DAP #034 - Google Ads Customer Match API Changes, Big Meta Ad Measurement Update, BigQuery Legacy SQL Retired, Google Tag Manager Bug Fixed, Overcounting ROAS in Ads Explained
Here is the top content I’ve read over the last couple of weeks.
🔥 Hottest news
Google Ads Customer Match API Change Coming April 1, 2026
(Ed.: I decided this is the most important news due to its potential impact on data) Arpan Banerjee discusses the upcoming changes to Google Ads Customer Match, which will stop supporting uploads through the API starting April 1, 2026. Users must switch to the Data Manager API to continue uploads, as the change aims to enhance data security and streamline processes.
📢 Product updates
Big Meta Update: Changes to Ad Measurement
Adriaan Dekker outlines significant updates to Meta’s ad measurement that will enhance accuracy in reporting. The changes include a new definition for click-through attribution and a shift in how conversions from engagements are categorized, which aims to provide clearer ad performance insights.
Bye-bye BigQuery Legacy SQL
Luka Cempre announces the retirement of legacy SQL features in BigQuery, specifically FLATTEN and WITHIN, highlighting the transition to more efficient SQL practices.
🛠️ Technical news
Google Tag Manager Bug Fixed
Simo Ahava reports that a UI glitch affecting Google Tag Manager’s Consent Mode in Preview Mode has been resolved. While the Consent Mode itself was functioning correctly, the glitch made it difficult to verify interactions during testing.
Overcounting ROAS in Google / Meta Ads
Luc Nugteren explains how many brands overcount their Return on Ad Spend (ROAS) by failing to account for returns in their conversion tracking. He provides a step-by-step guide to implement a return automation process in Shopify to ensure accurate ROAS calculations.
🎓 Other resources
Closing the Attribution Gap with Google Data Manager API
Doug Hall discusses the importance of the Google Data Manager API in bridging the attribution gap between ad clicks and revenue in CRM systems. He emphasizes that proper implementation can significantly enhance the quality of data fed into Smart Bidding.
Boost Google Ads Data with Robust UTM Strategy
Daniel Perry-Reed advocates for the use of UTMs in Google Ads to improve attribution tracking, arguing that relying solely on auto-tagging creates blind spots. He introduces an updated Google Ads script to automate UTM application across various campaign types.
Apple Attribution Impact - TEMPLATE
Dipesh Shah shares a Looker Studio template and GitHub repository designed to help visualize the impact of Apple’s Intelligent Tracking Prevention (ITP) on Google Analytics data. The dashboard highlights key metrics like attribution leakage and session fragmentation to better understand user behavior.