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. Let’s find out how these technologies are transforming distribution.
1. AI for optimized sourcing
More accurate sales forecasts thanks to machine learning
By exploiting advanced 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.
The Auchan Group uses Optimix’s AI solution to optimize its supplies in its hypermarkets and supermarkets, and has been able to reduce its overstocks by almost 60%.
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.
This is what Siplec Leclerc does with its non-food products, for example. 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.
Measure price sensitivity by product and customer type
Not all customers react in the same way to price variations. Similarly, price elasticity differs greatly between product categories. Thanks to machine learning, it is now possible to segment pricing strategies very finely according to these parameters.
Weldom thus modulates its lighting promotions according to the preferences of each customer segment identified by AI: -10% discount is enough to boost sales to its experienced DIYers segment, while -25% is needed to achieve the same effect with occasional buyers.
Optimize promotions and special pricing operations
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.
A skincare specialist like Yves Rocher knows, thanks to AI, that a “buy 2, get 1 free” offer on shampoos will generate more margin than a -30% promotion on its loyal clientele. On the other hand, occasional customers are more sensitive to immediate reductions in absolute value.
According to a CompTIA IT Industry study, 80% of retailers plan to invest in AI-driven price optimization solutions by 2027 to cope with competitive pressure.
3. Automate processes for greater efficiency and agility
Save time and increase reliability with automated pricing and forecasting processes
No more time-consuming and risky spreadsheets and manual processes. By entrusting their sales forecasting and price optimization tasks to a specialized AI platform, category managers can make their work much faster and more reliable.
A concrete example is Monoprix, which has automated the generation of its grocery sales forecasts and price lists. Result: 4 days a month saved for category managers, who can concentrate on higher-value tasks. And almost 2 points more margin thanks to much more in-depth analysis.
Orchestrate pricing across all channels
In a multi-channel environment, consistent, optimized pricing is a real headache. Unless you use an AI solution that centralizes data, calculates optimal prices and passes them on to all sales channels.
This is what Boulanger has put in place. Its AI system analyzes web and store prices, estimates elasticities, calculates recommended prices, and then distributes these prices on shelf labels, on the mobile app and on the e-commerce site. Everything is optimally synchronized in real time.
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Functionality |
Earnings |
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IA sales forecasts |
Anticipate demand at product, store and day level |
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Stock recommendations |
Adjust orders to actual needs to avoid shortages and overstocking |
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Measuring price elasticities |
Fine-tune pricing by product-customer pairing |
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Dynamic Pricing |
Be competitive at all times |
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process automation |
Save time and increase reliability |
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Unified multi-channel pricing |
Orchestrate your strategy across all customer contact points |
Measure the impact of your pricing and sourcing decisions
Track key performance indicators with advanced dashboards
By centralizing and cross-referencing its data in an AI solution, the retailer has a complete view of its business. Intelligent dashboards enable you to track sales, margins, stock-outs, promotional efficiency, etc. And this with the reading grid of your choice: by category, by store, by geographical area…
Take Gifi, for example. The home furnishings retailer has set up AI 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? With AI, it’s now possible to answer these kinds of questions in just a few clicks, by analyzing different pricing scenarios. Intelligent pricing tools incorporate advanced simulation models that enable different hypotheses to be tested before decisions are made.
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 use the IA tool to 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.
The Nielsen Price Barometer 2022 reveals that 47% of retailers consider their pricing impact analysis capabilities insufficient. AI solutions provide a concrete response to this lack of visibility.
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.
