There have been a number of buzzwords and defining technology trends in retail over the last decade, from Big Data to omni-channel and the ubiquitous, omni-present Cloud. And now the internet of things (IoT) and artificial intelligence (AI) have seemingly become the latest crazes and talk of the town.
Forrester expects investment in AI to triple this year. By 2020, 85 per cent of customer interactions will be managed by AI according to research by Gartner. It’s clearly 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.
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.
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AI is not necessarily a concept that’s all that new. With the tech industry’s love of jargon, various different names refer to more or less the same thing. Machine learning is used to steer self-driving cars. AI is proving instrumental in healthcare for identifying and diagnosing complicated ailments. In Fintech, all stock markets are now dominated by computer decision-making systems. And even everyday search engines like Google use AI to refine and improve the information it comes up with the moment you tap in a few keywords.
Plenty of examples in retail already fall under the hat of AI: for instance, online ‘chatbots’ being used by the likes of eBay or North Face to help with customer service; personal shopping assistants like Amazon’s Alexa that respond to voice prompts; or robots replacing information kiosks in stores at Lowe’s in the US. ‘Live Chat’ functions on retailer’s websites are also proving popular for replacing staff with always-on robots and providing a continuous machine-learning customer service experience.
Personalised service
The recently launched eBay ShopBot, an AI powered personal shopping assistant on Facebook Messenger, helps users find the best deals and sift through over a billion listings. These chatbots have question and answer recommendation capabilities that are much more personalised than previous systems. Nowadays, it’s difficult to know whether your questions about a particular delivery are being answered by a real human, or a machine.
They’re all examples of retailers trying to create a near human interaction. 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.
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.
The trouble with traditional retail IT systems is not only the breadth of fragmented data that’s so often meaningless and out-of-date; but moreover, it’s the over-reliance on linear computing techniques that are too simplistic when it comes to the complicated task of forecasting exactly what style, colour or size combinations are most likely to sell in any given area.
Machine learning
AI 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 eighty percent 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 important considerations, like the consumer’s browsing history, or even what they’ve tried on before in the fitting room (thanks to ‘smart’ RFID tags imbedded into garments).
Informed recommendations can 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.
Effective AI systems are looking for re-occuring patterns to help avoid out-of-stocks and unnecessary markdowns: for instance, 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 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 per cent 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 more than 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.
Artificial Intelligence offers the potential for a considerable reduction in labour costs for retailers. For consumers, it means getting more reliable information and personalised offers, not to mention considerable time-savings for both.
Human machines
A new report by PwC says that around 44 per cent of jobs in the retail sector are at risk of automation by 2030. Some of the mid-level employee positions will disappear – particularly warehouse staff and employees in the back-office. AI technology is extremely good at repeated tasks and number crunching, so lots of manual processes will undoubtedly be done by machines in future. For instance, 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 shopfloor.
In the not too distant future, it will be common practice to pull out your phone and ask it a question as you enter a store, rather than seeking out a sales assistant or searching through the rails yourself. For instance, your smartphone can immediately respond that a desired article is available in your 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 clearly the way forward. Talking interactive screens and self-checkouts in fitting rooms is something we’re already engaged with.
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 – including smart fitting rooms with interactive displays showing more buying options that digitally connect with sales staff – 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 or 70 per cent. Although RFID tagging and real-time stock monitoring offers near hundred percent 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 AI making forecasting and product selections even more accurate, it’s sure to become a reality.
Photographer Felix Mackel
