Four Ways AI Is Leveling The Retail Playing Field Versus Amazon

 I’ve been to dozens of retail-related conferences in the past year, most of which were attended by retailers, brands and early-growth companies seeking to gain traction in the retail ecosystem. Over the year, I’ve seen a continuous increase in the number of AI-driven technologies and companies showcasing solutions for retailers. I’ve also observed growing interest in these innovations among retailers.
Why? Because as multichannel retailers continue to fight age-old problems such as inaccurate inventory, stockouts and overstocks, overstretched and undertrained store associates, and suboptimal pricing, advances in AI targeting these problems are proving their ROI.
AI, or machine intelligence, is human-like or intelligent behavior exhibited by computers and machines that are “trained” by data to make autonomous decisions. What does AI help us do that we couldn’t do previously? It helps us process enormous amounts of data, typically from multiple sources, to identify opportunities and solutions.

This post is the first of three that explore AI in retail. Here, I’ll cover the primary use cases for AI in customer-facing functions and share some examples of companies that have developed AI applications for retailers.

Customer-Facing AI

At Coresight Research, we’ve introduced the CORE framework for retail AI use cases, which refers to communication, optimization of pricing, rationalization of inventory and experiential retail. The framework illustrates how retailers can use AI to better engage customers through communication and experiences, better manage inventory, and price products optimally.

The CORE Framework for AI in Retail

AI in Retail - Customer Facing Apps - Coresight Research
SOURCE: CORESIGHT RESEARCH

Communication

Retailers are using AI to communicate with shoppers through personalized online experiences, conversational robots and chatbots, and voice shopping. Its usefulness in terms of personalization is my focus here.

Great e-commerce websites meet customers on customers’ own terms: they show shoppers what they want to see and provide the information they need in order to make decisions and purchases. Mobile devices, with their small screen sizes, make personalization even more critical. AI can help retailers make the best use of screen real estate on mobile devices, presenting highly relevant content for each consumer. It can generate millions of personalized home pages and email variations and personalize in-app experiences.

One company that personalizes the e-commerce shopping experience by bringing the power of AI to behavior economics principles is Israel-based Personali. The company’s platform generates the optimal pricing offers and incentives for each shopper, based on the individual’s behavior patterns within the shopping session and in the past. Personali is paid only based on profit improvements, so the ROI is proven.

How Personali’s Incentive Personalization Platform Works

Personali Retail Pricing AI and Behavioral Economics
SOURCE: PERSONALI

Optimization of PricingCompanies such as Amazon have vast amounts of pricing data and sophisticated pricing applications that enable them to rapidly respond to changes in competitors’ pricing and consumer demand, but traditional retailers haven’t historically had the same tools. Today, AI startups and services are leveling the playing field for traditional retailers by offering applications that adjust prices automatically based on nonstore data such as weather, local events and competitors’ promotions.

Wise Athena, an emerging innovator based in Texas, predicts SKU-level demand for CPG companies by using machine learning and econometrics, dramatically improving the companies’ ROI on promotions. For context, according to consultancy Strategy&, US CPG spending totals more than $200 billion annually. Wise Athena’s application crunches data from competitors’ products across retailers, regions and categories. It also analyzes how a company’s products interact with each other. The application and algorithms predict likely outcomes from pricing strategies with regard to cannibalization and product cross-elasticity, so CPG companies can determine the optimal promotion strategies to deliver to retailers.

Example of the Insights Wise Athena Provides

Wise Athena AI Trade Promotion Optimization
SOURCE: WISE ATHENA

Rationalization of InventoryAI-powered retail applications not only identify gaps, forecast inventory and place orders, but also help reduce excess stock buildup, making retail more efficient. Excess stock often ends up being marked down, but AI applications can help identify products that are prone to be stocked in excess based on their historical tendencies, and prevent them from building up.

Startup Celect offers an Inventory Optimization Suite that uses predictive analytics and machine learning to optimize retail inventories by providing models of future buying patterns and behavior. The company’s Plan Optimization application helps retailers understand how various products affect overall assortments. By identifying underperforming products, the application highlights which store space could be made available for better-performing products. Retailers can also see which categories are being overallocated and those that have growth potential, so that they can rearrange assortments accordingly.

A View of Celect’s Assortment Optimization Dashboard, Which Recommends Increasing Receipts at a Selected Store

Celect AI Inventory Optimization
SOURCE: CELECT

Experiential RetailAI has offline retail applications, too, and retailers can use it to inform offline operations with online insights. India-based Talespin and US-based Pega are two companies that have developed AI-powered mobile-/tablet-ready applications to help store associates provide assistance and recommendations to customers. Other companies, such as Kairos, use facial recognition and AI to identify customers and inform store associates about their preferences, as well as to measure foot traffic and demographic trends throughout the day, and even detect shoppers’ moods and attention spans. Staff can take this information into account to deliver more personalized service, including presenting shoppers with offers that are triggered by their past purchase history.

AI technology can help companies use all of these data to deliver better experiences to their customers, and developers continue to find AI applications across business functions. We think that the sooner retailers adopt the technology, the greater the edge they’ll have versus their peers.

Retailers need to identify their specific strengths and weaknesses and implement AI accordingly to engage with customers effectively. The CORE framework can help retailers pin down which of their retail functions need immediate attention.

In my next post on this topic, I’ll examine AI applications at various points in the retail value chain and discuss how retailers are currently using the technology.


To learn more about AI in retail, see our Coresight Research report Artificial Intelligence in Retail, Part 1: Applications Across Customer-Facing Functions.


Deborah Weinswig
By Deborah Weinswig

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