Terabytes of information are available to retailers in the form of Big Data. These can, now, help them to make sense not only of the past and the present, but also to look more effectively to the future.

A traditional way of marketing to customers has been to use an RFM score (based on Recency, Frequency of purchase, and Monetary value of purchases). Unfortunately, with this rather static view of the customer, babies, for example, never get potty trained and always need diapers. In order to predict, retailers must, perforce, take a dynamic view of their customers(1). And take fully into account the fact that they do, actually, change over time.

Now, with artificial intelligence, neural networks, machine learning platforms, and the mass of data they have available to them, retailers are in a much better position to form that dynamic view.

Soon they should be able to predict not only that babies become toddlers, with changing needs (and no need to be changed), but also what those needs are! 

Business IP Traffic 2015-2020
(Petabytes per Month)

Source: Cisco: Cisco Visual Networking Index: Forecast and Methodology, 2015-2020
Note: 1 Petabyte = 250 bytes; 1024 terabytes: or 1 million gigabytes

About the Author:

Thomas Butcher is an independent writer, researcher and consultant focusing, amongst other things, on strategic materials, in particular metals. With 38 years of experience in the financial world, he has lectured and spoken around the world. Amongst other things, he writes the “Letter from North America” in the Minor Metals Trade Association's monthly publication The Crucible.

The article above is an opinion of the author and does not necessarily reflect the opinion of MV Index Solutions or its affiliates.


(1) www.insideBIGDATA.com, D. Gutierrez (Sept., 2016), How to Use Machine Learning to Further Retail Analytic Capacity