AI is revolutionizing the way retailers manage their supplies and prices.
By harnessing the power of machine learning, they can generate more accurate sales forecasts, have optimized inventories in real time, and set competitive prices that continuously adapt to the market.
In this article, we look at how these technologies are transforming retailing.
1. AI for optimized sourcing
More accurate sales forecasts thanks to machine learning
By exploiting machine learning algorithms, retailers can generate significantly more reliable sales forecasts. These models take into account a multitude of factors that influence demand, such as seasonal trends, weather data or promotional effects.
Let’s take the example of a DIY chain that wants to anticipate sales of summer barbecues. A machine learning model trained on sales history, previous years’ heat peaks and promotional planning will provide a much more accurate estimate of order quantities than a simple projection of last year’s figures.
Inventory optimized in real time to avoid shortages and extra costs
With sales forecasts refined thanks to AI,retailers are able to adjust their stock levels on an ongoing basis. No more out-of-stocks that generate customer dissatisfaction, and no more overstocks that weigh on cash flow and logistics costs.
Imagine a pharmacy selling antigen tests. In these times of pandemics, demand fluctuates rapidly and strongly. With an AI forecasting system linked to its inventory management systems, the pharmacy will be alerted in advance to upcoming peaks in demand, and will be able to replenish the right quantity of tests at the right time. Customers will be served and costs kept under control.
Localized sourcing to meet the specific needs of each outlet
Each store has its own catchment area and customer base. AI-based inventory optimization solutions integrate location into their calculations. They analyze historical sales on a store-by-store basis to define optimal assortments and quantities for each outlet.
A Carrefour hypermarket in the Yvelines will thus have a supply of cosmetics adapted to the profile and purchasing power of its local customers. Its shelf space will be optimized.
2. Setting the right prices with artificial intelligence
Adjust your prices to the market and the competition
Machine learning algorithms can continuously analyze a huge mass of market data and competitor prices. Thanks to this, distributors can adjust their prices in near-real time to remain competitive at all times.
The center collects prices from competing brands on the web and integrates them into its AI pricing system. It then recommends the appropriate tariff adjustments for each product category. This ensures that Leclerc always offers good value for money.
Measuring price sensitivity
Optimize promotions and special pricing operations
Not all customers react in the same way to price variations, and price elasticity can vary considerably between product categories. However, it is important to qualify this notion. In theory, to define price elasticity accurately, it is necessary to have tested a wide range of prices and to observe the real impact of these variations on demand.
In addition, external factors, such as general inflation, can influence these results. For example, if prices rise in tandem with inflation, which drives up the prices of many products and wages, the effect of a specific price change can be diluted, making it more difficult to interpret elasticity. Thanks to machine learning, it is possible to segment pricing strategies more finely by taking these complex parameters into account, but it remains crucial to take the overall economic context into account when adjusting these strategies.
Weldom thus modulates its lighting promotions according to the preferences of each customer segment identified by clustering model: a 10% discount is enough to boost sales among its experienced DIYers segment, while a 25% discount is needed to achieve the same effect among occasional buyers.
By analyzing huge volumes of data from loyalty programs and promotional histories, AI algorithms identify the best promotional mechanics to apply by customer and product type.
3. Automate processes for greater efficiency and agility
Orchestrate pricing across all channels
In a multi-channel environment, consistent, optimized pricing is a real headache. Unless you use a solution that centralizes data, calculates optimal prices and passes them on to all sales channels.
This is what Boulanger has put in place. Using optimix XPA, the system analyzes online and in-store prices, estimates elasticities, calculates recommended prices, then disseminates these prices on shelf labels, on the mobile app and on the e-commerce site.
|
Functionality |
Earnings |
| AI sales forecasts | Anticipate demand at product, store and day level |
| Stock recommendations | Adjust orders to actual needs to avoid shortages and overstocking |
| Measuring price elasticities | Fine-tune pricing by product-customer pairing |
| Process automation | Save time and increase reliability |
| Unified multi-channel pricing | Orchestrate strategy across all customer touchpoints |
Measure the impact of your pricing and sourcing decisions
Track key performance indicators with advanced dashboards
By centralizing and cross-referencing data in a solution such as optimiX XPA, retailers gain a complete overview of their business. Dashboards enable them to track sales, margins, stock-outs, promotional effectiveness, etc. And this with the reading grid of your choice: by category, by store, by geographical area…
Take Gifi, for example. The home decoration chain has set up dashboards that provide daily pricing indicators for all its stores. This enables the central pricing team to quickly detect margin discrepancies or price competitiveness problems and react immediately.
Analyze what-if scenarios to make the best decisions
What if I raised smartphone prices by 5%? What impact will this have on my sales and margins? Intelligent pricing tools incorporate advanced simulation models to test different hypotheses before making decisions.
This is an asset for a retailer like Carrefour. Before launching a large-scale promotion in a sensitive category such as fruit and vegetables, its category managers evaluate the operation from different angles: ROI generated by different promotional mechanisms, impact on related categories, volume vs. margin effects… All of which will inform the final decision.
By harnessing the power of AI, retailers gain a competitive edge to meet the twin challenges of operational excellence and customer experience. Whether to optimize supplies, boost sales via intelligent pricing or streamline their key processes, AI is surely their best ally. Many retailers have understood this and are already reaping the benefits of this technological revolution. The others will have to accelerate or risk stalling.
