Interview with Jerónimo Macanás about the uptick in streaming consumption and why converting trial users matters:
We all know that confinement measures in many countries have significantly increased TV and streaming consumption, but what about subscriptions? Do you think the rise in consumption will also be reflected in video service companies’ revenues?
Many analyst firms, like Strategy Analytics, are predicting an additional 5% growth in global video-on-demand subscriptions in 2020 compared to pre-pandemic forecasts.
This seems like good news in the short term for streaming businesses, but the global economic crisis that is expected to follow will force people to choose the streaming service that most closely delivers what they want. So the factors that make a streaming provider stand out from its competitors will make the difference when users are prioritizing which paid streaming subscriptions they will keep once the trial period comes to an end.
Additionally, with increasing competition in the OTT space, customer acquisition will become a more challenging and expensive proposition for video services.
Given the forecasted media consumption increase, I believe streaming companies are likely to see a significant increase in the number of trial subscriptions, not necessarily paid subscriptions. Whether streaming businesses will be able to capitalize on this special momentum or not will be determined by their ability to convert these additional unexpected trial users into paid users.
In order to make the viewer’s shortlist after his or her trial period has ended, it is essential that streaming businesses have the right conversion strategy. This strategy will be the key growth accelerator and will have a significant impact on profitability.
Is it really possible to predict future conversion rates based on past audience behavior?
Yes. It is. With today’s technology, it’s now possible to build vertical SVOD machine learning models specifically designed and implemented for the video industry. This means you will not only be able to identify those subscriber clusters with high conversion potential, but you will also be able to pinpoint the leading drivers for such successful conversion rates. You will therefore be able to take proactive steps to ensure it. Machine learning-powered trial conversion prediction models can learn from video services’ historical data so that it can both identify what kinds of users are likely to convert and generate conversion probabilities for current trial users.
With the increasing numbers of trial viewers using video services, there will be more information available about video trial users’ behavior than ever before.
We are encouraging our customers to use machine learning algorithms to identify those trial users that are highly likely to convert into paying users so their marketing teams can then focus their acquisition efforts on this segment, targeting these high-potential conversion candidates.
Once they feed this data into their video service’s CRM, customer support personnel are able to focus on those users who are high-probability/high-value conversion candidates, thus maximizing the number of trial users converted into paying subscribers.
How do you identify the best metrics to create an effective trial conversion model?
Performance metrics in machine learning are crucial; if you are going to use the results to influence your viewers’ behavior, you need to trust your data.
There are a bunch of metrics to ensure your predictive models are good enough, but the first and most important step is to define what you want to maximize with your predictive model.
Machine learning is not magic, and there is always a compromise between how accurate the prediction can get and the effectiveness of the target you are looking for, in this case predicting future converts, from trial to paying users.
You will need to decide which conversion approach best aligns to your overall business strategy: maximizing the video service’s profit, prioritizing the early identification of as many future conversion candidates as possible, or minimizing the potentially intrusive impact of acquisition marketing actions on “non-candidates”. This is just one of the business considerations you’ll need to address before going hands-on with the creation of your AI models.
To learn more about How to effectively predict and increase your OTT Service trial-to-paid user conversion rates, join our next webinar.
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