What is the difference between forecasting and predictive analytics?

I get this question a lot. I've asked this question a lot. I've heard many answers to this question…a lot.

Let me give you the answer of someone who looks at this from a practical, as well as scientific perspective. First, let's look at the origin of the words: forecast is a Germanic word, whereas predict is a Latin word. I think that says it all, doesn't it?

OK...all kidding aside, maybe there really is some insight into the different approaches based on the origin of the words. With reverence to the contrasting cultures of my childhood, there is a foundational difference that explains the two methodologies.

Forecasting is German

Life experiences with the German/Nordic side of my family revealed a straightforward, logical and rules-driven approach to the world. Many conversations were process oriented, "This is THE way you do X!" (insert anything from, eat supper to blink your eyes).

Structure, rules and logic explain why it makes sense that the Germanic concept of forecasting accepts the age-old presumption that what occurred in the past is the best indicator of the future. This method assumes that the reasons for demand of a widget can't be known, and therefore a mechanical method, primarily a statistical forecast, is used to duplicate the past in order to provide visibility to the future. These statistical models are well-defined, precise, verified, and often, if only coincidentally, accurate. They are the foundation of an engineering-based solution for attempting to reveal the future. A classically Germanic approach.

Predicting is Latin

My Latin side of the family comes from Argentina, the land of the Tango, all day drinking (yerba maté tea, of course) and big, loud expressive families. These experiences centered on the person and understanding how to engage, motivate and manage them. The belief that there is so much to know about every person, and that you learn more with every interaction.

Predictive Analytics takes a similar, more humanistic approach. Instead of relying on past historical activities, predictive analytics analyzes the influencers, interactions and activities of the actors in demand...the shopper/buyer. Revealing the future by getting into the head of the actors in commerce, rather than by analyzing the history of the object of commerce- the item. And, with the abundance of real-time consumer data available today, future demand for your organization’s products and services can be more precisely determined rather than relying solely on past activities. 

Predict the Actor, not the Widget

Like the words themselves, sometimes the difference between forecasting and predictive analytics approaches is revealed in the words used to discuss demand. For instance:

"What did this ITEM do last year?" exemplifies the notion that forecasting looks at item data, simulates the past, and attempts to apply it to the future. Truly, you don't need data to know what an item did. It didn't DO anything. An item is an inanimate widget, and the only thing it can DO, is wait.

"What does the SHOPPER do when X happens?" It is precisely the shopper/buyer's action that is revealed in predictive analytics. The shopper is the only player in this drama that can DO. The shopper is who the widget is waiting on.

An awakening that future action is what is being revealed makes it clear that intimate knowledge of the interaction with the buyer is the best indicator of demand. The customer transaction, the path to it, or the influencers on it, are what predictive analytics uncovers. The uncovered information reveals what will influence, motivate or drive the next transaction and what will deliver that shopper to act on the waiting widget.

Think of forecasting as a machine, cranking out data on what the item “did.” A technique that is duplicating the past and projecting this data on the future, or as I say, “post-casting.”

Predictive analytics is a deep intimacy with the people engaged in commerce, understanding what they’ll do next, showing what that is and why.