Adobe Analytics – Anomaly Detection

What´s Anomaly Detection?

Anomaly Detection is part of new & cool stuff from Adobe Analytics and provides a statistical method to determine how a given metric has changed in relation to previous data”

Is an anomaly the same thing as a spike or a dip?

Not exactly 100%

A spike or a dip is what happens when a metric dramatically increase or decrease
for a specific period of time. And it might be “created” or “expected”.
For example, if we run an extra £10000 PPC campaign, then it’s normal we will have an increase in traffic (due to that campaign). Thus, if we have 20% more of traffic and 17% more of conversions, that’s not an anomaly, just a spike.

An anomaly is more about the way that metric has changed and has an statistical approach.

For example, if one day 23% of the orders come from a specific campaign that represents just 3% of the traffic, that’s an anomaly, but can also be a spike or not.

It worth taking a digging, and the results are statistically significant (it’s highly recommend to thick the box “Show Only Statistically Significant Items”)

As we can see in the graph below we can see that there is an anomaly on the 29th of June, but it’s not really a spike

How can we get started with Anomaly Detection?

1- Anomaly Detection can be found within “Reports”, and then Site Metrics


2- Select the metric/s & the period

Just click on “Edit Metrics” and then choose a “Training Period”

  • Metrics

You can select one or more metrics (so you can see the relation between i.e. two metrics)
You can select every Success Event, and also the Standard Events related to eCommerce (cart additions, views, removals, orders etc.)

  • Period





The three training periods available are: 30, 60 and 90 days. Note a bigger training period may reduce the size of an anomaly.

3- Take a digging for a specific anomaly

Once you select the metric and timing, you will see a graph showing the evolution, pointing out the anomalies
As soon as we click on an anomaly, we see below the graph the actuals and what would be reasonable for that metric during that period of time. Additionally, we also see its impact on percentage (in green if it’s positive and in red if it’s negative).

Then we should click on analyze (above the graph) to see the “contribution analysis”

4- Check the possible reasons

Adobe Analytics suggest a range a “items” (that can be product, campaign etc.) in which an anomaly has been spotted.

Each posibitility has a contribution score that take values from 1 to -1:
1: complete association for a spike or complete inverse association for a dip
0: No association for contribution
-1: Complete association for a dip or complete inverse association for a spike.
In the image can see in the second row: 1% of x has generated 23 of y…

5- Create a segment and inspect it


Just click on one of the items (rows) and a button to create a segment containing that item (product, campaign, referrer etc.) will appear.

Next steps? Save the segment and apply it by referrer, device etc. in order to take a digging and know what´s going on..

As you can see, it’s very fast to identify what’s “unusual” and the segments we need for our analysis, and it will save us loads of time.

Any idea? Any comment? Any complaint? Leave your comment and I will get back to you. You can also contact with me via email o through my Linkedin and Twitter profiles

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