Data Under the Microscope
In science, a microscope is used to look for very small things that can have a large impact on our lives. Predictive analytics does the same thing. While it can identify the big trends that affect your entire customer base, it can also highlight small but highly profitable sub-segments of your customers.
For example, let’s say you’re selling commemorative plates. How valuable would it be to know that people in Wisconsin whose family income is more than $60,000 per year buy three times as many plates as any other customer group? Or that customers in the Green Bay area almost always by a plate with a U.S.A. theme, but rarely purchase one with a movie theme? And that they are four times more likely to make a purchase in June than in September? Do you think that would have an effect on your direct marketing efforts?
With traditional BI, it would be difficult to dig out this kind of data unless you already knew to look for it and ran a query or built a model to pull it out. Predictive analytics finds it for you automatically.
A good way to think of predictive analytics is as what-if scenarios on steroids. It spins through millions of pieces of information, finds the associations, considers the variables and then predicts what is likely to happen if you take a particular course of action.
Amazon.com is probably the best known user of this type of thinking. Once you register and make a purchase at Amazon, its predictive analysis engine starts churning. It looks at what you’ve purchased and looks at what else other people, who’ve also purchased what you’ve purchased, have bought for themselves.
The next time you return to the site, Amazon presents you with a list of merchandise you’d probably be interested in. In my case, I know their predictions are pretty accurate.
All of this happens automatically. There isn’t a person at the other end making the decision. They simply use statistical probabilities and all the data at their disposal to tempt you into disregarding your budget and spending more money at Amazon.com.
Your Thoughts Matter
Most of this discussion has focused on things that are relatively easily quantifiable. Every purchase has an SKU number, and while deriving patterns from millions of bits of hard data is a number crunching challenge, it’s still fairly straightforward.
One of the values of the better predictive analytics tools, though, is their ability to identify patterns in soft data such as comment cards.
If the simple text is input properly, predictive analytics can be used to identify patterns that can have a huge impact on the business. For example, if the words “customer service” and “sucks” show a pattern of association, you know you have a major problem that needs to be addressed, preferably sooner rather than later.
With a little digging, you can even find out if that’s an association that goes across the board or only pertains to a particular time period. The point is you’re able to see there’s a problem and do something about it before those customers abandon you and turn to a competitor.
Here’s another example. If the word “blue” shows up often in regard to a product line that doesn’t have any blue offerings, it may be an indicator that you need to offer the product in blue. At the very least, it’s worth considering, and it’s something you wouldn’t have known before.
Of course, at the end of the day a human being is still required to take action and make decisions on how to improve on a situation, good or bad. But at least the information is there and available, not hidden away in a file cabinet.
In next month’s column we will discuss some of the factors you need to consider when deciding between BI and predictive analysis tools and the presentation layer of each technology.
Mark Robinson is a Business Intelligence practice manager at Greenbrier & Russel, a business and technology services firm specializing in business intelligence, custom application development and enterprise solutions. He can be reached at [email protected].