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Demystifying Cross-Media Ad Impact
Yannis Pavlidis – VP, Data Science and Analytics, DISQO
In this session, Yannis Pavlidis of consumer insights and CX firm DISQO tackled the challenges of benchmarking, cross-media outcomes and brand lift due to incomplete data from siloed platforms and media channels. In the opening, Yannis provided a refresher on the importance of benchmarks and obstacles from existing approaches to benchmarking (e.g., inconsistent methodologies, outdated data and collection techniques). The discussion examined solutions to address issues in data collection concerning benchmarking ad impact, which streamlines the process using consented, single-source data. The presentation also examined calculating benchmarks based on data taken from one source group (rather than two unaffiliated groups), considered the recency of the campaign used and subsequent behavior(s) which then can be correlated with survey responses. The advantage of using consented single-source data is that it can lead to more insightful, relevant and consistent outcomes in benchmarks.
Key Takeaways
- Challenges with existing approaches to benchmarks included the following:
- Inconsistent methodologies across social networks make data comparison difficult when assessing cross-media campaigns.
- Behavioral data is often aggregated from more than one source, making data triangulation inefficient and unreliable (e.g., comparing audiences that are not the same).
- Outdated benchmarking data often fails to capture more recent substantial changes in the U.S. consumer landscape and the introduction of Generation Z to the consumer marketplace.
- Inefficiencies in the benchmarking process are addressed by using the same audience and methodologies across social platforms. Data and information gleaned from surveys and behaviors of consumers come from a single source. In addition, results from campaigns focus on the past three years, creating recency and relevancy.
- Calculating benchmarks are based on campaigns no further than March 2021. The median lift score is calculated using the difference between the exposed group and the control group.
- Different categories are considered when specific benchmarks are calculated. In addition, a threshold of 15 brands was implemented to create variety and statistical significance.
- Audiences surveyed are opt-in and tracked using metered data to assess ad exposure and downstream data. Surveys are provided to exposed and matched control individuals to assess attitudinal changes. Additionally, surveys and behavior can be correlated.