News Byte: Pitney Bowes Introduces the "Golden Customer Profile"

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Pitney Bowes refines its Spectrum
Pitney Bowes refines its Spectrum

Employing the same knowledge graph technology Google uses to provide users with related topics when they search a word, Pitney Bowes today introduced a new release of its Spectrum Technology Platform that it claims provides marketers with true real-time profiles of customers.

The company claims that the master data management (MDM) program allows marketers to slice through data stores of functional silos within their organizations. It aggregates, cleanses, and consolidates first-party data and enriches it with third-party data to provide what Pitney Bowes tech expert Navin Sharma calls a “golden profile” of the customer at any point in time.

“We have a major retail banking client with a global CRM initiative to drive the right experiences for high-net-worth individuals,” says Sharma, VP of product management, information, and solutions. “Recently, an American customer walked into one of their banks in Japan. He had millions of dollars of investments with them in the U.S., but the Japanese office had no idea who he was. Using this system would allow the Japanese bankers to match up all the data the bank has on this customer and treat him in the manner he expects.”

If a Japanese personal banker mistakenly enters the customer's name into a computer, Sharma says, the knowledge graph takes over and asks, “Did you mean Mr. XYZ?”

“Companies say they have a single customer view, but that's not right. What you're looking for is a multi-dimensional view,” Sharma says. “Customer service needs to view a customer one way, and marketing and finance another. The graph interrelates separate views of a customer within an organization, kind of like an organizational version of six degrees of separation.”

Other features of the upgraded Spectrum platform include a “whiteboarding” visual modeling approach, instant access to corporate and external data, and advanced clustering and in-memory chaching to process large volumes of data at high velocity.


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