Beyond ratings: Applications of Hadoop in media and entertainment
If you think it’s tough trying to choose a TV show to watch at night, try living a day in the life of a worldwide multi-system operator(MSO).
When you’re a cable company with tens of millions of subscribers, it can be a constant challenge to have the level of customer insight required to make decisions that drive real-time business performance.
One of those challenges is measuring program viewership.
The goal of moving beyond basic TV ratings and simple consumer demographics to establishing a secure analytics environment to integrate subscriber, set-top-box (STB) and third-party, enriched data in near real time is something leading media and entertainment (M&E) organizations continue to embrace. After all, they need to deeply understand and predict consumer behavior to effectively drive growth and retention strategies. These strategies include the ability to better, more accurately reach consumers across viewing platforms and enable dynamic, personalized content delivery.
I’ve said it before and I’ll say it again: This is no longer a competitive differentiator. It’s a consumer requirement.
One of the ways in which leading M&E organizations are delivering on this requirement is through the rapid adoption of Hadoop technologies. Hadoop (an open-source software framework for storage and large-scale processing of data-sets on clusters of low-cost commodity hardware) continues to be a go-to big data platform for measuring TV viewing or other digital media behavior. IBM InfoSphere BigInsights brings the power of Hadoop to the entertainment enterprise. BigInsights makes it simpler for media professionals to use Hadoop and, therefore, build big data and analytics applications that are ready for deployment in the shortest amount of time and at a lower cost of entry and ownership.
For example, BigInsights has a set of log analysis tools which can help mine STB viewing data, click streams and OTT/TV Anywhere application logs. This is extremely useful (particularly by Cable MSOs) to directly measure TV channel or program viewership. When successfully tied to subscriber information, it can be used for a multitude of insights, including affinity by different households and audience sentiment, and then help drive down further to the individual viewer.
You can deduce how this level of audience granularity and targeting can be valued by advertisers, especially at the local level. And for cable companies who want to boost CPM on incremental ad deals and see a reduction in effective CPM erosion through yield management, they know that analytics is tied to measurable outcomes that drive value for the enterprise across all functions.
Furthermore, this is where we get back to the “consumer requirement” I mentioned earlier. As cable MSOs continue to evolve from utility providers to lifestyle companies, their subscribers expect to receive the ads and offers that are relevant to them in the time and place in which they are viewed. High Expectation Consumers, as I like to call them, also want the tailored content and programming they want, exactly when and where they want it regardless of broadcast schedules, DVR and On-Demand cloud access or device type.
The primary question is: Are you ready to evolve?
Case studies for reference
Some additional ways in which leading M&E pros have deployed IBM InfoSphere BigInsights on Hadoop to support a Smarter Customer Experience:
- University of Southern California Annenberg Innovation Lab gains insight into public sentiment with near-real-time analytics of social media streams
- Wimbledon Championships serves up an ace performance delivering fascinating new analysis and insights direct to viewers via mobile, TV and web
How is your organization utilizing Hadoop to create a more developer and user-friendly solution for complex, large scale analytics? Let us know in the comments below!