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How Marketers Use Machine Learning In Retail

October 6, 2016 at 9:35 AM

Machine learning is revolutionising how companies are capitalising on Big Data to develop their marketing strategies. While the term encompasses a broad spectrum of technologies and approaches, in a marketing context it can be used to improve targeting, response rates and overall marketing ROI. To put it simply, machine learning involves the automated analysis of large volumes of data – such as consumer spending habits and purchasing behaviour, as well as demographic information – and using a mathematical algorithm and a computer to identify patterns and trends. The algorithm then tests predictions based on historical campaign data and learns from the predictions it gets right. With time, these algorithms become highly accurate as more data from campaign results is added.

When these trends and insights are used to develop a campaign or an entire marketing strategy, there’s considerably less guesswork and a greater chance of success. To get a better idea of machine learning in practice, let’s have a look at how two of the world’s top retailers are using machine learning to improve marketing ROI.

Targeting Customer Segments at Target

Machine learning is being used by marketers to identify patterns in purchase behaviour to develop relevant offers that are predicted to result in high response rates… sometimes a little too well. You may have heard about Target’s infamous machine learning incident where the company learned of a teenage girl’s pregnancy before her own family even knew. This is an interesting nugget in and of itself, but it’s important to understand the value of the machine learning methodology employed by Target to achieve such accuracy.

First of all, the company formed a strategic goal to target new parents as a customer segment. This demographic is perfect for a retailer, as they inevitably need a bunch of new products for the bundle of joy on his or her way. With this in mind, Target hired a statistician and machine learning expert, Andrew Pole, to help identify characteristics of soon-to-be mothers, who would then be sent coupons and vouchers pertaining to budding parenthood. Pole studied tons and tons of data with the aid of machine learning algorithms, and found out that women who suddenly bought large quantities of unscented lotion were very likely starting their second trimester, while women who started buying supplements like calcium, magnesium and zinc were likely in their first 20 weeks of pregnancy. Through machine learning, Andrew Pole was able to identify and locate Target’s ideal market, informing its marketing strategy and overall reach.

Read this blog to discover how marketers are using Machine Learning to anticipate customer behaviour, reduce churn rates and encourage up-sell.

Amazon is using Machine Learning to drive sales and anticipate demand

Amazon is one of the best-known examples of machine learning in action, as anyone who has perused their online store is sure to know. The recommended products shown once you log in are based on your unique purchasing and browsing history, and have been proven effective over the many years of its implementation. This type of machine learning technology also helps Amazon predict and forecast demand, thereby informing supply decisions to prepare for any given increases and wanes. Companies like Amazon have access to vast quantities of data that hold immense potential for not only estimating supply/demand, but for wider business decisions as well. By automating the intelligent analysis of huge sets of data, machine learning technology offers retailers a faster, easier and more accurate method of forecasting and planning. Keeping up to speed with fashion trends and styles is vital in such a competitive and seasonal industry. Retailers can’t afford to ignore the advantages that technology like machine learning brings to the table. (Click to Tweet!)

Machine learning can empower your marketing analytics with data-driven insights. Contact us to find out how you can use data to inform your marketing strategy.

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Image credit: http://www.zdnet.com/

Luke Turnbull
Luke Turnbull
Luke Turnbull was the Head of Customer and Lead Analytics at Principa, until the end of 2017, after which he returned to his home country of New Zealand. He worked in the financial services industry since 1995, during which time he worked in process, strategy and operational design across a range of organisations in New Zealand, the United Kingdom and South Africa. Luke had been with Principa for 9 years and led consulting engagements with Principa’s local retail clients across the customer lifecycle, with a particular focus on customer engagement and lead generation.

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