The years 2022 and 2023 were marked by high inflation!
(+4.9% in 2023 and +5.2% in 2022). In this context, it was easy for distributors to control their margins. Purchase prices were rising, and so were sales prices.
Today, inflation has fallen considerably.
In 2024, it reached +2%, yet consumption remains sluggish. Sales volumes are not picking up, and turnover is down for many retailers.
Margin management, as volumes decline and prices no longer rise, was therefore at the heart of discussions at the Retail Data Day, held in Paris on February 7.
The speakers emphasized the need to finely adjust prices and promotions, particularly by investing in data quality to make the right decisions. However, they remain cautious about implementing a pricing strategy based solely on elasticity.
Data quality: the cornerstone of a controlled pricing strategy
On the importance of data, there is little debate. All the distributors present agreed on the need to :
- Identify competitors and know their precise selling prices : The competitive perimeter, for each product universe, and the frequency of data collection must be defined. The various means of collecting data must be implemented (crawling and scraping for data accessible online), as well as in-store physical surveys where appropriate.
- Match your products, for an objective assessment of its price positioning: matching can be largely automated for identical products such as national brands; it is often more complicated for private label products, which are not always easily comparable or offered in different quantities. A suitable tool is therefore essential to simplify the work of users.
- Chain your products to guarantee consistent selling prices This involves defining various types of links to integrate variations in volume, brands, fragrances, tastes, sizes, colors and other criteria. Once again, this is difficult work, but essential if we are to keep our promise to the consumer of perfect price consistency.
There is general agreement that matching and chaining are key levers that need to be better mastered. Many expect AI to provide concrete solutions that simplify and accelerate this work.
Product segmentation to identify price-sensitive products
When it comes to pricing, not all products are created equal.
Certain products act as markers. At Retail Data Day, speakers focused on the importance of precisely identifying these “price marker” products.
Because they are the most purchased or symbolic products for consumers, these products need to be given special attention in retailers’ pricing strategies.
Some distributors have also sought to rationalize the process by calculating a price-sensitivity scorewhich reflects the customer’s vision. To calculate this score, they take into account criteria such as the product’s face value, its frequency of purchase, whether it is held by the competition, etc.
While it’s important to be correctly positioned on these products, you also need to remain consistent on other products, to avoid “killer prices” that destroy the Price image.
Elasticity: a difficult subject for retailers to grasp
The notion of elasticitywhich refers to the variation in demand in response to a price change, gave rise to a very interesting debate among participants.
Discussions showed just how difficult it is to grasp the subject and, above all, to use it as an operational lever to influence prices.
There’s no such thing as a magic formula. There are many criteria to take into account, all of which make very complicated elasticity assessment. For example, it’s not just a question of assessing the impact of a price change on a given product, but also the repercussions on demand for substitutable or related products.
Some distributors have preferred focus their efforts on measuring the impact of a promotionrather than on the impact of a price change at the back of the aisle:
The impact of a promotion on sales volumes is far more significant, and therefore “reasonably easy” to measure: depending on the type of promotion, volumes can be multiplied by 2, or even more.
Conversely, lowering the back-of-shelf price of an elastic product by 2% (coefficient of -2, for example), in principle means an increase in volumes of 4%. Is it really necessary to devote a great deal of computing power to such a limited and theoretical impact?
The debate remains open. But what emerges from the discussions is that elasticity should not be the only element in the pricing strategy.
Given the level of uncertainty that remains very high in retail, it seems appropriate for distributors to focus on 3 levers to optimize their pricing strategy:
- Invest first and foremost in data, to improve its quality: customer data, competitor price data, and above all product data.
- Better control of matching and chaining
- Invest in AI on well-targeted topics, enabling rapid productivity gains
For the time being, we remain cautious on other issues.
In all cases, the adoption of a best-of-breed pricing solution is now a necessity to leverage data and gain resilience.
