Predicting the Past

Predictions are guesses about the past, present, or future. Prediction is not reporting: reporting is an organized recap of what is known.

Why predict the past? Predicting the past is the starting point for predicting the future.

Wait, don’t we know what the past is/was?

Not usually. For example, economic events are so hard to identify and measure that we constantly revise last year’s GDP and continue to argue about when the recession started or ended.

Or, closer to home, “Why did churn increase last month?” The “answer” to that question will be a hypothesis: our competitors had positive press, our company had negative press, the weather changed people’s habits, who really knows?

The causes, influences, and interdependencies are rarely clear.

A prediction engine will use whatever data you can give it to create and evaluate hypotheses to answer your question.  The hypothesis that best explains the past is the starter kit for predicting the future.

And perhaps understanding the past allows you to repeat the parts you liked.

Published by Sue Aldrich

As a leading authority on worldwide customer requirements, practices, technologies, and governance for personalization, Sue researches the technologies and practices that help marketers get the most useful content in front of customers at the right moment: recommendations, search, discovery, targeted marketing, and web content management. Aldrich is an expert on optimizing the methods that help customers find what they need to make buying decisions and/or to solve problems. She helps clients develop personalization, marketing, discovery, and content management practices that will engage customers and improve results.

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