Guest blog by Alan Morris. Alan is the co-founder and brand ambassador of Retail Assist.
It could be argued that retailers can suffer from the Midas effect when it comes to specifying their requirements from technology; simply, they may not always know how to say what they want. Traditionally, we have written programs that tell the computer exactly what to do every step of the way. We define the input, create the calculation, and we set the criteria for the results. At no time do we allow the computer to consider the facts using its enormous capacity for performing simultaneous and complex calculations, and we never ask the computer to tell us what it thinks because computers have never been able to think for themselves. They may process millions of transactions, carry countless simultaneous calculations, and store massive volumes of data, but they don’t learn from it; only humans use data to learn and build experience so that they can make better decisions in the future. When it comes to decision support, technology has always been typecast into the support role.
Technologists have been thinking about this for a long while, and ever since the reigning world chess champion at the time, Garry Kasparov, was beaten at chess by IBM’s Deep Blue in 1997, they have been working to create a computer that will mimic the human brain. Surely machine learning provides this: using algorithms, it learns from data which factors are important in achieving a specific goal.
For example, if the goal is to improve the profitability of a particular product category, the system will learn which of the variables in play are important and why; it will also understand the relationships and work out what needs to happen so that the goal is achieved. Unlike traditional programming, the system will continue to learn as the variables change. This learning will continue to develop the computers thinking, expanding its experience so that it ensures the goal is met, regardless of how the business evolves. Given this explanation, you begin to appreciate that today computers can learn and gain experience and as they begin to provide better insights, we will soon be able to rely on them to manage some key processes without human intervention: this is machine learning.
Machine learning is already active in retail, with websites promoting other items we’d be interested in buying, based upon our past purchases. These sites are using machine learning to analyse our browsing and buying history to personalise our shopping experience and encourage us to spend more money. In other examples, machine learning is being implemented to improve the supply chain process. Adidas is co-creating a new supply chain with its customers. They use machine learning to look at hundreds of millions of pictures to determine trends in consumer desire and then translate that into a guided design of individualised products. Adidas typically take 18 months to turn trends into shoes, but the new prototype “speed factory” sees customers design their customized look with the finished product being shipped to them within 24 hours.
Not every retailer can be as cutting edge as Adidas, but there are many opportunities where machine learning can be used to make measurable differences for retailers. Customer touch points will become more valid and valuable as the data they generate is used to ensure that product assortments are better optimised for different channels based upon predicted demand. Financial plans will be more accurate and will identify improvements that will increase profitability, especially in relation to achieving sales which achieve full-margin. Inventory management and fulfilment across the different channels will improve as retailers are finally able to provide the personalised experience they want to offer to their customers.
Machine learning is in the constellation of artificial intelligence (AI) along with natural language processing. Many homes now use an Amazon Alexa to turn on the lights, play music, count down from six minutes to boil an egg, or place a grocery order. This powerful combination of technologies is going to further disrupt the retail sector and force businesses to think again about every aspect of their operation. What skills and experiences will it need if roles previously taken by humans are replaced by technology? Will merchandisers be replaced by data scientists? Will merchandise planning and WSSI systems be replaced by goal-based algorithms, which generate and action the insight needed to deliver success?
Time will tell, but there is one thing for sure: the idea of someone in a retail HQ calling out “Hey Merchi … improve intake margin on all products by 5%” is not as far away as some might think.
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