Signal measures and evaluates the association between a correlation event and a goal event and quantifies the correlation between the two. This facilitates a deeper understanding of the behaviors that drive customer conversions, and can help guide product decisions.
Note: This video is not current! While the general functionality of Signal hasn't changed, there are now more features available that are not highlighted in the video.
Signal Use Case
Using an example can highlight the value of quantifying correlations between events.
A music sharing app, for example, may want to understand the correlation between top events and users who purchase a song on the app. It is important to understand what the optimal actions that users take before purchasing a song are.
Building this query would look like this in Signal:
Values will be returned after running the correlation. For example, “Song Played” could have a strong positive correlation with purchasing a song. Most of the users who played a song later purchased a song.
This information can be used in future product decisions. For example, knowing that those who play songs are more likely to purchase songs, building tools to encourage song plays could be a next step. This would hopefully lead to a dramatic increase in the amount of users purchasing songs.
Signal Machine Learning Model
Signal calculates correlation using a well-known statistical algorithm called the phi coefficient. This value is further supplemented with an evaluation of how actionable this information is using Mixpanel’s opportunity measurements.
The phi coefficient is a single number between -1 and 1 and it indicates how closely an event moves with your goal event. 1 means that 100% of all users that did the goal event also did the correlation event. -1 means that 0% of users that did the goal event also did the correlation event.