The Retailer Spring_09.05_FA


How AI is rewriting the future of retail customer experience

Andrew Fowkes Head of Retail Centre of Excellence SAS UK & Ireland

ARTIFICIAL INTELLIGENCE IS REVOLUTIONISING RETAIL AS BRANDS LEVERAGE IT TO UNLOCK THE VALUE OF THEIR DATA, ARGUES SAS’ ANDREW FOWKES Today’s shoppers have more choice than ever. They’re more empowered and have much higher expectations of the brands they do business with. As such we now live in an experience economy, where the biggest differentiator for retailers is not having the best product or the lowest price, but offering the best customer experience (CX). However, to deliver the personalised interactions today’s consumers expect, brands need to process and analyse the vast amounts of data now available to them. They must also action the insights in a timely and cost-effective way to improve CX whilst managing costs and resources. This is why artificial intelligence (AI) has a bright future in retail. With the rise of AI, there are further opportunities for CX to evolve and grow, and provide an even more helpful, targeted service to customers that helps organisations differentiate themselves from the competition. Back to school Customer journey mapping and optimisation are common challenges in today’s omnichannel environment. From the moment a shopper lands on their homepage or enters their store, retailers must be ready to guide them along the shopper journey with the right ‘next best action’ content, message or offer at every step. However, traditional marketing automation and journey orches- tration solutions are hitting a wall. There are many permutations of paths in any single customer journey and myriad different actions, content, messages or offers from which to select. A shopper might see a product instore and research it online before returning to make their purchase, or they might browse a product on their phone before checking where the nearest store is. The scale and complexity of this challenge is a perfect opportunity for the application of AI in the shape of ‘reinforcement learning’. Reinforcement learning is a type of machine learning. At its core is the concept that the optimal behaviour or action is reinforced by a positive reward which allows the algorithm to learn on its own very quickly. Reinforcement learning takes the manual guess work out of marketing and optimises the customer journey by continually looking for the next best action to deliver the best outcomes. It continuously tries different actions to work out which will be the most successful – but every now and then it performs a random action just to be sure that the model is still fresh and up to date. This is an exciting step forward for retailers seeking the holy grail of customer journeys and ensuring next best action across every touchpoint.

The AI sales assistant There are typically two common approaches to recommendations. The customer-centric approach looks for similarities in the behaviour or characteristics of customers and recommends products that other similar users have bought. A product-centric approach looks for products that are associated with each other. This is helpful when you don’t know anything about the customer and their characteristics other than that they’ve shown some interest in a particular product. In this case the product that most closely matches the customer’s interests is recommended. Whichever approach is taken there are various challenges with every recommendation engine. During the early stages the engine may lack sufficient data to make relevant recommendations, while later in its lifecycle its recommendations database grows so large that it impacts performance. Fortunately, AI can give recommendations engines a real boost. Machine learning algorithms that combine both customer-based and product-based recommendation algorithms can overcome scalability, cold-start and sparsity challenges to improve results for the retailer as well as delivering a much-improved CX. Better insights equal happier customers These days, customers interact with brands in many ways: social media and digital channels, phone, email, webchats, letters, surveys and reviews, and in branches or stores. Each of these interactions provides an opportunity to garner rich insights into customer intent and sentiment, what customers want, and how they feel about the products, services, and experiences being delivered. Natural Language Processing combines machine learning, AI and linguistics and can automate and quantify customer feedback and insights across every customer interaction, regardless of channel. Capturing insights at scale allows you to accurately quantify the size and shape of opportunities to improve the CX and prioritise which investments will deliver the most impact, both to customers and to the business. What’s more, having this level of insight down to an individual customer gives businesses the power to make more effective and profitable decisions about customer strategy and fuel 1:1 brand experiences. Shop Direct is one retailer that already uses AI to enhance its personalisation capabilities. The pureplay retailer uses natural language processing for its customer service chatbot, the Very Assistant. This conversational user interface analyses data generated by customer interactions, learning their individual preferences so it can recommend products that are most likely to make an impact. Shop Direct also uses a range of powerful SAS analytics to help its website learn from browser behaviour, adjusting what products it displays in real time.

26 | spring 2019 | the retailer

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