Comment counts: How AI is key to the future of retail

Advances in artificial intelligence are destined to make our lives and shopping experiences stronger than ever – good news for the consumer, and even better news for retailers, writes Uwe Hennig of Detego.

The AI-powered Macy’s On Call mobile tool from IBM Watson and Satisfi

The AI-powered Macy’s On Call mobile tool from IBM Watson and Satisfi

There have been a number of buzzwords and defining technology trends in retail over the last decade, from big data, to omnichannel, and the ubiquitous, omnipresent cloud. Now the internet of things (IoT) and artificial intelligence (AI) have become the latest talk of the town.

Forrester expects investment in AI to triple this year. By 2020, 85% of customer interactions will be managed by AI, according to research by Gartner. It’s becoming big business across industries, and not just in retail: the value of AI is estimated to be worth $36.8bn globally by 2025, predicts US market intelligence firm Tractica.

With the proliferation and accumulation of so much data as people shop anytime, anywhere – whether online, in physical stores or increasingly via their mobile phones – the conundrum for many remains: there’s just too much information to be able to make any meaningful sense out of it.

And that’s where artificial intelligence comes in. AI relies on a continual process of technological learning from experience and getting better and better at answering complex questions. Algorithms powered by AI can rapidly come up with alternative options which are otherwise much more time-consuming and laborious using conventional computer-powered A/B testing. Like the human brain, AI adapts to the environment and gets better the more you use it. But unlike humans, the capacity for improvement is unlimited. What’s more, boring, repetitive tasks are never a problem.

Plenty of examples in retail already fall under the hat of AI: chatbots are being used to help with customer service; personal shopping assistants like Amazon’s Alexa respond to voice prompts; and robots are replacing information kiosks in stores like Lowe’s in the US. Live chat functions on retailers’ websites are also proving popular for replacing staff with always-on robots and providing a continuous machine-learning customer service experience. But the future of this space looks even more AI-enabled.


Personalised service and the human side of AI

Retailers have long since struggled with maintaining ever-increasing standards of customer service as consumer expectations continue to rise. As people continue to shop more via the internet, retailers have to provide a faster, more effective, personalised service specifically aimed at the needs and wants of individual customers.

AI is set to help. eBay’s ShopBot for instance, is an AI-powered personal shopping assistant on Facebook Messenger that helps users find the best deals and sift through over a billion listings.

Chatbots have question and answer recommendation capabilities that are much more personalised than previous systems. They’re examples of retailers trying to create a near human interaction. Yet an IBM study in retail deduced that traditional retailing is too constrained to cope with recent technological advances and that the technology to date is just not human enough.


Humans vs machines

In spite of that, a new report by PwC says that around 44% of jobs in the retail sector are at risk of automation by 2030. AI is extremely good at repeated tasks and number crunching, so machines will do lots of manual processes in the future. We’re already seeing some retailers wanting to close off stock rooms and using robots to make automatic decisions about what needs replacing on the shelves, or managing the flow of goods for deliveries and onto the shop floor, for instance.

In the not too distant future, it will be common practice for consumers to pull out their phones and ask it a question as they enter a store, rather than seeking out a sales assistant or searching through the rails themselves. The smartphone can immediately respond that a desired article is available in a specific size and that sales staff can bring it.

Voice recognition systems and speaking to a computer or smartphone (like Apple’s Siri) for answers is already taking shape. Macy’s used a version from IBM Watson to do exactly this (as pictured above), and talking interactive screens and self-checkouts in fitting rooms is something we’re also already engaged with.


Real-time recommendations

AI, or machine learning, learns from past behaviour, as well as trial and error, to come up with more intelligent solutions. It’s not just science, there’s an art to selling too. Old fashioned rules-based analytics will soon become a thing of the past.

At Detego, this means making more informed recommendations to retailers using predictive analytics. So, much like the practice of online retailers flagging up similar items you might like as you browse the web, some retailers are now taking this to the next level using AI – and not just online, but in their physical stores as well (where still over 80% of sales are driven).

For example, whereas a sales assistant might, if you’re lucky, recommend something that’s evidently there on the shelves, an AI system would be better at identifying what would be the best items to offer based on many more criteria. These would include fundamental credentials like real-time product availability and the resulting profitability for the retailer, as well as other considerations like the consumer’s browsing history, or even what they’ve tried on before in the fitting room (thanks to “smart” RFID tags embedded into garments).

Informed recommendations can also be made by tapping into social media and other factors that might influence product choices, like current fashion trends or weather forecasts in different regions.


Predictive AI

Effective AI systems are also looking for re-occurring patterns to help avoid out-of-stocks and unnecessary markdowns, such as by promoting underselling lines held in reserve that otherwise would later have to be discounted. Not only will such advanced technology know when shelves are empty, but more importantly, it will predict what will happen next.

One of the biggest growth areas where AI can make a significant difference to a retailer’s bottom line – for mobile, online and bricks-and-mortar retailing – is in this field of intelligent forecasting systems. Previously, retailers were only able to predict roughly the quantities of products to order to keep shelves fully stocked using (often out-of-date) inventory levels and historical sales data (usually going back a few years, at best). These days, AI can develop a much more accurate picture of exactly what types of products, sizes and colours are likely to sell, by looking at multiple scenarios in real time (fashion trends, consumer behaviour, the weather etc) and drawing on data from the internet. This means forecasting is no longer so much “stab in the dark” guess work.

Using AI, German online retailer, Otto, predicts with 90% accuracy what will be sold within the next 30 days and has reduced the amount of surplus stock it holds by a fifth. It has also reduced the number of returns by over two million products a year. It claims to be so reliable, in fact, that it now uses an automated AI system to purchase 200,000 items a month from third party suppliers with no human intervention. Humans simply wouldn’t be able to keep up with the volume of colour and style choices to be made.

While some fashion retailers are working with Detego to exploit many of the latest technologies to help encourage more people into their stores and improve levels of customer service, forecasting in fashion is generally quite poor. Despite more than 1,500 stores already equipped with Detego’s software and over a billion garments digitally connected, the wider industry average for forecasting accuracy in fashion still lags at a paltry 60-70%. Although RFID tagging and real-time stock monitoring offers near 100% inventory accuracy, relatively few fashion retailers have rolled-out digitally connected technology on a wider scale.

It’s still only the early stages of AI, but with the promise of it making forecasting and product selections even more accurate, it’s set to become a rapid reality. Now’s the time to jump on board.

Uwe Hennig is chief executive of retail tech specialist, Detego. Comment Counts is a series of opinion pieces from experts within the industry. Do you have something to say? Get in touch via info@fashionandmash.com.