Interesting discussion of descriptive, predictive, and prescriptive analytics

1. Descriptive analytics is mostly characterized in many Big Data discussions today. You have a lot of data but don’t know how to define it, organize it or tabulate it. We also interchange this term with “reporting.” It’s valuable information as a foundation, but it doesn’t tell you much about why the result happened or what might happen in the future.

2. Predictive analytics is what’s getting so much attention today. Here, you “use data from the past to predict the future,” according to Davenport. Don’t we all wish we could do this?

Skeptics hammer this concept because they insist correlation doesn’t mean causation. You might have the propensity to buy a hot dog from 7-Eleven at the same time you’re buying a Gatorade and a lottery ticket, but the reality of you buying a hot dog on your next visit, or any visit, is likely zero.

As Davenport says, “You don’t need to imply causation to apply predictions.” You’re simply predicting a likelihood of an action. For example, a certain type of customer might respond better to a certain type of email or product recommendation.

This is an important concept to understand whether you are using a model, a recommendation engine or simple business rules based on past behaviors. Predictions are just that — choices –and in a transient world moving faster, you’ll need to rely more and more on these. How far you stretch them is your RISK model.

3. Prescriptive analytics is what I think about on long walks. This is often where cause-effect analysis meets the real world. We mask this as “testing” in the marketing world. We all know the No. 1 rule of testing: You must have a hypothesis to test against.

Think of this area like a doctor writing prescriptions: fine if you are treating a common cold, but if you are trying to ascertain the relationship between increased purchase, profit and number of ad exposures over a given period of time by channel and segment, this becomes almost unfathomable operationally without a quant-geek team spending months on it in the back room.

So, what does it mean for you tomorrow?

Many discussions on the future are rooted in the questions you want to answer, how often you want the answer, from whom you want the answer and how you want to apply the answer to your business.  The importance of knowing this is what I’ve harped on for years:  We have more data, more transient consumers, more tools to choose from, and are expected to be faster and smarter than we were yesterday, all with the same budget.

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