Two factors make e-commerce the perfect proving ground for artificial intelligence technologies like machine learning: e-c ommerce is highly competitive and gaining an advantage over the competition requires the rapid processing of huge amounts of data.
Many areas of e-commerce are ripe for AI-driven innovation. Every improvement to logistics efficiency, marketing, pricing, or recommendations gives retailers an edge over the competition. Retail generates and consumes monumental volumes of data from dozens of channels. In fact, there’s far too much data for a human to know where to look or even what they’re looking for — the perfect conditions for machine learning.
Machine learning is the overarching name for various methods of data analysis in which computers find insights in data without being told exactly where to look for those insights. Machine learning algorithms, when exposed to massive amounts of data, can extract patterns and use those patterns to generate insights or predictions about future conditions.
When you upload a picture of a cat to Google Photos, it knows the object in the picture is a cat. The “cat identifying” code wasn’t written by a human: it developed as a consequence of exposing an algorithm to lots of cat photos (and photos of things that aren’t cats).
The same principle can be applied in many areas of e-commerce. To take one example, e-commerce merchants have become quite good at recommending related products, but anyone who shops online knows that recommendation engines frequently get it wrong.
Recommendation engines are limited because they have access to a relatively small set of data and because the ways they can “reason” about that data are constrained: “People who bought this product also bought that product” is not the best possible way to predict future purchases.
Machine learning will help merchants discover better ways of modeling user behavior so they can make more accurate recommendations about what a customer is likely to be interested in buying.
By exposing machine learning algorithms to truly massive amounts of data from purchase histories, social media data, web interactions, and anything else that might prove relevant, merchants can build automated analytical models that aren’t limited by the ability of humans to hypothesize about why certain people buy particular products.
This isn’t the technology of the future; machine learning is already here and companies like Boomtrain are using machine learning techniques to build retail personalization platforms.
Personalization is far from the only benefit of machine learning and other artificial intelligence technologies. They’re already widely used in fraud prevention, logistics and supply chain management, and pricing.
Over the next few years, the application of machine learning and artificial intelligence to e-commerce will become an increasingly important differentiator of e-commerce performance. Retailers who do not act to leverage the benefits of AI risk being left behind by early adopters who reshape the e-commerce market and the expectations of shoppers.