How to effectively predict and increase your trial-to-paid user conversion rates

With increasing competition in the OTT and Pay TV space, customer acquisition has become a more challenging and expensive proposition for video services.

As it becomes harder to win new customers, it becomes even more important to retain those customers you already have.

The right conversion strategy is a growth accelerator and will have a significant impact on your business profitability.

In this white paper we will explain how Artificial Intelligence algorithms allow video service providers to build and automatically run more accurate trial conversion prediction models, which predict future conversion rates based on past audience behavior.

There are good reasons you should use machine learning to predict SVOD conversion rates in your acquisition process.

With more and more information about your video users’ behavior, JUMP’s machine learning algorithms become more intelligent and can help identify those trial users that are highly likely to convert into paying users. You can then focus your acquisition efforts on this segment, targeting these high-potential conversion candidates.

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 identify 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.

In this whitepaper we will show you how to build a machine learning-powered trial conversion prediction model that will learn from your video service historical data so that it can both identify what kinds of users are likely to convert and generate conversion probabilities for current trial users.

By feeding this data into your video service’s CRM, customer support personnel will be able to focus on those users who are high-probability/high-value conversion candidates.

What follows is a step-by-step “how-to” guide for building your own model.