Today's digital media landscape is fragmented and diverse. Consumers expect on-demand access to rich (and continually updated) media content on a growing array of internet-connected devices. According to a recent study from Google, each day consumers spend 4.4 hours of leisure time in front of smartphone, tablet, PC, laptop, and TV screens. Considering that only 43 minutes of that time (on average) is spent in front of our old friend the television, media companies are under pressure to publish content to the web at an explosive rate. With the growing number of online touch points, the media landscape has undergone a radical change. The massive amount -- and increasing complexity -- of data that must be analyzed presents media companies with an enormous challenge.
Effective measurement and analysis are the keys to making money in the media industry. They drive advertising and subscription revenue, enhance SEO, feed recommendation engines, shape the social conversation, and impact a brand's reputation in the market. Effective data management can make or break a company's online fortunes. Yet, managing this challenge has never been harder.
In the early days of mass media, things were simpler. Before cable television and the internet, content access was more tightly constrained. Consumers read the local paper with their morning coffee and watched more or less the same programs on television at the end of the day. On the weekends, they piled the kids into the station wagon to check out the new releases at the multiplex.
There were only a limited number of data points for media companies to track and manage. Old measurement techniques often relied on extrapolations based on averages, and those data were put to use to measure limited factors like circulation numbers, ticket sales, viewership, and drop-off. Sophisticated models were applied to analyze these numbers, and the techniques used at the time were effective. However, the technologies involved were quite primitive in comparison to modern-day data collection methodologies.
Now, things couldn't be more different. Media companies have to accurately capture real-time data generated from multiple screens, mobile platforms, and social networks. And it's no longer sufficient to make broad generalizations about viewing habits and other consumer behavior. Each visitor has to be tracked and measured independently, taking into account specific user context (i.e., time of day, location, previous visit history, etc.). The possibilities are dizzying, and the amount of available information is potentially overwhelming. Media companies not only know that you watched "Breaking Bad" last night, but that you watched the entire first season last weekend on your iPad on the way to work and that you were inspired to do so based on a suggestion from a friend on your Facebook wall. Data generated from every user interaction with media can now include explicit interests and behaviors ("I like this") as well as implicit or predictive data (based on my behavior, I might also like that).
This wealth of data has the potential to be a valuable asset, but how can organizations determine which data will be most relevant to analyze in order to inform web content strategies and increase user engagement?
Capturing, analyzing, and acting on relevant data to optimize the customer experience and increase online revenue is easier said than done. There are a wide range of collection methods available, but they often pose technological, practical, and ethical challenges. Here are some important questions to ask:
The critical piece of the puzzle is not just which data points you collect but how you use them to inform your decisions and optimize the online experience for your audiences. New technologies that leverage NoSQL data stores, in-memory computing, and a semantic web are helping businesses overcome the technical challenges with measuring data, but we are still honing the craft.
So how will media companies manage this mountain of data in five years? Give us a few minutes to analyze some of the stats we've pulled, and we'll have some answers.
Doug Heise is product marketing director of CoreMedia.
On Twitter? Follow iMedia Connection at @iMediaTweet.
"Analyzing financial data" image via Shutterstock.
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