By Dale S. Laszig
DSL Direct LLC
Is big data revolutionizing the payments industry, or have we rebranded a traditional marketing concept to make it look shiny and new? Author Lisa Arthur suggested data and advanced analytics have been around for as long as marketing executives have been analyzing target audiences.
In "What Is Big Data," an Aug. 15, 2013, contribution to the Forbes marketing blog, Arthur wrote, "While I fully expect your company to add its own individual tweaks here or there, here's the one-sentence definition of big data I like to use to get the conversation started: Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis."
Arthur also noted people commonly think of big data as the information obtained from consumer behavior online and in social media. Many marketing and information technology executives define it to also include "traditional data derived from product transaction information, financial records and interaction channels, such as the call center and point-of-sale," she wrote.
How are advanced analytics transforming relationships between consumers and brands? IBM's full-page advertorial titled The Era of the Chief Executive Customer, described modern, enlightened shoppers who expect uniquely personal, real-time interaction with their chosen brands.
"On a smarter planet, we've seen how predictive analytics can help transform everything from how we fight crime to how we improve things like healthcare, food safety and utility grids," the ad stated. "For companies, this has raised interesting questions about how to take advantage of the unprecedented streams of information generated by today's customers."
IBM has pioneered the innovative use of big data to help companies forge lasting relationships with end users "by creating more than just a snapshot of a customer, but a lifetime view that can improve with every interaction. Predictive analytics brings science to the art of customer engagement, helping create a seamless experience that can give customers what they want, when they want it."
How can merchant level salespeople (MLSs) use advanced analytics to enhance customer service and improve the quality and longevity of MLS-merchant relationships? We recently polled payment executives and members of GS Online's MLS Forum to learn how they use big data to drive customer engagement and what kind of results they're having.
What follows is their advice on features, benefits, best practices and implementation.
A recent independent study on merchant retention done by ePay Consulting Services in cooperation with The Strawhecker Group addresses some of the inherent challenges of today's merchant acquiring market. The study advocates the use of advanced analytics in day-to-day operations so that ISOs can improve response times and anticipate the needs of their merchants. The full report can be found on www.ePayConsulting.com.
When interviewed for this article, ePay Consulting President Sherry Seetram stated, "There is a greater need now than ever before for merchant service providers to invest in technologies and operational improvements that will help them to offer customers personalized services and increase customer loyalty."
Data parsing methodologies used by ISOs and MLSs likely differ from those used by Tier One retailers, advertising agencies and consumer brands. Aggregate models rely on algorithms and trending analysis. MLSs generally are closer to their customers and track behavior patterns through a combination of customer relationship management (CRM) software and personal interaction.
Forum member Dee Malik wrote, "Although my preferred processor [offers] some great tools, I don't think I have a large enough pool of merchants to really utilize analytics very well, at the aggregate level. However, to some degree you read about how it helps others with understanding the size of merchants, for instance, that are most beneficial to them, etc. Therefore, to some degree we all use analytics."
Clearent wrote, "I think the role varies by where you are in the payment ecosystem. For example, an ISA or MLS would and should use analytics to define merchants at risk, high-profit merchants, low-profit merchants and even merchants with higher than average interchange costs. Along with defining these, good analytics should provide enough information to tell them why they fall into these categories."
Forum member ber added, "I use my CRM to track the source of business, the quality of the business I received from each source/campaign and timeline for deals from each source. It helps me better know where to spend my time and resources."
In addition to the valuable information that can be derived from CRM and transactional data, analytics can be used to analyze and protect an organization's information technology (IT) infrastructure. "We implemented a commercial big data solution initially to analyze and manage IT data security," www.paymentlogistics.com wrote. "We've been slowly using the solution to move toward analytics of merchant data. It's exciting, what a powerful big data tool can do, but it does take time to implement and refine."
IBM's description of data analysis by chief marketing officers sounds remarkably similar to MLS standard operating procedure: "Marketers can now use data to shape everything from how brands interact with customers to the products and services they offer to the structure and culture of the company itself," IBM wrote. "By radically rethinking their profession, marketers today are able to understand customers as individuals, use predictive tools to get ahead of demand and design truly social businesses.
"These CMOs are bringing more rigor to the ROI of their marketing investments and are ultimately proving that marketing can be less obtrusive and more personal, less of a pitch and more of a service to people than ever before."
Klinckphilip wrote, "Marketing, marketing, marketing. Every campaign has its own phone number which records the call. I use it to find out how much accounts cost to acquire and what ROI I get on different campaigns with different merchant types."
In addition to the aforementioned reasons why business owners rely on analytics for relationship management, perhaps the most basic use case is to stay connected in order to grow their relationships.
Clearent wrote, "As you move up the ecosystem, you will find processors using analytics to identify trends, at-risk partners as well as at-risk merchants, and overall any risk analyses necessary. The reason we don't talk about them much is that when we hear the word 'analytic' we tend to fog up – as it seems like a completed math equation. They don't need to be.
"I think solid data is what builds solid portfolios. If we make the reporting and analytics easy, they benefit everyone. And that is where the top of the ecosystem can play a critical role."
Most MLSs use data instinctively as part of the selling process. Let's just remember that selling is Job One. Other activities can take us away from our primary purpose which, simply stated, is to write new business, retain existing business and grow our merchant portfolios.
Clearent wrote, "[T]he key to any analytic practice is that it can't be time consuming to run the data. For, above all else, we are salespeople. And spending time crunching numbers is time spent away from sales."
While there are many ways to extract and analyze data from points along the merchant services value chain, these systems can enhance but never fully replace the feet on the street. MLSs have always delivered the lion's share of new business to the acquirer channel. They don't need sophisticated algorithms to understand their merchants and build meaningful relationships that stand the test of time.
Dale S. Laszig manages business development and strategic initiatives at DSL Direct LLC, a payments consulting company that helps clients promote, design, and deliver secure, leading-edge technology solutions. Her clients include software integrators, manufacturers, retailers, and value-added service providers. She can be reached at 973-930-0331 or email@example.com.
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