{"id":9080,"date":"2020-10-09T10:46:40","date_gmt":"2020-10-09T17:46:40","guid":{"rendered":"https:\/\/www.ispot.tv\/hub\/?p=9080"},"modified":"2020-10-09T12:05:25","modified_gmt":"2020-10-09T19:05:25","slug":"theres-a-better-way-to-measure-tv-advertising-lift-and-roas","status":"publish","type":"post","link":"https:\/\/www.ispot.tv\/hub\/theres-a-better-way-to-measure-tv-advertising-lift-and-roas\/","title":{"rendered":"There’s a Better Way to Measure TV Advertising Lift and ROAS"},"content":{"rendered":"\n

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\u2019s where lift analysis comes in. <\/p>\n\n\n\n

What is lift?<\/strong><\/p>\n\n\n\n

Lift measurements estimate the causal impact of TV advertising on consumer behavior<\/em><\/strong> 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: <\/p>\n\n\n\n

  1. People outside the brand\u2019s target, who are unlikely to convert regardless of ad exposure.<\/li>
  2. 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.).<\/li>
  3. People who only convert because<\/u> they\u2019ve been exposed to an ad.<\/em>
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    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. <\/p>\n\n\n\n

    Challenges with measuring lift for TV<\/strong><\/p>\n\n\n\n

    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\u2019s ad or not. The effect of the ad on the consumers\u2019 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:<\/p>\n\n\n\n