Mobile commerce and digital payments are producing a staggering amount of information about consumers. Making the best use of this data is a challenge that some companies are trying overcome with technology and analytics.
"The volume of data isn't necessarily the challenge. It's about using it intelligently and managing it in an efficient way," says Ross Christie, general manager of LoyaltyEdge at American Express.
LoyaltyEdge, which
LoyaltyEdge's clients, which use the service on a white-label basis, include First Bankcard, a division of First National Bank of Omaha. LoyaltyEdge manages FNBO's rewards programs and is also updating First Bankcard's rewards websites and is providing program management capabilities to improve customer experience. FNBO did not make an executive available for an interview.
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"The more data [card issuers] can collect when the transaction is occurring, that more it will help them," says David Wallace, global financial services marketing manager at SAS. SAS competes with companies such as Oracle and IBM in the business intelligence market.
Business intelligence usually informs fraud detection and credit risk, but it can also be deployed to generate marketing and special offers based on granular information such as consumer's current location combined with transaction history.
With that data, "you could have a real-time offer that goes directly to the mobile phone. But in order to do that, there has to be the contextwhere the consumer is at that moment. Are they browsing a website, or are they in a retailer's store, for example," Wallace says.
As shopping becomes mobile, payment companies will be challenged to deliver this context to the point of sale.
"There's an expectation that the [retailer or bank] will deliver what the customer needs without the customer having to say what they need," Wallace says.
The data explosion also creates latency challenges for retailers and payments companies as different channels and devices are used to accept payment. This can create a backlog if a payment company's data management technology cannot easily communicate with a third party such as a mobile carrier.
"What we are seeing now with intelligent targeting is more data mining is going on and happening within the customer sessionso if a consumer is getting instructions on what to do to access or redeem an offer, a lot of the information to drive that has to be transferred in real time," says John Knuff, general manager of global financial services for Equinix, which operates data centers.
Companies can avoid delays by vetting the location of an outsourcer's data management facilities. As strange as it sounds in a digital world, large distances can cause enough of a slowdown to impair the user experience, Knuff says.
Knuff recommends a geographically diverse strategy in which transaction and customer data can be accessed and analyzed as locally as possible, based on the user's location. Equinix, for example, operates a dozen data centers across the U.S.
"The thing to avoid is if the data set is on the West coast, but the session is on the East coast," Knuff says. "The data has to traverse three to five vendors and has to travel three thousand miles several different times during that session. That's really going to bog things down."
It's also important for payment companies to build or buy data accrual and storage architecture that's scalable, and can accommodate different ways of accepting payments, says Mark Sullivan,North America managing director of banking IT strategy at Accenture. It's a strategy that would encourage the use of open development techniques for data centers, as well as careful vetting of the interoperability of cloud services and other outsourcers.
"You want to get toward agnostic device models, so you can get real-time access to what is stored in data centers," Sullivan says.
