To Grow Revenues, Media Companies Must Harness Data


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At the end of last year, Laura Martin, senior analyst for entertainment, cable, and media at Needham and Co., LLC, authored a very interesting report on the future of TV. The report focuses on the continuously fragmenting video content landscape, which has made it increasingly more difficult for media companies to sustain the same level of profit per hour of content. In a world in which consumers' media attention is split among linear TV, digital subscription-based platforms, and digital ad-based platforms, media companies are faced with the increasingly difficult challenge of maximizing profits.

Most media companies have chosen to take the "chasing eyeballs approach"--distributing the same content across new platforms already distributed via linear TV. Martin argues that this is a mistake, as it undermines revenue growth by driving prices downward. Instead, she suggests that media companies develop different content under single content franchises, as appropriate for each platform respectively, in order to drive consumption across all platforms. This strikes me as the best approach, but I would add that a critical component to executing this strategy is leveraging data.

Audience development-Audience development is still a crucial element in growing revenue, and it can't be done effectively without valid audience data. By developing a better understanding of their audiences and using data, media companies can draw larger audiences for new and existing content franchises. For example, through better segmentation in their marketing efforts and deploying direct response tactics such as lookalike targeting, media companies can more effectively win new viewers and consumers. They can develop specific tactics based on the different demographic and psychographic characteristics of their audiences, specific to each platform, increasing conversion rate and tune-in.

Pricing and inventory growth-Another way that media companies can use audience data to increase revenue per hour of content is in the areas of pricing and inventory. For instance, if a media company knows that the audience of a particular show at a particular time is more likely to be in the market for a new car, then the company could conceivably charge higher prices to certain auto manufacturers for advertising in the timeslot. Moreover, if that company is to develop the capability to offer that timeslot to multiple advertisers at the household-level, then it could increase its inventory in addition to demanding premiums for household targeting. However, both of these capabilities require a high level of sophistication in both data capture and operationalization.

Programming-Distributing the same content across platforms hurts revenue growth not only because (as Martin explains in her report) it draws viewers away from premium platforms such as linear TV, but also because it misses the opportunity to fully optimize content across each platform respectively. By capturing and analyzing consumption data at a granular level by platform, media companies can identify which types of content work the best for each platform in drawing viewers/consumers and maximizing audiences across all platforms. Variables such as screen size, interactivity, and audio volume capacity can have a great impact on which content pieces are most appropriate for each platform. Media companies need to collect and analyze the data on the effect of these variables with their audiences in order to sustain and optimize audience growth.

As Martin points out, given their dual revenue stream business models, media companies are in an enviable position in the emerging video content ecosystem. However, diversifying content across all platforms is not the sole solution to overcoming the challenges to continuing revenue growth. The development of data infrastructure, assets, and capabilities is indispensable to driving profits. Moreover, it's difficult to imagine the creation of a robust diversified, cross-platform content strategy without a high level of investment in and deployment of data.