Making The New Model Work
Essentially, media content businesses are transitioning from a business-to-business model to a direct-to-consumer model. And this has dramatic implications for the way they need to handle and process data. Here’s a high-level rundown of what they need to consider:
- First, they will need the capability to reliably store and stream the content itself. That typically means access to reliable, scalable, cloud-based services that can guarantee sufficiently high data transfer rates, bandwidth, quality of service and availability, optimized for video delivery. Remember, reliable high-quality streaming video that doesn’t constantly frustrate viewers with stuttering playback, buffering bars or ‘content not currently available’ messages is just the baseline for entry into the OTT market.
- Where speed-to-market and/or social platform visibility are the most important factors, outsourcing the handling of content to the likes of YouTube may still make the most sense. However, an increasing number of content providers are finding that the big online video platforms don’t give them sufficient access to, or control over, their data (or, indeed, a big enough share of content revenues) to maximize their base of digital subscribers or the revenue-generating potential of their content.
- As a content business moves to an OTT model, the amount of user data it has to handle explodes. For example, HBO’s former CIO said that when the company launched its HBO Go streaming service, the levels of customer data it was generating grew 1000-fold. This is the natural consequence of shifting from an indirect model where you are serving a handful of large content distribution partners, to a direct model where you’re serving many individual consumers of content. This shift means OTT content businesses will need the capacity to handle a vast number of transactions and, depending on their business model, purchases.
- All this data can also give businesses unparalleled insight into consumer behavior. That is as long as they can capture it and have the capacity to correlate and analyze it intelligently. Again, they must be sure to work with technology partners that have proven expertise in this area. In addition, they need the requisite data science skills at their disposal to ask the right questions and draw the right conclusions from the mountains of data they’re analyzing. While machine learning algorithms and big data visualization tools are improving fast, without the right level of expertise at its disposal (whether in-house and/or through trusted partners) an OTT content business is unlikely to succeed.
- With growing competition for content everywhere, understanding how consumers think, act, behave and react to content is key to producing stuff they’ll respond to positively. This will also maximize the ability to promote it effectively. Therefore businesses need to analyze and correlate not just their own consumer data, but also what those consumers are saying and doing on social media in relation to the content they provide. They need to ensure their big data analysis extends to these external sources of insight. This is because correctly understood, this will both inform their content development plans and enable them to deploy far more sophisticated and personalized content marketing strategies.
- Those that make the leap successfully will be playing in a space that’s far more fast-moving and dynamic than the traditional broadcast media business. They will be able to deploy, on a smaller scale, the same kinds of personal intelligent content recommendation engines that we’ve become used to from the big boys like Netflix and Amazon. And while the aim of most content providers may be far more modest and specialized than either of those leviathans, with a scalable and reliable cloud-based architecture and the ability to leverage big data quickly and effectively, they will be able to grow as big and successful as their potential allows. In addition, they will also be reaping the maximum reward for their efforts.