When you’re working on multiple campaigns, you end up with tons of data. Say you’re running 1000 ad campaigns. Naturally, you want to understand if everything is working with each one.
That’s a lot of data to work through.
You might end up with that data on multiple spreadsheets. But reading and understanding it all could take hours. You’re almost certain to lose focus and miss something important. Maybe something that could change your approach completely and make you and your clients much more money…if only you knew about it.
You need a system for analyzing data
That system is anomaly detection. This is a problem-solving technique used by statisticians.
An anomaly is a statistical outlier. It’s easy to see outliers when you put the stats into a graph.
These are obvious outliers. But it’s not always so simple.
Consider this graph:
Is the last point on the graph an outlier? It could be, but whether it is depends on what you’re hoping to achieve.
Outliers are not the only anomalies. There are also inliers.
Here is an example:
This graph shows website sessions. The number of sessions increases on Mondays and the number of weekday visitors is around double that at weekends. Except in the last week, which shows Monday and Tuesday sessions stuck at weekend levels.
This is a good example of an anomaly that cannot be tracked by rule-based alerts.
Anomaly detection would highlight it automatically. It’s exactly the kind of unexpected anomaly that you’re likely to miss if you’re using spreadsheets.