Category: Analytics

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 […]

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 […]

So, is Web Analytics your dream job?

I’ve been working in Web Analytics for over a decade. During that time I had the pleasure to meet a lot of people: Analysts, product owners, marketeers, architects, developers, and so on. I hired a bunch of them, applied to others myself, onboarded and trained a whole lot over the years. No matter who I’ve been talking to, sooner or later, one type of question would always come up: Will this be fun? Am I going to be okay? There are a lot of articles out there focused on the skills needed to start with Web Analytics. As always, Google can help you find those (or go to Julien’s Blog if you want a recommendation). There also are some talking about the necessary mindset. With this one, I will try to give you an impression on the qualities I observed while talking to Web Analysts who love what they do. […]

Supercharge your Adobe Analytics Classifications with Google Sheets and Automation

Classifications are one of the best features of Adobe Analytics. They allow to enrich and translate tracked values by uploading classification files. One of the most common use cases is handling marketing campaign tracking codes, which can be translated from technical ids to understandable details about the campaign. This can be automated to a great extend, which is what this article will be about. We are going to look at the architecture of our solution and plan our implementation. Right after that, we will start building our spreadsheet in Google Sheets and create an automatic upload to Adobe Analytics using the Python programming language. If you are just interested in the final script, you can find it on Github. What we love and hate about Classifications When you are using Adobe Analytics, chances are pretty high that you are already using some form of classifications. Rightfully so, because they are […]

Adobe Analytics Introduction: Terms and Concepts

This is one of several post aiming to give an introduction into Adobe Analytics. They are intended as both tutorials and references for future use. While there already are a lot of good sources for this, some are quite dated and miss connections to recently released features and enhancements. In this post, I will explain some general things that are helpful to know when starting with Adobe Analytics. We will go over different interfaces to analyze data, explain Dimensions, Metrics, and Events and name some common integrations. Know what you are looking at: Dimensions One of the most important building blocks of Adobe Analytics are Dimensions. With Dimensions, we capture descriptive values on our websites or in our apps. Many people call them variables when explaining the general concept. On a website we might record the name of a certain page in a dimension. This would allow us to report […]

Integrating Adobe Target and Adobe Analytics into Voice Assistants

With digital experiences on the rise, interactive Voice Assistants like Amazon Alexa, Google Home or Apple’s Siri are still gaining popularity. Companies now need to meet their customers expectations and allow them to interact with their brand however they like. Those new possibilities require a clear strategy to avoid wasting time and resources on products which nobody actually uses. Adobe Analytics can help understand digital experiences better and drive value through customer feedback. With Adobe Target for personalization and experimentation, nothing can stop you from delivering the right experience at the right time. This article describes how both Analytics and Target can be integrated in Voice Assistants’ backend systems to track and test how users are interacting with your App. We will be using the a direct integration with the Experience Cloud ID Service, sync identifiers and use them for Analytics and Target. Target will then be used to personalize […]

Generating more business value with the Adobe Analytics dashboards App

The Adobe Analytics dashboards App has been out for some days now. It has been one of the most demanded features among Analytics users for years. Personally, I had to disappoint my business users for quite some time whenever they asked for an App. So naturally, I was quite happy when it finally came out. Before the app arrived, we had to build workarounds to enable people to take Analytics data wherever they go. At my company we utilized Power BI to pull data from Analytics and offer it in some form of mobile app. That was a huge pain, since we had to rebuild things we already had in Analysis Workspace and maintain two products. We also had to make huge compromises regarding interactivity with data and visualizations. I’m very happy we don’t need to do that any more! One of the concerns I had before I gained Beta […]

Trying out the new Adobe Analytics App

Adobe Analytics still is the most complete solution for Digital Analytics. But for years, there has been one thing missing: A mature way to use dashboards on the go, without using your computer. While Analytics is usable on mobile browsers on a technical level, it is not the best user experience for both Analysts and Business Users. This is why a real Mobile App has been one of the most requested features over the years. And guess what: Adobe just released one! Who this App is made for There is one important thing to know about this new App before diving into the features and interface. Let’s ask ourselves first who the target audience for this app is, because it most likely is not primarily made made for you if you are an Analyst. It is not made to offer the same feature set that Analysis Workspace offers and I’m […]

Building an Enterprise Grade OpenSource Web Analytics System – Part 7: Analytics Dashboard

This is the seventh part of a seven-part-series explaining how to build an Enterprise Grade OpenSource Web Analytics System. In this post we are building an Analytics Dashboard in Kibana for our data in Elasticsearch. In the previous post we build the connection from Kafka to Elasticsearch and Clickhouse to store the data. If you are new to this series it might help to start with the first post. We have come a long way in this series. We built everything from the client implementation with Snowplow to the processing and enrichment pipelines with Kafka and Python and stored all the data in Elasticsearch. Now it is time to make that data accessible in an appealing way to analysts and business users. The obvious solution for Elasticsearch is Kibana, which is developed by the same company and is designed to work perfectly with Elasticsearch! Webanalytics Dashboard in Kibana In Kibana, […]

Building an Enterprise Grade OpenSource Web Analytics System – Part 6: Data Storage

This is the sixth part of a seven-part-series explaining how to build an Enterprise Grade OpenSource Web Analytics System. In this post we are taking a brief look on what we can do with the data we collected and processed with Clickhouse. In the previous post we built a persisted visitor profile for our visitors with Python and Redis. If you are new to this series it might help to start with the first post. During this series we defined multiple topics within Kafka. Now we have different levels of processing and persistence available. If we want to keep any of it, we should put it in a persistent storage like a Data Lake with Hadoop or a Database. For this project, we are using Elasticsearch and dipping our toes in a database called Clickhouse for fun! Feeding Data into Elasticsearch From the previous part, we have a nice Kafka […]

Building an Enterprise Grade OpenSource Web Analytics System – Part 5: Visitor Profile

This is the fifth part of a seven-part-series explaining how to build an Enterprise Grade OpenSource Web Analytics System. In this post we are going to build a visitor profile to persist some of the data we track with Python and Redis. In the last post we processed the raw data using Python and wrote it back to Kafka. If you are new to this series it might help to start with the first post. Now that we have a nice processed version of our events, we want to remember certain things about our users. To do this, we are going to create a Visitor Profile in Redis as high performance storage. The process for persisting values will look like this: Building our Visitor Profile First things in this part, we are setting up a little helper script that will take our processed tracking events and flatten them. It looks […]

Building an Enterprise Grade OpenSource Web Analytics System – Part 4: Data Processing

This is the fourth part of a seven-part-series explaining how to build an Enterprise Grade OpenSource Web Analytics System. In this post we are building the processing layer to work with our raw log lines. In the last post we used Nginx and Filebeat to write our tracking events to Kafka. If you are new to this series it might help to start with the first post. At this part of the series, we have a lot of raw tracking events in our Kafka topic. We could already use this topic to store the raw loglines to our Hadoop cluster or a database. But it would be much easier later on to do some additional processing to make our life a litte easier. Since Python is the data science language today we will be using that language. The result will then be written to another Kafka topic for further processing […]

Building an Enterprise Grade OpenSource Web Analytics System – Part 3: Data Collection

This is the third part of a seven-part-series explaining how to build an Enterprise Grade OpenSource Web Analytics System. In this post we are setting up the tracking backend with Nginx and Filebeat. In the last post we took care of the client side implementation of Snowplow Analytics. If you are new to this series it might help to start with the first post. Now that we have a lot of data that is being sent from our clients, we need to build a backend to take care of all the events we want. Since we are sending our requests unencoded via GET, we can just configure our web server to write all requests to a logfile and send them off to the processing layer. Configuring Nginx with Filebeat In our last project we used a configuration just like the one we need. As web server, we used and will […]

Building an Enterprise Grade OpenSource Web Analytics System – Part 2: Client Tracking

This is the second part of a seven-part-series explaining how to build an Enterprise Grade OpenSource Web Analytics System. In this post we are setting up the Client Tracking using the Javascript tracker from Snowplow Analytics. In the last post we took a look at the system architecture that we are going to build. If you are new to this series it might help to start with the first post. When building a mature Web Analytics system yourself, the first step is to build some function into your app or website to enable sending events to the backend analytics system. This is called client side tracking, since we rely on the application to send us events instead of looking at logfiles alone. For this series we are going to look at website tracking specifically, but the same principles apply to mobile apps or even server side tracking. Almost every mature […]

Building an Enterprise Grade OpenSource Web Analytics System – Part 1: Architecture

Some time ago I wrote a litte series on how to amp up your log analytics activities. Ever since then I wanted to start another project building a fully fledged Analytics system with client side tracking and unlimited scalability out of OpenSource components. This is what this series is about, since I had some time to kill during Easter in isolation 😊 This time, we will be using a tracker on the browser or mobile app of our users instead of logfiles alone, which is called client side tracking. That will give us a lot more information about our visitors and allow for some cool new use cases. It also is similar to how tools like Adobe Analytics or Google Analytics work. The data we collect has then to be processed and stored for analysis and future use. As a client side tracker, we will be using the Snowplow tracker. […]

Creating more Business Value with Product Driven Analytics

When working in Analytics, it’s easy to view all the requests you receive as disconnected, one-off events. This is the traditional approach for Analytics departments, where Requests would be collected, prioritized and answered accordingly. But there are some issues with this way of handling things: It’s hard to see the bigger picture behind those business questions, especially between departments. Different people may have the same questions but ask them in a different way, leading to different answers. When requests are handled individually, it becomes hard to maintain a standard for the way they should be answered, especially if you are working as part of a team. Since not all stakeholders are equally Data-savy, they may ask for the wrong thing without letting you know what the questions is they are trying to answer. This leads to some awkward situations. People will ask for the wrong or suboptimal reports. You as […]