Business forecasting and predictive analytics are merging to leverage Big Data as a growth driver.

Predictive analytics does not have to be complicated and Demand Planners can learn these models and methods to drive business insight.

Organizational processes to support the application of predictive analytics insights are arguably a bigger challenge than the models. 

IBF spoke to Eric Siegel, author of Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie, Or Die and former Columbia Professor, who revealed just what predictive analytics is and how it crosses over into business forecasting.

“Predictive analytics is basically applications of machine learning for business problems”, says Siegel. Machine learning learns from data to render prediction about each individual [thing being examined].” That individual thing can be a customer, product, machine, or any number of things.

When asked why predictive analytics is the latest evolution in information technology, Siegel responded “Because predicting by individual case is the most actionable form of analytics because it directly informs decision for marketing, fraud detection, credit risk management etcetera”.

But How Does Predictive Analytics Actually Work?

“Data encodes the collective experience of an organization so predictive analytics is the process of learning from that experience. You know how many items you sold, which of your customers cancelled, or which transaction turned out to be fraudulent.”

Siegel continued, “You know all this – that’s the experience, and you learn from that experience and the number crunching methods derive patterns. And those patterns are pretty intuitive and understandable. They could be business rules. For example, if a customer lives in a rural area, has these demographic characteristics and has exhibited these behaviors, then they might have a 4 times more likely chance of buying your product than the average.”

“That may be a relatively small chance but when improving something like mass marketing, finding a segment that is 4 times more like to buy than the average, that has a dramatic improvement on business performance.”

It is clear then, that by identifying patterns in data, predictive analytics can reduce risk and identify valuable commercial opportunities.

Predictive Analytics Meets Business Forecasting

“There is a continuum between forecasting and predictive analytics”, Siegel notes. But he does highlight key differences in their current applications:

• Forecasting is about a singular prediction, i.e., about sales in the next quarter or who will win a political election.
• Predictive analytics renders a predictive score for each individual whether it is a consumer, client or product, and as such provides insight into how to improve operations relating to marketing, fraud detection, credit risk management etc. more effectively.

Siegel laments the current disconnect between the two fields, “There should be a lot more interaction between what are two very siloed industries but have a lot of the same concepts, a lot of the same core analytical methods, and a lot of the same thinking. Both belong under the subjective umbrella know a as ‘data science’”.

Ultimately, both forecasting and predictive analytics serve to gain business insight but approach it from different starting points. Every business decision starts with a lag between what you know now and what occurs. Whether you’re forecasting sales or the likelihood someone will buy something in response to a marketing initiative, you’re generating a prediction.

Siegel said of the similarities between forecasting and machine learning, “the methods on the business application side include decision, trees, logistic regression, neural networks and ensemble models while forecasting uses time series modeling, but there are ways these two classes really do interact and really build on one another”.

Predictive Analytics Isn’t Scary

When challenged that complex predictive analytics methods can scare people off, Siegel insists that “they’re totally intuitive” and that machine learning and predictive analytics can be “accessible, understandable, relevant, interesting, and even entertaining”. That should reassure Demand planners looking to adopt predictive analytics methods and models.

Talking of the apparent complexity of machine learning models, Siegel commented that even neural networks, which represent the more advanced modeling on the predictive analytics spectrum, are modular and each if its components are in fact very simple.

Even if the model as a whole is difficult to fully understand (even for the people who invented them) you can test them and see how well they work, meaning that regardless of how complicated the models are to understand, their actual application is relatively straightforward.

Whether it’s through his Dr. Data YouTube channel (complete with rap videos), his book, or his Coursera program, Siegel is on a mission to make predictive analytics accessible. When it comes to the data that predictive analytics uses, he again highlights the simplicity, “It can be simple as a two-dimensional table on an Excel spreadsheet where each row is an example and each column is an independent demographic or behavioral variable”.

How Can Demand Planners Start Using Predictive Analytics?

It goes without saying that training in data science and predictive analytics is necessary when it comes to demand planners applying these techniques. Most of the training available on predictive analytics is technical, however, and that’s just part of equation warns Siegel, “There’s another side to machine learning if you’re going to make business value out of it which is the organizational process – the way you’re positioning the technology so it’s not just a cool, elegant model but is actually actionable and will actually be deployed.”

That’s a theme that Demand Planners will recognize all too well and it’ll come as no surprise that supporting process and culture are vital to leveraging predictive analytics insight in an organization, “Organizational requirements like planning, greenlighting, staffing, and data preparation are foundational requirements.”

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One of the key themes raised by Eric Siegel is that forecasting and predictive analytics are merging to meet the business needs of today. To find out more about the future of these fields and how they impact demand planners and forecasters, check out Eric Wilson’s upcoming book, Predictive Analytics For Business Forecasting, published by the Institute of Business Forecasting, which is available to preorder now.

To get up to speed with the core concepts underlying predictive analytics, head over to Eric Siegel’s Machine Learning Course on Coursera.