Mo’ big data, mo’ big problems.

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A friend of mine works for a prominent university where one of his primary responsibilities is actively engaging with the alumni and athletic boosters, both directly and through social media channels, to garner large donations. Recently his department compiled a tribute video for one of their most prominent alumnae (and donor) using recorded messages from some of their past outstanding football players. It was a great idea in theory but tracking down this old player data proved to be rather difficult.

The life in academia is very much like our corporate lives. We have TONS of data about past and present customers (students) but it’s not easily accessible, not well organized and definitely not easily analyzed. My friend’s task sounded easy – track down football players that played for the university between 1974 and 2010, contact them, and get them to agree to appear in the tribute video that would air during this year’s homecoming celebration. What seemed like a straightforward request, turned into nothing short of a Big Data nightmare. You must be thinking, ‘I can get past customer addresses – what’s the big deal?’

The big deal is that my friend’s problem goes far beyond getting addresses. What kind of analytics are they using to determine who are the most valuable alumni (customers)? The university likely picks the person(s) who have given large amounts in the past (bought more) but who in their population is not connected, is not giving, has not been engaged with the university. There are invaluable relationships among the data that will increase donations (purchases) but you cannot just “eyeball” the answer.

My friend’s organization has big data problems AND people problems, and they’re not alone. I bet you can relate because you spend too much time gathering input for an Excel spreadsheet, too much time waiting for some tech person to get information for you, too much time guessing at how data are related. It’s not possible to eyeball complex relationships within or across data sources – if you even have them in a form where you can access them.

Here’s more information on Big Data as a managed service.

Some of these customers know all too well what happens when big data problems affect customer experience:

“I spent over an hour on the phone today while a call center agent fumbled through my customer record trying to locate a return order. I sent this dress back over six months ago but my account was never credited even after five calls about it. Why doesn’t your return process work?”

“I tried to redeem a plane ticket from points I had accrued from the airline’s frequent flyer program but I was informed that I wasn’t enrolled in the program, even after I gave over my frequent flyer number. This is crazy to me because they email me flight offers all the time but can’t find me by my frequent flyer number?”

“It was such as waste of my time to do the chat with your agent. I tried to serve myself and couldn’t so I chatted to get some help. I think the online banking agent should be able to tell me where to click to submit a request to get a missing account linked to my online banking profile. If I wanted to call to ask, I would have done that!”

Happy Wednesday!

Republished with author's permission from original post.

Jodie Monger
Jodie Monger, Ph.D. is the president of Customer Relationship Metrics (CRM) and a pioneer in business intelligence for the contact center industry. Dr. Jodie's work at CRM focuses on converting unstructured data into structured data for business action. Her research areas include customer experience, speech and operational analytics. Before founding CRM, she was the founding associate director of Purdue University's Center for Customer-Driven Quality.

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