IBM Watson, AlphaGo, Amazon Alexa – Artificial Intelligence (AI) is all around us, and holds the promise of connecting us and simplifying our lives like never before. But what about applications of AI in retail? Are they just as impressive and effective? Geoff Hueter, chief technology officer at Certona, explores how AI has the potential to transform retail as we know it, helping brands to overcome the obstacles presented by an ever-demanding, always-on, unforgiving and brand promiscuous shopper.
In 2014, a film called Ex Machina brought the concept of AI to the masses. The premise of the film, in a nutshell, sees a budding programmer win a competition with an interesting prize: to test the human qualities of a robot who is just a little bit too self-aware and cunning. Nowadays, when consumers hear the words ‘Artificial Intelligence,’ the most accessible concept is an insidious robot hell bent on causing destruction or deceiving those around it.
But AI isn’t just for sci-fi films. In fact, it’s been powering retail for years, and retailers have only been scratching the surface of what’s possible with the technology. With so much hype around AI – and the wariness of some in the industry – it can be challenging to adopt the technology to best effect. What’s more, whilst new pure-play e-commerce brands will have built their businesses using new technology, many retailers still rely on legacy systems and manage segregated channels. A combination of the right solutions and an intelligent approach to adoption and data management will help all retailers reap the benefits of AI.
Old approaches causing new problems
Traditional retailers are currently battling to create the required connected, tailored, omnichannel shopping experience. Overhauling an IT system is a substantial and costly investment, and in many cases, is pieced together through multiple vendors. Keeping pace with system updates and enhancements is challenging and can be problematic, resulting in poor integration and communication between systems.
This problem is widespread: according to one study by eConsultancy, 43% of the respondents and 39% of supply-side recipients claimed that integration with legacy IT systems remains the biggest challenge in e-commerce, and is one of the top three technology barriers to business growth.
When it comes to data, siloed systems or a patchwork of different IT systems can make it difficult to share information across sales channels accurately, quickly and efficiently. It is not only old technology holding retailers back; many workforces also lack the new skills and expertise required to drive and manage new business models.
An intelligent approach
Enter artificial intelligence.
AI and machine learning can deliver results which would either be too cost-, resource-, or time-intensive (or, just plain impossible) for humans to achieve. This includes gathering and analysing huge volumes of data in real time, determining patterns and predicting future outcomes. This automation removes the requirement for many manual processes, is unencumbered by the possibility of human error, and allows retailers to make accurate forecasts and optimise operations.
Traditionally, the in-store sales person would use information gathered about a shopper to recommend products, upsell and cross-sell. Now, however, retailers are sitting on a vast goldmine of information, gathered from numerous channels and interactions. AI solutions can be used to replicate the traits of the in-store sales person and deliver above and beyond traditional capabilities. Softbank seemed to take this concept literally, with one of the most widely-known examples of AI: Pepper. This humanoid robot, apparently capable of perceiving human emotions, has been trialled in a number of bricks-and-mortar stores in the states, with one store reporting a 70% increase in footfall.
However, the real benefits of AI lie not in gimmicky hardware but in the capability of AI software solutions to gather and process historic and real-time data from numerous touchpoints. This can then be used to deliver personalised insights and experiences seamlessly, at scale, across channels.
For example, a shopper’s movements and behaviours can be tracked across all channels – including in-store –and this information can then be used to create an accurate profile of that individual. AI-powered solutions will then map their future actions in order to suggest the right products via the right channel, at the right time, and in the manner which most appeals to that individual and is most likely to lead to a sale (without the need for a humanoid robot!).
Scratching the surface
Despite the benefits, many retailers are under-utilising AI capabilities. Whilst the technology and approaches are being developed and the opportunities are there, the retail industry is only just scratching the surface of what’s possible.
A recent Forrester report found that retailers are unlikely to invest heavily in AI, with almost half (42%) of retail businesses and technology professionals having no plans or not ready to invest, saying there is no defined business case. This may be attributed to the recent hype around AI, with many retailers unable to distinguish fact from fiction and understand the benefits this technology can deliver to their business.
Adopting AI technology – like any IT system upgrade or overhaul – is not without its challenges. As such, retailers must ensure they have strong support and comprehensive knowledge of any new capabilities and how this will integrate with and enhance existing systems.
Tools which utilise artificial intelligence and machine learning are available to retailers now; these include chatbots, apps for in-store staff with real-time product information and omnichannel personalisation solutions. Images of sci-fi-esque, futuristic tech accompany many consumer and business imaginings of AI, yet the reality is more low-key, and – most importantly – easily accessible for retailers today.
Personalisation and optimisation
It is that latter example of AI technology – omnichannel personalisation – which can really benefit retailers. This type of in-depth personalisation goes beyond the simple recommendation strategies many retailers are currently using. It instead combines real-time context with behavioural profiling, dynamic merchandising, as well as – where possible, and where legal – data from third parties such as a shopper’s Facebook engagement and information from other online marketplaces. The real-time-context could include things like the shopper’s location and how they navigate and browse a store, as well as the time of day, the weather, which device they are using to shop, and so on.
The AI solution will then use this information – thanks to deep learning algorithms – to make predictions about future shopping behaviour and intent to influence decisions. This will allow a retailer to anticipate the next best step in a shopper’s journey, and deliver the most relevant individualised experience. The shopper is then presented with products that are to their taste, in a way which appeals to them. The retailer too stands to gain, optimising business, driving sales and customer lifetime value.
AI in action
So, how does this all work in practice? Imagine a shopper is browsing on a fashion website for a new workwear shirt. At the back-end, the retailer is using an AI solution which not only tracks real-time click behaviours but also has the ability to use historic and profile data including their wish list information, delivery preferences, comments and reviews they’ve written about products, which items they’ve abandoned in the past, and so on. If the shopper then buys a blue striped shirt, the retailer will automatically not suggest that particular item or similar blue striped shirts. Instead, the retailer may suggest a matching tie or cufflinks. This data is also leveraged in real time and injected into automated targeted marketing emails to ensure highest performing content and products are presented on point of email open. This allows the retail marketer to avoid recommending previously purchased items, rather focusing on other relevant work apparel products which would complement the purchase and persuade the shopper to make additional purchases.
This kind of innovation may go unnoticed by the shopper, who will simply enjoy a more seamless experience. However, integrating AI into the bricks-and-mortar store could mean the physical shopping experience of the future will be rather different. Examples of this already in action include tools such as Snap.Find.Shop, which allows customers in US store Neiman Marcus to take pictures of items and have the app display similar products from the store’s catalogue. Beauty retailer Sephora has reinvented their in-store and mobile app experience by rolling out an artificial intelligence-based augmented reality application that helps shoppers find specific shades of lipsticks, the right skincare products or virtually select the right eyelash extensions. Through this AI enabled technology, Sephora can automatically recognise the most compatible items and recommend in stock items.
The AI of tomorrow might be humanoid robots and intelligent supercomputers, but the AI of today – and especially the AI in retail today – is underpinned by advanced data management and analytics, and intelligent systems integration. Implementing AI and machine learning solutions allows a retailer to deliver true personalisation, increase customer loyalty and drive sales. In today’s crowded retail marketplace, AI application is a real competitive advantage.
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