Adobe Real-Time Customer Data Platform

Currently in Realtime CDP, using tracked event data that is also evaluated in Adobe Analytics, it is not possible to segment profiles based on the number of visits (as metric “Visits” in Adobe Analytics) that they have since there is no “visit-id” that is exposed on the UI or even the possibility to select audiences based on “count unique” [element]. The number of visits in a given timeframe is an important KPI for our department and we would like to select profiles in different intervals of it.Thanks!

I would like to share a key feature enhancement request for Adobe Experience Platform (AEP) Real-Time CDP architecture regarding data modeling flexibility. Ideally for B2C, B2B & B2P editions as we have consolidated all into single Prod & Dev sandboxes as advised by Adobe Ultimate Support.The Feature Upgrade:Enable native Many-to-One (N:1) schema relationships between non-B2B XDM schemas. This will allow multiple custom data records to link directly to a single parent account record within standard XDM profiles, without requiring both B2B XDM classes.Use Case & Business Need:Current Scenario: We have custom data entities (e.g., competitor products, telemetry, certifications) where multiple rows of data map to a single core business account. The Problem: Standard non-B2B XDM schema relationships are restricted to lookup options, which force data into arrays. This results in complex architecture and confusing schema building UI. Proposed Solution: A flexible parent-child rela

We cannot get the Computed Attributes value distribution to show anything but non-zero values. We were recently informed by Ultimate Support that a minimum success rate of 30% is required for Computed Attributes. This is also where we learned that the 30% was not only a minimum threshold for the Value Distribution/Sample Profiles QA piece, but also for getting the Computed Attribute in and of itself to populate at all. This is an extremely high target threshold and unrealistic for many of our real-world applications. We did ask Ultimate Support if and how clients have adopted Computed Attributes given those requirements and we learned that there have not been many Adobe clients who have, and confirmed that many other clients have expressed similar concerns as we have. Can the Computed Attributes value distribution minimum success rate be optimized & improved beyond the current 30% minimum threshold? 

We cannot seem to get the Value Distribution for Computed Attributes to show any non-zero values? We were recently informed by Ultimate Support that a minimum success rate of 30% is required for Computed Attributes. This is also where we learned that the 30% was not only a minimum threshold for the Value Distribution/Sample Profiles QA piece, but also for getting the Computed Attribute in and of itself to populate at all. This is an extremely high target threshold and unrealistic for many of our real-world applications. We did ask Ultimate Support if and how many clients have adopted Computed Attributes given those requirements and he shared that there have not been many Adobe clients to his knowledge who have, and confirmed that many other clients have expressed similar concerns as we have. Hence the idea & feature enhancement request raised here.

Agentic AI is rapidly transforming marketing by enabling autonomous decisioning, content generation, audience creation, and journey orchestration at unprecedented speed. Within Adobe Experience Platform (AEP), Adobe Journey Optimizer (AJO), and Real-Time CDP, these capabilities are becoming increasingly real—not theoretical.However, with this acceleration comes a critical challenge: control, trust, and governance have not evolved at the same pace as AI capabilities.Without the right governance framework, agentic AI can introduce unintended risks across:Brand safety and consistency Regulatory and compliance adherence Customer experience integrity Decision transparency and explainabilityThe key question is no longer “Can we use AI?” but rather:“How do we scale AI responsibly while maintaining control, trust, and accountability?”This article outlines practical governance models that enable organizations to scale agentic marketing safely and effectively.Why Governance Becomes Critical in A

Description -Why is this feature important to you - as there are lot of data generated in Marketo Engage Email engagement data, having this filter option will enable only to ingest specific type of event records into platform, and not all the email engagement data. there are like millions are mails sent daily and we dont need all the email send events.How would you like the feature to work - similar to adobe analytics connector which has filtering optionCurrent Behaviour - there is no filter option for marketo engage source connector.

Description - When a field group is created for a specific class (lets say individual profile class), this field group by default wont be available for experience event class. where as we can extend the field group to experience event class through API. it will be beneficial to have this configuration done in UI.Why is this feature important to you - it is very useful to use the same field group for both individual profile class and experience event class. and this is possible now through API, but not UI.How would you like the feature to work - kind of a toggle button to extend the field group.Current Behaviour - currently it can be done through API, but not UI.

Description -Why is this feature important to you - in most cases UPSERT flag is required for a dataset. the option to enable upsert flag can be done only through API. it will be good if we have this option in UI.How would you like the feature to work - enabling UPSERT flag in UI as a toggle button.Current Behaviour - currently UPSERT flag can be enabled only through API.

Description:It would be highly beneficial to display the size of an individual profile directly within the Profile Viewer in Adobe Experience Platform (AEP). Why is this feature important?Currently, while we can view various attributes and audience memberships in the UI, there is no visibility into the actual size of a profile. Having this information readily available would help us better understand data volume, optimize storage, and troubleshoot performance issues more effectively. How would you like the feature to work?Within the Profile Viewer, alongside existing profile attributes, display the total size (in KB/MB) of the profile. This could be shown as a summary metric or as part of the metadata section. Current Behavior:There is currently no way to determine the size of an individual profile through the UI, which limits visibility into data usage at the profile level.

Descripción - Me and my colleagues are using the set auto_to_json function a ton while querying, partially it is because it only works for a single session. Therefor, one day, I thought that it would be a low effort hight reward implementation inside the query service UI.  This function might be implemented at an account level or just by adding a little toggle inside next to the "copy" or "undo text" functionalities. Something like this: I am really sorry for my drawing skills, as you might have already figured out, I am not an artist haha.

Description - It would be beneficial to have the ability to delete profile-enabled schemas, specifically in lower environments   Why is this feature important to you - Dev's that are 'learning' how to use AEP/RTCDP may incorrectly enabled schemas for profile, thus causing any type of schema clean-up efforts, unable to be completed.   How would you like the feature to work - The simple ability to remove enabled toggle, once tied dataset is removed from schema.   Current Behaviour - Unable to be currently done, verified this with adobe support that the ability was taken away due to issues.

Description:We would like to see update on Trade Desk connector to connect directly on a partner level rather then on advertiser level. It would be great to have both. Why is this feature important for us: We work with several countries and therefore we have a lot of advertisers. As we can not connect on a partner level - it means we have to manually configer destinaiton with mayn different adverticer rather then just configur it once with the partner. How would you like the feature to work:The same as Trade desk connectro works now, but would have possibility to chose between advertiser or partner. Current Behaviour:Only Trade Desk Adverticer ID connections are possible. 

Description:Add a built‑in Data Quality Dashboard in RT-CDP that continuously monitors incoming streaming data for schema deviations, missing key attributes, duplicate records, and outlier values. When anomalies exceed configurable thresholds, trigger:Automated alerts (email or slack)Visual drill‑downs showing the offending eventsGuided remediation steps (e.g., map this unexpected field, fill missing email, merge duplicate IDs)Why is this feature important?Proactive Hygiene: Catch ingestion errors the moment they occur, before they corrupt unified profiles.Faster Troubleshooting: Teams spend less time hunting for what broke in the data pipeline and more time fixing it.Higher Confidence: Marketing and analytics users trust that every profile and audience segment is built on accurate, complete data.

Description:Introduce a Destination Activation Replay feature that tracks failed or missed activation (e.g., due to API downtime, schema mismatch, rate limits) and allows for automatic or manual reprocessing once the destination becomes healthy again - ensuring no critical activation is permanently lost. Why is this feature important?Ensures activation reliability - campaigns or journeys shouldn't silently miss users due to transient destination errors.Helps marketers and engineers troubleshoot and recover from downstream failures without rebuilding segments or pipelines.Supports auditability and compliance, with logs showing which profiles were successfully activated, which failed, and which are pending retry.Prevents data loss during outages, especially when targeting time-sensitive audiences like flash sales or churn interventions.Current Behavior:If a destination (e.g., Facebook, Google Ads, webhook, custom API) fails during profile export, the activation fails silently or log

Description:Introduce a Source Health Dashboard that monitors the status, freshness, volume, and latency of incoming data sources (batch and streaming) in real time. Add alerting features for drops in ingestion, data schema mismatches, or unusual data volume patterns. Why is this feature important?Ensures data quality and trust across the platform by surfacing real-time issues with source integrations.Reduces diagnostic time for ingestion failures that silently impact audience building or segmentation.Proactively alerts technical and marketing teams to source failures, stale data, or delays, preventing downstream impacts on campaigns and decisioning.Supports data governance and operational excellence in data-driven organizations. Current Behavior:Source connection status (e.g., for streaming or scheduled batch uploads) is not actively monitored in a centralized dashboard.Failures may go unnoticed until audience segments look wrong or campaigns underperform.There is no built-i

Description:Introduce a real-time audience fatigue scoring mechanism that tracks user responsiveness (or lack thereof) across channels (email, push, ads, etc.) and automatically adjusts frequency caps or suppresses the user from further campaigns until engagement improves. Why is this feature important?Prevents over-messaging and burnout, which leads to unsubscribes, opt-outs, and negative brand perception.Allows more intelligent campaign pacing - showing restraint can often improve long-term engagement.Reduces manual guesswork around send frequency by making decisions based on real-time engagement signals (opens, clicks, site visits, etc.).Optimizes deliverability and compliance, especially in regulated regions with communication limits.Current Behavior:Frequency capping is usually static and channel-specific (e.g., max 3 emails/week).No native cross-channel fatigue detection or unified scoring that evaluates all touchpoints.CDP does not dynamically adjust campaign eligibility ba

Description:Introduce a predictive LTV (Lifetime Value) model natively inside Real-Time CDP that scores customers dynamically based on their engagement, transactions, browsing behavior, and other attributes - allowing marketers to automatically prioritize higher-value users for premium campaigns, offers, and loyalty experiences.Why is this feature important?Maximizes ROI by investing marketing resources in users who are likely to bring more revenue over time.Enables smarter segmentation beyond simple past-purchase or recency-frequency models.Enhances personalization by aligning offer types, discount levels, and campaign intensity based on user potential value.Improves strategic planning by giving marketing, sales, and service teams a predictive view of customer quality, not just quantity.Current Behavior:Marketers manually segment users based on historical transactions like total purchase value or number of orders.No predictive scoring available natively in Real-Time CDP; advanced LTV

Description:Enable dynamic suppression of users from active marketing campaigns in real time based on recent negative experiences - such as failed transactions, customer complaints, or product returns - using Adobe Real-Time CDP signals and event-based triggers.Suppressed users can be re-evaluated for reactivation once an issue is resolved. Why is this feature important?Prevents wasting marketing budget by sending promotions to frustrated customers who are temporarily disengaged.Improves brand perception by respecting user sentiment and avoiding tone-deaf marketing outreach during sensitive moments.Reduces churn by showing customers you are listening and responding appropriately to their issues.Current Behavior:Audience suppression typically relies on static exclusion lists updated periodically (daily or weekly).No native real-time suppression based on live customer service data, failed transactions, or dissatisfaction signals.There’s no automated mechanism in Real-Time CDP to tem

Description:Introduce a feature in Adobe Real-Time CDP that utilizes machine learning to predict customer churn based on real-time behavioral and transactional data. The system would allow marketers to define churn-risk thresholds and automatically activate personalized retention journeys (e.g., email offers, push notifications, or loyalty incentives). It would also maintain a version history of churn prediction models for transparency, comparison, and improvement over time. Why is this feature important?Increases ROI of marketing campaigns through targeted retention efforts.Enables real-time action, which is crucial for saving at-risk customers.Provides transparency and iterative improvement through model versioning.Supports personalization at scale with minimal manual effort.Reduces revenue loss by proactively identifying customers likely to churn. Current Behavior:Adobe Real-Time CDP allows segmentation and audience activation, but lacks built-in churn prediction and autom

Description:Introduce the ability for multiple users to collaboratively build and refine audience definitions in Adobe Real-Time CDP with support for version history, change tracking, and inline comments.This feature would help cross-functional teams (marketers, analysts, data scientists) work together more efficiently while ensuring traceability and reducing errors in audience logic. Why is this feature important to you:Audience building often involves multiple team members, and right now, it’s hard to track who made what changes or why.Sometimes, edits overwrite each other or lead to confusion without proper documentation or context. How would you like the feature to work:Each saved audience should include a version history, showing what was changed, when, and by whom.Users should be able to leave inline comments on specific filters or conditions (like commenting in Google Docs).Rollback functionality should allow users to restore a previous version if needed.Notifications

Description - It would be nice to have at profile-level the amount of emails each profile have received and opened in the last 30 days.Why is this feature important to you - It would be possible to address journeys based on communication-engagement withouth having to do it at audience-level. How would you like the feature to work - a computted attribute that returns the count of emails a profile have received/ opened in the last 30 days, something link:xEvent[_experience.customerJourneyManagement.messageInteraction.interactionType in ["open"]].count() Current Behaviour - Computed Attributes currently do not support System generated datasets

DescriptionIntroduce the ability to generate real-time lookalike audiences based on existing high-performing audiences using AI/ML models within Adobe RTCDP.This feature would allow marketers and data practitioners to automatically expand reach by targeting users with similar behaviors, attributes, or engagement patterns - without needing to define static rules manually. Why is this feature important to you:We want to reach more customers who behave like our recent buyers without manual effort.Manually creating similar audiences is time-consuming and often misses key traits. How would you like the feature to work:When viewing a audience like "Recent Purchasers," click a button to generate a lookalike.RTCDP should use AI to find similar profiles and update the audience in real-time. Current Behaviour:There’s no option to automatically find and target similar users.Marketers must rebuild rules or export data to external tools for this purpose. Use Case:A brand wants t

Why is this feature important to you:This feature is essential because it improves data organization and management within Adobe Real-Time Customer Data Platform (RT-CDP) when activating datasets to Amazon S3. By allowing separate paths for each dataset, we can ensure more precise data handling. How would you like the feature to work:I envision this feature working by enabling users to define unique paths for each dataset within a connection. When setting up a connect for S3 dataset activation, users should have the option to specify a distinct path for each dataset, allowing for more granular control over data flow and storage. This could be implemented through an intuitive interface where users can easily assign paths during the connect configuration process. Current Behaviour:Currently, RT-CDP does not support separate paths for each dataset under a connect for S3 dataset activation. All datasets are managed under a single path, which can lead to challenges in data organiz

Why is this feature important to you? Currently, understanding Adobe documentation in Experience League requires reading through lengthy content, which can be time-consuming. A summarization feature would make it easier to grasp key concepts quickly, save time, and enhance learning efficiency.How would you like the feature to work?A “Summarize” button on each documentation page should generate a concise summary of the content, highlighting key points, definitions, and steps. This summary should be clear, structured, and easy to read, helping users quickly absorb the essential information without going through the entire document. Current Behavior:Users need to manually read and navigate through long and detailed Adobe documentation in Experience League to understand concepts. There is no built-in summarization feature, making it difficult to extract key information quickly.

Description - When creating an audience within audience builder, we sometimes will use other audiences as a baseline. When we go to look at the available options to use, it does not group them by the folder structure that we have created, meaning the audience you’re looking to use is harder to find. We want to make the folder structure available for use within the builder search menu Why is this feature important to you - A big part of RTCDP for my team is the ability to reuse audiences. We have created a folder structure to make it easier to find the audiences that we want to use as "building blocks" in our new audiences that we create. If we cannot navigate the folder structure in the Audience Builder (AB) UI, then it becomes almost obsolete. Additionally, since we will have multiple teams operating and creating hundreds of audiences in our platform, it is much too difficult for our users to look for the correct audience "building block" in an unstructured AB. How