As the shopping experience becomes more and more integrated, retailers tend to adopt an omnichannel sales approach. This means that a customer may seamlessly switch across the multitude of sales channels, shopping online from a desktop or mobile device, by telephone or in a bricks and mortar stores.
Not only does this allow customers to get the best out of their shopping, but it also provides retailers with an enormous amount of data generated by customers. This digital trail left by customer’s interactions with the retailer, both online and offline, provides marketers exabytes of data. Bluetooth beacons in-stores drove $4 billion in sales in 2015 alone. Certainly, lots of precious insights can be found in this plethora of information. But crunching that is a tough row to hoe. And here’s where machine learning (ML) comes in.
This term is often used interchangeably with artificial intelligence (AI), yet they are not exactly the same. AI is, basically speaking, a machine capable of intelligent behaviour. ML is, as Stanford Dictionary puts it, “the science of getting computers to act without being explicitly programmed.” ML uses algorithms that learn from data to build predictive models that choose where to look for insights. This technology opens a great deal of opportunity for businesses.
Applications of ML are almost limitless when it comes to retail. Product pricing optimisation, sales and customer service forecasting, precise ad targeting, website content customisation, prospect segmentation—these are the most obvious examples of how ML can boost your sales and save your marketing budget.
As usual, numbers speak best for the success of ML in retail. Fifty-five percent of Amazon’s sales come from personal recommendations made by machine learning algorithms. Target Corporation achieved 15 to 30% growth in revenue with the help of machine learning predictive models. At least 40% companies surveyed by Accenture Institute for High Performance already use machine learning to improve their sales and marketing performance. And, frankly, I’m not too optimistic about the future of the remaining 60%.
Obviously, data-driven decisions have been defining the success of retailers long before AI and ML were even invented. Choosing the right mix of products based on customer demand, setting prices and offering discounts based on competitor policies—those things have always been about careful data analysis.
But the crucial factor to thrive in our “be quick or be dead” time is the speed at which you make decisions, as well as their quality. Businesses should not only look back, analysing the data obtained in the past. Cutting edge data processing happens in real-time and changes are being made on the fly.
Adaptive analytics, for instance, prevent customers from abandoning your website by sensing the first signs they might drop off and causing live chat assistance windows to pop-up.
