Handling dozens of ad campaigns on one platform is something any marketer can manage. But when you scale to hundreds of ad groups and thousands of ads, details that can make a difference easily slip by. This is where you need the help of machines.
Performance marketing has been a game of large numbers for quite a while now. A multitude of channels, dynamic search ads on Google, programmatic networks… there are many things going on, many of them “under the hood”. Analyzing performance and optimizing can be really tough if you don’t have the right data in the right tool. And even if you do, there could always be nuggets of information that could turn out to be very useful, but are often missed.
The most important thing you need to know when it comes to campaign performance is finding out as soon as possible if there are any problems with its delivery. Any behavior that deviates from the standard can be seen as a threat to the campaign’s performance. And in that forest of data, how can you find these differences?
Enter – anomaly detection.
What is anomaly detection?
Anomaly detection is a process of identifying any patterns that deviate in any way from the standard behavior in any given dataset. It is used by machine learning algorithms to find any outliers to already detected patterns in datasets.
It can be supervised, semi-supervised or unsupervised, depending on the level of detail and precision which is needed, and on the type of datasets analyzed. Anomaly detection (sometimes referred to as “outlier detection”) is an important technique that highlights information that could easily be overlooked by humans as they try to make sense of large datasets.
What is anomaly detection used for?
There are numerous applications of anomaly detection and it has in fact been a common feature of modern systems for a very long time. Remember that time your bank blocked your credit card? That was a form of anomaly detection built in into the banking systems that analyze all card transactions and flag if a card has been used in an unusual location or with a strange, repeating pattern. And yes, we all know it could use a lot of refinement, but it is nevertheless very useful protection against widespread credit card fraud.
Adverity's Anomaly Detection feature
Why is anomaly detection important for marketers?
When it comes to marketing, especially performance marketing, anomalies can be both a good and a bad thing. Anomaly detection can help you to identify technical issues with your website that would otherwise go unnoticed for days, or it can show you that one single ad in a large ad group is performing much better than all the others.
With this important ability to spot outliers even in the largest datasets, anomaly detection is an important ally of a data-driven marketer. Not only can it prevent huge problems from snowballing next time technical things go belly-up (and this will happen, sooner or later), but it can also help massively improve performance by helping you identify that one channel/creative/social post that works significantly better than others, which you can then replicate in future campaigns on other channels.
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How can I use anomaly detection?
You can use anomaly detection on any type of marketing data, from impressions and clicks, to budget spend, or website page visitors. Applying it to common data sources used by marketers, such as Facebook Ads or Google Analytics, can do wonders for your marketing ROI.
To gain these benefits, you need to have a system that continuously feeds the anomaly detection engine with new data from all relevant sources. You can do this manually, but unless you are only looking at a very small amount of data, we don’t recommend it.
Slight plug moment - yes, platforms like Adverity offer both the data integration engine and an AI-powered system that powers the anomaly detection machine. However, self-promotion aside, no matter how you want to do it, anomaly detection is a hugely powerful tool that any data-driven marketer should have in their armory.
Rather than sleepless nights over the performance of your ads or websites, instead rely on machines to keep you posted on the latest developments, and even give you advice on what you can do differently. Isn’t that a huge step forward in managing marketing campaigns than making conclusions based on boring, outdated charts printed from PowerPoints?
Yes, we think so too.
Related articles:
- What is Marketing Mix Modeling (and how can it help with marketing attribution)?
- What is a Cohort Analysis (and how can marketers use it)?
- What is the Difference Between AI and Predictive Analytics?
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