Adobe Analytics

 Hi, I am trying to create a monthly performance dashboard to compare last month’s performance with the month before. To analyze metrics like visits month over month, I am using the "compare time period" feature. However, I’ve noticed that the comparison of visits and the percent change do not update automatically each month, which leads to incorrect data comparisons. Is there a fix for this issue, or is this feature functioning as expected?   

Hi,I've been having this issue, since 8 hours ago, and everyone in my team has the same issue.When we use "Target Activity > Experience" and overlay segments inside of A and B, the segments only show under 1 of the variations. When deleting in one of the variations, all of a sudden they appear in the other one, as if they were there but not displayed. Tried:- Logging out- Clearing browsing data / cache- Restarting computer- Trying with new reports- Trying with old test reports SO this bug is also happening in old reports, that already had the segments overlaid fine. Does someone know the solution or experienced something similar?For now we can just keep them as separate tables but it slows down the work a lot and makes harder to compare the data.

Hi,  We are in the process of creating a custom metric in Snowflake that will enable us to combine Adobe data with other data sources. Is it possible to import a metric definition or rule from Snowflake into Adobe Analytics? Our goal is to create the metrics in Snowflake and then import them into Adobe Analytics.

I am currently exploring the use of Page events to develop a calculated metric that will help me better understand the intricacies of these events. As part of this process, I want to perform tests on the metrics I've designed. To do this effectively, I need to identify the specific actions or conditions that trigger a page event to activate. What approaches or methods would be most effective in uncovering these triggers? Can I use Data Feed for this purpose?

I have an Adobe Analytics Web SDK implementation using the data object and we are working on migrating to CJA. This implementation is accurately sending data to AA but in CJA, my event counts show as 0. My mapped eVars and props work fine but I'm probably just missing something simple with the events. I have tried for example, data.__adobe.analytics.events.event1 mapped to a custom schema field web.webInteraction._test.clicks where that field is set to integer. I have also tried changing web.webInteraction._test.clicks to a "measure" and instead mapping to web.webInteraction._test.clicks.value to mimic the AA ExperienceEvent template but both scenarios result in counts of 0 in CJA. What am I missing?

With the new EU rule about cookie consent. I just want to clarify, what can and cannot be tracked? Some further questions in case cookies are denied: 1. Can Visits still be tracked?2. I understand that Unique visitor might not get tracked but can an aggregate number of visitors be ascertained?3. Would number of visitors be an inflated number?4. Deeper engagement tracking for example, users that have engaged for over 2 pages in 1 visit, can a metric like so be tracked? Thank you so much! 

Hi! I am looking for some clarity on the date ranges within Adobe Analytics. Why are the two numbers different? And which one is more accurate for monthly reporting?    

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

We rolled out a new version of a portal in our website back in June and I am trying to combine actions that are the same within that portal but were named something different before and after the switchover.  I have a freeform table that shows all the different page names before and after the switchover to the new version within the portal that we want to measure and looking at them by Unique Visitors for the last year.I thought I might be able to create a segment that would look at each specific action in the portal we are measuring and it would combine the UVs between the two different page names from before and after the switchover for the same data range, However my UV numbers when viewing by Page Name in the Freeform Table are significantly higher than what each Segment is showing.For ExampleWe have an action in the Portal named xyz:view:idcard before the switchover and after the switchover to the new version of the Portal it is named xyz:ss:view:idcard.In the Freeform t

We sell multiple additional products for customers to add to their policy and I measure these as a % penetration rate. Order of optional extra / all Orders For some of our brands and journeys we include these products for free which makes their penetration 100%, and brings up the rate of our combined metrics.I do pass value of each product with my purchase tag, so was hoping to create a segment (or condition in custom metric) that excludes products where the value is £0 - is this possible?

  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

Hi, We recently changed cookie from Onetrust to Transcend and I noticed there is almost 60% drop in traffic in Adobe Analytics after implementation. When I checked Google Analytics, there is not huge drop as in Adobe analytics. What can be the reason for this? Thank You,Garvita.

Is it possible to create a segment that evaluates something in the segment over a period of time that is not dependent on the filter in your workspace view?  For example, If I want IP addresses excluded that had >1,000 visits over the last 12 months without an order in my workspace view that is filtered for today.  In other words, I'm excluding those IPs today (filtered in my workspace view) that met the above criteria.

I run into this a lot and curious to see how others handle it. Even when the numbers are accurate, stakeholders will often question funnel drop-offs or channel attribution. It feels less about the math and more about how much confidence people have in the way the data is configured and/or presented.How do you deal with that? Do you show the rules and assumptions up front, compare against other tools, or do something else that helps build trust?Would love to hear what’s worked (or not worked) for your teams.

 What product are you using?Adobe AnalyticsWhat area of the product are you using?ProjectsWhat are you trying to achieve?Restore deleted projectsWhat’s the problem or error?I would like to restore a deleted project, but it is not possibleWhat have you tried so far?Search for solution only. Also I have created a support ticket for this.Screenshot 

Has anyone else struggled with the Page URL dimension hitting cardinality limits in Workspace? On heavier traffic months, I’m seeing 10–15% of URLs rolled into Low Traffic, which makes the stakeholder dashboards pretty unreliable.I know Adobe generally recommends leaning on Page Name or classifications, but that doesn’t always cut it when the request is for an exact URL list. We’ve tried a few things:Classifying off a clean URL key (helpful but introduces latency + governance overhead).Segmenting by page categories (good directional view, but not enough detail for stakeholder asks).Falling back to Data Warehouse pulls (accurate but not self-service friendly).Curious how other enterprise teams are dealing with this:Any practical ways you’ve kept URL-level visibility in Workspace without blowing up cardinality?Are there approaches you’ve found that give business users “exact URL” access without sending them to Data Warehouse every time?And has anyone leaned on CJA as a workaround here —

Hi @jennifer_dungan  Further to the query at https://experienceleaguecommunities.adobe.com/t5/adobe-analytics-questions/flow-chart-starts-with-vs-contains/m-p/576850 I'm seeing the same 'Next' column Visit figures for each entry, regardless of whether I use 'Starts' or 'Contains' and that can't be right? The 'Start' values right-hand column entries should surely be lower?For example, using the previous poster's link and 2 screenshots, using 'Start' they initially had 1,788,992 page views, and in the 'Contains' image it had a higher 22,208,466 page views. So far so good. However, for 'Contains' in the far right column the top www.churchofjesuschrist.org entry says 1,610,978 ...but in the 'Start' image the far right column the top www.churchofjesuschrist.org entry also says 1,610,978. Surely the 'Start' with 'Next' page volumes should be much lower? Thanks,Steven Biggs

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'm working with a variable in Adobe Analytics that captures multiple values separated by hyphens. I'm looking for the best way to visualize this data in a heatmap. The goal is to split these delimited values into individual components and represent their frequency or intensity visually, allowing for easy analysis of patterns or trends. Any advice on how to achieve this effectively would be appreciated.  Dimension X captures:  Equity Compensation-Retirement Planning-Executive Financial Planning-Financial Education-Nonqualified Deferred CompensationExecutive Financial Planning-Financial Education-Nonqualified Deferred CompensationRetirement PlanningRetirement Planning-Financial EducationI would like to see my dashboard show each value independently and provide counts of how many instances it showed up. For example: Retirement Planning = 3Equity Compensation = 1Executive Financial Planning = 2 Is it possible to do this without using a list.var? 

We created a segment in CJA that has criteria using seconds as an inclusion for consecutive email clicks. We found several examples where people are getting pulled into the segment even though their email clicks are well outside the 1 second range. Has anyone used this type of logic on email deliveries? Curious if there is something wrong with the seconds dimension or if there is adjustment needed on our side.