But you’re busy—you don’t have time to watch KPI indicators all day long. That’s where Anomaly.io comes in. By combining our detection algorithms with your Salesforce data, you can automatically detect problems and notify the appropriate personnel to ensure that speedy corrective action is taken.
Monitoring Key Performance Indicators (KPIs) is essential to running a successful business. As one example, you should examine your lead generation KPI several times a day, to allow you to detect and correct problems as quickly as possible.
Monitoring key performance indicators (KPIs), sales or any other product data means working within an ecosystem where very often you will see metrics correlating with each other. When a normal correlation between two metrics is broken, we have reason to suspect something strange is happening.
As an example, take a look at anomaly.io analytics during its early days (a long time ago). In the graphic above, the new users are shown in green and the returning users in red. Clearly, something strange happens in the middle of November. Let’s use some techniques to find out more!
To detect the correlation of time series we often use auto-correlation, cross-correlation or normalized cross-correlation. Let’s study these techniques to understand them better.
Previously, we looked at using Twitter Breakout (EDM) to detect Anomalies. As with the popular E-Divisive, EDM detects mean shift and changes in distribution. Both algorithms work with seasonal time series, but perform even better without seasonality. Read More
Time series decomposition splits a time series into seasonal, trend and random residual time series. The trend and the random time series can both be used to detect anomalies. But detecting anomalies in an already anomalous time series isn’t easy.
Time series decomposition works by splitting a time series into three components: seasonality, trends and random fluctiation. To show how this works, we will study the decompose( ) and STL( ) functions in the R language.
It can be difficult to detect the underlying trend of a time series in the presence of anomalies, due to unwanted noise. Fortunately, there are techniques to take into account those anomalies, so you can work with this kind of time series. One of them is the moving median.