On May 1, 2026, Google announced a significant change to Google Ads data retention policies. Starting June 1, 2026, Google will enforce a 37 month retention limit on granular reporting data. Daily, weekly, and hourly data older than 37 months will be deleted. Reach and frequency metrics will be limited to 3 years.
The good news: monthly, quarterly, and yearly aggregates stay for 11 years. The bad news: you have less than four weeks to act. Many performance marketers do not know this is happening. If you rely on historical benchmarks, year over year analysis, or automated reporting pulling old data, you need to prepare now.
This guide walks you through exactly what is changing, who is affected, what data to export before June 1, and how to build a long term data strategy so you never lose control of your Google Ads history again.
What is changing
Google is implementing a tiered data retention policy. The retention period depends on the granularity of your data.
Hourly, daily, and weekly data is limited to 37 months. This means any data collected more than 37 months ago becomes inaccessible through standard Google Ads reporting or API queries. June 1, 2026 is the enforcement date. After that, queries requesting data older than 37 months will return a DateRangeError.
Reach and frequency metrics have an even shorter window: only 3 years.
Monthly, quarterly, and yearly data is retained for 11 years. This covers most standard performance reporting needs. If you are doing year over year comparisons at a monthly level, you can still do that. The constraint is on granular daily breakdowns.
What this means for your account
If you run API queries that pull daily data going back more than 37 months, those queries will fail after June 1. If your reporting tool backfills BigQuery or your own warehouse with historical data, those backfills will overwrite with null values. If you are using Google Ads API in automated workflows pulling data older than 37 months, those workflows will break unless you update them to handle DateRangeError responses.
Who is affected
Not every advertiser will feel the impact equally. The retention change matters most if you fall into one of these categories.
You are affected if you do year over year analysis beyond 3 years. Seasonal businesses (retail, travel, education) often compare this year to the same period two or three years ago. If your business cycles run on longer horizons, you need historical data.
You are affected if you manage agency accounts with long client relationships. Agencies hold onto account data for compliance, benchmarking, and quarterly business reviews. Losing granular data from client accounts creates liability.
You are affected if you export to BigQuery. Many teams set up automated BigQuery pipelines to build independent data warehouses. If your pipeline backfills old data, that process will now write null values instead of pulling historical records.
You are affected if you use automated reporting tools that query the API. Tools pulling daily or weekly data for dashboards, email reports, or data warehouses will break when querying beyond 37 months.
You are affected if you need baseline metrics for planning. Seasonal planning, budget allocation, and performance benchmarking all rely on historical data. Losing access to granular daily data makes it harder to plan accurately for future campaigns.
What to do before June 1
You have three weeks. Here is the practical checklist to protect your data.
Export historical data now
Do not wait. Download your Google Ads reports going back as far as you need. Use the Google Ads UI export feature, the API, or BigQuery to pull all daily and weekly data you want to keep. Focus on the last 3 to 5 years depending on your business cycle. Include campaign performance, ad group data, keyword metrics, search term reports, and conversion data. Store these exports somewhere you control: your own database, a data warehouse, cloud storage, or even local files. The format matters less than having the data outside of Google's control.
Audit your BigQuery setup
If you use Google Ads Data Transfer to BigQuery, check your backfill settings now. If you have backfill enabled for historical data, disable it or adjust the date range before June 1. Backfilling data older than 37 months after the enforcement date will overwrite existing records with null values. You do not want your historical warehouse polluted with null data. Review your tables. If they contain data older than 37 months, you need to export and archive that data separately before June 1.
Update API queries and automation
If you have scripts or tools that query the Google Ads API for historical data, audit those now. Any query requesting daily or weekly data older than 37 months will fail after June 1. Update your code to handle DateRangeError gracefully. Add logic to catch the error and either log it, alert your team, or skip the old date range. Test the changes before June 1 so you are not discovering broken workflows in July. For tools you do not control (third party analytics platforms, reporting software), contact the vendor now to understand their roadmap for supporting the new retention limits.
Store monthly aggregates separately
While daily data gets deleted, monthly summaries stay for 11 years. Calculate and store monthly, quarterly, and annual summaries of your key metrics: spend, conversions, cost per conversion, ROAS. Create a simple spreadsheet or database tracking these monthly aggregates. This becomes your long term benchmark for year over year comparisons and seasonal planning. You lose daily granularity, but you keep the ability to analyze trends at a higher level.
Document your baseline metrics
Create a documentation file listing your key historical metrics: average monthly spend, typical cost per conversion by campaign type, seasonal patterns, best performing quarters, peak traffic periods. Write down the story of your account performance. This becomes your reference baseline for future campaigns and budget planning. When you cannot access the raw daily data, having these documented aggregates means you can still make informed decisions.
How to export your data
You have three options. Which one you choose depends on how much data you have and how automated you want the process to be.
Option 1: Google Ads UI export
Go to Google Ads Reports. Set your date range to cover the data you want to save. Download the CSV file. This is quick and simple. The downside: you can only download one report at a time, and you are limited to what Google Ads UI reports show. Use this method if you have a small number of accounts and only need summary data.
Option 2: Google Ads API
Use Google Ads Query Language to extract data programmatically. This is more flexible and scalable. You can query specific metrics, filter by campaign or ad group, and automate the extraction. The downside: you need technical skills or developer resources to set it up. Use this method if you have engineering support or if you manage multiple accounts and want to automate the export process.
Option 3: BigQuery Data Transfer
Set up Google Ads Data Transfer to automatically push data to BigQuery. This is the best long term solution. Data flows automatically on a schedule. You can query it using SQL. You own the data in your warehouse. The downside: it requires BigQuery setup and ongoing costs. Use this method if you want a scalable, automated system that runs forever without manual intervention.
What to preserve
Whatever export method you choose, focus on preserving daily data for the last 37 months (back to June 2022 from the enforcement date). Include campaign, ad group, keyword, search term, and device performance data. Include conversion metrics and cost data. Store this with your business data.
For data older than 37 months, monthly aggregates are fine. You do not need daily detail for the past 5 years. But you do need reliable monthly summaries you can reference for year over year planning.
Building a long term data strategy
One time export is not enough. You need a system that protects your data permanently.
Do not just export data once and assume you are done. Google's policies can change again. Build a process that continuously stores your Google Ads data independently. Set up automated exports on a regular schedule. Weekly or monthly exports work well. Push the data to your own data warehouse, BigQuery, Snowflake, or a simple database. The goal is to own your data, not rent it from Google.
Document the schema and retention policy for your warehouse. Define which metrics you store long term and at what granularity. For data older than two years, storing monthly summaries instead of daily detail saves storage costs while preserving the insights you need.
Many performance marketing tools are building independent data storage to solve this exact problem. Tools like aubado store your Google Ads performance data independently, so you are not reliant on platform retention policies. Your data stays yours, regardless of what Google changes next. If you manage multiple accounts or want to consolidate Google Ads data with other channels, that approach eliminates data loss risk entirely.
Ready to protect your data permanently? Try aubado free and keep your Google Ads history safe forever.
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