Google shipped API v25.1 on August 19, and the headline features are not about automation or creative generation. They are about measurement. Specifically, the release adds programmatic access to Brand Lift and Conversion Lift study data, along with new fields that surface your upcoming AI Max migration dates. For B2B marketing leaders who have spent years defending upper-funnel spend with directional data and gut instinct, this is the release that finally gives you board-grade numbers.
The timing is not accidental. As recent industry analysis shows, 60% of senior marketers now trust incrementality testing more than any other measurement method. Google is responding by making lift measurement a first-class citizen in the API, not a side feature you access through a rep.
What Actually Shipped
According to Google's official release notes, v25.1 introduces two major measurement capabilities. The first is Brand Lift measurement support, which adds five new read-only dimensional resources: LiftMeasurementAgeRange, LiftMeasurementCampaign, LiftMeasurementDevice, LiftMeasurementGender, and LiftMeasurementVideo. These let you pull lift study results segmented by audience dimension directly through the API, rather than exporting them manually from the UI.
The second is Conversion Lift measurement support, which is where the real CFO-grade math lives. The release adds 24 new Conversion Lift metrics, including winner score metrics for detailed statistical analysis. You can now retrieve incremental conversions, relative lift, incremental cost per action (iCPA), and incremental return on ad spend (iROAS) programmatically. A new LiftMeasurementConfig resource represents the lift study itself, and a LiftMeasurementFlight resource captures flight details including start and end dates.
For teams running experiments at scale, this means you can finally build dashboards that show true incrementality alongside attributed conversions, without manual data pulls or spreadsheet gymnastics.
The AI Max Migration Clock Is Now Visible
The release also adds two new campaign fields: Campaign.aca_migration_date_time and Campaign.broad_match_migration_date_time. These surface the dates when your campaigns will auto-migrate to AI Max.
This matters because the migration timeline is split. As Google announced in June, Dynamic Search Ads migration was pushed to February 2027, but campaigns using Automatically Created Assets (ACA) and campaign-level broad match settings still migrate in . The new API fields let you see exactly which campaigns are on which clock, so you can prioritize testing and baseline measurement before the switch happens.
If you are running B2B lead gen campaigns with long sales cycles, the September deadline is roughly six weeks away. That is not enough time to run a proper incrementality test from scratch, but it is enough time to pull historical data and establish a pre-migration baseline you can compare against post-migration performance.
Why Lift Measurement Matters for B2B
The standard objection to lift testing in B2B is that sales cycles are too long and conversion volumes are too low to achieve statistical significance. That objection is valid for some accounts, but it misses the larger point.
Google's Conversion Lift documentation describes the methodology: you split your audience into a treatment group that sees ads and a control group that does not, then measure the difference in downstream conversions. The result is incremental conversions, the conversions that would not have happened without the ad exposure.
For B2B, this is the number that actually matters in a pipeline review. Attributed conversions tell you what happened after someone clicked. Incremental conversions tell you what happened because of your ads. The difference between those two numbers is the gap between what your dashboard shows and what your CFO should believe.
The new API access means you can pull these metrics into your BI stack and model them against pipeline stages, deal velocity, and revenue. You can calculate incremental cost per opportunity, not just incremental cost per lead. You can show the board a sensitivity table that accounts for holdout size, study duration, and confidence intervals.
The Practical Pilot
If you have not run a Conversion Lift study before, the new API access does not change the setup requirements. You still need to work with your Google rep to configure the study, and you still need sufficient conversion volume to achieve statistical power. Google's guidance recommends aiming for 90% certainty of lift, with budget guidance provided if your setup falls below that threshold.
What the API access does change is what happens after the study runs. Instead of exporting a PDF and manually transcribing numbers into a board deck, you can automate the data flow into your reporting infrastructure. You can set up alerts when lift falls below a threshold. You can build a historical record of incrementality by campaign, audience, and creative that informs future budget allocation.
For teams already running lift studies, the immediate action is to update your API integration to v25.1 and start pulling the new metrics. For teams that have not run lift studies, the immediate action is to talk to your rep about feasibility before the AI Max migration changes your campaign structure.
The Migration Baseline
The combination of lift measurement access and migration date visibility creates a specific opportunity: you can measure incrementality on your current campaign structure, then measure it again after AI Max migration, and compare the two.
This is the kind of before-and-after analysis that turns a vendor-driven migration into a controlled experiment. Google's headline claim is that AI Max campaigns see an average of 7% more conversions when using the full feature suite. That number is based on comparing full-suite AI Max to matching-only AI Max, not on comparing AI Max to your current DSA or broad match setup. Your mileage will vary, and the only way to know by how much is to measure it.
The new API fields tell you when the migration will happen. The new lift metrics tell you what to measure before and after. The rest is execution.
What This Means for Your Measurement Stack
The broader signal from v25.1 is that Google is treating incrementality as infrastructure, not as a premium feature for enterprise accounts. The API access democratizes what used to require manual exports and custom reporting. The dimensional breakdowns let you segment lift by the same dimensions you use for targeting and optimization.
For B2B marketing leaders, this is the moment to stop treating lift measurement as a nice-to-have and start treating it as the ground truth that validates everything else. Your multi-touch attribution model tells you which touchpoints get credit. Your marketing mix model tells you which channels drive aggregate growth. Your lift studies tell you whether any of that credit is actually causal.
The CFO does not care about attribution models. The CFO cares about whether the money you spent created revenue that would not have existed otherwise. Google just made it easier to answer that question with math instead of narrative.
