Published in: Pricing

Thanks to Data, Auchan’s Revenues are Rising!

Auchan is recovering from the storm, after nine complicated years, the turnover is on the rise again and the ongoing projects are numerous!

+6,2% in revenues in 2022, despite the successive crises. According to Philippe Brochard, the new General Director of Auchan France, the shift in the balance has been supported by a solid commercial dynamic driven by the teams’ commitment, at all levels, and the increase in confidence in the company’s projects (+25%).

“We’re back to doing business the way we did fifty years ago… but with data, IT on top of it.” Philippe Brochard in interview with LSA

Numerous actions have been implemented internally and have driven this growth upwards. Several major challenges have been identified: the organizational and operational model, data and digital.

Renewal of a large part of the management committee, construction of new IT, data and e-commerce teams, all in order to implement a common business project and put an end to the organization in silos. These were the objectives of the new CEO of the retailer. And to ensure that everyone is involved, the organizational and operational changes have been accompanied by an internet transformation and adaptation plan for change.

A vision and investments that are already starting to pay off!

Data is already being used for pricing strategies. The data and IT teams have worked on developing a tool to measure SKUs price elasticity. This use of data to optimize prices opens up new opportunities in the context of commercial strategies and negotiations with manufacturers.

As pricing experts, it is possible to implement the calculation of price elasticity using historical data in order to obtain more accurate business impact simulations. These simulations can then be used to refine pricing strategies and create value in negotiations with suppliers.

However, we do not recommend using this method of computing price elasticity based on historical data for price optimization. This is because there are too many external factors that can influence demand and therefore impact the measurement of demand, while historical price variations are by definition quite small.

To counter this, at PricingHUB, we develop an experimental approach with control groups. We activate our machine learning AI to optimize our clients’ prices and improve their business performance.

To learn more about our tool, click here!