Tag: Adobe Customer Journey Analytics

Import Google Analytics data into Adobe Analytics using Data Sources

On one hand, Adobe Analytics remains my favorite web analytics tool on the market. The longer I use it, the more I appreciate all the well thought-out features, from data collection to processing, storage, and analysis. Those features are even more impressive when compared with what Google Analytics has to offer. And yet, on the other hand, even I can’t avoid having to work with Google Analytics in some way or another. In a large, global company, it is basically unavoidable to find Google Analytics on some small, long forgotten marketing landing page in some market. It gets even worse: Up until last year, I personally had to maintain an inherited Google Analytics instance on a legacy website and app. What a cruel world! Besides those cases, where someone in your company actually wants to use Google Analytics, there are also more forgivable cases. For example, a company may be […]

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 […]

Web Analytics with Adobe’s Customer Journey Analytics, Part 8: A new home

This post is the eight and last post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In the previous post, we were creating the connection from Experience Platform to Customer Journey Analytics. In this post, we are going to take a look at our web analytics data and explore some use cases. Believe it or not, but this series of posts is almost finished! Starting with nothing, we have created a sophisticated schema for our data in Experience Platform, created a tracking implementation using the new Web SDK, enriched our data in Query Service, and pulled all that data into Customer Journey Analytics. If you have been following since the start of the series, I want to say: Thank you, hope you enjoyed the ride! Now it is time for the finale, where […]

Web Analytics with Adobe’s Customer Journey Analytics, Part 7: Customer Journey Analytics Backend Configuration

This post is the seventh post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In the previous post, we were enriching our basic web analytics data with some advanced fields in Query Service. In this post, we are creating the connection from Customer Journey Analytics to Experience Platform. At this point in this series, we have a world-class dataset of web analytics data in Experience Platform, ready to be analyzed. I’m personally very proud of the things we were able to achieve in Query Service, especially with the pathing dimensions. With all of that, we have even more than what normal Adobe Analytics would give us! With all the data enriched, we now have only one step left before we can start analyzing our digital user’s behavior. First, we need to pull data […]

Web Analytics with Adobe’s Customer Journey Analytics, Part 6: Advanced Data Processing in Query Service

This post is the sixth post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In the previous post, we took a look at processing some basic data we need for our web analytics use case utilizing Query Service in Experience Platform. In this post, we are creating some advanced fields to our data in Query Service. I think it’s fair to say that even with just the information from the previous part, we could have a very useful web analytics tool already. But if you know me, you know that I like to take things to the next level wherever I can, especially if it involves writing code. And is SQL not some sort of code too? Entry and exit page were a nice start last time, but we have some fields still […]

Web Analytics with Adobe’s Customer Journey Analytics, Part 5: Basic Data Processing in Query Service

This post is the fifth post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In the previous post, we took a look at doing the implementation using Adobe Launch, the Adobe Web SDK, and Client Data Layer. In this post, we are going to processing some basic data we need for our web analytics use case utilizing Query Service in Experience Platform. This series of posts is coming along quite nicely. If you followed all the previous posts until now, you will now have a functioning Web SDK implementation that tracks your data into Experience Platform following the Experience Data Schema we have tailor-made for our use case. Nice! Now we are ready to feed our data into Customer Journey Analytics, right? Well, we could. If we are just interested in the plain […]

Web Analytics with Adobe’s Customer Journey Analytics, Part 4: Capturing Data with Web SDK (Alloy)

This post is the fourth post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In the previous post, we took a look at our business questions and how we can structure our data most effectively. In this post, we are doing the actual implementation using Adobe Launch, the Adobe Web SDK, and Client Data Layer. On our way to creating a full-scope, front-to-back implementation of Customer Journey Analytics to track a web site, we are now ready to think about our actual implementation. Since we have the data structure in place and already have an awesome Experience Event Schema, we just need some actual data. The logical choice to feed data to the Adobe stack is, of course, by utilizing their client-side tools as well. Specifically, we are going to use Adobe Launch […]

Web Analytics with Adobe’s Customer Journey Analytics, Part 3: Data Structure in Experience Platform

This post is the third post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In the previous post, we took a look at the different possible solution architectures we can use to bring data into Customer Journey Analytics and decided on the best one. In this post, we will take a look at our actual business questions and how we can structure our data most effectively. From the last post we already know that we want to track data using only the new Adobe Web SDK going forward. To make that work, we need to create a schema in Experience Platform first, which defines the structure of the data that we want to capture. While some people (sometimes me included) see schema management as one of the more tedious tasks in Platform, I […]

Processing Adobe Analytics Data Feeds with Apache NiFi for Adobe Experience Platform

In the series of posts that is currently being released on this blog I’m showing how companies can move from Adobe Analytics to the brand new Customer Journey Analytics to utilize the many advantages of the new tool. However, I feel like the current Adobe-provided solution for bringing data from that old to the new world lacks some essential information. I did an extensive comparison in the most recent post of the series, but will give some of the reasons here again. When we use the Adobe Analytics Data Connector to bring data from an Adobe Analytics Report Suite into Experience Platform, we are dealing with some limitations: The data is based on what Adobe calls mid-values. Those sit between raw, unprocessed data, and fully processed data in the processing chain. Because of this, we don’t have access to dimensions like persisted Evars, Visit Number, and other data points we […]

Web Analytics with Adobe’s Customer Journey Analytics, Part 2: System Architecture in Experience Platform

This post is the second post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In the previous post, we discussed the motivation and scope of this project and why, eventually, existing Adobe Analytics customers will start moving to Adobe’s Customer Journey Analytics. In this post, we will take a look at the different possible solution designs we can use to bring data into Customer Journey Analytics and decide on the best one. Adobe’s Customer Journey Analytics is built on Adobe’s brand new Experience Platform. With that, it is very flexible in terms of how data can be brought into the tool. Depending on the setup it may seem very easy to bring data in quickly. However, all that flexibility also means we have many ways to deviate from the ideal path, so we […]

Web Analytics with Adobe’s Customer Journey Analytics, Part 1: Goodbye Adobe Analytics, my Old Friend

This post is the first post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In this part, we discuss the motivation and scope of this project and why, eventually, existing Adobe Analytics and new customers will start moving to Adobe’s Customer Journey Analytics. If you found this article, chances are high you work in or adjacent to the field of digital analytics or web analytics. It doesn’t really matter if you are an existing Adobe Analytics user, on the Google stack, or just looking for your very first web analytics tool. If you have been following the trends and discussions in our industry in the recent time, you will likely already have caught on the massive changes that both our industry and Adobe’s products go through. With changes to privacy requirements and cookie […]

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 […]

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 […]

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 […]

Building the ultimate auto tracking implementation with Adobe Experience Platform and Web SDK

I think it’s no secret that a lot of companies, agencies, and analyst dread the amount of effort it takes to implement a sophisticated analytics tool like Adobe Analytics. That may come in part from the correlation between company size (and thereby business complexity) and choice of analytics tool, but it is quite clear that implementing Adobe Analytics in a way that fully utilizes both all of its countless features and what can be collected from a page is a challenge to even the most experienced specialists. This is where other tools like Google Analytics or smaller solutions like Matomo have their place. If your use case and business situation are right, they may be a quicker solution for you. The simplicity is quite tempting but would not be enough for larger businesses. That leads to a funny situation when people from agencies or small companies join a large corporate […]

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 […]

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 […]