Identify churn triggers and propensities using real-time viewing behaviour and reduce the impacts of churn

Understand the critical viewing dynamics affecting retention for Pay TV & OTT

Identify disengaged viewers and improve engagement

Create highly predictive customer segments based on portfolio viewing behaviors

Intercept churn attempts earlier to help reduce retention discount offers

Consumers now have a wide variety of choice in TV consumption with the establishment of many providers. Both subscription and advertising-funded services are now out-competing traditional public service broadcasters (PSBs) and viewing to digital platforms such as YouTube is substituting a large amount of viewing minutes that used to be spent on conventional TV.

The critical success factor for the business performance of any TV operator, whether Pay TV, advertising funded or PSB, is to attract and retain viewers. This is becoming increasingly challenging with the explosion in competition for consumer viewing attention.

Acquiring new viewers, users or subscribers is arguably the simpler, although often expensive, task. After all, consumers can usually be tempted to try a free service, whether that is because it is advertising funded or offers a free trial period.

The real pressure on any TV operator’s business plan, however, is the ability to keep consumers viewing on the platform and, in subscription models, paying. The costs of ensuring retention (customer churn), in spending on premium content or retention-driven discounting, can be very high.

Understanding what drives customers to churn and when they are most likely to leave or become dormant, is therefore a highly valuable capability.

And that’s why ThinkChurn is such a powerful solution.

ThinkChurn identifies the viewing behaviours that predict churn

Working with scores of TV operators across the World over many years, ThinkAnalytics has extensive experience of the predictive power of viewing data.

We know that the intensity of viewer engagement has a strong relationship to loyalty and churn propensity.

The ThinkChurn engine analyses real-time viewing data, from linear, VOD or catchup services, to enable a clear understanding of the dynamics of engagement strength in your viewing audiences and create scores that predict the changing churn propensity for every user. This provides a valuable early trigger to prompt downstream churn mitigation activities.


Identify likely customer cancel requests before they happen to reduce retention discounting and increase retained revenue

Determine which viewers are most likely to become dormant or leave the platform

Predict the free-trial bingers who will not remain on the platform or transition to Pay TV

Understand which viewers only use your platform to access specific content and then leave once that content is consumed

Establish which channels and shows act ‘gateways’, capable of transitioning your viewers up the engagement hierarchy

Identify disengaged viewers and understand what content might engage them

Better understand how to profit from the viewer household composition effect on loyalty and retention