31 may 2022
Note: This data is a comparison of assortments at stores for retailers such as Albert Heijn, Jumbo, Coop and Hoogvliet in The Netherlands, comparing April 2021 to 2022 in terms of inflation.
Is Jumbo on to something with its different brand vs private label pricing strategy?
In analysing data available at Daltix, we can confirm what NielsenIQ broadly outlines in their recent article (link to article – in Dutch only) on price inflation. Price inflation is a fact for nearly all products at all retailers. Considering the current competitive landscape putting increased pressure on margins, you would expect branded products to have higher price inflation rates than the private label products. This is true in most cases, especially at Albert Heijn and Hoogvliet.
However, Jumbo is trending in the complete opposite direction. All price changes on private label products exceed the price changes for branded products, except for vegetables and non-food store items. The most striking example in this comparison, is on frozen and babycare products, where inflation on private labels rises 7% more vs brands in these respective categories at Jumbo.
How Daltix can help with your data needs: Daltix collects, structures and cleans large amounts of retail data on a daily basis. The data is so qualitative that our customers can easily track price changes in the competitive field on a detailed product level. Additionally, they can make objective comparisons, eliminating assortment changes in different product categories across years, making an analysis such as the one we made for you very easy.
If your data provider doesn’t give you a clear outline of how they test their own data for quality, you should get suspicious. In order to deliver data you can trust, we’ve developed the Daltix Data Quality Indicators. Read about what our DQIs are and why it’s good for your data here!
In the final blog post of the data architecture series, we’ll take a look at how Daltix can provide everyone who requires their data access in a way that suits them. To answer that, we’ll examine our two data setups and explain our experiences and how they fit into our data architecture.