The success of any marketing action needs to be measured with the right KPI’s (Key Performance Indicators). The Financial Times found a unique KPI that allows outstanding predictions of their churn rate. Read further to learn more.
KPI’s are difficult to set
The very definition of those KPI’s is a crucial part of any project. If you take the wrong KPI’s you’ll get a biased perspective on what you are doing. If you chose the right ones, you’ll be able to monitor your actions and improve your performance.
KPI’s are especially important in Big Data assignments where too much data makes it difficult to decide on what to monitor.
RFV : a meaningful KPI for the media sector
The KPI chosen by the Financial Times to monitor its performances is particularly clever. They are using the RFV formula (Recency – Frequency – Volume) from the retail sector and applied it to the media sector the following way. The KPI is calculated (R x F x V) for each user as follows :
- Recency – when did they last visit?
- Frequency – how often do they visit?
- Volume – how many articles have they read?
Astonishingly, the RFV is so meaningful that it predicts cancellation rate.
Y=-0.030437239 ln(x) + 0.0314616343
This is the relationship between the cancellation rate (churn) and the engagement score (the RFV score). As you can see on the graph below the regression is amazingly good. The more engaged the readers, the less likely they are to cancel their subscription.
To learn more about the Financial Times Big Data strategy, have a look at the presentation below. It was given by Robin Goad, Head of Customer Analytics, at the EBU Big Data conference on 21 March 2016 in Geneva. I was presenting right after him on the use of third-party data.
The right Big Data KPI’s are those which you can correlate back to some marketing indicators like churn, retention or customer lifetime value (CLV).
As the example of the Financial Times shows, sometimes the inspiration can come from other industries. Therefore, make sure you don’t stay in your bubble and be curious of others’ business practices.Tags: data mining