At Wicket Labs, we believe you can when it comes to subscribers and your service. That’s why we’ve developed a new way of thinking about user health, our Customer Happiness Index, or CHI® Score. This concept expands beyond traditional consumer satisfaction indices, which are typically based on interviews and surveys. Instead, we’re using data + artificial intelligence to uncover unique, predictive, and most importantly, actionable insights into your customer cohorts.
Our approach is to leverage one of the core value propositions of the WIcket Scorecard which is to build a data set from many sources. Using data harmonization and applying machine learning to this data allows us to look at consumer behavior across many dimensions. CHI derives correlations between each factor and churn probability, which, not surprisingly, grows inversely to happiness.
This approach gives our customers powerful tools to work with. Every subscriber receives a CHI Score, an aggregate of scoring across four weighted categories used to track dozens of features. The categories we use are:
Here we’re focused on the number of series watched, types of content viewed, whether a user is a fan of a series, whether they watch new content upon release, etc.
Features in this category focus on frequency and intensity of viewing. In other words, how often does a user engage with the service, and when they do, how much do they watch? Are they using multiple devices? Do they watch in a discernable pattern?
This includes features like subscription length, acceptance of price increases, consecutive months with usage, renewals, etc.
This category can include data about customer service interactions, correlations between primary viewing device and overall ratings for the app on that device, percentage of session time spent watching content vs. browsing for something to watch etc.
The Power of CHI
With a unique CHI Score for every subscriber, we can unlock several new insights for our customers regarding their users’ current and future behavior.
As alluded to above, there is a direct relationship between CHI Score and churn probability. A glance at the CHI Score for a given subscriber is a good indicator of health and likelihood that they will be around next month and beyond. The power in this comes from understanding why someone is satisfied, or likely to churn and then exploring ways to act in either scenario.
To make this concept more actionable, CHI Scores can be viewed in multiple ways. You can pick a dimension like primary viewing device, and look at CHI Scores for each device broken out by category, enabling you to identify what is driving churn risk, isolate the issue and have a meaningful impact on retention. The Scorecard also allows you to look at CHI scores across other dimensions like sales channel and pricing plan to uncover and address additional areas of concern or opportunities to improve your experience.
To take a deeper look at CHI on a particular device class, we illustrate the variation of user scores vs. ideal score across each category, and how the mean score for each device compares to the overall mean score. This is a great way to identify areas of improvement (below average) and where they might be most effective (concentrated distribution of scores).
Contact Us to learn more about the CHI score, how it’s been integrated into the overall Wicket Scorecard, and how it will help reduce the churn numbers in your video service.
Tags: audience lifetime value • churn • content engagement • lost customers