Understand your Conversion Lift based on geography measurement data

In this article, we explain the meaning of metrics within your study and geo-based Conversion Lift reporting.


How do we collect the data?

Conversion Lift based on geography measures the causal, incremental impact of your campaigns by aggregating unattributed conversions into non-overlapping geographic regions and isolation differences between baseline and exposed. Conversion Lift based on geography typically requires a higher budget than the user-based alternative. Results start being shared during the study, but we recommend waiting until the end of your study to get the most accurate results.


Conversion Lift based on geography metrics and statuses

Note: The instructions below are part of the new design for the Google Ads user experience. To use the previous design, click the "Appearance" icon, and select Use previous design. If you're using the previous version of Google Ads, review the Quick reference map or use the Search bar in the top navigation panel of Google Ads to find the page you’re searching for.

Conversion Lift measurement data is available at the conversion level. Here’s how you check your Conversion Lift based on geography measurement data:

  1. In your Google Ads account, click the Goals icon Goals Icon.
  2. Click the Measurements drop down in the section menu.
  3. Click Lift measurement.
  4. Click on the study name and you’ll get the test results in the “Details” page.



Incremental Return on Ad Spend (iROAS)

Incremental conversion value/Incremental Cost = iROAS

The interpretation of this metric is that based on the results of the experiment and your current media spend for every dollar invested in the campaign or channel that was tested, you get [iROAS] additional dollars.

For example, if your study results in getting an iROAS of 2, that means that for every $1 USD invested, your business generated $2 USD net new in conversion value that otherwise wouldn’t have existed. If you want to learn more about the math behind this metric, you can review the white papers of the underlying methodologies:

Incremental conversion value

The incremental conversion value, in local currency, driven by your campaigns in the experiment period.

Incremental conversion count

The incremental amount of conversions driven by your campaigns in the experiment period.

Incremental cost

The difference of cost among treatment and control during the experiment period.

Confidence interval

This metric is the estimated range in which your Conversion Lift based on geography estimates fall. For example, you may find that your iROAS is 2.2. This is your point estimate. In brackets, you’ll find the confidence interval from at least 1.3 to at most 3.5.

Cooldown date range

This is the period of time after the test finalizes when the campaigns go back to business as usual, but we continue collecting information to calculate iROAS. This range is optional, but it’s recommended for clients that have a conversion cycle longer than a few weeks.



“Significant Positive iROAS”

It means that the net new revenue generated during the experiment is greater than the amount of money spent on the campaigns. This metric is a solid indicator that the campaigns under observation are driving efficient incremental returns to your business.

“Not enough data”

“Not enough data” means that based on the date range you’ve selected in your account, the number of conversion values received in that date range is below the minimum threshold required to surface results. In general, this is a typical status to find at the start of the experiment. But if it doesn't update, you should check for the following issues:

  • Make sure that you’re running studies at the recommended budget for “High” feasibility.
  • Don’t over-target the campaign with audiences.
  • Control that there are no location exclusions.

“No significant lift detected”

Even when we have enough data, it’s possible that results are still neutral and that “No lift is detected”. This happens when there was no statistically significant difference between the response generated by regions impacted by the ad and those that didn’t view it. If you don't have lift at the overall level, check if you have lift in specific conversion slices. Consider focusing on those segments with positive lift. As with any media channel, some metrics are more difficult to move than others.


Related links

Was this helpful?

How can we improve it?
Clear search
Close search
Google apps
Main menu