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How to do Lift Analysis Using Multi-Touch Attribution Data

By December 16, 2025No Comments

If you’re on LinkedIn, you’ve undoubtedly heard some of the criticisms of multi-touch attribution (MTA), specifically around how it can’t track brand or other non-click-based interactions. And, let’s be honest, there is a lot of truth to that. Let’s look at the Display channel, for example.

With Display advertising, the average click-through rate is somewhere between 0.05% and 0.15%, give or take. With an MTA system that relies on clicks, your Display channel will consistently be underrepresented. Especially considering that Display can still have an impact (and is designed to have one) beyond clicks.

So, does that mean that MTA platforms–such as Marketo Measure, CaliberMind, and Dreamdata–just can’t track the impact of channels such as Display? No, it doesn’t. We just have to use a different methodology. And that methodology is Lift Analysis.

What is Lift Analysis?

Lift Analysis is a methodology that has been in use long before multi-touch attribution systems were invented. It’s related to, but isn’t the same as, incrementality testing.

What Lift Analysis seeks to do is to understand the “lift” generated by a marketing tactic against a specific KPI. That KPI could be…

  • New Leads
  • General engagement
  • Web traffic
  • Followers on social
  • MQLs
  • Pipeline
  • Sales/Revenue
  • … and many others!!

Lift Analysis does not require a click to measure the impact of an ad/campaign. So, how do we leverage multi-touch attribution data to do Lift Analysis? Let’s go…

Using MTA to do Lift Analysis

The first thing we need to do is to take a look at the components of the campaign. How are we running this campaign? Is it Paid Search, Display, or multi-channel? Who is the target audience? We need to answer these questions and a few others, but the target audience question is the most important for Lift Analysis.

Once we have those questions answered, then we need to look at the goals/objectives of the campaign. What is this campaign hoping to achieve? New leads? MQLs? Demo Requests? What are we hoping to drive with this campaign? That is going to determine the KPIs for which we measure the “lift”.

Now that we know who our audience is and which KPI(s) we want to track, we can design our Lift Analysis strategy.

Let’s use the example below, because I think it’s a very relevant and real example to use…

  • The example campaign in question is a product launch campaign that is using Paid Social, Paid Search and Display to market the launch of a new product, Product XYZ.
  • The goals for the campaign are to generate web traffic, new leads, MQLs and Demo Requests for the new product.
  • The audience is all existing customers (for cross-sell) and a specific target account list that we feel are going to be the best accounts to market this to.

Now, let’s design our strategy.

We know that our KPIs that we want to track are; new leads, MQLs and Demo Requests. We also have two separate account lists that we’re targeting, along with all other accounts that we aren’t targeting. These are the two most important things we’re going to focus on.

For web traffic, we’re going to set up a report that looks at new web touchpoints generated for the existing accounts and the target accounts. Then, we’re going to set up a report that looks at new leads generated at the non-target accounts. In these reports, we want to set up a time frame for 30-days prior to the launch of the campaign, and a time frame for 30-days after the launch of the campaign. In these reports we also want to include information about the Marketing Channel they came through. BUT, we aren’t using the Marketing Channel or any UTMs as filters. This is because, for the purposes of Lift Analysis, we want to see all sources of web traffic.

We’ll set up the exact same reports for the new leads, the MQLs and the Demo Requests. Again, we won’t be using any Marketing Channels or UTMs as filters. They’re good to have in the report so we can see how people are engaging with us, but we don’t need to use these as filters.

Now that we have our reports set up, it’s time for the “Analysis” part of Lift Analysis. What we’re looking for is “lift” for KPIs we’re tracking, for the targeted accounts and the non-targeted accounts, pre- and post-campaign launch.

  • If the “lift” for the targeted accounts is higher than the “lift” for the non-targeted accounts, then this campaign appears to be having a positive impact
  • If the “lift” for the targeted accounts is the same as the “lift” for the non-targeted accounts, then the campaign isn’t responsible for the “lift”… it’s possible that something else is
  • If there is no “lift” for the targeted accounts or the non-targeted accounts, then the campaign is having no impact at all
  • If the “lift” for the targeted accounts is lower than the “lift” for the non-targeted accounts, then the campaign appears to be having a negative impact

This is traditional “Lift Analysis”. And, much of what we’re doing above doesn’t really need MTA to work.

But, MTA can add a ton of value by adding context. The MTA data can help us understand which Marketing Channel people are actually using to engage with us. It can help us understand–for the ads that do generate direct clicks–which ads are performing better than others. It can help us understand which forms people are filling out. It can help us understand which keywords are being searched. It can help us understand which pages people are landing on prior to filling out a form.

The MTA data can help us optimize the campaign based on what it tells us. Now, it won’t help us optimize the Display ads… because that data would be limited to impressions at best. But, it can help us quantify the impact of the whole campaign–including the Display ads–by looking at the data holistically without using any Channel or UTM data as filters.

Another thing that you could do here, which is more similar to Incrementality Testing, is to isolate a small control group of targeted accounts and not run the Display ads to them… or pick and choose the Display ads. This gives you another cohort to compare the “lift” against, which would help you isolate the impact of the Display ads specifically.

Final Notes on Lift Analysis

As you can see based on the above example and the strategy built for it, MTA is not actually required to do Lift Analysis. BUT, MTA can help out quite a bit by helping us understand more about the “lift” itself. MTA can provide that additional data that helps us optimize due to the granularity of the data provided.