More ways to misread customers: the problem with more data for marketers
Why are marketing conversion rates stuck at one percent despite increasing customer touchpoints and analysis tools?
More channels through which to interact with a customer might make it harder to follow him or her through a single “journey,” according to Sri Raghavan (pictured), senior global product marketing manager at Teradata Corp.
“We have to go to ‘place A’ where there’s a lot of [Customer Relations Management] information, for instance, or ‘place B’ where there’s a lot of online information, weblogs and web servers and what-have-you,” Raghavan said during the DataWorks Summit in San Jose, California.
In this tangle, one encounters not only conflicting and confusing content, but also data formats that may not blend easily together, Raghavan told George Gilbert (@ggilbert41), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio. (* Disclosure below.)
“Weblog servers are very different in their data makeup as opposed to CRM solutions,” he said, adding that accurate translation across channels and formats in crucial. For example, a customer may not be visiting a store because they are buying online; that knowledge alters the way a company should effectively communicate with him or her. But where is that information? In the CRM or any number of other places? And how does a company find it and integrate it with everything else?
“We need a way to be able to bring them together to connect a singular user ID across the different sources,” Raghavan said.
Universal language
A blanket tool for such messy and disparate data ought to be somewhat agnostic about sources and other products in use in a data center, according to Raghavan.
“We’ve put in place a technology, an architecture, which allows us to be able to connect to all these various sources, be it Teradata products or non-Teradata, third-party sources — we don’t care,” he said.
Teradata’s analytics software, including its AppCenter reiterative framework, cover the map, rendering disparate data in a structured format and feeding outcomes back into algorithm models for retraining, Raghavan explained.
“Each part of the journey is interrelated, and you need to understand this,” he said.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s independent editorial coverage of DataWorks Summit. (* Disclosure: Teradata Corp. sponsored this DataWorks Summit segment on SiliconANGLE Media’s theCUBE. Neither Teradata nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE
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