Organizations are largely attracted to business analytics and big-data by their desire to capture greater value from their data. They do this by plumbing areas of unstructured information originating from outside sources such as the Internet.
No one is feeling this urge more than the folk in corporate marketing departments. For years, marketing has struggled with customer relationship management (CRM) and other marketing intelligence systems, trying to purchase and integrate appendices of data that give teams a clearer picture of who customers are and how they can best sell to them.
I remember working with one such marketing intelligence system several years ago. We could get basic customer information, such as age, sex, and where they lived. But we had to pay our provider to append more data when we wanted to add dimensions to the intelligence like our customers’ professions and whether they had children.
Sometimes it was worth it. If you were trying to target young teens to open savings accounts for their college educations (thereby beginning relationships with the bank), it helped to know which of our established customers had children and if the parents were employed in professions requiring college degrees.
But there were also inherent risks, dangers that haven’t appreciably changed over the years...
Is the data right? A Time magazine writer from Los Angeles was reported as living in New York, and at least one data analytics program profiled him as a woman between the ages of 18 and 19. At least 8.7 percent of Facebook profiles are from fake users. In plain numbers, this adds up to 87 million users out of an estimated 1 billion account-holders as of year's end 2012.
The moral of the story: Marketing departments should pre-assess the degree of accuracy they believe their data will have before engaging a third-party data mining shop to help them get it.
Will customers approve data collection? There are customers who love it when you are intuitive and anticipate their every need -- and there are those who don’t. There's no way you can create a targeted marketing campaign that's granular (or accurate) enough to figure out individual customers' opinions about this tactic.
Some companies get around the situation by having an “opt in” policy for customers who wish to receive promotions. Others send out the company's data privacy policy to customers each year (something regulators in the healthcare and financial services industries require). A corporate privacy policy should clearly explain to customers just when, how, and by whom their personal data can be used, so at least there are no surprises.
Is your third-party data mining source reliable? Overwhelmed by the amount of data they can mine, many companies turn to professional data mining and aggregation houses for help. What they discover is a treasure trove of information that can be mined for them at a fraction of a penny per match. In one case, Maggiano’s Little Italy engaged Rapleaf, a third-party data mining and aggregation firm, to transform Maggiano’s extensive customer email contact list into a marketing database that appended demographics and psychographics to better understand the lifestyles of Maggiano’s basic and premium customers and how to increase sales to them.
In another case, United Oil Co., a chain of more than 125 gas stations throughout Southern California, used business analytics to tap into point-of-sale (POS) information it had collected from its service stations to determine which product mixes worked best in which geographical locations. No matter whether it was a restaurant or a gas station, both organizations evaluated the credibility of their data and their data-gathering sources before making the investment in analytics -- and, in both cases, the investment led to business intelligence breakthroughs.
"We were surprised at the amount of data that was coming through our POS systems and how much micro-analysis potential there was," says Bill De La Espriella, director of technology at United Oil. "This is why we’ll be focusing most of our IT efforts in the area of analytics."
— Mary E. Shacklett is president of Transworld Data
"We were surprised at the amount of data that was coming through our POS systems and how much micro-analysis potential there was," says Bill De La Espriella, director of technology at United Oil.
As you say in your article, Mary, the amount of data is going up, up, and away, not only from POS, but from sensors, social media, and all sorts of other input devices and applications. It's imperative, then, that organizations figure this out. And fast!
It is overwhelming! But as you pointed out, it's one of those tasks that has to be done -- and done right. It's also a reason why rogue cloud deployments are such a bad idea; they make risk management more challenging since IT and legal have no idea where data resides, who is hosting this information, how many versions exist, and so forth.
The service station example is an intriguing one. By tapping into POS systems, companies can find out what customers really want, as opposed to what customers say they want.
Since it "must be true" if it is on the Internet, too many organizations assume that "data availability" and "data accuracy" are the same thing.
Over the years, in order to test applications and social media, I have built several fake profiles (it was easier than explaining to my connections that they should ignore my next 25 Facebook posts as I tested to make sure all of a websites social media links worked). I am constantly amazed by friend requests to a fake profile and my fake people have even been tagged in photos - but maybe those are other fake people. But most interesting, one of my profiles recently jumped from online marketing to direct postal marketing and I am now getting physical junk mail. I guess multichannel marketing works - but bad data on one channel probably equals bad data on another channel.
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Social media has been with us for a decade -- but employer policies and the law are anything but firm about the most appropriate usage of this powerful tool.
The average yearly tuition cost for brick-and-mortar colleges and universities in the US today is more than $20,000, an expense that has increased 1,120 percent since 1978. By comparison, students of all ages can often earn an online education for one fourth of this annual cost -- and more businesses are starting to acknowledge online degrees.
Multi-tenant clouds assure security for clients, but not necessarily for their ideas. Here's one thing you should discuss with your cloud provider before you sign on.
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Facebook's Graph Search may face some profound challenges and risks, first, because Facebook users haven't been thinking of their posts as product reviews; and second, because Facebook will now have to contend with the social-network equivalent of SEO "gaming" of results.
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While NFC's original goal was to enhance mobile commerce applications, it is finding its way into a number of other uses, which is creating both opportunity as well as challenges for IT departments.
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Edmunds separates customers into segments based on the info it collects on its site and from partners, and uses that to push out custom content, said Brian Baron, director of business analytics for Edmunds.com, at Predictive Analytics Innovation Summit.
The automotive website uses propensity modeling to target ads and customer registration forms, said Brian Baron, director of business analytics for Edmunds.com, at Predictive Analytics Innovation Summit.
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