Dogo Store:
Data Match Quality Through Meta Catalog Optimization
Dogo Store was receiving incomplete conversion data on Meta due to data loss caused by browser restrictions and outdated feed structures. Technical catalog work raised catalog match rates to around 83% over a 28-day period.
The Problem
Dogo Store could not fully track user interactions in its Meta catalogs across the Turkish, French and US markets. Remnants of the old feed structures and catalog synchronization errors kept match rates in the 32%–43% range, which directly constrained its ad optimization processes.
Work Done
To solve this problem, the catalog infrastructure was optimized on the software team's side and the data flow was restructured.
The product data coming from the dataLayer at the Product View, Add to Cart and Purchase steps was checked against the catalog.
item_ids that did not match Meta were corrected by the brand's software team, preventing data pollution.
A 28-day learning cycle was completed so that pixel signals would match the catalog fully.
Results
The overall score in the TR catalog was raised from 43.3% to 83.6%.
The overall score in the FR catalog was raised from 38.7% to 78.8%.
The overall score in the US catalog was raised from 32.1% to 78.9%.
At the Add to Cart and Purchase steps, a stable match quality above 90% was achieved across all catalogs.
Impact
Thanks to the catalog optimization, Dogo Store began feeding its ad algorithms with far healthier data sets. Campaign optimization could then be managed more efficiently on realistic data with high match quality, instead of on incomplete data.
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