AI Adoption Led by Tech-Retail Hybrids Adoption of AI in retail is being led by e-commerce firms and particularly by platform giants such as Amazon, Alibaba Group and JD.com, whose AI capabilities are strengthened by their status as tech companies as well as retailers. Perhaps not coincidentally, these companies are establishing dominant or near-dominant shares in e-commerce. To compete against these giants, more and more store-based retailers are embracing some of the technologies that the platforms deploy. Coresight Research data, collated for the second of our  Artificial Intelligence in Retail reports, found that major retailers are deploying AI in substantial numbers across multiple functions: Our analysis of 30 large retailers found the technology was being deployed most in the supply chain, as well as for in-store and e-commerce functions. [caption id="attachment_88519" align="aligncenter" width="720"] Source: Company reports/Coresight Research[/caption]   The CORE Framework For those retailers that have deployed AI to a limited extent, and those that are yet to introduce AI to their operations, our proprietary CORE framework highlights the potential application of AI in four areas of retail. We show these in the four quadrants below and discuss them further in the subsequent sections of this report. [caption id="attachment_88520" align="aligncenter" width="720"] Source: Coresight Research[/caption]

Communication More and more, offering a personalized online experience is becoming a must: best-in-class retailers are already personalizing homepages, emails and apps, and this help shoppers to navigate the near-endless choice of products available online. We see the ongoing growth in browsing and shopping on mobile devices adding greater urgency to the demand for personalization, for two reasons:

• First, mobile sees low conversion rates, so retailers must work harder to convert a visitor to a customer.
• Second, small screens limit browsing of products, so shoppers see fewer products on each visit, compared to when they use a laptop or desktop computer.

Retailers can use AI to help surface content that is relevant to each consumer, thus making the best use “screen real estate” on mobile devices. Retailers can:

• Generate millions of personalized homepage and email variations and personalize in-app experiences.
• Test alternative options for website design based on the conversion rate generated by each variant.
• Adjust what products are displayed in real time, based on individual consumer behavior.
• Aggregate data on buying habits, lifestyles and preferences to form a single view of each customer. The experience can be personalized using data from inside and outside the business.

Beyond personalization, AI offers the possibility of better, more efficient communication between retailers and shoppers.

• AI voice assistants such as Amazon Echo and Google Home are moving AI-powered consumer engagement from text to voice. As shown in Figure 1, more than half of 30 major retailers we reviewed are already using voice to reach shoppers. However, voice also sees the retailer-customer relationship disrupted, as tech giants manage the interactions—shoppers speak to Alexa or Google instead of to the retailer.
• Chatbots are well established, with many retailers using them to answer customer queries, and some are going beyond offering basic responses to detect users’ moods based on the words they use and the tone of their messages—enabling personalization in tone as well as content. Our analysis of 30 major retailers found half are using chatbots (see Figure 1).
• Personal shopping apps allow shoppers to send virtual assistants on “missions” to search out a specific product or seek out the best price online.

In this world of multiple touchpoints, retailers must stand ready to serve and advise consumers, through whichever channels, devices and technologies those consumers choose. Optimize Pricing E-commerce platforms such as Amazon are changing prices so quickly that it is now almost impossible for traditional competitors to match them on price. These tech giants rapidly adjust their prices on millions of items, based on consumer demand and competitor pricing. AI offers rivals the chance to compete on the same terms and automatically set optimal prices based on market conditions and external data such as competitors’ sales and promotions, weather conditions and calendar events. According to Profitero data cited by Retail Dive in 2018, one-quarter of the products sold by Amazon itself (i.e., excluding third-party units) change in price multiple times a day. We show one example of Amazon’s dynamic pricing in the chart (Figure 3). It is clear that competing retailers cannot hope to match Amazon’s prices by making manual adjustments. To be truly price-competitive, retailers selling online need to adopt similar, data-driven approaches to pricing. [caption id="attachment_88521" align="aligncenter" width="720"] Source: CamelCamelCamel/Amazon[/caption]   AI vendors can provide retailers with price-optimization services that include:

• Automated price decisions for each product, by channel, drawing on market conditions and details such as sales, promotions, weather and events.
• Real-time monitoring of localized market conditions and in-store metrics, translated into recommended actions that help retailers offer products at optimal price points.
• Optimal entry price points for newly launched products that have no sales history.

Rationalize Inventory Inventory management remains a hige challenge for retailers, with many retailers holding too much inventory and allocating that inventory inefficiently. In our February 2019 inventory survey report, we noted the scale of the inventory problem, with markdowns costing US retailers an estimated $300 billion in lost revenues in 2018. AI can help retailers reduce their inventory by using historical transactions and variables such as weather conditions, calendar events and consumers’ website searches to predict future demand. Moreover, retailers can use real-time sales data to reallocate stock between stores for maximum full-price sellthrough. AI can help such retailers reduce their inventory and maximize full-price sales by:

• Offering retailers prescriptive plan, buy, allocation and fulfillment recommendations.
• Automating store replenishment to reduce out-of-stock rates.
• Recommending actions to adjust the buy or reallocate by pairing under-allocated stores to over-allocated stores, based on real-time sales data.
• Providing an overview of consumer demand and market supply using data across social influencer buzz, online product searches, consumer shopping patterns and SKU data.
• Powering robots that undertake in-store shelf audits based on visual recognition.
• Formulating planograms to ensure that items are displayed optimally, including with other items that can be cross-promoted.

Our February 2019 survey of US retail decision makers found that a large majority see advanced analytics as beneficial for planning and running operations. Confirming the potential for technologies such as AI, fully 86% of respondents were able to identify specific ways in which advanced analytics could help their retail sector sell more products at full price. Those surveyed view the technology as particularly helpful in informing decisions regarding how much stock to buy, which promotion schedules are optimal and what kinds of products to stock. [caption id="attachment_88522" align="aligncenter" width="720"] Survey question summary: In which three, if any, of the following ways do you think advanced analytics tools would most benefit your retail sector with selling through products at full price?
Source: Celect/Coresight Research [/caption] Experiential Retail As more and more shopping switches to e-commerce, and to that channel’s functional retailers such as Amazon, Tmall and JD.com, store-based rivals across both discretionary and nondiscretionary retail must offer better in-store experiences; they must drive out friction from the shopping process, deepen engagement with their customers and close the information gap that exists between e-commerce and physical stores. In doing so, retailers stand better placed to delight their shoppers, drive more traffic to their stores and, crucially, sell more effectively. AI offers opportunities to enhance the store experience in a number of ways. Retailers can:

• Engage shoppers with online assistance and recommendations, as well as in-store robot advisors.
• Use facial recognition to flag “VIP” shoppers to store associates, provide personalized, tailored in-store experiences, and authenticate payments.
• Help shoppers discover products, through visual search, shoppable video, magic mirrors and personalized advice on everything from skincare to fashion.
• Make it simpler to buy, with automated, checkout-free stores. This is one of the more nascent uses of AI: As we showed in Figure 1, only 13% of 30 major retailers are using AI to operate cashier-free stores or other online-to-offline (O2O) store formats.

Technology vendors can help legacy retailers retrofit their stores to offer greater engagement and automation. In doing so, they will be competing head-on with companies such as Amazon, Alibaba Group and JD.com, which are, variously, offering checkout-free stores and payment by  facial recognition. Key Insights AI has become the technology driving change in retail, and many longstanding retailers have adopted the technology to a limited extent, while others are already using it widely across business functions. Retailers are likely to continue to see the need to extend AI’s deployment across various operations, and they can look to our CORE framework to understand the key opportunities for AI deployment.