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: Analytics
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 […]
Please, stop comparing Adobe Analytics to Google Analytics
This post is going to be a deviation from the “normal” content on this blog. Its purpose is to address one of the questions I received most often from a lot of people reading my posts. The title might already give away what that question is: “Frederik, in your opinion, should companies buy Adobe Analytics or Google Analytics?” And I think there is something fundamentally wrong with this question. I think the above question can only be answered through some absurd level of generalization that does not do justice to both tools. There are some agencies or consultants who end up doing this comparison to either appear neutral and independent, or drive SEO traffic to their own sites. This annoyed me to a point where I started writing this post to have my personal answer ready at hand in the future. Bear with me on this one. To be able […]
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 […]
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 […]