With the seismic shift in television content and viewing, capturing TV consumption by households and individuals at scale is more critical than ever. Until now, it has been difficult, if not impossible, for advertisers to answer the question, “Who watched my ad?” as well as for content creators to answer, “Who watched my show?”
The understanding and measuring of television consumption habits are the operational driver of various marketing activities, ranging from ad budget planning and allocation to attribution. The classic measurement framework against which brands plan and allocate their TV ad budgets - currently about $70B annually in the US alone, has historically been the use of panel data.
Data from traditional panels can provide some insight for marketers, but also present disadvantages
Disadvantages of Panel Data
1. Panel Bias
Panels represent a small subset of the population and under-represent certain demographic groups like blacks, Hispanics, and young people. Further, there are studies showing that people’s report of their own media usage can differ considerably from independent assessments.
2. Household Bias
Most panel data is generated at the household level and doesn’t reflect individual habits and preferences. Ad planning needs are much finer, encompassing behavioral as well as nuanced demographic characterizations based on individual actions and engagement.
3. Content Bias
Due to the viewing preferences of the small panels and the limitations of data collection methodology on televisions (OTT content is not detectable via cable box), much of the content now consumed on the TV screen remains undetected and immeasurable.
These disadvantages highlight the need for a clear solution that can record consumer viewership of TV content with the following characteristics:
At a scale substantially larger than traditional panels
At an individual level - not just at the household level
Across multiple content sources - linear TV, OTT, etc.
The Mobilewalla consumer intelligence platform offers the industry’s largest, and most comprehensive consumer data repository with demographic and behavioral data from 1.5B devices across 30+ countries. Mobilewalla’s extensive database contains detailed geographic, behavioral, demographic and activity-pattern consumer data that enables precise audience segmentation using proprietary machine-learning algorithms and verified heuristic techniques.
By combining these data assets with granular content consumption data, now available from the TV ecosystem, Mobilewalla has created the first individual viewership database. Revealing, at both the household and individual levels, content that was consumed on TV screens anywhere in the United States. The breadth and depth of this data positively impacts two key areas:
Current measurement standards are deeply flawed due to panel size. Leveraging data from a highly representative sample of the population presents a measurement framework that yields results of markedly higher precision and accuracy.
Mobilewalla data reflects the activities of over 90% of the 18+ population in the US. Combined with our at-scale visibility of TV ad consumption, this results in the fulfillment of two hard-to-achieve but essential objectives of accurate attribution:
• Observing post-exposure behavior of consumers who saw the ad.
• Creation of look-alike control groups of similar scale as exposed groups.
Connect with our data experts to get detailed look at how Mobilewalla can provide you with the deep insights around TV attribution and measurement your brand needs for better ad planning and budgeting and understanding the impact of your TV advertising spend.