Salesforce Anomaly Detection using Anomaly.io

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detect anomalies in salesforce

Running a business successfully you have to monitor some important Key Performance Indicators. For example, you should always look at your lead generation KPI several times a day.

Why checking your KPI several times a day? To detect problems as fast as possible. Of course, you can’t spend your time simply watching the KPI all day long.

Using Anomaly.io detection algorithms with your Salesforce data, it’s possible to automatically detect when something goes wrong.
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Detect anomalies in correlated time series

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anomaly in time series correlation

Monitoring KPI, sales or any product you’re looking at an ecosystem. In an ecosystem, things behave in harmony (kind of). Very often you will see your metrics correlating with each other. When a usual correlation between two metrics gets broken we can suspect something strange is happening.

Let’s look at anomaly.io analytics at its beginnings ( a long time ago ). See the above graphic. In green the new users and in red the returning users. Clearly, something strange happens in the middle of November. But let’s use some techniques to detect more!
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How to seasonally adjusted a time series in R

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seasonal-adjustment

What is a seasonal adjustment? Why and when to use it? How can it be calculated? A seasonally adjusted time series is a time series with a removed seasonality. To compute this metric, we need to calculate the seasonality. Then, we remove it from the original time series.
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Moving Median is robust to Anomaly

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moving-median

It might be difficult to detect the underlying trend a time series follow in the presence of anomalies. It often has unwanted noise. Hopefully, there is some technique to overpass those anomalies in order to work with this kind time series. The Moving Median is one of them.

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