Retail, in its most traditional sense, has had a pretty catastrophic 2018. The almost simultaneous announcement that high street stalwarts Maplin and Toys R Us had collapsed in February, is indicative of modern times. Homebase has since followed suit, while House of Fraser has been snapped up by the imperious Mike Ashley for just a pound, a year on from his bargain-basement acquisition of GAME.
Closer to our industry, Dixons Carphone is hanging in there – although it will close 92 stores in the coming months. And the problem is not a new one. The death of the traditional retailer stretches back a decade to when high street favourite Woolworths came tumbling down during its centenary year.
The ‘Amazon effect’ is often blamed for the death of bricks and mortar retailers, but the truth is that technology can be a driver for success across the board.
As CEO of Daisy Intelligence, Gary Saarenvirta explains, artificial intelligence actually offers smaller players a level playing field when it comes to competing against the online juggernaut. “This landscape is ideal for AI because the disruptive capabilities driven by AI can allow mid-market companies to develop the same capabilities as the dominant players; the impact of AI in retail is therefore magnified,” Saarenvirta says. “Ten years ago, the cost of AI systems was prohibitive and only affordable for very large companies. Today, almost every business can afford to invest in AI.
“Early adopters of AI will succeed at the expense of those companies that are late to the game.”
Be it floating distribution centres, humanless checkouts or online chatbots, the use of AI in retail is already there to see. AI has already been adopted by two-fifths of retailers to leverage customer insights, while machine learning (ML) has seen even greater uptake.
Almost half of retail executives use ML to boost sales figures: predicting trends, tailoring special offers and monitoring customer behaviour. And, while the technology may be new, its success boils down to a simple rule of retail which stretches back centuries: the customer is king. As Thibaut Ceyrolle, VP of EMEA at Snowflake Computing explains: “AI, paired with big data, brings back an asset long lost to commerce – knowing your customer. As organisations shift from product to customer-focused strategy, data aggregation is exploding.”
He added: “Those at the forefront of innovation are reaping the rewards: some 54% of shoppers have bought something based on automated recommendations or cart reminders, while over 70% have been nudged into purchases through coupons and discounts.
“Retailers who fail to deliver a personalised experience may lose customers who have now come to expect online retailing to cater for their individual needs.”
And getting to know your customer allows retailers to tailor how and what they sell in order to improve the customer experience and boost profits at the same time.
AI, at its core, offers the possibility of a circular society which continues to improve a service. As Ceyrolle explains, getting to know your customers allows you to record and predict shopping patterns. “Most AI/ML solutions we see today are in stock management and supply chain, but they extend to customer relations, the use of chat-bots, and tailored services,” he says.
“Through bespoke marketing, brands can see low-loyalty customers become significantly more engaged and likely to make a purchase. Forecasting accuracy, operational efficiency, excess and depleted inventory, are all better managed by implementing these tools.” He adds: “The ever-growing appetite for personalised retail experiences is not restricted to online, but also within physical stores.
“Data can help understand the journeys customers take in-store and how they react to the shop layout. By tapping into this data, ML can help provide important insights, such as where best to position certain clothing lines or optimise the interface of self-service checkouts, thus reinventing the dynamics of a store layout to boost sales.
“Retailers will also be able to extend ML in other retail processes, such as predicting supply and demand for products and optimising prices.”
Likewise Graeme Provan, global director of Business Automation at Genesys believes that tracking customer trends is the biggest advantage of successfully deployed AI within the retail space. He explains how AI has a broad scope to improve the shopper’s experience through reducing friction in the sale, while leaving the customer in control of their journey. For example, AI can suggest certain items that the retailer knows is in stock –including the right size – for a specific consumer.
AI helps retailers to tailor the experience so it can optimise the consumer’s experience, knowing the attention span of the consumer is shortening.
“As customers interact with retail in both the store and online they are leaving digital footprints,” Provan explains. “These digital footprints can be used to identify overall patterns in customer behaviour and provide personalisation. I have seen businesses that use this information strengthen relationships with their customers in high and low-end retail.
“They’ve done it by tailoring their customers’ digital experiences to make their regular purchases easy, suggesting only items that have been purchased by people like them and through personalising all communications.
“This is driven through AI and the level of personalisation is resulting in increased business because the retailer knows what its customers expect it to know about them.”
He adds: “Automation of routine tasks through AI is being used to reduce costs, optimise the value chain and to identify unusual behaviours in internal processes and in stores.”
The benefits of AI in retail are already being seen. From reducing human error to automating routine tasks, AI harness the power to revolutionise the retail space – both online and in store. The key thing now is getting retailers to embrace it rather than reject it. Those that do will reap the rewards through a happier, more engaged customer base. And it doesn’t take artificial intelligence to work out that a happy customer equates to a happy retailer.