Adobe Analytics

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

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. 

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 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 -  

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

  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

Whether you are just starting out with your mobile data collection or migrating from the Adobe Experience Cloud based extensions to the Edge Network extension, the sendEvent API is essential to your implementation. In this post, we will take a deeper look at the sendEvent API to understand the required and optional fields when configuring Edge data collection for Adobe Analytics.   The sendEvent API allows you to send ExperienceEvents to the Edge Network to track events like screen views and user actions, and it will replace your trackState and trackAction calls when migrating from the ACP or solution extensions to AEP/Edge. In general, the ExperienceEvent is structured like this: //create experience event from dictionary: var xdmData : [String: Any] = ["eventType" : "SampleXDMEvent",                               "sample":

  Introduction In today’s multi-device, multi-channel landscape, customers often engage with brands across multiple platforms and touchpoints on their path to conversion. A customer might discover your product on their mobile device during their morning commute, research it on their work computer during lunch, and finally make the purchase on their tablet at home. Without person stitching, these would appear as three separate customers in your analytics, leading to fragmented insights and missed opportunities. Organizations increasingly need to effectively combine customer data from multiple touchpoints to gain meaningful analytics insights. This is crucial for Customer Journey Analytics (CJA), which uses a single declared field identifier per dataset to maintain consistent person identity. To meet these data integration needs, CJA for person-based stitching offers two distinct approaches: Field-based Stitching (FBS) and Graph-based Stitching (GBS). These stitching methods help

Navigating complex datasets just got easier! Adobe Customer Journey Analytics now offers a Data Dictionary: your go-to reference to explore and understand every dimension, metric, and component used in your projects. Why it matters: Simplifies data governance Enables collaboration across teams Provides clarity on definitions, owners, and usage Reduces analysis errors by aligning teams on the same definitions Whether you're a business user, analyst, or data engineer — this feature brings transparency and trust to your CJA implementations. Link: https://video.tv.adobe.com/v/3418028/?quality=12&learn=on#_blank   Tip: Start tagging your components with meaningful descriptions, ownership info, and usage notes. Your future self (and your teammates) will thank you! #AdobeCJA #CustomerJourneyAnalytics #AdobeAnalytics #DigitalAnalytics #DataGovernance #DataDictionary #CXInsights #DataDriven

Introduction The Adobe Data Insights Agent is an AI-powered assistant that enhances Customer Journey Analytics (CJA) workflows by working alongside your team. Within Analysis Workspace, this new interface boosts the efficiency and productivity of your analysis processes. Building on your existing CJA analysis techniques and expertise, it streamlines tasks while surfacing valuable insights. This blog post explores how the assistant enhances your CJA practices, accelerates daily workflows, and helps your team extract more value from customer data while preserving your established analysis methods and domain expertise. Managing Access to Data Insights Agent Access to the Data Insights Agent within CJA is governed by several key parameters. As part of a limited access program running until November 30, 2025, the agent is available to all eligible CJA customers. Organizations must agree to specific legal terms related to generative AI before implementation, and users require proper perm

  Introduction For organizations using Adobe Customer Journey Analytics (CJA), understanding the data processing and ingestion process from the Adobe Experience Platform (AEP) data lake to CJA is crucial. This is because CJA does not work with a fixed data size, which means Adobe cannot provide a standard time frame for data ingestion. As a result, users should understand the factors that can affect the queue status, data processing speed, and any deviations from the expected timeline. With this knowledge, CJA users can plan their data management strategies more effectively, ensuring efficient data ingestion and maximizing the benefits of using CJA. Note: The latest ingestion prioritization and latency guidance for CJA as of June 12th, 2024, is as follows: Ingestion Prioritization and Latency You can now ingest your events data in Customer Journey Analytics within 90 minutes (SLT), regardless of whether the data is 24 hours, 48 hours, or 7 days old. Note that this capability

NoteIn part one of this series, I provided the reason why you would want to prevent collecting data from bot traffic and described the basic process on how to do so.   This article provides a solution that helps you identify spam-like activity on your site that isn’t explicitly recognized as coming from a bot but nevertheless is worth blocking because of its useless nature.IntroductionMany clients that Adobe Consulting has worked with over the years have noticed web traffic patterns that could not be identified as explicit bot activity yet appeared robotic "enough" to warrant a second look. In many of the cases, the patterns showed normal looking "users" that would continually (and very quickly) hit a page over and over and over again to the point where they would generate thousands(!) of server calls without any recognizable reason for doing so.  In response, my Adobe colleague, Jeff Branson, produced an ingenious solution to this problem and called it the Hit Governor.

  Introduction This blog post explores the data egress cases offered by Adobe Experience Platform (AEP) and Customer Journey Analytics (CJA). We'll examine each option's diverse functionalities, providing you with a clear understanding of their capabilities. We'll also analyze the strengths and potential limitations of each method, empowering you to make informed decisions when selecting the most suitable egress option for your specific needs and requirements. Our comprehensive review examines a broad spectrum of factors, including data processing capabilities, integration potential with external systems, scalability for handling large datasets, and user-friendliness of extract initiation. By thoroughly exploring these aspects, we aim to provide you with a robust framework for evaluating and comparing the diverse egress options available within AEP and CJA. To provide an up-to-date comparison of the various AEP and CJA data egress options as of May 2025, here's a summary matrix

Thank you for joining our Tech Session! Here are some additional resources to aid you in your migration journey: Data Variable Mapping  XDM Variable Mapping  Configure top and bottom of page events in Web SDK Customer Journey Analytics (CJA) Implementation - A must watch summit presentation! What goes into designing an XDM Schema?  Migrate from AppMeasurement to the Web SDK Migrate Target from at.js 2.x to Platform Web SDK Migrate from Analytics mobile extension to Adobe Experience Platform Edge Network  Migrate components and projects from Adobe Analytics to Customer Journey Analytics Set up consent with Platform Web SDK  Adobe Experience Platform Edge Network API (1.0.0) (Edge API similar to the Analytics Data Insertion API)