3 questions to ask as media transition from the precision era to the predictive era
Digital Subscriptions Blog | 23 November 2020
In a recent conversation about data and measurement, it became clear that the way we look at data is changing. With changes in third-party data and cookies, we become first-party focused. With better tools and machine learning, we lean away from our historical reporting and start to focus on predictive moments.
These changes are an escape from data overload. Our perspective on what to measure and valuable events is shifting to indicators that help promote action.
If you’ve looked at analytics software and wondered what to measure or what data is important, then the predictive era — even if in its simplest form — can provide a new set of lenses with which to identify data that can help influence action.
These are three questions I’m starting with as we enter discussions on how data can help us predict users’ behaviour and decide what journeys and campaigns with which to experiment.
1. Can we take action based on this data alone?
If your answer is no, then you may want to look at this data throughout the user funnel. What actions do users who visit blank pages take? Why are they visiting the page? Who is visiting this page the most?
Looking at data as if we were storytellers can provide insights leading to opportunities to improve retention and churn. It can even attract potential customers.
If your answer is yes, then what actions and what teams will spear head these tasks?
2. Can the data predict an action users might take?
If your answer is yes, how can we intercept this moment? What else do users who visit this page do? What can we do to change the action?
If your answer is no, what combination of events can help predict an action? For example, if users visit a blank page, they are more likely to take blank action.
When we look at data as tools to predict behaviour we have the opportunity to intercept an undesired action, or multiply the effect and impulse actions aligned with our goals. These data points are the building blocks for experimentation and new journeys. Some of the first places to start are where the users pay, interact with support, and within profiles.
3. Looking at the data, what can we replicate?
This question, while simple, is the greatest indicator of potential. If we are unable to identify what is worth doing again, then all the effort, learning, and changes are lost. Data’s greatest promise is becoming a roadmap of actions that, when completed, result in the actions we want to see.
As we enter the predictive era, the possibilities of scale continue to grow. Crossing these practices and identified data with efforts in marketing and retention offer promise, and they allow our teams to focus on experimentation instead of analysis.