In what has become one of the most read series on this blog I am showing some examples of what Adobe Analytics has to offer in regards to statistical analysis. In the previous posts we took a look at simple averages and standard deviations, regression analysis and even forecasting. In this post we are going to use a variation of the simple mean called moving average. When dealing with time series data we might encounter what is called “noisy data”. Instead of showing as a steady line our KPIs might go up and down from day to day, making it hard for us to judge where the general trend is headed. One way of solving this is through the regression modeling we did before, which gives us a straight approximation line. But what we can also do is average the data for a defined window along our series, which is […]

# Tag: Statistics in Adobe Analytics

### Advanced Time Series Analysis through Linear Regression â€“ Statistics in Adobe Analytics

Previously in this little series, we took a look at how we can describe our trended data by using the statistical Mean and Standard Deviation. While this works quite well for data that doesn’t change much over time, it is rather limited in regards to take trends into account. With this post, we are doing something about that issue by using Linear Regression techniques. At the end of this post, you will get an Analysis Workspace project like below, where we can judge trends in data and see changes over time: Let’s get our hands dirty! Limitations of Mean and Standard Deviation Before we start, I want to explain the problem outlined above a bit better. Please consider the following graph I generated with the Workspace from the previous post and some demo data: What we see is a clear trend in our data, since our daily Unique Visitors are […]

### Simple Time Series Analysis through Standard Deviation – Statistics in Adobe Analytics

In my last post, we took a look at how Descriptive Statistical Analysis can help us understand our site performance using the simple Mean. I introduced the concept of conditional counters to help us identify our top- and bottom-performing sites. Today we are going to extend our knowledge of descriptive statistical methods by using Standard Deviation on trended data and apply conditional counters to it as well, but with a new spin. If conditional counters are new to you, it might help to check out that last post! As last time, we are setting ourselves a goal for this post. At the end, we want to have a nice workspace to help us understand our trended data better. We need a way to judge if the fluctuation in our data is within an expected range and how often it is not. This is what we are going to build: Let’s […]

### Simple Mean and Conditional Counters – Statistics in Adobe Analytics

In my last post, we took a look at how we can predict the future through Regression Analysis with Adobe Analytics and visualize it in Analysis Workspace. While that was a quite advanced post, there are a lot of things we can do using basic statistical analysis. This is what we are going to look at in this post, exploring some ways to describe our data in a standardized way. At the end of this post, we want to describe our relative page performance for a website like this, showing us top- and low performing pages and how many there are of both: Describing ranked website performance relative to the Mean This first part will show how we can level-up our ranked reports. Let’s pretend we want to judge how certain pages on our website are performing. To do this, we might start with a simple table containing our Page […]

### Predictive Regression Analysis – Statistics in Adobe Analytics

Adobe Analytics is awesome for analyzing historical data. Besides Segments, Drilldowns or Derived Metrics, it also offers some advanced statistical functions like Regression Analysis. Here are some examples for the different regression models that are available today: It would be really cool if we could use this functionality to predict the future with some regressive models! This is what this article is going to describe by using advanced calculated metrics. In the end, we want to have a graph like this, with the historical and future data in the same visualization: We will go through the whole process of generating a metric like shown above. If you just want the result, you can scroll down to the bottom of this article, where I show the complete metric. Let’s start! Statistics 101: Simple Linear Regression in Adobe Analytics To start things off, let’s remind ourselves what regression analysis does. To keep […]