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Determining the Value of Emotional Engagement to TV
Pedro Almeida – CEO, MediaProbe
Context matters—not all reach is equal, and so, we need a way to qualify each impression and valuate each of these impressions. Metric of valuation needs to be valid, reliable and have predictive power for business outcomes. The research focus: 1) What can we say about the value of emotional engagement (EE)? 2) Can we model the value of EE via its impact on memory? 3) Can we use EE to optimize and valuate content and ad positions? How? Methodology: MediaProbe used Galvanic Skin Response with participants who were exposed to content through a MediaProbe panel (U.S., 2,700 households). Data gets delivered second by second and data extracted goes toward creating an impact measure of how much people are reacting to what they are watching. The platform calculates an impact value that enables comparisons across media platforms. There was an added layer to see whether participants are leaning into the content and are engaged. U.S. TV dataset includes over 45,000 participants, reaching over 85,000 hours. More than 1,000 TV hours are monitored and over 42,500 ads. Using a subset of 16,351 ads and 329 “premium pod” formats, participants watch content and are then asked which ads they remember. Findings:- Enhancing the emotional impact of an ad in 150 EIS points equates to adding a second 30’ ad unit. This will increase probability of brand recall by 15%. For each 100 points, this increases probability of brand recall by 10%.
- Single best predictor of whether someone will respond to an ad is how much a person was engaged with the content prior to the ad. EE carries over to the ad break. It’s more engaging pre-break, in earlier breaks and earlier position in break, which leads to higher ad impact.
- However, this is different across genres. Genre moderates pre-break emotional patterns. This is further differentiated within genres. For instance, people will react differently to ad breaks when watching soccer vs. some other sport. MediaProbe shows that there is 66% similarity between various award shows in terms of EE to ad breaks. They use this data to realize the value of different ads placed in different breaks (1st, 2nd, etc. break) and pods. Emotional engagement helps better predict ads performance.
- Additional findings show that first-in-break still rules and that premium pods deliver higher recall.
- Ad EIS is systematically associated with ad recall.
- It is possible to optimize ads for estimated impact by advertising in the most engaging content and being present after the most engaging moments.
- Different genres tend to have typical pre-break engagement morphologies. This allows to estimate the delivered value of each pod position (and order in break when relevant).