The Challenge of Price Data in the DMA Era: How Dataedis Ensures ‘Apples-to-Apples’ Comparisons on Google Shopping

The digital market is constantly in motion. For businesses that rely on up-to-date and accurate price data, like ours, it is crucial to closely track these changes. The recent adjustments in the user interface (UI) of Google Shopping, driven by the Digital Markets Act (DMA), have a significant impact on how product information is presented to consumers and, more importantly, how we collect and process this data.

The DMA Impact on Google Shopping: Breadth versus Precision

The Digital Markets Act (DMA) is European legislation aimed at limiting the power of large technology companies, the so-called ‘gatekeepers’, and creating a fairer digital market. A direct consequence is the obligation for Google to display its Shopping results differently, by also including alternatives and variants to offer the consumer a slightly broader scope.

Important to note: Google is continuously adjusting the format and display of its Google Shopping results and rolling out updates. They are incrementally figuring out what works sufficiently for DMA compliance while remaining commercially viable.

This has led to a continuous change in the UI, resulting in the increase in the presentation of “similar items” or product variants.

However, what is a ‘broadening’ for the consumer is a ‘blurring’ for the retailer.

For retailers, it is critically important to be able to compare the price of the exact right item, and not just similar items or other variants. This is the difference between an effective and a failed pricing strategy: the comparison of Apples-to-Apples, and definitely not Apples-to-Oranges.

The Risk of Unfiltered Data: The Treacherous Nuances

The danger lies in the fact that the items in the new Google Shopping display often do not differ greatly, but rather in small, specific aspects or variants that are presented to the consumer as “similar.”

For retailers who want to influence their competitive position with a competitive pricing strategy, this is disastrous, as even the smallest variant is enough to fundamentally undermine a pricing strategy.

Consider the following scenarios:

• The Nuance Difference: You sell a Laptop Model X with 16GB RAM. The Google Shopping results also show an offer for a Laptop Model X with 8GB RAM.
  • The Consequence: The 8GB model is logically cheaper. Without accurate filtering, your repricer fetches the lower price and prompts you to lower the price of your 16GB model. You unnecessarily lose significant margin purely because of the difference in a single specification.
• The Subtle Variant: You sell a Shoe (Color A, Size 42). The results also show an offer for the same shoe in Color B (a less popular variant), which is on clearance.
  • The Consequence: The color variant is temporarily discounted and does not accurately reflect the true market price for the popular Color A. Your strategy is based on a temporary, specific promotion of a variant, leading to incorrect conclusions about your market position.

This means that data quality sharply declines, jeopardizing the reliability of the insights within dynamic pricing and repricer dashboards.

The Unique Dataedis Solution: Pure Data, Sharper Strategy

At Dataedis, we have not only recognized this challenge but have directly addressed it. Our mission is to provide SaaS providers of dynamic pricing and repricer services—as well as the retailers we serve directly—with the purest and most usable data on the market.

Our unique service goes far beyond standard scraping:

We have developed advanced filtering mechanisms to ‘purify’ the Google Shopping results. This means we have the capability to:

• Variant Elimination: Filter out all deviating colors, sizes, and, crucially, different technical specifications.
• Accessory Exclusion: Ensure that your item is not compared with an accessory, a bundle, or a different model year.
• Focus on the Unique Item: Our output guarantees only the price data of the exact, unique item (based on EAN, MPN, or other unique identification).

The result? Our data provides the exact right overview for your repricer dashboard, allowing you or your customer to confidently implement dynamic pricing. You are assured of a true ‘apples-to-apples’ comparison, ensuring you do not lose unnecessary margin based on incorrect variant comparisons.

The changes on Google Shopping under the influence of the DMA are a fact. But thanks to the specialized filtering from Dataedis, this challenge is converted into a competitive advantage for you as a retailer, distributor, manufacturer, brand, or B2B service provider to (online) retail.

Are you ready to ensure your pricing strategy is based on absolute purity, not pricing errors? Contact Dataedis today to learn how our unique ‘apples-to-apples’ data can protect your margin and sharpen your competitive edge.