchant. That information can prompt acquirers to make sure the transaction takes place during the business day, when approval rates are higher, says DeAngelo.

Deeper Relationships

For many acquirers, however, hiring staff to leverage big data is not an option. For smaller acquirers, the way to get in the big-data game is to contract with a third-party analytics firm. San Francisco-based Womply, for example, works with more than 35 acquirers and processors, including BB&T, Moneris, North American Bancard, and TSYS.

The company offers a suite of analytic and marketing solutions. “We collect and analyze everything from hourly precipitation and temperature data to gross domestic product, business closure statistics, competitors’ performance, and everything in between,” says Nathan Scripps, head of special projects. “Knowing more about their customers or potential customers and using that information to service them helps acquirers cross-sell and retain them better.”

A recent study by Womply of acquirers and processors that use its Insights application, which among other things analyzes a company’s online reputation and Web-site health, revealed that the users experienced 17% lower merchant attrition on average compared to those not using the application. The study tracked more than 400,000 merchants across more than 10 acquirers and processors over three years.

While acquirers are making the move to harness big data, payments experts agree that they are still in the early stages of understanding just what it means to their business. As the amount of internal and external data grows, it will only become more complex to manage, payments experts say.

To get the most out of big data, acquirers will need to completely change their view of data analytics. “The key to leveraging big data is not viewing it as another value-added tool that can help with merchant acquisition and retention, but as a way to gain insights and develop new products that deepen customer relationships and grow a merchant’s lifetime value,” says Drieling.


Big Data by the Numbers

$187 billion

Projected sales worldwide of big data and business analytics in 2019, up from $122 billion in 2015

$98 billion+

Forecast of spending by U.S. companies on big-data and business-analytics solutions in 2019

$22.1 billion

What the banking industry will spend on big data in 2019, second only to discrete manufacturing, which will spend $22.8 billion

$55 billion

Projected sales of big-data software in 2019, nearly half of which will come from purchases of end-user query application, reporting, and analysis and data-warehouse management tools

Source: International Data Corp.

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