How to measure paid social’s impact on PPC

If your paid social campaigns aren’t converting, you may be undervaluing their impact. Your brand’s exposure on social media can influence other parts of your marketing that platform metrics don’t capture.

Here’s how to design and measure a test to understand how paid social influences your other marketing channels, including PPC.

Step 1: Determine your hypothesis

Start with what you want to learn, then define a hypothesis you can realistically evaluate with your data.

For example, this is a common hypothesis for measuring paid search lift from social traffic:

• Search lift hypothesis: Increasing spend on social media will increase brand search volume and overall PPC CTRs.
• Logic: 
  • Social ads build brand awareness. As more people become familiar with our brand, they will search for it more often when making research and purchase decisions. 
  • As more people are exposed to our brand, they will increasingly click on our PPC ads regardless of their search term (i.e., increasing non-brand and brand CTRs).
  • People exposed multiple times to our brand will have a higher trust factor in our products, and therefore, our conversion rates will increase. 
• Measurement: 
  • Impression and click volume for our branded terms.
  • CTR changes for brand and non-brand terms.
  • Conversion rate changes for brand and non-brand terms. 

Your hypothesis could have a different scope, such as measuring paid and organic lift from social spend or an increase in direct traffic. 

Step 2: The test

The next step is to set up the test parameters. Generally, measuring before and after a change is a mistake, as seasonality or other factors can affect your test results.

The most common test setup is a geographic split. In this test, we’ll increase social spend for only a set of geographies. Then we’ll examine the PPC data for the geographies where we ran the test and compare them with areas where we did not.

As you choose geographies, you’ll want to control for other variables that may affect your test. Here are some common issues that companies have run into and need to control for in their tests and measurements:

• You sponsor a sports team, and they’re playing during your test.
  • If the game is regionally televised, this can dramatically affect your test results.
• You’re running TV commercials in only certain regions.
• You choose experimental geographies with many out-of-region commuters, such as New York City, and include New Jersey and Connecticut in your control group.
  • In these instances, grouping a region and its surrounding commuter areas together, and placing other cities with similar characteristics, such as Chicago and Philadelphia, in a different group, can help balance these tests. (Note: in this example, we’re splitting New Jersey in half.)
• Seasonal or local events. Large conferences, festivals, or major weather events can affect your data.

Your control and experimental groups should be statistically similar across factors such as income levels, and urban versus rural regions.

As you set up and measure your test, consider your budget. If you increase social spend and expect higher clicks and conversions for your PPC campaigns, ensure you have the budget to capture the increased demand.

Examine your impression share and impression share lost to budget before and after the test to ensure budget limits won’t severely impact your results.

Dig deeper: Why PPC tests in 2026 call for nuance, not winners

Step 3: The measurement

Measurement can go from very simple to extremely complex.

At a simple level, you can compare platform data to see how your data changed. In this case, a Google Ads report shows how pausing social spending and influencer campaigns across all social platforms (TikTok, LinkedIn, Facebook, YouTube, etc.) affects performance.

For this test, pausing social spending yielded mixed results for conversion rates. As brand searches decreased, conversion rates in some regions increased, while in others they fell.

However, what was consistent was a dramatic drop in conversions.

You can get more sophisticated in your testing. Depending on your analytics setup, some companies want to measure touchpoint differences for their conversions. Others will want to measure overlap rates between social and paid search visitors, or examine attribution touchpoints and models.

Before you set up your test, ensure you have the measurement capabilities needed to understand and interpret the results.

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Step 4: Evaluation beyond the test criteria

As you run various tests, you want to measure the results against your hypothesis. However, it’s useful to list other variables worth evaluating beyond your test criteria.

This is where search consoles, analytics tools, CRM, internal data, and even the paid and organic report can come into play.

In one example, a company was running a test to see whether pausing several advertising channels, from social media to TV ads, would dramatically change its brand search volume. They hypothesized that their brand was so well known in the marketplace that they could cut back on several forms of brand advertising and reallocate that budget to other channels and non-brand advertising.

While the simple paid and organic report in Google Ads won’t tell you the full story about in-store revenue and direct traffic changes, it can serve as a signal to form an overall picture of a very complex test.

They had recently launched a new product line, and that line continued to see a large increase in traffic during the test. However, their most common brand terms saw significant declines from the test. This was a year-over-year comparison across a set of geographies, rather than a period-to-period comparison, to help correct for the increase in holiday traffic that would have occurred during the previous period.

The results were by far the most dramatic I’ve ever seen in this type of test, to the point it was clear other variables had to be in play that could affect the test.

This takes you to the sniff test. Rely on your experience with data to make common sense adjustments. If you look at the data and it just doesn’t seem right, ask yourself whether this makes sense, if it’s a math quirk (common with low data), or if other unforeseen variables are in play.

In this example, no one believed the results should be this dramatic. The company stopped running the test and began an internal evaluation of its organic presence, including Google’s recent updates, changes to AI Overviews, AI engagement, and other factors affecting its web presence beyond its usual marketing channels.

Dig deeper: Are your PPC ads still authentic in the age of AI creative?

What to do with your social impact tests

The test setup is simple:

• Determine your hypothesis.
• Decide how you will test. The easiest setup is a geographic split.
• Make sure you can measure the results.
• Launch the tests.
• Evaluate the metrics for your hypothesis.
• Examine other metrics for insight or additional testing ideas.

For some companies, Facebook and other social channels are their top conversion channels, and these tests won’t be applicable. For others, social media advertising results often look poor when evaluated in isolation.

In these examples, the companies were already running many social media campaigns, so the test was to reduce social media spend. If you don’t run much social media, your test will be to increase your social media spend to see how it affects your data.

I’ve seen a lot of these tests, and the results are highly inconsistent across companies. Many companies will increase their social media spend and see little change in their data. Others will increase their spend and see a nice lift in overall performance. These are tests you need to run yourself, as your results will vary by company.

Running geographic split tests in your social media campaigns and then measuring the results on paid or organic search traffic can give you insights into how to leverage social media campaigns for other marketing channels.

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