The importance of the “user journey” in video services and how to analyse it easily

We know that the way people interact with a video service represents a journey, which, of course, can start and end at any point.

The more we understand the behavior of our user base – their unique preferences for discovering our video service, how they become paying users, and how they consume video content – the better we can create a video entertainment offering that leads to the highest possible number of users reaching engagement status.

First of all, it is important to know how many users have explored our service so that we can evaluate if our service captures potential customers’ interest and if our awareness advertisement activities seem to be working (creating those potential users). When we look at these metrics over time, we can analyze a tendency in order to identify both positive performance and areas of improvement.

If we split the potential users’ data by different dimensions we learn more about the “service discovery” details. For instance, it is a valuable insight to understand which devices potential new customers used to discover our video service.

Great! The first step is done, and we have attracted “new users” to our video service! Now we have to convert these users into paying customers and finally compel them into achieving “loyal user” status. To do this, we need to understand how many new users registered and became “in promotion users” and finally, of those, how many converted into “paying users”. We will want to see differences in a reasonable length of time in order to identify trends and issues in the onboarding process.

It’s crucial to identify the trends associated with conversion issues during the onboarding process and understand at which point of the life cycle we lose clients.

Wonderful! We’ve attracted users and have converted them into paying users, now we have to monitor their activity to verify they continue using the service and do not turn into “sleeping user’s” (the precursor to losing them). As an example, understanding the trend related to content type watched prior to a user becoming a “sleeping user” would be extremely useful to our operation.

Now we take it to the highest level and look at our “loyal users”. We combine loyalty indicators with user activity to identify our most valuable users. Again, we want to understand if there are changes over time and detect trends to understand their behavior (lifetime, watching intensity), level of loyalty status, etc.

Unfortunately, sometimes customers have to or want to leave us. It is important to understand at what point this happens and if it represents a greater trend (more and more people are leaving our service). We call this group the “lost users”. As always, we need to identify trends and understand customer behavior (lifetime, watching intensity), etc.

If you understand your customers’ user journey you can put controls in place across your video service and then you are empowered to influence your users’ behavior.

Do you want to know more? Don’t hesitate to ping me or drop me a line.





Extreme personalisation and artificial intelligence in the world of video
Video recommendations and Machine Learning