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    Dustin CNew Participant

    Tracking of Pull Conversations & Balanced ticket assignmentsSubmitted

    We recently started using Pull Conversations to help reduce cherry-picking and create a fairer workload across our support team. It’s still early days, but we’re already seeing some positive effects, our inbox feels more manageable, and the team is more focused.That said, one big gap we’ve noticed is the lack of visibility into how teammates are actually using Pull. For example, we can’t tell who’s clicking “Pull Conversation” versus who might still be going into the inbox manually and picking off easier tickets. There’s no data point or reporting on how a conversation was assigned - whether it was through Pull, manual selection, or something like Round Robin.Because of this, we had to take some pretty aggressive steps just to drive the behaviour change we wanted. We removed the general inbox from view and locked down the ability to change views altogether. It worked, but it felt heavy-handed. We’d much rather guide behaviour with data instead of restrictions.What we really need is better tracking of teammate behaviour around conversation assignments. Just something simple like:• “Was this conversation assigned via Pull?”• Who’s using Pull regularly• How are Auto Assignment features like Balanced or Round Robin actually being usedThis kind of visibility would help us understand if these features are working as intended and where we might need to coach or adjust our processes. Without it, we’re kind of flying blind.Would love to hear if others have found workarounds or if this is on Intercom’s radar already.

    Suggested articles based on URL Context Sensitive contentSubmitted

    The problem: The Suggested Articles widget in Messenger shows AI-generated suggestions, but they're often not relevant to the specific page the user is currently viewing. There's no way to target suggested articles by URL — we can't assign specific Help Center articles to specific pages of our app. Users on a specialized screen get generic suggestions, give up on self-service, and open a conversation instead.Example: A user is on our Special Orders dashboard (www.ourapp.com/specialorders). We have a Help Center article written specifically for that feature — but we can't make it appear there. Meanwhile the AI surfaces loosely related articles that don't answer the question the user almost certainly has on that page.The request: Let us assign Help Center articles to specific URLs and URL patterns, with wildcard support for dynamic segments — e.g., /account/*/settings should match every user's account settings page regardless of the ID in the URL.Intercom already supports current-page URL targeting for outbound messages, tours, and checklists — this request is essentially extending that same, proven targeting capability to Suggested Articles.Supporting details:1. Priority: Manually assigned articles should appear above AI-generated suggestions, so curated content always leads.2. Hygiene: If an assigned article is archived or deleted, it should automatically drop off the suggestion list — no broken or stale links.3. Reporting: Click/engagement data on assigned articles, so we can see which page-level suggestions actually deflect conversations and optimize accordingly.The payoff: more relevant self-service at the exact moment of need, fewer unnecessary conversations reaching the team, and better use of the Help Center content we've already written. This would also strengthen Fin — knowing which articles are most relevant to the page a user is on gives Fin better context to resolve questions on the spot, improving resolution rates for everyone. Thanks for considering!