Increase Engagement and Stickiness
This is the sixth and final blog in a series talking about how video streaming data, pulled from various parts of the workflow, can be used to support business goals. This post will focus on increasing subscriber engagement and stickiness.
Do you know how much content your viewers are watching? On what device? At what time of day? If you don’t, then you are missing a critical puzzle of long-term success for your streaming platform: user engagement. Understanding how often your viewers watch, and from where, and on what device, is the fundamental data of your business. Not only can this data help shape advertising, it can also help you determine the long-term viability of your platform. If few users are watching little content, if only a small portion of your subscribers are logging in every day, it may signal troubling times for your business. Thankfully, with access to the data from your streaming workflow, you can take action to increase engagement and stickiness.
The Two Core Values of Engagement and Stickiness: DVU and MVU
Two key metrics, Daily Views per User (DVU) and Monthly Views Per User (MVU), tell you how often your users are returning to your platform to watch content. Industry statistics tell us that users who are more engaged and return more often, are five times more likely to continue paying for the service. In short, a retained user is far more valuable than a newly acquired user!
Measuring these values and using them to experiment with a variety of levers in the platform (i.e., ad placement, subscription tiers, content recommendations, etc.) can help ensure you drive up the level of repeat engagement. Most streaming platforms, for example, get only about 20% of their subscriber base to continually engage. In fact, studies have shown that 80% of users churn after three days of subscribing. Improving those numbers, which will have a demonstrable impact on sustainable revenue, ad impression sales, and ad impression value, involves continual monitoring.
So how do you get users to come back and consume content every day, week, and month? You utilize player and delivery data to build content journeys for different user personas. For example, some users like to binge on content. So you offer them that experience. Other users like the slow burn, such as releasing a single episode each week at the same time (similar to traditional linear television). By looking at the N-day retention of users who perform the playback start event for a series, you can see how the airing date affects their engagement, and can segment and adjust marketing campaigns to re-engage accordingly.
Cohort Analysis: An Example of Using Subscriber Data to Affect Meaningful Improvement in Your Streaming Platform
As defined by Bill Su in his Medium article, cohort analysis is, “…an analytical technique that focuses on analyzing the behavior of a group of users/customers over time, thereby uncovering insights about the experiences of those customers, and what companies can do to better those experiences.”
Capturing data about individual users, for instance, customizing content recommendations, is important but understanding how groups of similar, or dissimilar, users behave is critical to improving the overall experience. For example, employing cohort analysis with data gathered through Datazoom, could provide you a deep understanding of consumer behavior over the first 72 hours (a critical OTT platform timeframe when users generally make the decision to stay or churn; of course, this could be aligned with a free trial time frame as well).
Using this data, you could then make needed changes to the content recommendation engines, platform features, etc. to try and mitigate that early churn. Another example might be to understand how a decline in viewing minutes relates to churn. You could look at users who have a certain percentage of decline over a given time frame. Then you could reference that against churn rates for that group.
Ultimately, you could set system alerts, perhaps automated emails that hit employee inboxes every morning, when multiple users have hit the start of the declination threshold enabling marketers to target specific programs at those users in the hopes of driving their viewing numbers back up.
Although cohort analysis setup can be complicated to ensure its reporting the right insights you need, it is becoming a valuable tool in the OTT operator’s toolbox for preventing churn and increasing subscriber satisfaction.
Keep Them Coming Back For More
Of course, having a rich and popular content library is sure to bring users back to your platform day-after-day. But even if you don’t have a billion-dollar content budget, you can still utilize viewer data to not only understand user receptiveness to your content, but also a host of other behaviors which all relate to how often your users will return to your platform. Keeping them engaged with the content that is most relevant to their behaviors will ensure they return often and, hopefully, bring others with them.
Use Streaming Data To Increase the Value of Your Advertising
This is the fifth blog in a series about how video streaming data, pulled from various parts of the workflow, can support business goals. This post explores how to employ streaming data to increase the value of your ads.
Of course, increasing the number of ad impressions is a priority for ad-supported streaming platforms as it directly relates to revenue. But you can’t just up the number of ads per break (going from three ads in a pod to four) as that may have disastrous consequences to your business: users could stop watching content, resulting in lower views per month per user, resulting in, ultimately, fewer impressions you can sell. To ultimately increase the value of your advertising, both the number of ads you can place and their CPM, you have to look carefully at several critical factors such as:
Understanding How Viewers Feel About Your Advertising
Of course, increasing the number of ad impressions is a priority for ad-supported streaming platforms as it directly relates to revenue. But you can’t just up the number of ads per break (going from three ads in a pod to four) as that may have disastrous consequences to your business: users could stop watching content, resulting in lower views per month per user, resulting in, ultimately, fewer impressions you can sell. To ultimately increase the value of your advertising, both the number of ads you can place and their CPM, you have to look carefully at several critical factors such as:
Tolerance for ads
Ad placement
Inventory forecast
Ad Tolerance
Without understanding your viewers’ tolerance towards ads, it’s impossible to know how to maximize the number of ad impressions they will accept within any piece of content. But this is measurable by taking the rate at which users drop off during ads, both across a piece of content and within individual ads. You can then segment that data by location, device type, subscription tier, content type, and content title, which will help determine under what circumstances users have more or less tolerance for ads. For example, there might be some content with which users are so enthralled (i.e., a very popular title) that they will accept more ads within the ad pods.
Ad Placement
The placement of ads is essential in overall AVOD success. Unlike traditional commercial television, ads do not need to follow a set pattern of the display. They can be stitched into streaming video at whatever time is deemed appropriate. So, looking at the distribution of playhead position on the heartbeat event within a content title can be a good indicator of sections within the video that are of most interest to users (i.e., before they drop off). You could then position an ad pod right before this content segment to ensure users will watch the ads and impressions will be recorded. You’ll also want to ensure that you’re showing your last pod of advertisements before the majority of viewers drop-off.
Inventory Forecast
Maximizing the value of ads to the business isn’t just about having a large ad pool; it’s about ensuring all available ad impressions slots are filled. There is nothing worse than having only a few advertisers try and fill all the slots, forcing viewers to watch the same ad over and over again! By looking at the historical ad request and impression counts, you can see how much ad inventory was left unfilled at the end of the month. Extrapolating the ad request count can help provide a forecast of inventory to sell the next month. Ultimately, you want to sell as much upfront as possible without going over (i.e. over-subscribing). To achieve that kind of accuracy, you can use a confidence interval on the forecast and train the model with more historical data.
Increasing Your Advertising Value With Higher Cost Impressions
Until your business reaches a certain tipping point, such as a number of subscribers, or a certain number of minutes viewed per month per subscriber, advertisers will not pay a premium for your impressions. Furthermore, some will require the ability for a third-party solution to measure your audience and performance as a way to ensure their money is being spent on the right people, in the proper context, and appearing as expected. But with a bit of upfront analysis, you can increase your ad space’s value by identifying the high-value users and providing this information to advertisers for targeting.
First and foremost, an ad is more valuable if users watch it all the way through. But an ad is even more valuable if there is some user action associated with it, such as a click. You can utilize the ad milestone event to determine which users are more likely to consume the majority of an advertising. Keep in mind that it may make more sense to break down behavioral patterns by content title rather than user ID. This is because some content can have a disproportionate amount of ad completions and clicks.
Second, providing personas to your advertisers can increase the value of your CPMs. Building these personas is readily available to you from delivery and player data about viewing habits. For example, users who watch fashion shows are probably interested in fashion. In short, the content genre often goes hand-in-hand with user interests. But using a custom metadata field called interest, you can add more information to the database of users. It’s possible, knowing basic demographic information about your users, that the personas you develop could include age and gender as well.
Finally, premium ad sales must be supported by data. Advertisers aren’t going to pay more for your ad slots than a competitor if you can’t show them that the extra cost is worth it. You can do this by sending back metadata in the ad request. For example, consider a user who is interested in fashion. This user’s ID is marked in the ad network, which is passed back when the player makes an ad request.
The ad network can utilize this data to deliver a more targeted ad. Of course, you can generalize this and serve fashion ads on the fashion content as well, but by tracking at a user level, you can target them across content and deliver those ads which would be most compelling to their interests, regardless of the content they are viewing.
Get Smart About Advertising To Increase The Value of Your Advertising
Although there is a lot of data you can pull from your ad servers about impressions and time viewed, it’s not married to your users. Using the data from within your streaming workflow, from CDN logs and the player, you can get a user-level understanding of ad views, tolerance, and more. This will provide much deeper insight and enable better ad forecasting, which helps to guide revenue projections and improve the growth of the business.
Improve Your Streaming Revenue With Data From The Workflow
This is the fourth blog in a series talking about how video streaming data, pulled from various parts of the workflow, can be used to support business goals. This post looks at the relationship between streaming data and business revenue.
Streaming platforms don’t exist for altruistic reasons. They are businesses that want and need to generate revenue from the content which is consumed through their apps and services. This is why it’s so critical to be able to tie in data generated from within the workflow to business objectives because it not only allows measuring the ROI of specific content titles, but it provides a true basis for understanding the bottom line.
And although streaming revenue platforms differ in many respects, there are two business needs which many of them share: balancing content types and encouraging new subscriptions.
Balancing Content Types
If your streaming revenue platform consists of multiple content monetization strategies, for example, AVOD (ad-supported video), SVOD (video only available to subscribers), and TVOD (videos purchased and downloaded), you can use the data within your delivery workflow to maximize the potential of each video type.
Start with taking the subscription rate as the number of unique users (who are current, paying subscribers) and have playback start events. This is your baseline. Once you have that, you can experiment to see how content availability within the different video types impacts revenue. For example, you can change some of the titles in the SVOD library to AVOD to make them available to non-subscribers. Also, you can do the opposite and put some of the normally free, ad-supported content behind the paywall to increase the value proposition for subscribing. With your experiment parameters set, you can target a subset of users based on their user ID or device ID.
Once you’ve completed this experiment, measure the impact by looking at the user ID and device ID targeted with these experiments. This can reveal how the changes impacted revenue (i.e., ad revenue lost versus subscribers gained). Furthermore, you can extrapolate to understand revenue-per-user, per minute, and conversion rate over time. Of course, experimentation involves iteration. With a steady flow of streaming behavior and delivery data, you can modify the experiment as you fine-tune the content balance across your video types. Some additional experimentation parameters can include ad-supported subscription tier (i.e., will users pay a lower subscription amount for limited ad inclusion), pricing of subscription tiers (i.e., will users pay a different amount for more access to content or for increased simultaneous device support), and the number/duration of ads.
Encouraging Subscriptions
There is no doubt SVOD will net the most long-term and sustainable revenue per minute. A primary goal then is to encourage subscription. One way to do that is to target marketing efforts toward users who are more likely to subscribe. Consider the following scenarios:
Ad-skipping. Users who frequently skip ads can be targeted with ad-free subscriptions, especially if they have many minutes viewed per month. In that case, they clearly see the value of the content but would rather not see the ads.
Win-backs. Part of any subscription service involves users who let their subscriptions lapse (or deliberately unsubscribe). Understanding why provides a clear opportunity to re-engage with those users and offer them an incentive to reactivate their subscriptions. For example, given their past viewing behavior (again, tied to that unique User ID), you can identify content-related behavior, such as favorite title, genre, actor, etc., and recommend content to them.
Different subscription tiers. For users who may watch sporadically or infrequently, you could experiment with different subscription types. Perhaps something much cheaper and based on the number of hours viewed each month.
Your Viewer Data is Tied to The Health of Your Streaming Revenue
Of course, the data gleaned from within the streaming workflow is integral to ensuring operational reliability, consistency, and high-quality viewing experiences. But that data is also foundational to the health and growth of the streaming business. Why? Because it provides unparalleled insight into what your viewers are actually doing. Are they watching ads? Are they watching specific content? Are they logging in each day? Data from the player, CDN logs, and other streaming technology stack components can answer those and other questions so that you can better fine-tune your business to better align with how your subscribers want to interact with your platform.