Over the next three years, the implementation of artificial intelligence (AI) in retail will grow from the current 40% to more than 80%, according to an analysis by IBM Corporation, and the investment in this technology by retailers globally in 2022 will be $ More than 7 billion, Juniper reported.
Machine learning techniques as well as artificial intelligence are proving to be indispensable allies for growth and efficiency in supermarket operations.
While AI helps shoppers find exactly what they are looking for in a fully automated manner, machine learning takes into account the infinite context of historical data and finds relevant patterns and trends to make accurate predictions.
For the ubiquitous optimization of supermarkets, a blog specializing in technology ThinkML highlighted in one of its articles the great potential of artificial intelligence and machine learning.
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“Artificial intelligence solutions are the future for delivering an exceptional customer experience. ThinkML says that brands that focus on implementing AI technology will be more personalized and efficient in a long list of service providers.
The use of technologies in supermarkets strengthens relationships with consumers and helps overcome the business’s biggest problems and challenges.
Similarly, the unified business planning platform Prisma outlines four points on how the value of data captured and analyzed by artificial intelligence benefits all Kirana store units.
- price fixing: Establishing an effective pricing strategy for each product or category means finding the point on the elasticity curve that provides the highest profit, balancing each item’s margin with the number of sales, but also taking into account competition’s prices and changes. takes into account. Market.
The use of data science and algorithms makes it possible to capture customer data and forecast and anticipate product demand in order to effectively define the price and quantity of products.
- Promotion: Retailers acquire information from their customers through their online and offline activities. Using data collected across all touch points, predictive analytics correlates it to actual purchases and helps shopkeepers anticipate customer needs.
Thus, it is possible to create personalized promotions and loyalty programs that generate increased sales. Thanks to predictive analytics, a retailer can know the complete profile of its customers, their purchasing power and their behavior to run promotional campaigns.
- New Products: When a product is new to the market, it becomes even more difficult to price, promote and position it.
New technical tools allow to predict the success of a new product. Qualitative data collected by artificial intelligence is combined with quantitative market data to identify new products and estimate the size of the opportunity.
The supermarket’s own data can be used to complete the picture, looking at customer loyalty to competing products, sensitivity to category promotions and the results of previous new product launches.
- stock management: Predictive analytics helps supermarkets to effectively forecast item demand and remove uncertainty from inventory management by suggesting better stock management strategies.
Additionally, store owners can identify new products to offer to increase revenue and reduce inventory shortages. The use of artificial intelligence lowers inventory costs, reduces the frequency of stock-outs, and increases sales.