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.
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