Newsday’s Predictive Data Analytics Driving Subscriber Retention
Overview of this campaign
The application of predictive analytics to audience data can provide an unprecedented level of intelligence into the quality of the relationship a publisher has with each and every one of its consumers; ultimately being core to retention driven decision making.
At Newsday, we have lead our data strategy by imagination, which has included intelligently understanding the following through predictive data analytics.
- The quality of the relationship we have with each of our subscribers
- The engagement habits and dynamics for each and every one of our subscribers for the various interactions they have with our brand
- The probability of canceling for each and every one of our subscribers
- The steps and stages of why and when our subscribers cancel
- Which subscribers will and will not be influenced by retention treatments
- The optimal personalized retention treatment for each subscriber
- The content types and consumption patterns that predict great retention and greater price elasticity
We have leveraged the advanced predictive analytical tools of churn and persuasion modeling to guide in the development of practical applications; delivering concrete results with significant margins in retention and revenue. Four case studies demonstrating such are.
- Dynamic messaging for engagment deviations - payments
- Surprise and delight retention marketing
- Optimizing surprise and delight retention marketing
- Determining niche product opt ins
Results for this campaign
The results of all four case studies have been strong; ultimately contributing to a 35% year end and 24% full year reduction in churn. Specific results are as follows.
- Dynamic messaging for engagment deviations - payments – reduction in formers is 13% at 3 to 4 months post launch
- Surprise and delight retention marketing –
- High churn subs – reduction in formers is 40% at 5 months and 20% at 9 months
- Accelerating churn subs – reduction in formers is nearly 30% at 5 months and 15% at 9 months
- Optimizing surprise and delight retention marketing –
- Modeling suggest a reallocation and incremental spend $80k will save 3700 more subscribers at 3 months.
- Reduction in formers via gratitude to high churn subs is 35% at 3 months vs. -15% prior to persuasion model
- Determining niche product opt ins –
- 12% reduction in churn probability upon opting in
- 5% more price increase with nearly no stops at 10 weeks post price increase
- 50% reduction in churn at 4 months post opt in
These case studies are just the beginning of the practical applications that we are developing with the advanced predictive analytical tools of churn and persuasion modeling aimed at improving the quality of the relationship with have with each and every one of our consumers. Our plans include, replicating the code and marketing of dynamic messaging for payment deviations into other customer touchpoints including digital consumption, emails, and push notifications. Also, the data related strategy for determining niche product opt ins and more broadly, content development considerations, is largely driven by predictive analytics.