Adobe Experience Platform

Step-by-step guide for using Adobe Experience Platform Log in to Adobe Experience Platform: Go to https://experiencecloud.adobe.com/ and log in to your Adobe Experience Platform account. Navigate to the platform: Once you're logged in, you'll be taken to the Experience Platform home page. From here, you can access all of the tools and features that are available in the platform. Create a schema: In AEP the data to be ingested need to be bound to a specific structure called schema. Click on the "Schema" tab in the left-hand navigation menu and then click the "Create Schema" button. Choose a base class for the schema and the name for your schema and add all the fields and identities to the schema. You can even leverage one of the predefined fieldgroups. I can cover schema base classes and fieldgroups in one of the next blog. Create a dataset: To start working with data, you'll need to create a dataset. Click on the "Datasets" tab in the left-hand navigation menu and then click th

Years ago, I was a Product Manager at MathWorks, working on Simulink—a tool that allowed engineers to design embedded algorithms without needing to write a single line of C code. I remember vividly being in a meeting with General Motors, presenting to some of the brightest engineers working on the next generation of hybrid vehicles. I was the nerdy kid in the room, passionately explaining how they could simulate smarter engine designs right on a canvas, bypassing countless hours of manual coding. After my presentation, one of their senior engineers walked to the podium. He smiled at me and said, “Saurabh, have you ever lifted and felt what an engine is like? We’re going to put this in someone’s car—it’s deeply personal. As much as we love your algorithms, we expect that you won’t take away the human element of testing whatever folks build on a computer out on the shop floor (their factory testbeds). We’ll test more, double-check ourselves, and ensure that it’s the absolute best fo

Dear AEP Community, Your time is precious, and we understand the importance of efficiency when it comes to troubleshooting issues with Adobe Experience Platform.That's why we're excited to again bring you our carefully curated collection of Top KCS (Knowledge-Base) Articles for December 2024, designed to help you resolve common challenges swiftly and effectively. Our Top KCS Articles are your shortcut to: Quickly identifying and resolving recurring issues.Accessing expert insights and best practices for AEP.Streamlining your troubleshooting process for maximum productivity.Empowering yourself with the knowledge to tackle any AEP-related problem head-on.Why spend hours searching for solutions, when our Top KCS Articles have already done the heavy lifting for you?Let us help you save time so you can focus on what truly matters – creating highly personalized digital experiences with AEP. Start supercharging your problem-solving skills today by exploring our T

It seems that ECID sets a “test” cookie, but it is set with SameSite=None, but not being set securely. My client’s IT flagged this as a security problem. How do I resolve this? I think basically ECID should not be setting that "test" cookie.ECID is loaded via the Adobe Launch extension (using v5.2.0 as of this writing). I have already verified its settings as follows:secureCookie = truesameSiteCookie = None

Hello there,We’re encountering a significant issue with data consistency in AEP during streaming ingestion of consent data from a single server-to-server source. Our expectation is that the most recent consent status will replace older information in the CDP/AEP Profile. However, instead, the older consent data is being retained. This data inconsistency is concerning, especially given that reliable, up-to-date data in AEP is crucial for many users. I’m aware of the upcoming Customer Timestamp Ordering feature which could help resolve this by using source timestamps to prioritize the latest data. However, this feature isn’t available for production yet, has several limitations, and lacks an ETA.Here’s an example of what we’re seeing:UserID Status Time1Skipped14:25:231Accepted14:38:01Despite the second record being newer by 13 minutes (based on the source’s wall clock), the profile sometimes ends up with the "Skipped" status instead of the expected "Accepted" status. This inconsiste

Modernising Data Management: The Transition to Lakehouse ArchitecturesIntroduction Data management has taken significant strides in recent years, with Lakehouse architecture emerging as a leading-edge solution. For organizations handling complex data ecosystems, the Lakehouse offers a unified approach that combines the best elements of data lakes and data warehouses. This hybrid model promises benefits like streamlined access to data, performance enhancements, and reduced costs. However, while Lakehouse architecture has the potential to redefine data management, it's important to remember that it is still a new and evolving approach. Adopting a Lakehouse may not be necessary or beneficial for every organization, especially those already operating efficient data platforms. For the largest Indian bank, transitioning to a cloud Data-Lake architecture has brought clear value, yet other organizations may find alternative solutions better suited to their needs. Here's a deeper look

OverviewData Distiller users need a convenient way to generate data insights to predict the best strategies for targeting users across various use cases. They want the ability to predict a user's likelihood of buying a specific product, estimate the quantity they may purchase, and identify which products are most likely to be bought. Currently, there is no option to leverage machine learning algorithms directly through SQL to produce predictive insights from the data. With the introduction of statistical functions such as CREATE MODEL, MODEL_EVALUATE, and MODEL_PREDICT, Data Distiller users will gain the capability to create predictive insights from data stored in the lake. This three-step querying process enables them to easily generate actionable insights from their data.Augmenting Fully Featured Machine Learning Platform Use CasesData Distiller's statistics and ML capabilities can play a crucial role in augmenting full-scale ML platforms like Databricks, Google Cloud AI Platfor

IntroductionIn this tutorial, we will explore how to create powerful, enterprise-grade dashboards that rival the best-in-class dashboards found in traditional business intelligence (BI) tools. The goal is to demonstrate that you don't need to rely on expensive BI vendors or invest extra resources for users to build and consume insights effectively. By leveraging SQL as the foundation for creating these charts, we can unlock the full potential of your data without the need for additional software or tools. The tutorial is designed with two key audiences in mind: the data team and the business users. The data team will focus on building foundational charts, ensuring that they include the necessary filters and dimensions to provide flexibility. This allows business users to perform deep, drill-down analyses and gain actionable insights directly from the dashboards. This approach streamlines the workflow, enabling teams to quickly create interactive and insightful dashboards without h

Use this thread to ask any questions related to the Experience League LIVE session titled,Harmonize Audiences in Experience Ecosystems - Federated Audience Composition in Experience Platform. Experts are monitoring this thread to ensure your questions are answered. Thanks and we hope to hear from you!

Hi all,   I have a POC which should set or update Profile information based upon a web form which information is supposed to be sent through Web SDK / sendEvent. The Profile schema contains a section for person information such as first name and last name (standard Adobe XDM schemas used).   The dataset is updated and contains this field group.   Now, when I try to send the information via custom code     const data = { xdm: { identityMap: { myPersonId: [ { authenticatedState: "ambiguous", id: "12345", primary: true } ] }, person: { name: { firstName: "John", lastName: "Doe" } } } }; alloy("sendEvent", data).then(response => { console.log("Profile updated successfully:", response); }).catch(error => { console.error("Error updating profi

Case Study OverviewWe’ll be using the Data Distiller Query Pro Mode Editor, a web-based editor that lets you view results of a query with limited options- 50, 100, 150, 300, or 500. While this may seem limiting, it’s important to remember that the editor operates within your browser, which has memory constraints for displaying results. If you use a dedicated client installed locally on your machine, such as DBVisualizer, you can handle much larger datasets—up to 100,000 rows or more. However, even with local clients, you’ll eventually hit application memory limits.This brings us to an interesting challenge: how can we efficiently paginate through the result set when the client editor imposes a limit on how much data can be displayed at once? The answer is Data Distiller Cursors.What is a Data Distiller Cursor?A cursor in Data Distiller is a database object used to retrieve, manipulate, and traverse through a set of rows returned by a query one row at a time. Cursors are parti

Subscription analytics involves tracking and analyzing key metrics that measure the health and growth potential of a subscription-based business. These metrics provide insights into customer behavior, revenue trends, and overall product engagement, which are crucial for making informed business decisions. Common metrics in subscription analytics include churn rate, Monthly Recurring Revenue (MRR), Average Revenue Per User (ARPU), Customer Lifetime Value (CLTV), Daily Active Users (DAU), and Monthly Active Users (MAU). Monitoring these metrics helps companies understand how well they are retaining customers, growing revenue, and encouraging product usage over time. Subscription analytics and metrics like the above are widely used across various many industries:Software as a Service (SaaS): SaaS companies use subscription metrics to monitor user adoption, gauge engagement, adjust pricing, and reduce churn. Key metrics like MRR, ARPU, and CLTV help them assess profitability and the l

Calling all marketers looking for a new intelligent addition to your team, Join us for an exciting live session where Adobe product and engineering experts will give you an exciting sneak peek at our latest groundbreaking innovation: AI Assistant for Audiences, coming soon to Adobe Real-Time CDP to supercharge audience insights and actions. Using natural language, marketers can leverage AI Assistant for Audiences to estimate audience size, understand reasons for fluctuations, and define powerful goal-oriented audiences, unlocking more use cases and reaching audiences that matter. Can’t attend? No worries—register anyway, and we’ll send you the recording! Watch here:  Youtube Live page.

A Guide to Creating Schema Using SQLCreating an schema via SQL is a powerful way to streamline your data queries within the platform. This guide will take you through the process step-by-step, showing you how to set up XDM-compatible schemas using SQL constructs directly within your queries in query service. By following these SQL syntax, you’ll be able to write efficient, clear queries that simplify the schema creation at runtime.Getting StartedBefore beginning, ensure you understand the data structure you’ll be working with, as this will guide your schema creation.Types of Adhoc Schema CreationType 1: Basic Adhoc SchemaUse this approach to create a simple schema structure.Create TableSchema demo_query_schema1 AS (name1 text ,age1 text); Type 2: Adhoc Schema with Identity Map (With or Without Primary Identity)This version allows you to designate a primary identity namespace, making it compatible with identity mapping.Create TableSchema demo_query_schema2 AS (name2 text PRIMARY ID

Hi, All! My team is working on upgrading the Adobe Mobile SDK and had the following questions:  Our IOS dev is working on updating the core/identity extensions in our SDK. He has completed the items below, however, we're unable to see data populate when using a proxy and therefore aren't able to validate. We have registered the extensionsPopulated the Environment IDsHe implemented trackState and trackAction callsAdded relevant dependenciesPublished Launch Env to StageHas anyone come across this issue?

Authors:  @horiag, @Rachel_Hanessia, @Danny-Miller, @shaistah Design: @BrookeBe  Editors: @NamitaKr1, @asults, @HuongVu Have you ever worried about unexpected consequences while updating critical audiences or attributes in Adobe Experience Platform? You’re not alone. Data engineers and analysts often face this challenge when managing interconnected data systems. Changes to shared objects like attributes and audiences can disrupt workflows for other teams, complicating processes, and risking important marketing efforts. In this post, we’ll explore how AI Assistant can help avoid these disruptions and simplify the management of shared objects in your sandbox.  Previously, data professionals had to manually track where an object was used and analyze the impact of updating it a time-consuming and error-prone task that could take days. One data engineer recalled a challenging project from last year: "We needed to upd

The Adobe Experience Platform (AEP) Data Lake is a comprehensive data hub that brings together datasets from a wide array of Adobe applications, including Adobe Analytics, Adobe Campaign, Adobe Audience Manager, Marketo, and Adobe Commerce. When you combine this wealth of data with that from Adobe Real-Time Customer Data Platform, Adobe Journey Optimizer, Customer Journey Analytics, Adobe Mix Modeler, Marketo Measure, and Adobe GenStudio, you have the entire Adobe ecosystem at your fingertips—empowering you to personalize at scale like never before. These diverse datasets provide a rich foundation for creating highly targeted and dynamic segments, helping you better understand and engage your customers. Beyond raw customer data, each of these systems generates system datasets that contain critical information about personalization and customer interactions. Whether it's journey insights from Adobe Journey Optimizer or behavioral data from Adobe Analytics, the AEP Data Lake allows

Dear AEP Community, Your time is precious, and we understand the importance of efficiency when it comes to troubleshooting issues with Adobe Experience Platform. That's why we're excited to again bring you our carefully curated collection of Top KCS (Knowledge-Base) Articles for October 2024, designed to help you resolve common challenges swiftly and effectively. Our Top KCS Articles are your shortcut to: Quickly identifying and resolving recurring issues.Accessing expert insights and best practices for AEP.Streamlining your troubleshooting process for maximum productivity.Empowering yourself with the knowledge to tackle any AEP-related problem head-on. Why spend hours searching for solutions, when our Top KCS Articles have already done the heavy lifting for you? Let us help you save time so you can focus on what truly matters – creating highly personalized digital experiences with AEP. Start supercharging your problem-solving skills today by exp

Adobe AEP — Solving DCVS ingestion errors Updating Media  Barry Mann17 min read 1 day ago  Updating Media What are ingestion errors?In Adobe Experience Platform (AEP), ingestion errors can occur while importing data, either in batches or streamed. There is no special reason to give the two ingestion methods different treatments.Any dataset can be affected, here we shall consider only the Web Event Dataset, as this is where problems are concentrated in day-to-day operations. The current client has 62 datasets.The dataset “prod — Web Event Data’ is associated with our schema constructed from field groups: Adobe Analytics ExperienceEvent Template, Adobe & Full extension, plus several other field groups added in a cumulative ad-hoc fashion.Ingestion errors should not be confused with dataflow run errors: Updating Media INGEST-1554–400 Errors — Within datflow   Dataflow error preview.These are also ingestion errors, b

OverviewSubscription analytics involves tracking and analyzing key metrics that measure the health and growth potential of a subscription-based business. These metrics provide insights into customer behavior, revenue trends, and overall product engagement, which are crucial for making informed business decisions. Common metrics in subscription analytics include churn rate, Monthly Recurring Revenue (MRR), Average Revenue Per User (ARPU), Customer Lifetime Value (CLTV), Daily Active Users (DAU), and Monthly Active Users (MAU). Monitoring these metrics helps companies understand how well they are retaining customers, growing revenue, and encouraging product usage over time. Subscription analytics and metrics like the above are widely used across various many industries:Software as a Service (SaaS): SaaS companies use subscription metrics to monitor user adoption, gauge engagement, adjust pricing, and reduce churn. Key metrics like MRR, ARPU, and CLTV help them assess profitability a

What is Differential Privacy?The key idea behind differential privacy is to ensure that the results of any analysis or query on a dataset remain almost identical, whether or not an individual's data is included. This means that no end user can "difference" two snapshots of the dataset and deduce who the individuals are. By maintaining this consistency, differential privacy prevents anyone from inferring significant details about any specific person, even if they are aware that person’s data is part of the dataset. Consider a database that tracks whether people have a particular medical condition. A simple query might ask, "How many people in the dataset have the condition?" Suppose the true count is 100. Now, imagine that a new person with the condition is added, increasing the count to 101. As the data scientist, you know that your neighbor has been very ill and that there is only one medical care provider nearby. Without differential privacy, this information could allow you to

What is NPS?At its core, the Net Promoter Score (NPS) is calculated by asking customers a single, simple question: "On a scale of 0 to 10, how likely are you to recommend our product or service to a friend or colleague?" Based on their responses, customers are categorized into three groups: Promoters (scores 9–10): Loyal enthusiasts who will keep buying and actively recommend your brand to others.Passives (scores 7–8): Satisfied but unenthusiastic customers who are vulnerable to competitive offers.Detractors (scores 0–6): Unhappy customers who can damage your brand through negative word-of-mouth.The NPS score itself is calculated using this simple formula:                                                            NPS=%Promoters−%DetractorsThe percentage of Promoters (%Promoters) refers to the proportion of customers classified as Pr

Harmonize Audiences in Experience Ecosystems - Federated Audience Composition in Experience Platform Register Here: Harmonize Audiences in Experience Ecosystems - Federated Audience Composition in Experience Platform  Event Details Gain expanded access to enterprise data warehouses to compose audiences and power brand-initiated and in-the-moment experiences. In this session you will learn how to: Access critical warehouse-based datasets without copying underlying data to minimize duplication. Compose and enrich high-value audiences using warehouse-based datasets. Activate those audiences across multiple channels to support low latency use cases and power personalized experiences. Join our Adobe product experts to explore how Federated Audience Composition allows you to accelerate execution of brand-initiated and in-the-moment use cases. This live session gives you the opportunity to ask questions in the chat and get answers from the experts. Can’t make it? Sign up anywa

Harmonize Audiences in Experience Ecosystems - Federated Audience Composition in Experience Platform Register Here: Harmonize Audiences in Experience Ecosystems - Federated Audience Composition in Experience Platform  Event Details Gain expanded access to enterprise data warehouses to compose audiences and power brand-initiated and in-the-moment experiences. In this session you will learn how to: Access critical warehouse-based datasets without copying underlying data to minimize duplication. Compose and enrich high-value audiences using warehouse-based datasets. Activate those audiences across multiple channels to support low latency use cases and power personalized experiences. Join our Adobe product experts to explore how Federated Audience Composition allows you to accelerate execution of brand-initiated and in-the-moment use cases. This live session gives you the opportunity to ask questions in the chat and get answers from the experts. Can’t make it? Sign up anywa

Visual Studio Code (VS Code) is one of the most popular, lightweight, and versatile code editors available today. Developed by Microsoft, it’s designed to work with a wide range of programming languages and tools, offering support for extensions that make it even more powerful. When paired with SQL, VS Code becomes a powerful environment for querying databases, managing data, and writing efficient SQL scripts.Why Visual Studio Code for SQL?Download JSON: DBVisualizer and many other tools charge for downloading JSON data as part of their premium offerings, which can be a significant limitation, especially when working with Data Distiller. This editor, however, provides much more flexibility without those restrictions.Lightweight and Fast: VS Code is designed to be lightweight, allowing you to run it efficiently on various systems, including machines with limited resources. Despite its small footprint, it packs powerful features.Run Python Notebooks: Visual Studio Code supports Jupyter-s