Answering the What & the Why of Consumer Behavior

For more than three decades, MarketCast’s market research has helped some of the world’s biggest media and entertainment companies, sports leagues and consumer brands launch new products and content. Today, our analysis of fan opinions help marketers and researchers better understand their core audiences and avid fans, including their preferences, tastes, opinions, as well as how they might act in a given situation.

Traditionally, we’ve done this by having researchers survey audiences with sample sizes ranging from a few hundred people to a few thousand, depending on the project. However, in the last few years, a fundamental shift has occurred from traditional research-based ways of gauging audience beliefs and behaviors to data-driven ways of analyzing how they actually behave, including what they watch, buy and listen to.

At MarketCast, we believe the fusion of primary research and big data science marks the industry’s most significant change since Arthur Nielsen pioneered market research in the 1930s.

While big-data-driven research is still in its infancy, the main difference between the two is that the big data approach relies heavily on passive data collection from larger audience samples and a wider range of data sources.

Passive datasets typically reflect consumer activity rather than intent. If a market researcher asks someone about their plans to watch a movie or purchase a brand of beer, the answers will create a picture of their preferences but might not reflect their actual behavior. Whereas, if we look at the data about what they are watching on a streaming service or purchasing from the grocery store, we will understand their actions, but we might not understand what motivated them to act.

The ideal approach is to combine big datasets that reflect customer behavior with primary market research that can validate the big data’s representativeness and explain the motivations that lead to these behaviors.

Consider how MarketCast uses market research to help Hollywood studios, streaming services and TV networks generate feedback and understand viewing intent from test audiences. Our mix of qualitative and quantitative surveys and analysis helps studios fine-tune their marketing assets and teaser content to better appeal to target fans, helping them drive box office results and tune-in.

We ask potential audiences, including fans of a specific genre or franchise, survey questions related to the movie or TV show being tested. This often includes their opinions about the characters, storyline, narration and soundtrack. As long as the respondents represent a fair cross-section of the film or TV-watching public, or a segment thereof, our studio and TV network clients can use the feedback of those interviewed to create a picture of fan opinions.

With the addition of data science and advanced analytics, we can augment this survey approach by analyzing big datasets of an audience’s previous viewing habits, demographics of TV and movie viewing, social media activities, media spend, and moviegoing behavior. Combining this with the primary audience research allows for far more opinions to be taken into account, creating a richer and more insightful portrait of the fan.

For example, take a concept to re-create a 1970s crime drama or a Gen-Z update to a sitcom like Friends. Data scientists can forecast how popular revivals will be by looking at the success of similar re-runs on cable TV and other revivals’ success on streaming. We can also determine which genres or cast members may perform well by analyzing relevant social media activity data. Just as traditional market researchers have to combine good survey development skills to ask the right questions, data scientists need to connect the right questions with identifying the best datasets to analyze.

In both approaches, the questions are likely to be the same. While the market research approach can engage hundreds of individuals to understand their preferences and motivations, data science can connect with millions of consumers to overlay actual behavior. Combined, we can answer both the what and the why of consumer behavior, offering a powerful combination for marketers.