We are truly steeped deep in the age of the customer, where ‘choice’ is the recipe for success.
When it comes to entertainment and video, the case is quite the same. The rise of high-speed internet and the proliferation of the smartphone culture has completely changed the way we consume content. Once ‘cable’ dependent, today, we are no longer hostage to a cable or broadcaster. This is all thanks to the rise of ‘Over The Top’ or OTT channels where content is delivered through an internet connection.
Today broadcast media companies are going on the OTT mode and are delivering video content to their subscribers via multiple channels. As the OTT market matures, it also gets more competitive. For instance –
- Customer churn is one of the greatest challenges for OTT businesses
- Ensuring the highest customer lifetime value out of the customer base is difficult
- With more providers entering the market, OTT providers have to come up with ways to get their customers to stay with the service after the initial viewing experience and get them to become avid customers
How big data and analytics can change the world of OTT
The key to a great OTT service starts with an understanding of the customer and responding to their needs promptly – whether it is for content, the user experience, or the business model.
Since the ‘viewer’ lies in the heart of the business, OTT managers have to look at big data and analytics to enable actionable learning of customer behaviors and manage business rules.
Understanding customer churn
OTT viewers today are spoilt for choice. The market is getting overcrowded. Along with the number of OTT players, the choice of providers is also increasing for the customer. Customer churn is a real problem to solve to maintain profitability in the OTT universe.
Most OTT services struggle with retention once they launch. Customer acquisition is also becoming more expensive and challenging as markets become more populated. However, big data and analytics can level the playing field here by providing detailed churn analytics that answers questions like ‘which customers are most likely to churn next month’?
Big data analytics gives OTT providers the capacity to aggregate disparate data sets and develop a 360-degree customer view. OTT providers can use more-accurate churn prediction models and use real-time and historical data, user data and user behavior, and other associated data to identify subscriber clusters with a high risk of churn. They also get detailed insights into the main causes of churn and can proactively take measures to solve this problem.
How do brands use OTT to advertise?
Accurate Audience Targeting
People use their email accounts to log in to OTT platforms. Thus, marketers get valuable data associated with users like age, gender, region, and type of device. Brands make the best use of this data to create customised marketing campaigns. Consequently, they access the viewers with the highest potential, and launch tailored ads for better engagement, which are less expensive, and boost ROI.
To evaluate the effectiveness of their marketing activities, marketers utilise analytics to track and measure data that is not available on traditional platforms, such as viewability, interactions, and delivery efficacy. This information assists marketers to understand what is working and helps them optimise future ads accordingly, to increase conversions.
Advertising via OTT channels is an innovative way to reach your customers/target audience. OTT platforms allow advertisers to distribute their material across all devices (e.g., computers, TVs, phones, and tablets) when viewers access the OTT platform, resulting in “second chance” business. For example, suppose a customer looks for but does not purchase a pair of headphones. In this case, cross-channel marketing can retarget this customer with headphone advertisements on an OTT platform, using the email address of the customer it is linked to.
Besides in-show content, digital marketers leverage OTT channels to reach consumers through unconventional promotional collaborations. These collaborations try to capitalize on the popularity of specific OTT series by developing parallel narratives that sync with the brand’s identity.
PepsiCo’s recent advertising agreements to capitalize on the hype around Netflix’s Spanish drama ‘Money Heist’ is one such instance. PepsiCo began selling limited-edition cans with photographs from the web series as well as a QR code directing the purchaser to an enrollment form for a virtual fan gathering.
Crossing the content chasm with personalization
Personalized, relevant, and contextual content is what OTT viewers demand. OTT has now become mainstream, and the viewers want a lot of content on multiple services.
However, with new streaming services that come online almost every other week, there is more content today than ever has been produced in history. The recommendation engines need more customization and personalization powers to deliver the right content to the users.
OTT content needs to leverage big data and analytics to get to that ‘Spotify’ model where content can easily be served based on individual preference. By combining large data sets of user data and metadata for analysis, OTT providers can fine-tune their recommendation engine and ensure that the right content reaches the right user.
Deep big data analytics also gives OTT providers deeper audience insights. It helps them understand genres of content that are in high demand, what content the audience demands at what time of the day, when do they pause, or what do they skip. Based on this data, OTT providers can make informed decisions on content dissemination.
Improve customer experience
Understanding territory-specific nuances of user behavior and gaining insights into device demographics and platform infrastructure becomes essential as OTT providers look at wooing international audiences. Additionally, gaining granular insights into real-time across live and on-demand services also becomes essential to improve customer experience and stay on top of the OTT game.
Big Data and Analytics play a significant role in providing deep insights into all the influencers of customer experience by looking at all the data intelligence. Analytics helps in getting a complete and multi-dimensional understanding of viewer experience and gives OTT providers information granularity to benchmark things that matter most, identify disruptions that impact engagement, and make smart business decisions without ambiguity influencing it.
Using behavior-based audience insights and fan analytics enables OTT providers to profile the viewers accurately. This helps them make more informed business decisions on programming choices, marketing effectiveness, predictable cross-selling, and upselling opportunities, making it more relevant and contextual to the viewer.
There are many online tools and resources that can help you enter the OTT space with a background in data analytics, digital marketing, and big data. With a little research and creativity, you can have a successful career in the OTT space.