There is an universal truth in life: Inspiration always strikes when and where you least expect it. The same happened to me the other day, when I was reading High Output Management by former Intel CEO Andrew Grove. While the book is definitely worth reading for anyone interested in management, analysts can benefit just as much from reading it to get inspiration for valuable performance indicators and visualizations. Quite early in the book Grove presents one of his favorite visualizations to track progress towards specific goals: The linearity indicator. This chart shows the current progress towards a set target and where the performance might be heading. Here is his example for a hiring target from the book: My initial reaction was “wow, this is super cool and simple to understand”. If the current progress is above the linear progress, we’re in good shape to reach our goals. If it is […]

# Tag: Calculated Metrics

### Calculated Metrics in Segments are finally here… Sort of, in Adobe’s Customer Journey Analytics

If you have been following this blog for a while (thank you!), it shouldn’t surprise you if I claim: Adobe Analytics is the best web analytics solution available today. But if we’re honest, it has been around for a long time, which has been leading to a situation very familiar to anyone working in the tech industry: The things that we build today might limit us in the future when new technology becomes available. This is also true for Adobe Analytics. When Adobe Analytics was created, it was necessary to build features like the Visitor Profile or Props in a certain way with what was available at that time. Back then, it was necessary to store Visitor Profile information in a database and add it to the data as it was processed (something I also used in a previous series of posts). The database engine on top of that data […]

### (Time-)Normalize Performance over time in Adobe Analytics’s Analysis Workspace

In Digital Analytics, one of the most common requests from business stakeholders is to compare the performance of two or more items on our websites, like marketing campaigns or content pages. While it is immediately obvious why this comparison is important to the business, it quite often leads to graphs like this, where the analyst tries to visualize performance over time: This solution is technically correct but makes it hard to really compare how both pages perform in direct comparison with each other. They went public on different dates and while Page A is rather stable in regards to traffic, Page B got a boost at around the middle of its time online. So, how do we make this simpler? When enjoying my free time between jobs, I caught up on some older videos from the Superweek Analytics Summit’s Youtube Channel. In 2019, Tim Wilson demonstrated how to align dates […]

### Cool Approximate Count Distinct Use Cases – Adobe Analytics Tips

One of the things that really sets Adobe Analytics apart from other solutions is the ability to create sophisticated Calculated Metrics and Segments on the fly. You don’t need to be a highly trained Analyst or Data Scientist to create your very own set of Measures and Dimensions unique to your business question. The best thing for me personally is that we can create those metrics from the same interface where we do our day-to-day analysis and reporting. It doesn’t matter if we want to quickly create an average or build advanced time series analysis dashboards, it’s all right there at our fingertips. Today I want to tell you about one of my personal-favorite functions called Approximate Count Distinct. This functionality allows us to count how many different values from a dimension we tracked and use that number in both Calculated Metrics and Segments (making this function the closest we […]

### Retention Analysis in Adobe Analytics â€“ Part 2: Custom Segments and Metrics

User Retention is crucial to any digital offering. If you optimize your offering to a point where users come back on their own, you can not only save on marketing cost but also engage your existing users more. This makes retention analysis a prime example for how digital analytics can provide tangible business value. In the previous post, we used Cohort Tables and some builtin features of Adobe Analytics to analyze User Retention. But there is a lot more Adobe Analytics has to offer once we start using Segments and Calculated Metrics. In this post we are going to build our very own Segments to see how many of our Users we are able to retain. Based on those Segments we will then define some Calculated Metrics to make our lives even easier. I’ve also put the results on the Open Adobe Analytics Components Repository. Let’s start building! Simple User […]

### Call for contributions! Introducing the Open Adobe Analytics Component Repository

Over the last few months I created quite a lot of Calculated Metrics and Segments for this blog. While the feedback has been great, it became more and more difficult, for me and others, to keep up with all the different metrics and where exactly I used them. I’ve been using a privat Github repository to keep track of everything I create which I now make available to the public. I will put all the metrics and segments I have already created on there as I migrate them from my private repo. The same will be true for future posts on my own blog. My hope is that this will help me stay on top of all those components and maybe help somebody else to find them more quickly. But since I host it on Github, why not make this a collaborative effort? Share your work and earn kudos From […]

### Time Series Analysis through Moving Averages – Statistics in Adobe Analytics

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 […]

### 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 […]