Dr. Eric Siegel cuts through the buzzwords surrounding predictive analytics to reveal what it is, how it works, and how it is driving major improvements in business—including in demand forecasting. He discusses the persuasion paradox, reveals how companies can get started in predictive analytics, and dispels the myth that all consumers of this technology need to be technically minded. A civil liberties activist, he also warns that misuse of this technology can further marginalize society’s most vulnerable groups.
“Predictive analytics has become a critical best practice for all businesses that operate at scale”.
What is predictive analytics?
Let me give three definitions. The first is that it’s the business application of machine learning. The second, more common functional definition, is that it’s technology that learns from data to make predictions about an individual person or case in order to improve company performance. The third definition—where predictive analytics meets forecasting—is that it’s the deployment of machine learning methods such as decision trees, log-linear regression, ensemble models and neural networks. These methods serve to predict demand. When applied for forecasting, rather than generating a prediction for an individual customer, corporate client, or transaction, which is what predictive analytics is usually employed to do, it simply provides a forecast for the item and time range in question.
How does predictive analytics work?
Imagine you have rows of data available from which to learn. In the case of a classical business application, each row corresponds to a customer and contains the outcome or behavior about the customer you’d like to predict—such as whether they committed fraud, whether they canceled their subscription, whether or not they turn out to be a good credit risk, whether they defaulted on their debt, or whatever outcome is relevant to your business. In the case of forecasting, each row includes or summarizes everything you knew up to this point in time about a particular SKU or whatever you’re trying to predict demand for, along with the outcome, i.e., what the actual demand ended up being over the next week or next month. These rows compose your training data from which correlations are discovered between the data points you hold about the product and the outcome.
Are machine learning and predictive analytics synonyms?
It depends on the context, but, when you’re talking about these kinds of business problems, they’re entirely synonymous. A lot of the confusion surrounding this technology is due to nomenclature. A few years ago, the industrial world started using the word machine learning. Before that, the term was only common within academics and research and development. When it comes to this technology and demand forecasting, you can just as well call it machine learning as predictive analytics. Whether we’re predicting the likelihood of somebody defaulting on a loan or predicting demand for a product, the same exact core analytical methods apply.
To complicate things further, what about prescriptive analytics?
Prescriptive analytics is a superfluous term, which has never been agreed on as far as its definition. The way it’s usually used is to imply that predictive analytics is not enough and now you need additional technology in order to translate what you’ve predicted into the actual action to be taken. But predictive analytics has all along been intended to directly inform actions to be taken such as whether to make marketing contact, whether to audit for fraud, or approving someone for credit card application. The term prescriptive analytics implies there’s entirely new field or technology. There isn’t. Predictive analytics as a field already includes field deployment, i.e., the translation into action.
The above is an excerpt of the original article The Journal of Business Forecasting. Click here to access a major excerpt of the interview.
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The full article covers dozens of additional questions, including:
How does predictive analytics differ to more traditional statistical modeling?
How can companies get started in predictive analytics?
What kind of data do companies typically hold that could be used for predictive analytics?
One of the most famous cases of a company benefiting from predictive analytics was Target when they marketed to women they identified as likely to be pregnant. How did they figure that out?
Do predictive analytics models assume that correlation equals causation?
In your book you say it’s impossible to predict with high accuracy. In business, what kind of accuracy do you need?
There are now off-the-shelf predictive analytics/machine learning tools that are very cheap. Are these black box systems and can they be trusted?
What worries you about predictive analytics?