Identify your most important anomalies with Millimetric’s weighted data sources

AI anomaly detection and automated KPI tracking startup Millimetric introduces new weighted data source feature to their SaaS platform

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Understanding the impact of specific anomalies on your digital performance can be difficult, especially when you’re analysing data across many different data sources, some of which are more valuable to you than others. That’s why AI anomaly detection and KPI tracking startup, Millimetric, has introduced a new way of ranking different anomalies by data source to make understanding the impact of anomalies easier for you.

Previously, anomalies in any of the data sources integrated with the platform were equally ranked with Millimetric’s innovative ‘impact score’ rating. This meant that anomalies in every integrated data source were treated with the same weight. For instance, if you had an anomaly in your Facebook Ads data which had an impact score of 7, the same anomaly in your Google Ads data would also be rated a 7.


A Google Ads anomaly in the Millimetric platform


However, the new feature, weighted data sources, allows the user to choose how much weight each data source should carry in their anomaly calculations, which is then reflected in the ‘impact score’ number the anomaly is assigned.

With this new approach, users can ensure that they are being made aware of the most impactful anomalies and performance opportunities in the data sources that matter most to them. For instance, if Google Ads is your most-used ads platform, you can choose for anomalies in this data source to be assigned higher impact scores than Facebook Ads. This means that a Google Ads anomaly with an impact score of 7 would now be rated as a 4 when it appears in Facebook Ads, since you’ve indicated it’s less important to you.

You can set up you weighted data sources by logging into your Millimetric account and heading to ‘settings’. By default, all the sources are weighted the same, but you can adjust by sliding the weight bar up or down accordingly.

Adjusting the weight of data sources in the Millimetric platform


Note that this will only work for anomalies that occur after you’ve set the data source weight – historical anomalies will appear as they always have.

With data ranking on the Millimetric platform, you can know the issues that need to be dealt with the most importantly. Try it out today.

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