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Unanswered questions

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Bill Hoerr
 Product Q&A
Engage and Sendgrid Integration conundrum

Hello!I’ve run into a particular problem with integrating SednGrid with Engage. The email volume I have is nowhere near needing a dedicated IP for SendGrid, but Segment requires The Pro 100K account in order to connect via subusers. Is there any way around this? SendGrid support informed me it’s not possible to get a shared IP and have subuser management. Is there a way to utilize SendGrid with Engage without having to use subusers in SendGrid? Or is there a workaround for this to ensure the same functionality?

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Vrinda Aggarwal
 Product Q&A
How to collect audience of one of the randomised branches?

Hi, I have created a journey in which I want 85% of the audience to be sent to google adwords and 15% as holdout. How do I know which people are in holdout like how to collect these people?

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David Alejandro Flores Ihuit
 Product Q&A
Inconsistent Audience Counts Between Overview Section and Builder Preview

 We have recently noticed that the display options in the Overview section for 'Audience Details' and 'Identifier Breakdown' show 0 users, while the 'Audience Preview' in the Builder section shows 1,767 users in this case. Could this be an error related solely to the information preview, or might it be affecting the actual audience content? Could this just be a temporary glitch? Previously, audiences have processed without errors in the preview during recent days. However, the data now appears as 0 (as shown in the attachments). Additionally, in other Overview sections like 'Audience Growth' and 'Computations', active users can be observed.   

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Segmenter
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David Alejandro Flores Ihuit
 Product Q&A
Reverse ETL update

I have some questions about Reverse ETL functionality and was wondering if additional documentation or guides exist on this topic. Scenario:A relative date query identifies users who requested quotes within the last 5 days with 10-day lead times before their arrival and do not have a confirmed booking. These users are flagged as TRUE via a trait (e.g., early_quote_user). From an initial group of 10 tagged users: 2 later convert to confirmed bookings (no longer meeting criteria). 3 new qualifying users are added as TRUE the next day Key Questions: Should the logic to mark converted users as FALSE be implemented directly in the SQL query? If not explicitly handled, will those users' traits remain TRUE indefinitely? Are there alternative methods to automatically transition users from TRUE to FALSE when they no longer meet criteria?

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Segmenter

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