Subscriber Retention is a Team Sport

Who cares about subscriber retention? The real question is, who doesn’t? Sure, we can point to the lifecycle marketing team or executive P&L owner. But anyone working in media and entertainment today knows that engaging noncommittal subscribers is a team sport. It takes strategy, coordination, and training across disciplines to have a shot at defeating the greatest rival OTT platforms face: subscriber churn.

There are a great many variables that determine how and when – and even why – OTT service subscribers will churn.

Better TV shows on another platform? Call the content team.

Bad streaming app experience? Get product development in here.

Too expensive? Have finance model new pricing scenarios.

No matter what the reason is – and it’s personal to every subscriber – the bottom-line churn rate indicates whether an OTT service will successfully scale or collapse over time.

A successful vMVPD or specialized OTT video service requires many people in a variety of roles to make it all work.

Finance

First: The finance and revenue team will hold a stake in subscriber retention. When subscriptions increase, they are the happiest folks in the building. Break out the champagne, we’re on our way and subscribers are coming in the door quickly! More subscribers mean more revenue and no one is happier than the finance group when a service is thriving and growing.

When the trend line turns and revenue isn’t flowing in because of churn, the finance folks will want to know why. So will their counterparts in every other department (more on that in a minute). It shouldn’t surprise you to learn that the reasons will span across multiple components of the service and subscriber base.

It is impossible for finance to combat increasing churn and deliver actionable intelligence without understanding why it’s happening. Financial data must be exposed to a bench of stakeholders (who must in turn share data with the finance team) to move the trend line in the right direction.

Product

Second: The product organization will also be front and center with a keen interest in churn and how to keep their audience engaged and avoid subscription changes. Product and service evolution is listed as a top transformational priority by 65% of OTT executives, according to Ernst & Young. To compete with the 600+ (and growing) OTT video platforms, providers are investing in product, content, and customer experience optimizations that will keep subscribers from turning to the competition.

It is mission critical for product teams to identify, analyze and act upon the valuable data available about audience behavior. How are they interacting with the content? What is the most watched content? How was it promoted? What ancillary content can and should be represented to the user and audience? Are certain shows performing better with certain sets of viewers, perhaps by age or geography?

Reviewing all these questions, as well as using AI tools to understand subscriber data, will provide OTT product teams with a strategic look at the business to determine some tactical actions that they can consider to improve audience engagement, drive revenue, and reduce subscriber churn.

Sales and Marketing

Sales and marketing teams hold the most obvious stake in customer churn. After all, they’re the ones beholden to KPIs that directly reflect retention – like customer acquisition cost (CAC), customer lifetime value (CLV), average revenue per user (ARPU).

The challenge for sales and marketing teams is getting ahold of the data they need to figure out why subscribers are churning, before it happens. CRMs only track a limited amount of data; usually, OTT teams can see a subscriber’s basic demographics and some in-app behaviors. But what about quality of service (QoS), billing data, and online activities outside the app?

Assuming that the available data is the most relevant data is a huge mistake. It opens the doors to human bias, manual errors, and undetected correlations. Let’s look at segmentation as an example. A typical OTT provider today can segment customers by factors like service tier, content preference, or product expiration date. But they can’t tell you whether a specific promotional offer would work best for high-risk subscribers based on home wifi speeds

Pulling in all the data that does matter to subscriber churn takes a lot of API work, heavy reliance on business analysts, and time. With month-by-month subscription terms, OTT providers can’t afford to wait that long just for a historical snapshot of what happened. They need to know what’s going to happen next month, next quarter, next year.

Data Science? Yep, Them Too.

When it’s time to answer business-critical questions about subscriber retention and churn, teams inevitably turn to data science and engineering… most likely when business analysts and domain experts have hit the limits of tools at hand. The CRM can’t read transactional billing data, customer satisfaction scores can’t connect with other systems of record, predictive revenue forecasts need to be validated, and so on.

But with data science teams spending 45% of their time just preparing data, there’s not much bandwidth left to analyze it for every team that needs insight, urgently, ASAP, please!

For problems like churn that pose an existential threat to OTT providers, some data science teams turn to custom in-house solutions. Content recommendation engines are a common example. If existing software and internal models aren’t powerful enough to understand what subscribers want to watch on a service provider’s specific platform, they naturally gravitate toward building an engine that is.

But data science teams are discovering the pains of building solutions to manage churn; according to Gartner, 85% of AI projects will deliver erroneous outcomes by 2022 – thanks to bias in data, algorithms, or the teams that took on the noble task of managing them. Not to mention the cost, maintenance, and machine learning training it takes for the thing to work. (More on that in our Enterprise AI Guide for Media and Entertainment .)

With all eyes on data science when it comes to providing the insight that teams rely on to understand every aspect of churn, organizations need solutions that are accurate, scalable, and ready to deploy ASAP to the endless list of stakeholders that play a part in subscriber retention.

Winning the Subscriber Retention Game

See? Churn can’t be understood nor remedied by one person, nor one data source.

The people charged with analyzing, managing, and mitigating churn are all part of a team. The same rules and expectations apply to the data they’re trying to manage. Each data source – authentication, payments, audience behavior, playback – is another player on the team. To provide unique insights and robust intelligence in this case, they must all play by the same rules.

There are two key points to remember if you want to win the game: first, you need a playbook. If everyone is reading different data sources and building different models, executing strategies is a messy (and losing) proposition. Second, you need the right equipment; if teams aren’t able to view and analyze real-time data under real-world conditions, they’ll never understand what it takes to retain subscribers.

As any good coach would tell you, teams are the successful combination of great individuals. As any good player will tell you, developing the skills to play your part is key. As OTT providers seek to improve subscriber retention, they need to provide every player with a common playbook and the right equipment.