How to Measure Incremental Lift

How much impact did your digital advertising campaign make? That’s what you want to know. But with the ad tech landscape growing more complicated by the day – walled gardens, media plans divided across measurement platforms, the coming third-party cookie deprecation and the rise in identity and privacy concerns – finding these answers can be elusive.

But it’s still possible. You may not be able to directly observe your digital advertising campaigns’ impact, but here at Goodway Group, we can tell what reactions are triggered using incremental lift.

Defining Incremental Lift

If you shut off your advertising campaign tomorrow, customers will still make purchases. But how much more would they have made had the campaign been live? This “more” is called incremental lift – what happens when all else is held constant and something changes in response to a stimulus. In other words, incrementality measurement lets you understand the true impact your marketing strategies have on a customer’s decision to purchase.

Understanding Incremental Lift Types

Each lift study type below seeks a baseline for comparison but from different angles:

Pre-/Post-Lift Study

This is the simplest lift study by far, yet it’s most likely to be skewed by seasonality and trends.

How It’s Done: Measure your audience or market response for two weeks. Then run your ad campaign for two weeks and measure the change in conversions.

Year-over-Year (YoY) Lift Study

This lift study type makes room for seasonality and requires less data-gathering than others. Yet, regionality and industries affected by climate changes could impact results.

How it’s Done: Run a campaign and compare it to the previous year’s campaign results.

Control vs. Exposed Lift Study

This lift study is the gold standard in public health because it requires a random selection of the target audience to receive an ad, while another random selection of the same target audience doesn’t.

How It’s Done: Identify two user ID pools of the same data-backed qualifications: One ID pool should receive a campaign ad, while the other should receive an unrelated public service announcement (PSA) ad.

Choosing the Right Lift Study

Determine Your Ultimate Goal.

What is it you want to do? Increase awareness? Foot traffic? Sales? Trial rates? The goal you choose will guide all your measurement decisions, so be sure your advertising efforts match your goal.  

Identify Available and Measurable Proxies.

What were the transaction amounts? Number of downloads? Certain correlated on-site activities? (Don’t count clicks, though. Online engagement and conversions don’t correlate with clicks.)

Executing a Lift Study

Understanding incremental lift requires a strict scientific method approach to determine direct cause and effect. Use the scientific method to ensure accuracy and replicability: Observe, hypothesize, test, implement, repeat.

  1. Observe a desired outcome.
  2. Hypothesize the impact a stimulus will make.
  3. Test two randomly selected samples, both with and in the absence of the stimulus and then confirm or deny the hypothesis.
  4. Implement the results into strategic, budgetary or optimization decisions.
  5. Repeat since the quest for better results never ends.

Tap into ad tech’s vast data ocean and start analyzing now. When your data collection, organization and maintenance are comprehensive enough, you can use statistical modeling to help you find the correlation strength across cause-and-effect relationships (how much impact each variable has on the outcome), which can help you make solid data-driven decisions about your future campaigns.

Ongoing test-and-learn lift studies across advertising audiences, channels, geographies and tactics and so on will continue to reveal incrementality. Take these results, for example.

Growing a Retailer’s Presence and Driving Online Sales

When a leading mattress retailer wanted to grow its presence to attract new high-value customers, we used a suite of cross-channel measurement solutions to examine the entire customer journey from upper-funnel media exposure to on-site purchase. Our client was then able to track the incremental performance lift that programmatic video and connected TV added to display advertising performance beyond last-touch attribution. When CTV or programmatic video preceded display advertising in the consumer journey, we observed a 7x lift in revenue per exposed user.

This retailer also wanted to determine the effectiveness of advertising efforts on driving online sales. We developed a rigorous measurement solution, a ghost bidding methodology, that seamlessly created exposed vs. control groups without wasting budget on PSA ads, which allowed us to directly correlate digital media spend to the customer purchase decision. We found a 12.1% lift in display advertising, a 17.4% lift in native advertising – totaling more than a 29% lift in combined channels.

Accelerating a Client’s Customer Acquisition

Another client of ours, a baby nutritional product line, wanted to make informed decisions about its media mix and budget allocation. Using Amazon Marketing Cloud, we found the purchase rate was 3x higher when customers were exposed to both display and Sponsored Product ads than Sponsored Product ads alone and 13x higher when customers were exposed to both prospecting and remarketing ads than remarketing alone.

Embracing the Science of Marketing

“The days of intuition-driven marketing are coming to an end,” our president Jay Friedman told Advertising Week. “Marketing is becoming a science, and most of the world still resists acknowledging this transition.” While the world is resisting, take the opportunity to embrace this transition – to break away and lead the industry.

Reach out to us to learn more about our novel incremental lift approach and measurement solutions. We can help you implement a lift study on your next campaign so you can clearly see the impact you’re making and confidently share the results with your C-suite.

As director of data strategy and business planning at Goodway Group, Nick guides internal teams and clients to find meaning in data, and he uses math to help make clients heroes. Leveraging years of industry analytics experience from Digitas and Millward Brown Digital, Nick structures knowledge development to ensure all client campaigns succeed.