Tag: Web Analytics Dashboard

Keep track of goals using the Linearity Indicator in Adobe’s Analysis Workspace

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 […]

Monitor Adobe Analytics usage for free with Power BI and Python

Adobe Analytics is, without a doubt, the most complete and feature-rich product for Web Analytics and Reporting on the market. I won’t even try to list all its features, since I would definitely forget some or would have to update the list in a few months as new functionality is released. And while I, as an analyst and power user, love to have all those great tools available, they create a challenge for me in my other role as an analytics admin. All of those features bring complexity to the every-day work of our business users. For example, when Analysis Workspace was released in 2016, it meant that users had to learn a new interface to get the most value out of Adobe Analytics. But as an admin who knows their users, I have a strong feeling that some people still use the old Reports & Analytics interface in 2021. […]

Visualizing Adobe Analytics Report Suites for free with Python and Power BI

Adobe Analytics is super flexible in the way it can be set up to exactly match all requirements of business users, analysts, and developers. A crucial part of any implementation is the creation and configuration of the Report Suites, which can be seen as the backend database of Adobe Analytics, that will hold the events sent to Adobe Analytics. In theory (and practice in some setups), each and every Report Suite can have a completely individual set of variables and metrics. However, having the option to create an individual configuration of dimensions and events for each Report Suite comes with a hefty long-term cost. For example, each and every setup needs to be implemented in Adobe Launch, where the on-page data layer needs to be matched to the dimensions and metrics of the Report Suite. If every Report Suite is configured differently, a lot of work needs to be put […]

Implementing Adobe Analytics in a First-Party context

One of the things I love most about Adobe Analytics is how flexible it is. That is not only true for its interface, Analysis Workspace, and the numerous integrations, but also for everything that happens on the actual website where it is implemented. Almost every functional detail can be configured and tweaked, including the destination where the data is actually sent to. For years Adobe has offered Analytics customers the Managed Certificate program, where Adobe would allow us to send data to a server that looks like it belongs to our own company (or, more specifically, our company’s domain). But upon closer inspection, those requests are only disguised as first-party and are actually still sent to Adobe’s servers directly instead of our own. And in addition to that, Adobe Launch and the Experience Cloud ID Service will still load their data from different domains that don’t belong to your company […]

Tracking Apps with Adobe’s new Experience Platform SDKs and the Edge Network

Tracking mobile apps has always been fun for me. Compared to measuring websites, apps are a more controlled environment, which is great for data consistency. Sure, there are some less-fun cases (like those pesky hybrid apps) but the general experience on major platforms like Android and iOS has been quite great! Changes even happen a bit slower compared to browsers and there are way less moving parts to keep track of. Just like when tracking websites, Adobe has been most helpful in providing us the tools we need to collect data in the most effective and consistent way. If you have been tracking mobile apps with Adobe for a while, you will know about the similarities between websites and apps (like having a trackState function that increments Page Views and trackAction for Custom Links) and nice comfort features (like automatically tracking App ID, Launches, Upgrades, etc.) that make it so […]

The Visitor Profile: Adobe Analytics’ Big Advantage

We live in some very exciting times for our industry. There is a lot going on in the analytics space with Adobe’s brand new Experience Platform, Customer Journey Analytics, Web SDK, and Launch Server Side. All of those new innovations will fundamentally change how we track data and process data once it has been collected. But since I got the opportunity to try out most of those exciting things myself, people often ask me why I still love Adobe Analytics as much as I do. My answer to that usually covers multiple areas. For example, I like how the App Measurement Library in Launch helps me to collect data efficiently. Analytics’ Processing Rules and Marketing Channels are another great tool to enrich our events after the collection. In a previous post, I already explained why I love prop-type dimensions quite a lot, since they can provide us with metadata on […]

Using Flow and Fallout Visualizations like a Rockstar in Adobe Analytics

It’s no secret: I love Analysis Workspace. In fact, I think it is the main advantage Adobe Analytics has over Google Analytics. That is because Workspace allows for seamless collaboration between analysts, marketeers, product owners, and other business stakeholders. With enough enablement, there is no difference in which tools different groups of analytics users would use: It’s always the best one! Workspace is the perfect combination of sophisticated functionality and an appealing user interface. But because of this user-friendly interface, not every advanced function or use case is immediately apparent to every user. This can lead to funny situations, where experienced analysts never really use certain parts of Workspace that could save them a lot of work. In today’s post we will take a close look at two of the most undervalued features: The Flow and Fallout visualizations. While they seem quite similar in functionality and trivial to understand on […]

(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 […]

Retention Analysis in Adobe Analytics – 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 […]

Retention Analysis in Adobe Analytics – Part 1: Cohort Tables and Builtin Functionality

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 this post, we are going to take a look at how we can analyze user retention with the most advanced digital analytics tool, Adobe Analytics. We are going to start in this post with the builtin analytics dimensions and metrics, then take a look at cohort tables, and in the next post even build our own Segments and Calculated Metrics to help us understand retention. Let’s get started! Builtin Retention Metrics and Dimension To start things off, we will take a quick look at what Adobe Analytics has to offer out-of-the-box to help us understand […]

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 – 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 […]