Consumers are constantly bombarded by advertising to the point that determining the impact of one ad versus another has become incredibly complex. For most brands, TV is the biggest investment in the media mix, so quantifying the value is critical. But with all the noise, how can you give TV ads the credit they deserve? That’s where lift analysis comes in.
What is lift?
Lift measurements estimate the causal impact of TV advertising on consumer behavior and help quantify the business value driven by TV advertising. When we think about advertising impact for a particular brand, we separate potential consumers into three categories:
- People outside the brand’s target, who are unlikely to convert regardless of ad exposure.
- People who are likely to convert whether they see a TV ad or not (e.g., who find out about the brand through other advertising channels, word-of-mouth, etc.).
- People who only convert because they’ve been exposed to an ad.
The third group is the most valuable to marketers because it contains the people who are most likely to be influenced by TV advertising. With lift measurement, brands can identify where these people tend to watch TV, track campaign performance over time, and quantify the incremental business caused by ad exposure.
Challenges with measuring lift for TV
The most precise way to estimate the causal effect of advertising would be to run a controlled experiment in which consumers are randomly assigned to either see an advertiser’s ad or not. The effect of the ad on the consumers’ likelihood to convert in each group could then be measured. Methods like this are relatively common for digital advertising, but they are prohibitive in the linear TV advertising space, in that such an experiment would require TV advertisers to do one of the following:
- Go dark for some period
- Invest in advertisements that do not directly benefit the brand (e.g., PSAs)
- Only measure the impact of local ads, where the airing schedule can be varied across different markets.
These strategies can take a tremendous amount of time and money to implement. They’re also necessarily idiosyncratic — they are not a readily scalable solution when your goal is to provide real-time measurement of ad impact for hundreds of brands.
Thankfully, there’s a better way to measure TV advertising lift and ROAS. Download the full article to learn about the data science and methodology behind iSpot’s unique lift attribution model, or contact us if you are interested in seeing a demo.
About the Author
Nicole Lawless DesJardins (Ph.D.) is a Principal Data Scientist at iSpot. She helps lead the overall strategy for data science at iSpot and she mentors other scientists. Her Ph.D. is in personality psychology and her academic research focused on making first impressions and gaining and asserting social status.