Artificial intelligence (AI) and machine learning technology are proving to be indispensable allies for growth and efficiency in supermarket operations. In the next three years, the implementation of artificial intelligence in retail will increase from 40% to more than 80%, according to an analysis by IBM Corporation. Investment in this technology by retailers globally in 2022 exceeded $7 billion, reported Juniper.
While AI helps shoppers get what they are looking for in a fully automated way, machine learning considers infinite historical data references and finds relevant patterns and trends to make accurate predictions.
Regarding supermarkets’ omnichannel optimization, the technology specialist blog ThinkML highlighted how artificial intelligence and machine learning have great potential in one of its articles.
“Artificial intelligence solutions are the future for delivering an exceptional customer experience. Brands that focus on implementing AI technology will stand out as more personalized and efficient in the long list of service providers,” ThinkML notes.
Using technologies in supermarkets strengthens relationships with consumers and helps overcome significant business issues and challenges.
Likewise, the unified business planning platform Prisma explains in four points how the value of data captured and analyzed by artificial intelligence benefits all grocery store units.
- Pricing: Setting an effective pricing strategy for each product or category means finding the point on the elasticity curve that provides the highest profit, balancing the margin on each item with the number of sales, and considering competitor prices and market changes.
Data science and algorithms make it possible to capture customer data and make forecasts and predictions of product demand to define the price and quantity of products effectively.
- Promotion: Retailers receive feedback from their customers through their online and offline activities. Predictive analytics, which uses data collected at all touchpoints, relates it to actual purchases and helps retailers anticipate customer needs.
Thanks to predictive analytics, a retailer can know its customers’ complete profile, purchasing power, and behavior to carry out promotional campaigns. This makes it possible to create customized promotions and loyalty programs that generate increased sales.
- New products: When a product is new to the market, it is even more challenging to determine its price, promotion, and positioning. New technological tools make it possible to estimate a new product’s success. The qualitative data is combined with quantitative market data to identify new products and estimate the size of the opportunity.
Managers can use the supermarket’s data to complete the picture by analyzing customer loyalty to competitors’ products, sensitivity to category promotions, and the results of previous new product launches.
- Inventory management: Predictive analytics helps supermarkets eliminate uncertainty in inventory management by effectively predicting item demand and suggesting better stock management strategies.
In addition, store owners can identify where to offer new products to increase revenue and mitigate inventory shortages. The use of artificial intelligence results in reduced inventory costs, less frequent stock-outs, and increased sales.