Despite Faster Tools Real Time Marketing Remains Elusive

Rapid improvements in big data analytics have moved marketers closer to making personalized offers in real time, but analyzing fresh data fast enough to close a sale or retain a customer remains an elusive goal for most companies.

Tapping the power of real time personalization has been a key goal for marketers since the dawn of the dot-com era two decades ago, and many companies have mastered the art of tailoring web content to satisfy discerning visitors. However, real time analytics still hasn’t produced the type of actionable intelligence that can help an employee personalize a promotion during a conversation at a customer contact center or on a retail store sales floor.

Scoring Your Customers

Srividya Sridharan.png

“I think the technologies have somewhat caught up to the reality of producing analytics in real time,” said Srividya Sridharan, a senior research analyst for Forrester Research specializing in customer insight. “But there’s a very big distinction between whether the analytics is actually produced as opposed to being executed in real time.”

The difference rests in how marketers “score” the propensity for a customer to reach a purchase decision, say, when seeing a kiosk in a retail store or during a conversation with a call center service agent. “That is based on data from the past, so it’s predicting the future,” said Sridharan.

That’s not a bad approach, but it’s not new either. Traditionally, scoring has been based on data that has been gathered in the past — three months, six months, even a year or two — not on what an angry customer said five seconds ago.

That’s no longer good enough. As Sridharan pointed out in a research report more than two years ago, marketers have lost control of the customer relationship because “consumers today look to their networks for answers and receive them by the bucket load in an incredibly short period of time.”

Instant Gratification

So a retail consumer armed with a mobile phone can conduct real time price comparisons at other stores and online vendors — making that customers’ past data largely irrelevant. The same goes for a customer seeking help through a call center while surfing the web. Such consumers have developed heightened expectations for quick responses when prices are too high or something goes wrong with their service.

“Successful marketers recognize that they must leverage customer insights, often in nanoseconds, to remain relevant to the customer,” Sridharan wrote back in 2011. In an interview with CMSWire, she said, “It hasn’t changed really. I think what has happened is the technologies that enable real time analytics to become a reality are much more widely available now.”

Whether the analysis is being executed effectively depends largely on the channel. On a website, for example, a customer’s background combined with current activities can trigger highly personalized offers at that moment. But in process-intensive call centers, Sridharan noted that the caller’s “voice still needs to be transcribed into text, and the text needs to be minded for themes and content, so there’s a lot more processing involved in a channel like that.”

Data from social sites present another complication. To the extent data can be tied to the consumer in a store or on the phone, it can be used to help personalize the response to that individual. However, that’s the exception. More typically, it’s used to respond to problems and complaints on the social network itself. Salesforce Radian6, for example, is a social listening platform that helps companies spot and respond to complaints.

Service, Not Sales

“The real time application [in social media] is more in the service context, and not so much in the promotion or offer or marketing context where I’m actually trying to promote something because you interacted with me in social media,” Sridharan explained.

Perhaps the most advanced use of real time analytics at call centers is in fraud prevention, particularly at credit card companies where new technologies can help to detect tone, voice and other anomalies.

Sridharan believes similar systems will evolve for retail call centers, where fraud also eats into revenue.

But for phone or cable companies trying to retain and upsell customers on new services, or for retailers looking for a better response to fickle showroomers, real time analysis has not yet arrived.

Title image by DeiMosz (Shutterstock).