Artificial intelligence (AI) has been making news all year, but the hype might steer retail leaders towards the wrong AI use cases for their business.
Before a retailer can integrate AI into its strategy, it must first determine which solutions will have a significant impact on the key performance indicators (KPIs) it wants to improve. For some, priority KPIs might be established through better conversion rates, stronger margins or reduced fraud.
To cut through the “AI noise,” retailers should consider solutions that have proven to have long-term impacts, as opposed to what’s being deemed “the next big thing in AI.” Three tried-and-true solutions include personalization, price optimization and fraud detection.
Personalization Fosters Relevancy and Loyalty
We’re all aware that customer acquisition is expensive, costing anywhere from five to 25% more than retaining existing customers. This is due to the quantity of resources needed to reach new audiences, including advertisements and lead generation. To accelerate business development, retailers should instead focus their efforts on fostering long-term relationships and growing the value of existing shoppers.
One way to bolster customer relationships is with strategic, AI-driven personalization. This technology ensures that customers’ needs are met, even as their desires change. With AI, retailers can evaluate and re-evaluate a customer’s behaviors in real time, offering promotions and recommendations that are relevant to them. These behavioral insights are derived from basket sizes, online clicks, purchases, in-store point-of-sale information and more.
For example, imagine a shopper begins purchasing dairy-free alternatives to the products they’d traditionally bought. An AI-driven personalization engine can automatically stop offering useless coupons for milk-based products and instead begin showcasing recommendations for lactose-free ice cream in the shopper’s preferred flavors or brands.
Personalization increases the value that a retailer provides loyal shoppers by improving the relevancy of offers and recommendations. And what’s more, personalization happens in real-time behind the scenes across any consumer touchpoint and does not disrupt the customer journey, meaning it doesn’t require a change in the shopper’s existing behavior. As a result, personalization is an easy-to-integrate, yet effective, AI use case.
Price Optimization Enhances Competitive Advantages
During times of inflation, consumers become increasingly price sensitive. In fact, according to Deloitte’s State of the Consumer Tracker for August 2023, 32% of consumers visit several stores in the hopes of finding the best deal. With price maintaining top-of-mind prioritization for shoppers, retailers would be wise to focus their AI investments on pricing.
AI-generated price optimization helps retailers automatically determine the price elasticity for every SKU in the assortment. This optimization considers sales forecast, channel, competitor’s prices, prices of complementary and substitute products and more.
Price optimization is beneficial for any retailer. For example, 55% of U.S. consumers reported being the most aware of rising grocery costs. AI can help grocery retailers find the best price for any item, including those with upcoming sell-by dates, thereby reducing waste. Similarly, for apparel retail, AI can set the best price to efficiently sell-through seasonal styles and minimize excessive markdowns.
The ability to optimize price points across all SKUs is incredibly valuable for retailers. AI protects margins while maintaining customer trust and loyalty.
Fraud Detection Builds Trust
Many retailers have experienced an increase in theft in recent months, including Target which reported losing half a billion dollars due to theft. With this growing risk, loss prevention is a clear priority for retailers and AI can provide the best path forward.
One area that retailers can focus their AI-based fraud detection efforts is the self-checkout kiosk. According to the Loss Prevention Research Council, 58% of shoppers believed committing theft was “easy” or “very easy” at this un-manned point-of-sale.
To combat this belief, self-checkout kiosks can be equipped with AI-driven technology that uses scoring algorithms to determine the probability of fraud for any shopping basket. For example, if the number of items in the shopper’s cart doesn’t match the time spent scanning the items or the basket total, the fraud detection tool will recommend a rescan or human intervention. This alert will stop the current instance of theft and deter the shopper from future fraudulent attempts against that retailer.
With retail theft on the rise, the benefits of AI-driven fraud detection are tremendous. The theft issue is vast and requires a multi-part, data-driven approach which only AI can provide.
Evaluate AI Based On Lasting Outcomes
While recent “flashy” AI stories may sound exciting, retailers should focus on addressing the problems facing their shoppers and their businesses today. There are thousands of AI use cases for the retail industry, but only some solutions will deliver improvements to KPIs immediately. Personalization, price optimization and fraud detection tools effectively target the needs of the customer and the retailer, without disrupting the shopping experience.
Michael Jaszczyk is CEO of GK Americas and chief digital transformation officer of GK Software SE