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Explaining Way To Blue's buzz charts

The purpose and methodology of Way to Blue’s buzz charts, published by ScreenDaily.

Benchmarking Performance

Way To Blue use social data to identify the extent to which audiences engage with films, TV series, and entertainment brands during the lifecycle of their campaigns. We have been tracking film performance in terms of social analytics metrics for over three years. 

At Way To Blue we go beyond measuring volume of conversation in the social space (we liken the volume metric to awareness) to understand the extent to which audiences anticipate or intend to see the film at the cinema. We call this metric “intent to view”. More so, we have statically validated this “intent to view” measure; it provides a significant and unique relationship with theatrical performance.

Additionally, we aim to understand who is engaging with the title, where they are engaging online, why, and what elements of the film are driving their social conversations. We gauge these social conversations by understanding the sentiment and themes around various marketing assets. Together, and often with very quick turnaround that the social space affords us to immediate access to social data, we are able to inform our clients of the ingoing likely success of their campaign, and understand the relative impact of various components of the campaign and marketing strategy.  

Taking all these social metrics together with what we know about how past or comparable titles have performed, and what types of movies they are, what kind of audience rating they have (e.g. all ages, parental guidance), and what weekend of the year they are released, we can make comparisons about the social performance of the films in the social space at any point in the campaign, before they have opened at the box office.

In order to understand the extent to which a title is over or under performing on social metrics, we have developed norms by county and a number of genres at each point in the theatrical campaign—from the first teaser to the night before box office opening. This normative data, used as benchmarks, are the product of aggregating every major film opening in the US and UK in the last 1 year on social analytics metrics, what kind of films, and finally theatrical performance. Contextualizing film performance with benchmarks, based on recent and robust, historical data allows for more granular, accurate, and insightful comparisons of films when evaluating the social media performance.

Every Friday we report on films at different stages in the theatrical campaign – two weeks pre-release, one week pre-release, films releasing this weekend, and films in their second week at the box office.

By benchmarking performance on social analytics metrics against our proprietary norms, we can contextualize performance on proprietary key metrics. And by taking into account title-level variables that we know are linked to box office performance, we can provide more robust analysis of marketing efforts and their likely impact on the films box office performance.

Key Terms Explained

Conversation volume: Overall amount of conversation about a film

Intent to view: Amount of the conversation that audience members showing proclivities to attend in the theater

Sentiment: Amount of the conversation that is positive, negative, and neutral

Time period: we don’t generalize data going farther back than one year as we recognize the social/digital landscape is rapidly evolving

Coming Soon – we analyse social conversations across the preceding Friday to Thursday period, two weeks prior to release

Next Week – we analyse social conversations across the preceding Friday to Thursday period, in the week prior to release

Openers – we analyse social conversations across the preceding Friday to Thursday period, week of release

Holdovers - we analyse social conversations across the preceding Friday to Thursday period, first week at box office

Genre Norm: Benchmark for US and UK at which half the titles in the last year of this genre preformed above and below

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