Winter '27 release notes are live.
It is actually Salesforce that released this, is this not happening anymore?
Winter '27 release notes are live.
It is actually Salesforce that released this, is this not happening anymore?
Excellent example for improving performance when using WhatsApp, especially for those with many agents using this tool. Enable subtitles in your language and 1080p quality:
https://www.youtube.com/watch?v=kHTemjVWEG0&list=PLYStC_2TZBSU
We’re happy to share Winter ‘27 Release resources! 🩵
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Hi @Jamie Zenker, a gentle nudge, do you have any updates on the Release Overview deck winter 27? Thanks in advance!
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Hi, When I was preparing for the certification it was so helpful for me to follow the trail, it is structured and well organised. Check it out:
Hi Team
I have published segment in SFMC from data cloud whihc uses relative attributes of DMOs. Because of these relative attributes the data is coming in JSON format.
I am using SSJS script to parse this data however script worked for few records but failing for large data. I have to parse around 3 lakh records or may be more than that.
Is there any other solution to parse segment data so that I can use it further.
Hi Ashwini, thanks for the detail, and good news, your current setup is basically the point 2 approach done well.
On point 1, the important one: flattening upstream does not conflict with all campaigns needing the data, it is the opposite. If you pre-shape those related attributes into scalar fields in Data Cloud with a Calculated Insight and activate those instead of the raw related attributes, the data lands flat for every campaign, so no SSJS parsing is needed anywhere downstream. That removes the timeout entirely rather than managing it. If the campaigns only filter on a handful of derived values, model just those in the CI.
Point 2 (chunking) was simply this: never parse all 300k in one Script Activity run, slice the DE and process one batch per run so each run stays under the script time limit. What you built, an initial load in chunks then hourly deltas of only new or updated rows, is exactly that pattern, so you are on the right track.
One refinement as volume grows: drive the delta from a reliable last-modified watermark rather than comparing two full DEs, it stays cheap even at millions of rows. But if the Calculated Insight route is feasible, that is still the cleanest long-term fix since it deletes the parsing step altogether.
Hi Everyone,
We're planning to migrate from a 3rd-party SMS Activity to SFMC MobileConnect for sending one-way transactional SMS using an Alpha Sender ID to UK customers
Note: Our SFMC Business Unit is dedicated to transactional messaging only and is not used for marketing communications
Current architecture
Based on my understanding & questions:
Based on my understanding, marketing SMS requires consent, while transactional SMS does not. Since our consent is managed in Service Cloud, I'd like to validate the recommended Salesforce approach.
Has anyone implemented a similar architecture or can share Salesforce best practices?
Thanks!
Since this is SFMC/MobileConnect (Journey Builder, Alpha Sender ID), not core Apex/Flow - this is Marketing Cloud territory, and the consent/Journey questions below sit at the seam between Service Cloud (Contact Point Consent) and Marketing Cloud (Journey Builder), which is exactly the kind of cross-system sync point worth being explicit about.
1. Should Journey Builder check Contact Point Consent before sending?
Yes - but the check should be scoped correctly, and that scoping is the actual design decision here. Under UK PECR, a purely transactional message with no promotional content (order cancellation notice, no upsell/cross-sell copy) is generally exempt from PECR's marketing consent requirements, so legally you don't need marketing SMS consent to send it. But "don't legally need consent" isn't the same as "don't check anything" - you still want to confirm the number is valid/deliverable and hasn't been globally suppressed (e.g. customer explicitly said "don't text me at all," a hard bounce/carrier complaint flag, etc.). So check consent, but check the right purpose
, not blanket marketing consent.
2. Should the SMS still send if SMS Contact Point Consent is Opted Out?
This depends entirely on what that Opted Out flag actually represents in your data model, and that's the thing to nail down before building rather than assume:
My recommendation: model (or confirm you already have) a distinct Transactional/Service SMS consent purpose in Contact Point Consent, separate from Marketing SMS, and have the journey's decision split key off that specific purpose. This sidesteps the legal ambiguity entirely rather than making a judgment call in Journey Builder logic. This is a genuine "pause and confirm with legal/compliance + whoever owns the CPC data model" item, not something to default on silently.
3. Do you need a MobileConnect keyword for the one-way Alpha Sender ID?
No - and functionally it wouldn't do much even if configured. Alphanumeric sender IDs only support one-way messaging by default because the sender isn't a real phone number carriers can route replies to, so standard keyword-based opt-outs like STOP don't work natively with alphanumeric IDs. There's no inbound path to that Alpha ID for a keyword to catch. Practical implications:
One architecture risk worth flagging explicitly: confirm whether the CPC data your journey checks is synced into SFMC via Marketing Cloud Connector (near real time, so there's a sync lag window) or whether you need a real time lookup back to Service Cloud at send time. Given this is triggered off a same day event (order cancellation → immediate SMS), a same day opt-out landing in that lag window is a plausible edge case worth a decision, not an assumption.
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Our marketing team has been historically creating a new lead for each and every time a person "raises their hand" for a specific product -- like if they filled out a form online or attended a webinar or visited a both, etc. This has resulted in duplicate leads. We are working on correcting this, doing some de-duplication efforts, and making use of campaigns and campaign members to record what marketing interactions a person engaged with. This works but the sticking point is how do we track product interest
-- for example prospect was interested in Product A in January and Product B in July. Could be a current customer or someone we've never done business with before.
My gut reaction is that we use opportunities for this -- every interest in a product is it's own opportunity record that they can work through all the stages -- however sometimes the sales person, account manager, BDR, or Solution Engineer finds out quickly that they are not actually qualified for that product and DQ the "lead" -- if we use opportunities like I was thinking -- we'd have a ton of "garbage" opportunities.
We are migrating from Marketing Cloud Engagment (fka Exact-Target) TO Marketing Cloud Account Engagement (fka Pardot) -- so we are using this as an opportunity to clean house and fix processes
Thoughts/Ideas on how to handle this?
#Marketing Cloud #Pardot B2b Marketing Automation
Hi @Ahmad Helal, Mounir has provided the details, I am happy to connect to see whether we could help