What problem does it solve?
A grocery or general merchandise retailer reprices thousands of items across hundreds of stores every week: regular price changes, promotions, and markdowns on seasonal and perishable stock that will not sell at full price. Done by hand in spreadsheets, this is slow, inconsistent between analysts, and reactive: a marked down shelf is a decision made after the item has already lost value, not before.
Revionics, the pricing platform used by Delhaize America and Rimi Baltic, describes the manual alternative directly: before adopting price optimization software, Delhaize America's pricing analysts managed prices with complex, unwieldy spreadsheets, a slow manual procedure that lacked scalability, and pricing processes and data collection were not standardized, which made it hard to ensure pricing strategies were followed enterprise wide. For perishables, the cost of getting the timing wrong is thrown away stock: at Albert Heijn, Supermarket News reports that prices on chicken and fish are reduced automatically based on their sell by date, with a higher discount for items that need to be sold soonest.
How does it work?
- Bring in the signals. Point of sale history, cost and margin, current inventory, competitor prices, and, for markdowns, the sell by or best before date, feed a pricing engine continuously.
- Estimate demand at different prices. The engine estimates price elasticity per item and location: how much volume changes as price changes, learned from historical sales, not fixed rules.
- Recommend or set the price. For everyday and promotional pricing, the engine proposes a price within the retailer's guardrails for an analyst to approve; for markdowns on electronic shelf labels, it can act automatically within a small set of fixed discount steps, choosing the step and the timing so the item is more likely to sell before it expires.
- Push the price to the shelf. The new price reaches paper tags on the next print run, or an electronic shelf label in near real time, so a fast moving markdown clock is actually visible to the shopper.
- Feed the outcome back. Sell through, waste and margin by item and store retrain the elasticity estimates, and pricing analysts review exceptions and set the strategy: which categories run automated markdowns, and what the outer guardrails are.
- Audience
- Employee facing
- Autonomy
- Supervised agent
- Adoption
- Early adopters
- Channels
- Internal tools, Kiosk and branch
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
No public deployment has disclosed a measurable outcome yet.
Value drivers: Revenue growth, Employee productivity, Speed and cycle time.
Indicative value
A grocery retailer with 500 stores and USD 4 million in average annual perishable and seasonal sales per store
USD 1.5 million to USD 21.9 million
Perishable and seasonal margin recovered per year
How this is calculated
Formula: stores * perishableSalesPerStore * unsoldAtFullPriceShare * perishableMarginRate * marginRecovered. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Stores stores, stores | 500 | 500 | The reference retailer. |
| Perishable and seasonal sales per store per year perishableSalesPerStore, USD per store per year | 3,000,000 | 5,000,000 | Editorial assumption for a full line grocery store, replace with your own perishable and seasonal category mix. |
| Share of perishable and seasonal sales sold at a markdown or written off unsoldAtFullPriceShare, fraction of perishable and seasonal sales | 0.04 | 0.1 | Editorial assumption, replace with your own waste and markdown rate by category. |
| Gross margin on perishable and seasonal sales perishableMarginRate, fraction of sales value | 0.25 | 0.35 | Editorial assumption: a typical gross margin range across produce, meat, dairy and general merchandise categories, replace with your own category margin mix. |
| Share of that lost margin recovered by better timed, better sized markdowns marginRecovered, fraction of the margin at risk in markdown and waste | 0.1 | 0.25 | Editorial assumption, conservative because none of the deployments on this page discloses a percentage or currency margin result: Revionics' customer case studies for Delhaize America and Rimi Baltic state results in qualitative terms only ("improved markdowns planning and margins" for Delhaize America, "positive lifts in units and revenue" for Rimi Baltic), and Albert Heijn has not disclosed a percentage or currency figure for its dynamic discounting rollout. A separate Microsoft customer story about a related Albert Heijn initiative (cited on this library's demand forecasting page) reports a weight based saving, "This now saves 250,000 kilos of wasted food per year", which is not a margin or currency figure and is not used in this estimate. |
What it leaves out: Gross margin recovered only. It leaves out the cost of the pricing platform, electronic shelf labels and integration, the risk that a wrong markdown clears stock too early at a needless discount, and any change in footfall or basket size from more visible in store discounting.
Who already uses it?
11 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Albert Heijn
Netherlands · Retail and ecommerce · 2019
Albert Heijn, the Ahold Delhaize supermarket chain in the Netherlands, built an in house algorithm that automatically discounts fresh produce, chicken and fish as their best before date approaches, so stock is cleared by the end of the day instead of thrown away. The algorithm was advised by anti waste specialist Wasteless and first tested with Pricer's electronic shelf label hardware in one store in 2019. It is now rolled out to every Albert Heijn store fitted with electronic shelf labels, working with three fixed discount levels (25%, 40% and 70%) that the software chooses per product from sales history, local and seasonal patterns, weather and current stock.
No outcome disclosed.
Academy Sports + Outdoors
United States · Retail and ecommerce · 2026
Academy Sports + Outdoors, a sporting goods and outdoor recreation retailer with more than 300 stores across 21 states, has used the Revionics Base Price and Markdown Optimization platform for more than a decade, and announced an extension of that use on April 29, 2026. Academy's Vice President of Pricing, Merchant Systems and Master Data Management, Russell Kolb, says the retailer grew from around 70 stores, when pricing was analysed manually store by store, to more than 300 stores now managed on the platform, but Revionics' announcement does not disclose a percentage or currency pricing result for the extension.
No outcome disclosed.
Grupo Ramos
Dominican Republic · Retail and ecommerce · 2026
Grupo Ramos, described by Revionics as the largest retailer in the Dominican Republic with more than 90 stores across hypermarkets, supermarkets and discount formats, replaced a manual, multi department pricing process with Revionics, rolling it out category by category across grocery food, grocery nonfood and fresh food, with differentiated strategies by store format.
In a Revionics hosted webinar, Grupo Ramos executives Herbert Torres (VP Commercial) and Jennifer Mejia (Pricing Manager) say the retailer has been live on Revionics for about a year and a half, has moved from an execution focused pricing team to a more strategic one, and that the return on the investment has been measured and is positive; no percentage or currency figure for that return is disclosed.
No outcome disclosed.
Coborn's, Inc.
United States · Retail and ecommerce · 2025
Coborn's, a 104 year old employee owned grocery retailer with nearly 80 stores across Minnesota, North Dakota, South Dakota, Wisconsin, Michigan and Illinois, selected the Revionics AI Pricing Platform, including base price and promotions optimization, to move its pricing process off manual, spreadsheet based methods and onto a data driven approach across its Coborn's, Cash Wise Foods, Marketplace Foods, Hornbacher's, Kessler's Food and Grocery, and Sullivan's Foods banners. Revionics announced the selection on December 9, 2025; the announcement states the platform will optimize pricing at the product, key value item group, zone and category level and improve promotion planning, forecasting and measurement, but does not disclose a percentage or currency result for the rollout.
No outcome disclosed.
Majid Al Futtaim Retail
United Arab Emirates · Retail and ecommerce · 2025
Majid Al Futtaim Retail, which runs Carrefour across the Middle East, Africa and Central Asia, built an AI pricing engine called "Spider" on Azure OpenAI Service. Spider manages pricing across 7 million SKUs in 14 markets, considering scenarios, recommendations and periodic promotions to keep prices competitive; the same engine also enhances product specifications so customers can find items matching needs such as gluten free or organic. Microsoft does not report a metric isolated to Spider: the disclosed savings and speed figures on the same page cover the combined Azure OpenAI programme (which also includes a customer feedback analysis tool and a personalization engine called "Geo"), not pricing alone.
No outcome disclosed.
Panda Retail Company
Saudi Arabia · Retail and ecommerce · 2024
Panda Retail Company, Saudi Arabia's leading grocery store chain, owned by Savola Group and serving more than 100 million customers a year, selected Revionics' base price solution to set prices at nearly 200 hypermarkets and supermarkets. Revionics' announcement says Panda wants a more data driven and customer centric approach to pricing that improves price perception and financial results as demand and local conditions shift, and describes Revionics' AI models forecasting demand changes and recommending optimal prices; Panda's Chief Marketing Officer is quoted on putting the customer first. No percentage or currency result is disclosed yet.
No outcome disclosed.
Tractor Supply Company
United States · Retail and ecommerce · 2024
Tractor Supply Company, the largest US rural lifestyle retailer with more than 2,200 stores, has been a Revionics customer since 2010 and selected Revionics' Base Price and Markdown applications to streamline and automate pricing decisions across its stores and products. Revionics' announcement, quoting Tractor Supply's Vice President of Pricing, Events and Presentation, says the scale and localized pricing of the platform have been strategic for the business; it does not disclose a percentage or currency result.
No outcome disclosed.
Drogaria Araujo
Brazil · Retail and ecommerce · 2022
Drogaria Araujo, a Brazilian drugstore chain with around 200 locations, moved its non medication items, about half of its revenue, onto the Revionics pricing platform and used Revionics' Key Value Item analysis and Store Zone Clustering to build a localized, elasticity based pricing and zone strategy. An independent Price Operating Model Maturity Assessment by Accenture, commissioned by Araujo's board, ranked the retailer at the "Leading" level, above the Brazilian national average of "Intermediary". Araujo's Competitive Intelligence Manager, Leonardo Lage, states in Revionics' case study that gross margins increased significantly and resulted in a higher EBITDA margin for the company, but the case study gives no percentage or currency figure for that increase.
No outcome disclosed.
Holiday Stationstores
United States · Retail and ecommerce · 2021
Holiday Stationstores, the gas and convenience store chain with more than 600 locations across ten northern US states, replaced category managers setting prices from market intelligence and manufacturer recommendations with Revionics' pricing platform, including its tiered pricing for multi buy promotions such as an item priced at 1.69 dollars that customers can also buy two for 3.00 dollars. Revionics' case study, quoting the retailer's Director of Pricing and Business Support, says Holiday reached its first year gross profit goal and now bases pricing decisions on facts rather than intuition, with results it describes as exceeding the cost of the system several times over; no percentage or currency figure for that result is given beyond the stated goal.
No outcome disclosed.
Rimi Baltic
Latvia · Retail and ecommerce · 2019
Rimi Baltic, the ICA Gruppen owned grocery and convenience retailer operating in Latvia, Lithuania and Estonia, replaced rule based pricing in its ERP system with the Revionics price optimization platform. It rolled out category by category in two steps, first moving its existing pricing rules onto the platform, then switching those categories to price elasticity based pricing, and completed the change across its full product assortment in all three countries in under a year.
No outcome disclosed.
Delhaize America
United States · Retail and ecommerce · 2014
Delhaize America, at the time the US subsidiary of Delhaize Group operating supermarket chains including Food Lion and Hannaford (this record predates the 2016 merger that formed Ahold Delhaize), replaced spreadsheet based pricing with the Revionics price optimization platform across all of its price zones and stores. The retailer moved from rule based pricing to scenario based, shopper centric pricing, then extended the same platform to markdown decisions, so the company could make fact based markdown and clearance decisions, including markdown cadence and depth, aligned with local shopper demand and store level inventories instead of blanket rules.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- At least two years of point of sale history by item and store, including promotions
- Current cost, margin and inventory position by item and store
- For perishables, best before or sell by dates at the item and batch level
- Competitor price data where the retailer competes on price
Systems to integrate
- Point of sale and pricing master data
- Inventory and warehouse management systems
- Electronic shelf labels or the store's price ticket printing system
- Promotion planning and calendar systems
Complexity: Medium
The pricing logic itself is a mature, well understood optimization problem. The work is integrating clean, current cost, inventory and competitor data per store and item, and, for automated markdowns, electronic shelf labels that can update fast enough to make the discount clock real.
- 1
Start with one lever and one category group
Pick either everyday pricing or markdowns, and a limited set of categories with clean data and clear ownership, before touching the full assortment.
- 2
Set the guardrails before the model runs
Agree the price bounds, the fixed markdown steps if any, and which categories or events (a supplier price change, a local competitor promotion) always route to a human.
- 3
Decide what runs automatically and what needs approval
Automated markdown execution on electronic shelf labels needs the tightest guardrails, since there is no review step before the shopper sees the price; everyday price changes can start as recommendations an analyst approves.
- 4
Pilot in a subset of stores with a holdout
Run the new pricing in a sample of stores against a matched set that keeps the old process, so sell through, waste and margin can be compared honestly.
- 5
Roll out category by category
Rimi Baltic moved its existing pricing rules onto the platform first, one category at a time, before switching those categories to elasticity based pricing, which kept the change manageable and let the team catch mistakes in the rules logic early.
- 6
Give analysts the exception queue, not the whole catalogue
Once trust is established, most items should not need a human look; analysts spend their time on flagged exceptions, strategy and the categories still outside automation.
Guardrails
- Hard floor and ceiling on any price or discount change, with no override outside approved limits
- A fixed, published set of markdown steps for automated execution, not an unbounded discount
- Competitor and cost data source and freshness checked before it can move a price
- Exceptions (a data outage, an unusual price swing, a new item with no history) routed to a pricing analyst, not applied automatically
KPIs to instrument
- Sell through and waste by category and store, automated versus a holdout
- Margin by category, automated versus a holdout
- Analyst time spent on exceptions versus routine repricing
- Price change accuracy: the share of published prices matching the system's recommendation
Human in the loop
Pricing analysts own the guardrails, the category rollout plan and every exception the system flags, and review a sample of automated markdown decisions each week. Category managers sign off before a new category moves from recommendation to automated execution.
Common failure modes
- A stale signal moves the price
- Bad inventory or cost data pushes a wrong price or markdown live. Check data freshness before applying a change, and cap the size of any single move.
- Discount steps train shoppers to wait
- If the first markdown step always appears at a predictable point, shoppers learn to wait for it. Vary timing within the guardrails and monitor sell through at each step, not only at the end.
- No holdout, no honest result
- Comparing automated stores to their own past performance overstates the effect, because seasonality and the wider market shift too. Keep a matched holdout for as long as the programme runs.
- Automation outruns the guardrails
- A category is automated before its guardrails, exception rules and electronic shelf label coverage are actually ready. Gate the move to automation on a checklist, not a launch date.
What are the risks and rules?
EU AI Act
Minimal risk
Setting or recommending retail prices for goods is not listed in Annex III and does not evaluate the creditworthiness, employment, or access to an essential service of a natural person, so this is minimal risk under the EU AI Act. It would need reassessment if a retailer used the same engine to set an individual price per identified customer rather than per product and store, which raises separate consumer protection and non discrimination questions the deployments on this page do not describe.
Rules that apply
Controls to put in place
- Published, auditable guardrails (price bounds, markdown steps) that automated execution cannot exceed
- A permanent holdout group of stores or categories for honest measurement
- Change log of every price and markdown decision, automated or analyst approved, with the data it used
- Regular review of automated categories for unintended patterns, such as discount timing shoppers can predict and exploit
Frequently asked questions
- Does AI dynamic pricing mean charging each shopper a different price?
Not in the deployments on this page. Delhaize America and Albert Heijn price by product and, for markdowns, by store and remaining shelf life. Rimi Baltic's elasticity based pricing runs at the product and category level; its sources do not describe store level pricing. None of the three prices by identified shopper. Individual level pricing raises separate fairness and disclosure questions this page does not cover.
- How much does AI markdown optimization actually save?
None of the deployments on this page publishes a percentage or currency result for markdown or margin impact. Revionics' own case studies state results at Delhaize America and Rimi Baltic in qualitative terms only ("improved markdowns planning and margins" for Delhaize America, "positive lifts in units and revenue" for Rimi Baltic), with no percentage or currency figure attached. Albert Heijn has not disclosed a percentage or currency result for its dynamic discounting rollout either.
A separate Microsoft customer story about a related Albert Heijn initiative reports that it "now saves 250,000 kilos of wasted food per year", a weight, not a margin or revenue figure, so treat any margin or revenue number you see elsewhere as an industry estimate, not a result from these deployments.
- What is the difference between everyday pricing and markdown optimization?
Everyday pricing sets the regular shelf price from demand, cost and competition. Markdown optimization decides how and when to discount stock that will not sell at full price, typically seasonal goods or perishables close to their best before date, which is why Albert Heijn's system works from remaining shelf life rather than from a fixed markdown calendar.
How to cite this page
Blits.ai AI Use Case Library, "AI dynamic pricing and markdown optimization for retail", last verified 29 September 2026, https://www.blits.ai/ai-use-cases/retail-dynamic-pricing-and-markdown-optimization. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 30 September 2026: Added deployments: Majid Al Futtaim Retail.
- 29 September 2026: First published