Adobe Journey Optimizer

I see the new 13 month in lake and 90 days in profile store have been implemented across the AJO experience tracking dataset. How is unsubscribe being managed now? I’m seeing cases of where customers have opted out of a communication by clicking the unsubscribe link, being opted back in. the unsub is in the lake, but not in profile any more. What is Adobe doing to manage this? I’m seeing massive GDPR issues here, unless I’m hopefully mistaken regardsMaec

I am trying to build a journey using the wave feature to send over 10 days. The send will be flighted each day over the 10 days. I have tried to dry run / use simulation to test the journey, and I keep drawing an error to say last wave must be within 6 days and 18 hours from journey start. Can waves not go beyond this time? Do we have to keep it within 6 days and 18 hours from journey start or is there a way around this? Thanks!  

Hi All I have a requirement for a landing page with five Opt-in/Opt-out services.If a user selects one or more specific services for Opt-out and then clicks the Call-to-Action (CTA) button, only the selected services should be opted out. If the user clicks the CTA button without selecting any specific service, the user should be opted out of all five services by default.How can we implement this logic in AJO? Thanks Gowtham Raj

Is there something to consider for push daily capping? I've applied capping to push but users are receving the push even with the daily capping.

Rules are the quiet workhorses of decisioning. They decide who’s eligible, who’s excluded, and which conditions have to line up before an offer, a message, or an experience is allowed through.They’re also, historically, one of the more technical things a marketer is asked to build — a careful stack of attributes, operators, values, and logic that has to be exactly right to work at all.The gap has always been the translation step. You know, in plain language, exactly who you want to target: “loyalty members in New York who haven’t opted out.” Turning that sentence into a valid rule expression — the right attribute paths, the right operators, the right nesting — is where the friction lives.AI-assisted rule authoring closes that gap. You describe the rule you want in everyday language, and the AI Assistant generates a ready-to-use rule suggestion, complete with the underlying expression, that you can apply to the builder with a single click. This post walks through where to start, how it

Hi! I’m working on a use case where an email was not sent because the profile had reached the daily frequency cap.The client would like to keep that communication on hold and retry it the following day. In other words, if the email is excluded today due to capping, it should automatically be sent tomorrow once the profile is eligible again.Is there a way to achieve this behavior in AJO?Thanks! 

Hi Everyone,I am not able to find where I can get the contextual data from API triggered campaign payload within AEP/AJO datasets after email sent out.Normally you would see some metadata on AJO Interactive Messaging Profile Dataset, however I could not find contextual data in it. Thanks!

Hi all,I have a requirement for a landing page with five Opt-in/Opt-out services.If a user selects one or more specific services for Opt-out and then clicks the Call-to-Action (CTA) button, only the selected services should be opted out. If the user clicks the CTA button without selecting any specific service, the user should be opted out of all five services by default.How can we implement this logic in AJO? Thanks Gowtham Raj

How would you debug Ranking Formula in Decisioning?Create selection strategy in code base campaign with test profiles and check if for specific profiles best offer is returned?  

Hi everyone,We are trying to better understand the actual behavior of the “Trigger after batch audience evaluation” option in AJO, because in our case it does not seem to behave the way the documentation and UI tooltip suggest.From what we have observed, if the first run of a daily journey happens before the export job for that day is available, the journey ends up using the previous day’s snapshot instead of waiting for the current day’s audience evaluation/export to complete. After that, each subsequent run appears to keep following the same pattern, since it only checks whether a newer snapshot is available compared to the one used in the previous execution.That makes us wonder whether this feature is really behaving as expected. In practice, it seems that if the first run happens too early, the journey can stay one day behind indefinitely, which makes the configuration much less useful for daily journeys than we would expect.The behavior we are expecting is that the journey should

Hi ,we are working on ajo web channel personalizarion to show real time banners. while workign we found that we cant uise event contextual attributes inside web channel. we need to call aem content fragment based on the current page url which user has visited.is it possible to use window.location.href ? will it fetches the current browser url so that we can pass this as input to content fragment.Please help with inputs . ThanksUsha

Building a Decisioning rule or a ranking formula is only half the job. The harder question is always the same one: will it actually behave the way I think it will?Until now, the honest answer was “let’s find out in production” — you’d wire the rule into a decisioning strategy or a campaign, push it out, and watch the results trickle in. That’s a slow, nervous way to work, and it’s an expensive one when a subtle logic error means the wrong offer reaches the wrong profile.Simulation changes that. You can now test any rule or ranking formula against sample or AI-generated data before you put it to work — right inside the authoring experience, in a few clicks, with no hand-built data and no guesswork. This post walks through both: simulating a rule to confirm it qualifies the right profiles, and simulating a ranking formula to confirm it orders your offers the way you intend. The UsecaseConsider a common scenario. You’ve authored a rule that should only apply to profiles whose favorite cat

I created:Profile-enabled Schema Profile-enabled Dataset Identity configured (Email namespace) Ingested customer data successfully Profiles are visible under Real-Time Customer Profiles Created a Batch Audience using Email Address Created a Journey using Read Audience as the entry activityHowever, the audience always shows:Qualified Audience Size = 0As a result, no profiles enter the journey. What I have already verifiedSchema & DatasetProfile-enabled schema Profile-enabled dataset Data ingestion completed successfully No failed batches in the current datasetProfilesThe profile exists and contains:Email Address First Name Last Name Identity MapAudienceAudience is Published Origin = Segmentation Service Evaluation Type = Batch Population remains 0 MonitoringIn Monitoring I noticed:Last Evaluation Job = No runs (for multiple audiences) Last Activation Job = No runsEven after waiting more than 24 hours, no evaluation job appears.AI Assistant FindingsI also checked using AJO AI Assista

Hi! I can't find anywhere the total amount of campaigns (Action campaigns) supported by ajo. Journeys limit is 200 but what about Campaigns? Thanks!

Your Adobe Journey Optimizer experience could win you $50. 🎁We're gathering feedback from Journey Optimizer customers, and we want to hear from YOU, whether you're in the platform daily or driving the strategy behind it.Two short surveys are live now:✅ Practitioners: Tell us how you use Journey Optimizer's channels, capabilities, and workflows day to day (~5 min)✅ Decision Makers/Leaders: Share what drove your investment in Journey Optimizer and the business impact you've seen (~4 min)👉 Practitioner Survey: https://lnkd.in/gMqTBS85👉 Decision Maker/Leader Survey: https://lnkd.in/g9aJv--cYour input directly shapes what we build next. Every completed survey is entered into a random drawing for a $50 gift card.

Hi all,I'm trying to understand a specific aspect of AJO's decisioning capability. Does AJO's decisioning engine have the ability to generate/select offers purely on its own — using only internal AJO/AEP configuration (like offer eligibility rules, priorities, and constraints) — without needing to pull any profile or event data from an external/3rd party source into AEP?

Im using the following query to consult ClientIDs that entered a specific action (Email action): SELECTDISTINCT identityMap['clientId'] [0] ['id'] AS node_enter_profilesFROMjourney_step_eventsWHERE_experience.journeyOrchestration.stepEvents.journeyVersionID = 'd67b3772-71a9-4d4c-b78f-a7c5373f8034'AND _experience.journeyOrchestration.stepEvents.actionID = 'b5a8420a-899b-437b-97fd-4a425f7a4e71'AND COALESCE(_experience.journeyOrchestration.stepEvents.journeyNodeProcessed,false) = trueAND (_experience.journeyOrchestration.stepEvents.dryRunID IS NULLOR _experience.journeyOrchestration.stepEvents.dryRunID = '')AND COALESCE(_experience.journeyOrchestration.stepEvents.inTest,false) = false Is there a way to include in this query, the date of the start of the journey as well as tags related to the journey?

Adobe will stop supporting Batch Audiences in the Audience qualification start to a journey, and wants either streaming audiences (so 24 hour period max) or use of the read audience start. That means a read audience can’t determine which are new profiles in the audience and just sends to everyone in the audience, which just doesn’t work, or we need to define the audience in batch with very defined limits and have exclusions of people who have been messaged, which is not practical. Campaigns have an iterative type of read to process only new profiles entering the audience, journeys  dont. And campaigns arent an option for Whatsapp or logic around comms segmentation Are there any work arounds that anyone has put in place to negate the potential issues here? I really feel this is a very ill advised change to something that was working very well.

We are investigating unexpected email click-count behavior in Adobe Journey Optimizer and Customer Journey Analytics.For one email recipient, CJA displays:61 Total Clicks 1 Unique ClickThe profile’s email interaction data contains 20 message.tracking click events associated with the same profile, message execution, batch instance, campaign, and message.Here are some findings from within those records. Each click event has a different event _id. There are 19 distinct link labels and urlID values. The recorded links include buttons, ticket links, social links, app-store links, an Add to Calendar link, and other destinations throughout the email. All 20 events occurred between 22:38:45.166 and 22:38:46.383, a period of approximately 1.2 seconds. One link was recorded twice; the remaining events represent different links. Every event has "isBot": falseWe also reviewed the HTML used for the send. The links are represented as separate anchor elements, and the buttons reviewed do not contain

How are organization planning for changes to AJO platform which take you by surprise? Are there any pre-release notes that your Account Manager shares a month before new releases? Or is a there a release plan that you have aligned with your Account Manager so that new releases are first done in DEV environment and then pushed to PROD a week or so later.