Unify Marketing Operations with Data Management and Predictive Analytics

At the 10th annual MIT Chief Data Officer and Information Quality Symposium last month, all conversations about marketing solutions and/or customer data became conversations about this year’s hot topic for technology executives who work with data: data management.

“Probably the best way to think of [data management] is as the creation of an up-to-the-minute, integrated customer view,” said Andy Cutler, Director of Global Customer Solutions for RedPoint Global, in an onsite interview at the Symposium. Cutler went on to explain that the inherent problem, therefore, with data management is that large martech heavies like Salesforce and Oracle “don’t really have as much ability to tie the data together. 

“That’s what companies are trying to do; they’ve struggled to do it due to data limitations … and organizational barriers,” said Cutler, who illustrated this struggle thus:
You do the email piece; you do the catalog piece; you do the website; you do customer service…” (ad nauseam).
“You need a tool before you get the marketing tool,” Cutler concluded.
Or maybe not. Cutler’s company, RedPoint, offers data-management solutions for omnichannel marketing—a.k.a. campaign management, which, said Cutler, goes hand in hand with data management for today’s digital marketer or brand manager. 

“Social media interactions, web traffic—this stuff has historically been problematic for companies because it’s been unstructured,” said Cutler.  Additionally, “a lot of companies have their data updated, but only daily or weekly…so their understanding of who the customer is is now out of date.”
RedPoint seeks to solve this problem, said Cutler, through Hadoop-based predictive analytics that can go so far as to provide real-time data and analytics related to customers—while still allowing sales and marketing teams to build multiple channels, and relate to customers across those channels, in direct support of an entire brand or marketing campaign. 

“We have a very unique way to help companies use [and organize] their customer data,” observed Cutler, “and the most powerful data you can have is when someone has actually
bought something.”
Referencing “the classic marketing funnel,” Cutler was emphatic that the data upon which RedPoint’s predictive analytics rely is actual purchasing history.
“Surfing data is good, but it’s not as predictive,” Cutler noted. “The most powerful data you can have is when someone has actually bought something.”
From there, boasted Cutler, the combination of effective data management and predictive analytics—such as those offered by RedPoint—can predict such customer factors as details of the customer’s most probable next purchase, the customer’s purchasing timeline, a customer’s likelihood of defection, and the like. 

“Part of what our software does … is it builds the customer profile over time … so if I buy clothes from Orvis, they have a history of what I’ve purchased in the past and what I’ve looked at,” offered Cutler. “All of these things are helpful to you in deciding ‘How much do we want to invest in this customer?'”

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