Bagging discounts on Black Friday and Cyber Monday have become a yearly ritual for many of us. The roots go back to the early 20th century – a folk belief is that it is the day retailers go “into the black” after running at a loss for the year. In reality, the term was first connected with the day after Thanksgiving as a comment on the nightmarish congestion generated by the crowds in Philadelphia.
In the 1980s sports games gave way to shopping as the public’s favourite post-Thanksgiving pastime. Cyber Monday arose in the mid-noughties when marketers realized that workers returning to the office following Thanksgiving breaks were making use of high-speed internet connections to shop online bargains. As with Black Friday, it has become an online battleground between retailers keen to leverage whatever technological edge is available, to grab as big a slice of the pie as they can.
In today’s climate, this increasingly means Artificial Intelligence (AI) – specifically machine learning powered by big data – and this has altered the playing field dramatically.
In retail, this technology has two main uses – predicting demand and personalizing services.
Traditionally, retailers used Black Friday as a means of shifting stock which hadn’t sold during the year, at a reduced price. Thanks to the huge increase in the amount of data which we generate, and which retailers can capture and analyse, today it’s possible for them to predict what we will spend our money on with greater accuracy than ever before.
This means that pricing, inventory and distribution can all be managed more efficiently, with global retailers such as Amazon and Alibaba able to stock distribution centres according to regional buying habits. The overall savings on transportation when they discover that, say, there is a low demand for ice in Alaska over the holiday period, leads to overall reduced operational costs and eventually lower cost to the consumer.
That’s a trite example of course, and in reality, today’s sophisticated machine learning engines are built around artificial neural networks designed to mimic the learning process of the human brain. After all, it’s the most capable information processor known to exist and is capable of finding far more subtle signals and relationships. AI – of which machine learning is the current state-of-the-art – seeks to augment our classification-based learning system, honed through millions of years of evolution, with the perfect recall, lightning speed and infallible logic of a computer.