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    3157 Ideas

    Matej Bosnjak
    Matej BosnjakNew Participant

    Product Request: Customizable Article Reaction Workflow TriggerSubmitted

     Hello Intercom community,  When a customer clicks 😞 or 😐 on a Help Center article, Intercom automatically opens a conversation and sends a built-in reply:German:"Schade, dass du die benötigte Antwort nicht finden konntest. Möchtest du das Team noch einmal um Hilfe bitten?"English:‘It's a shame you couldn't find the answer you were looking for. Would you like to ask the team for help again?’There is currently no way to: Edit or replace this default message Trigger a Workflow at the moment of the reaction The "customer sends first message" workflow trigger never fires in this context because the built-in reaction flow opens the conversation automatically and takes over before any workflow can run.Who is affected Any team that: Uses Fin AI Agent as their primary support agent Wants to collect structured feedback on why articles fail Cares about brand tone consistency across all customer touchpoints What we want Option A - Editable reaction reply text Allow admins to customize the default message that appears when a customer reacts negatively to an article - either globally or per Help Center.Option B - Dedicated Workflow trigger for article reactions Add a new Workflow trigger: "Customer reacts to article" (with filter options for 😞 / 😐 / 😃) that fires immediately when the reaction happens - before any conversation is opened.Ideal Workflow this would unlock Customer clicks 😞 or 😐 on an article A customizable message appears asking why the article didn't help Structured reply buttons are offered (e.g. "Too technical", "Missing information", "Didn't match my situation", "Something else") A short free-text follow-up is collected based on the button selected A closing message confirms the team will review the article The conversation is assigned to a teammate internally for action Why this matters Catches missing or unclear content early - before it turns into a support ticket Provides structured data on why articles fail, rather than just a reaction count with no context Enables teams to close the feedback loop and improve content proactively Ensures a consistent brand tone across all Fin and Messenger interactions Current workaroundNone available. The built-in reaction flow cannot be overridden or customized.Thank you for your cooperation. Best regardsMatej - reevSenior Customer Support Manager

    Add customization options for the "The team can also help" text when using "Set AI Agent expectations"Submitted

    When configuring Fin under Settings → Channels → Messenger → Conversations tab → "With Fin," selecting the recommended option "Set AI Agent expectations" automatically inserts the subtitle "The team can also help" beneath the workspace name in the Messenger conversation header.This string is currently hardcoded — there is no way to edit, replace, or remove it anywhere in the Messenger or Fin settings.We'd like to see several customization options for this text:1. Custom text — the most important one. Let us provide our own wording so it matches our brand voice and support model (e.g., "Our support team is here if you need a person" or "Ask to speak with our team anytime").2. Keep the default — workspaces that are happy with "The team can also help" should be able to leave it as-is with zero extra setup.3. Hide it entirely — a toggle to remove the subtitle for teams that don't want a second line in the header at all.4. Per-language customization — the custom text should support Intercom's multilingual settings so each supported language can have its own version, just like other Messenger strings.5. (Nice to have) Availability-aware text — the ability to vary the message based on office hours or team status, so we're not promising "the team can also help" at 2 AM when no one is online.Suggested implementation: an option set within the "Choose how Fin sets expectations" section — Default / Custom text / Hidden — with a multilingual text field when Custom is selected. This would mirror how "Start conversation button text" is already editable directly below this setting, so there's an existing pattern to follow.Thanks for considering!

    Gerald PradoActive User

    Make Help Center Articles Report Data Available via the Reporting API for use with OperatorSubmitted

    What I'm requesting:Expose the data from the legacy Help Center Articles report (found under Reports > Proactive Support > Articles) through the Intercom Reporting API, so it can be surfaced and used within AI-assisted analytics tools like Intercom's own Operator/Copilot assistant.The data I'm referring to includes:Article viewer counts (per article, per time period) Reactions (happy / neutral / sad) Conversations triggered per article Last updated dateWhy this matters:We use our Help Center extensively as a first line of support. This data already exists in Intercom — it's visible in the legacy report — but it's siloed from the reporting API, which means AI tools can't access or reason about it.Right now, when I ask Intercom's AI assistant to analyze help center content performance, it can tell me which articles Fin cited in conversations or what resolution rates look like by topic — but it can't cross-reference that with how many customers actually viewed those articles or how they reacted to them. That's a significant blind spot.The use case is straightforward: combining article view and reaction data with Fin's resolution rates, topic breakdowns, and conversation outcomes would give support teams a complete picture of how their knowledge base is performing — not just from Fin's perspective, but from the customer's.What good looks like:Being able to ask the AI assistant: "Which articles have high views but low positive reactions or high follow-up conversation rates?" — and get a data-backed answer without having to manually cross-reference two separate reports.Who benefits:Any team using the Help Center seriously — especially those using Fin as their AI agent — would immediately benefit from this connection being made. It closes the loop between content publishing and support outcomes.