Adobe Analytics

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Applications frequently face scenarios where the single standard datastream setup isn't sufficient to meet specific use case requirements. Whether it's refining data consumption, integrating custom processing logic within mobile apps, or managing the mobile app code release cycle, adjustments are often necessary. This discussion explores methods for modifying and overriding default datastream configurations by leveraging Data Collection (Tags), eliminating reliance on the mobile app code push cycle. As a result, updates to the datastream override can be implemented rapidly without going through the mobile app release process. The key benefit is the ability to configure datastream overrides at the Data Collection (Tags) level, rather than within the mobile app itself.  Additional Use Cases: There are various scenarios where your app may require adjustments to its datastream or dataset override configuration. In this blog, I will highlight a few key use cases to demonstrate when and

June' 25 release notes Check out what's new and updated in the latest Adobe Customer Journey Analytics product release update: Release Notes   New capabilities Analysis Workspace left panel no longer opens and closes on hover: The left panel in Analysis Workspace is used to add things like components, panels, and visualizations to your project. The option to temporarily open the left panel by hovering over one of the icons on the far left is no longer available. Instead, simply click one of these icons to keep the panel open, then click the same icon to close it. Customer Journey Analytics B2B Edition: Customer Journey Analytics B2B Edition helps B2B companies align their marketing, sales, and product teams by providing actionable account insights that drive revenue growth. With the account placed at the center of the data model, all analysis focuses on the account journey. Adding a new layer of entities (accounts, opportunities, and buying groups) on top of p

In our last Edge Data Collection Concepts blog, we introduced the concept of the data object as a flexible and schema-free method to collect data for Adobe Experience Cloud solutions. In this post, we'll dive deeper into how you can use the data object to implement Adobe Analytics. Whether you're migrating from AppMeasurement.js or starting fresh with Web SDK, the data object offers a straightforward method of implementation.   When configured as discussed below, the data object will contain values immediately recognizable to Analytics and to implementers of Analytics. If you previously set s.eVar1 to a value of "foo", setting data.__adobe.analytics.eVar1 to "foo" achieves the same result and can be done very simply in the Tags interface similarly to how those values can be set when using AppMeasurement. Implementing the Data Object The data object starts as an empty variable, and we can build up and clear values with each of our rules in a way that is similar to how we set

I've been on this forum for a few years now, and I've seen the level of activity ebb and flow. Sometimes we have a lot of activity, other times you can hear crickets. But one of the things that I've learned about improving a community is that you need all of the voices not just a few. So, I want to hear your thoughts - what do you want to see in this community? What draws you to it? What are you hoping to get out of it? What do you want to see more of?   I think that we have a great thing here, the ability to connect with other experts from around the world, share ideas, tips and tricks, and learn something new. One of my favourite things is reading questions from others, seeing what kind of challenges different companies are facing. I'm drawn to this community because there are so many different opinions, people with different ways of doing things that can help you see a problem in a new light and find solutions. 

I am really looking forward to the Adobe Analytics Challenge 2025 and noticed that the official calendar has already posted the following dates:Kickoff Call: September 16, 2025Submission Opens: September 16, 2025Submission Deadline: October 7, 2025Champions Announced: October 23, 2025Champions Event: November 10 & November 13, 2025One detail I could not find is when the registration window officially begins. Does anyone have any insight on whether registration has already opened, or when it will be available?

Adobe has stated that since the latest code was rolled back we should prevent further unexpected values. That doesn't seem to be the case.One issue we don't recall seeing prior is that some marketing channel values are returning numerous rows as "Unknown" though once you search for any variation of the characters in "unknown" nothing shows up.  Any other issues you're seeing out there with data we might need to take into account?(And we already working with Adobe support directly, but just wanted to understand what we might want to be aware of until this is fully resolved)

Adobe Analytics seems to be quietly testing a new ‘Generate Slides’ feature, available to a select group of users. This feature allows users to convert Analysis Workspace templates directly into PowerPoint slides, making it easier to share insights and reports with stakeholders.  Instead of manually creating presentations, users can now generate slides that include charts and metrics from their workspace templates—saving time and ensuring consistent formatting. While it’s still in limited testing, this feature shows promise for improving workflow efficiency and simplifying the reporting process. Are you one of the lucky ones selected by Adobe?Like or comment if you’ve got this feature enabled! Let’s see how many of us are in the test group. 

Introduction This blog post is a resource for practitioners of Adobe Customer Journey Analytics (CJA) who want to gain a deeper understanding of the main navigation elements and access patterns. It offers a summary and breakdown of the different menu areas and functionalities within the CJA main interface as of February 2024. The aim is to assist users in developing a clear mental map of the navigation elements within CJA.           Figure 1: Customer Journey Analytics - Main Navigation Header This post highlights the need for CJA Product Admin privileges and administrative authority to access certain user interface elements. The Connections and Data Views options in the navigation menu are hidden for non-admins. Note that full access to Connections also requires extra permissions related to Adobe Experience (AEP), as detailed in Product Admin additional permissions. The main aim of this post is to equip users with the essential knowledge to maximize the us

  Introduction Derived fields is a powerful new feature of Customer Journey Analytics (CJA). It allows users to transform their CJA schema fields at the Connections-level in various ways. Using the derived fields interface, users can instantly and retroactively make non-destructive derivations to their CJA dataset fields. This can potentially save time and effort by avoiding data rework and reprocessing, while enhancing the quality of the end CJA Data View data. As a result, it leads to faster time to business value and insights. The CJA derived fields feature empowers users to perform more accurate and insightful analyses, leading to better business decisions and outcomes. By providing CJA administrators with the ability to manipulate their CJA Data Views in ways that were previously not possible, this feature is an invaluable tool to maximize the potential of already captured data and uncover insights that can drive business success. June 2024 Update: Review of Derived Fields

Introduction Organizations using Adobe Customer Journey Analytics (CJA) need to clearly understand when to create new Connections versus Data Views for analytical success. This framework examines the differences between these two foundational CJA layers and provides guidance on when to use each approach. By following these principles, organizations can build an efficient, scalable analytics foundation that delivers richer insights while managing costs effectively. Think of the CJA framework like building a house. Connections are similar to the foundation and structural framework because they determine what materials (datasets) you use, how rooms connect, and the overall integrity of the building. Data Views are like different interior designs and room arrangements within that same structure because they don't change the foundation but offer different ways to experience and utilize the space. Just as you wouldn't rebuild your house's foundation to redecorate a room, you shouldn't cre

I have so many, it's hard to choose from, but at it's most basic, I'd say right-click, right-click, right-click!  Also documentation.But I want to hear from others...what are some great tips you have on using Analytics?  Could be ones you've heard from others or ones you came up with yourself. 🙂

Have you wished you had a guide for how to use advanced functions in your calculated metrics? Check out the link at the bottom of this post to see a new guide that I’ve written to help you understand the various functions available.   Calculated metrics are one of the most powerful features available in Adobe Analytics. Creating your own metrics gives you the flexibility to define KPIs like conversion rate or add-to-cart rate rather than simply relying on the events captured on your website (or Adobe’s out-of-the-box metrics). There are several features in calculated metrics that you can use: adding segments, containers, static numbers, and of course, functions.   Each function performs some type of mathematical computation in order to give you options for how you want to build your metrics. Some of these functions, like mean or median, are pretty basic yet still very powerful. You can use them to get an average of a particular metric without worrying about dividing it yourse

Have you ever attempted to use the Adobe Experience Platform Debugger to do some very simple troubleshooting tasks, like examining only your Web SDK payloads?  I have, and occasionally, my experience doing so hasn't been all that great, as exemplified by the following internal dialog:"Alright, I need to find a specific XDM object within a server call's payload"."I think I know which server call contains the correct payload and I think I know where to look within the payload itself""Okay, so I'll click here, then click there, then click here.""Is that it? Nope, darn. Okay, go back.""Click there, click here"."Wait, what is this?""Click here, click there, click, click, click.""Hmmm, why can't I just see everything all at once?"If you couldn't tell, it's all the clicking and guesswork that drives me crazy.  So, if you've had the same type of internal dialog as I have, please know you can set up a completely different method to look at your AEP Web SDK payloads – with a lot less c

In part 2 of this series I gave you a basic technical guide on how to get Adobe Analytics working with Web SDK. In this article, I will expand out more details of my recommended approach, particularly around how to efficiently populate the XDM Object. However, before we get on to what to do, I'm going to start with what not to do and my biggest mistake! Mental model Prior to using the Adobe Web SDK, based on years of successful deployments(!), I had a very strong mental model of how Data Collection - Tags should be set up to support Adobe Analytics.  My basic mental model went like this: Set a data layer in the website Read the data layer into Data Elements, normally on a one-to-one basis Use rules (which contain most of the logic) to: apply conditions to our data processing concatenate Data Elements together map Data Elements to Adobe Analytics variables send data back to Adobe Analytics My biggest mistake was initially sticking rigidly to this mental model and trying

I am a professor looking for assistance in how to register a team for the Adobe Analytics Challenge (https://adobeanalyticschallenge.com/). The registration links are not working. Could somebody please reach out to me at marthaud@ksu.edu with more information?Marne L Arthaud-Day

                                   Spectacular customer experiences don’t just happen— they’re imagined, built, and brought to life by Experience Makers like you. Now it’s your turn to take center stage. Submissions are open through October 17, 2025, at midnight PT. Whether you are scaling personalization, uniting content and data, or transforming engagement through AI, we want to hear how you’re reimagining customer experiences with Adobe applications.   Explore the 2026 award categories Each company can apply in up to three of ten categories, with opportunities for individuals, teams, and companies: The Advocate – Demonstrates a customer-first approach to delivering personalized journeys with Adobe applications. The Ambassador – Shares Adobe product expertise and best practices to help peers and colleagues create exceptional experiences with Adobe applications. The

I've read different articles a number of times and just absolutely fail to understand how and why this populates.Low-traffic value in Adobe Analytics | Adobe Analytics It says:Adobe Analytics uses two thresholds to determine which unique values are displayed in reports each month: A low threshold and a high threshold. These thresholds can be adjusted by Adobe from time to time. The current threshold limits are: Low threshold: 2,000,000 unique values during the month. High threshold: 2,100,000 unique values during the month. Reporting is not affected if a variable does not reach the low threshold in a given month. When a variable reaches the low threshold, data begins to be bucketed under a dimension item labeled Low-Traffic. Each value beyond this threshold goes through the following logic: If a value is already seen in reports, add to that value as usual. If a value is not yet seen in reports, initially add it to the Low-Traffic dimension item Now I have a table showing v

I have been using Adobe Analytics for more than a decade, so I know exactly how I feel about using it in comparison to what's now been happening with Customer Journey Analytics for the past number of years. My question now goes out to my fellow community of users.  What is/are the biggest challenge(s) you face or have faced with Adobe Analytics/CJA, and how have you chosen to address them?  In fact, are you addressing them?  Do you still have unanswered questions? Super interested to hear your thoughts! In fact, feel free to -  

Is there a dimension or metric that allows us to check whether the cart contains products or is empty when a user visits the site?We're experiencing an issue where the cart is automatically emptied due to cookies, and we want to understand what percentage of users are affected by this.Although there is a metric called 'Cart Removals (event)', the data shows zero, so we’re looking for an alternative way to check this.

We all use Adobe Analytics (or CJA) day in and day out. And, let’s be honest, most of us are probably at least a little bit nerdy. I’m willing to bet that you’ve got a favourite feature (or two or three). So, let’s nerd out a little bit about our favourites - what is your go-to feature? Why do you think it’s the best? Why do you think everyone should be using it?   Is it a certain visualization that helps explore your data? Is it a type of segment or metric that you like to build? Or is it the way you set up your processing rules?   It probably comes as no surprise to anyone who has seen me talk at Summit or in the Skill Exchange, but my favourite feature is using functions in calculated metrics (and metric building in general). A lot of the functions are vastly underused, but they’re super powerful and can definitely help you up your reporting game. Building custom metrics is already a pretty powerful tool, but with the added use of the functions, it's a whole other level. F

  Introduction Adobe Customer Journey Analytics (CJA) represents a significant advancement in analytics capabilities, enabling organizations to connect disparate data sources and analyze customer journeys across multiple touchpoints. The potential of this tool is remarkable, with many companies already realizing its benefits. However, like other analytics products and perhaps even more so due to its vast scope, your team's success with CJA depends on defining and understanding the right set of use cases to pursue. A well-defined framework for use case development helps organizations and teams move beyond tactical reporting to strategic analysis that drives business outcomes. Without such a framework, you may find yourself stalled immediately, either haphazardly combining data or uncertain which data to combine. Your colleagues won't know when to leverage CJA's powerful ability to quickly answer unique questions, and your progress with translating customer insights into action wi

  Introduction Adobe Customer Journey Analytics (CJA) offers various methods for calculating bounces and bounce rates, each providing unique insights into user engagement patterns. These approaches have distinct implications, potentially influencing how we interpret and act upon user behavior data. In this blog post, we'll explore the different bounce rate calculation options available within CJA, examining their strengths and potential drawbacks. Bounce rate has been a cornerstone of web analytics for decades, evolving alongside the digital landscape. From its origins in early website traffic analysis to its current implementations, it remains a widely-used metric for understanding user engagement. It's both amusing and impressive how this seemingly simple concept has stood the test of time, adapting to new technologies and user behaviors. As we explore its various incarnations within CJA, we see how this venerable metric continues to provide insights, albeit with a modern sla

I found the definition of "Reloads" a bit confusing.From what I understand, it seems to be an automatically collected event that's counted as '1' when the current page view is based on the same page as the immediately previous page view. Is that correct..?If so, aside from refreshing the browser, are there any other cases where this could happen? I would really appreciate it if you could kindly let me know 🙂

  Introduction Data Views in Adobe Customer Journey Analytics (CJA) function as powerful, customizable lenses that determine how your Adobe Experience Platform (AEP) datasets are interpreted from CJA Connections. These Data Views contain component configurations that define all dimensions and metrics available to users in Analysis Workspace and control how these components are logically structured. Unlike traditional analytics implementations where changes required extensive coding or time-consuming data reprocessing, CJA Data Views provide flexibility through retroactive, non-destructive, report-time processing capabilities thus allowing you to make adjustments without disrupting your data collection infrastructure. Building on this foundation of Data Views functionality, this blog post explores best practices for effectively managing and thoughtfully curating your CJA Data Views. These practices will maximize operational effectiveness, ensure analytical consistency, and driv