Analyze Fin

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Hey everyone 👋As Fin handles more customer conversations, I'm curious how teams are checking the actual responses being sent to customers.Not just whether Fin resolved the conversation but whether the response itself could create a problem.For example:promising a refund or exception that isn't actually allowedgiving incorrect pricing or policy informationmaking a commitment the business can't honorexposing sensitive customer informationgiving inappropriate financial or medical advicemissing an important AI disclosureThe difficult cases aren't necessarily obvious. A response can look perfectly reasonable on its own but become risky when you look at the customer's situation and the company's policies.How are you handling this today?Manual sampling? QA? Fin's monitoring tools? Internal rules? Something else?I'm working on this problem myself and would genuinely like to understand how other Intercom teams are approaching it.

Hey everyone For those using Intercom with AI/automation, how are you currently monitoring the conversations happening in your workspace for things like:AI making an incorrect refund or pricing promiseIncorrect policy information being given to customersSensitive financial/medical advicePII or other sensitive information being exposedAI responding without the appropriate disclosureI'm curious whether you're manually reviewing conversations, relying on Intercom's existing tools, or using another compliance/QA solution.I’ve been building ChatShield around this exact problem it connects to Intercom and monitors workspace conversations for potentially risky interactions, rather than only checking the AI bot itself.If anyone here is dealing with this problem, I'd genuinely love to hear how you're handling it today.ChatShield: https://chatshield.cloudNot trying to sell anything here mainly interested in understanding how other Intercom teams approach this.

Hello,I wanted to consult with my fellow Knowledge Content and Fin AI operators about an issue I’ve noticed while working through “Edit Article” Recommendations (previously called Suggestions).I’ve identified a couple of flaws in the editor panel that loads to the right of the Recommendations list:When an “Edit Article” Recommendation impacts one or two steps within a numbered list, the editor strikes through all steps (sometimes entire paragraphs) in the list instead of highlighting only the specific step(s) affected. Accepting these edit recommendations often breaks the formatting of our articles.   Similarly, when an “Edit Article” Recommendation impacts a section containing images (such as UI screenshots), accepting the edit removes the images entirely and leaves only the ALT text behind once the edits are applied.I’m curious if anyone else has experienced these issues or found reliable workarounds for them. At the moment, we’ve had to manually review and reformat affected articles

We need to implement a double approval process for FIN AI production releases.Before any deployment goes into production, a second level of validation and authorization should be required to ensure proper security and quality control. This will allow Team Leads to have visibility and governance over changes being released.Production deployments should never depend on a single person developing and pushing changes directly to production without an additional review and approval process.

Hi everyone,I'm currently exploring different ways to improve customer support workflows using AI. I was wondering how other teams are measuring the quality of responses generated by Fin AI.A few questions I have:What metrics do you monitor the most? Have you noticed improvements in first-response resolution? Do you use Fin mainly for FAQs, product support, or live conversations? Any best practices for training AI with a growing knowledge base?I'd love to hear how your team approaches this and what has worked well in real-world use cases.Thanks! 

Issue: Fin sometimes have a follow up question, “Is that what you were looking for?” / “Did that helped?”Customer replied: “ok” “okay thanks” “cool” “got it” BUT, they’re not totally done with their concern. This is affecting Fin CSAT. 

Hey folks, I'm Brian, I work on the customer education/Academy team at Fin.We're starting a new project atm, building out professional certification program and before we get too far into it, I want to hear from the people this is actually for.A quick bit of context on what we mean by certification: this isn't a course completion badge. It's a structured way to prove you can actually do the job, through real scenarios, assessments. Something worth putting on your CV or LinkedIn.Think less "I watched the videos" and more "I can demonstrate I know what I'm doing."Two things I'd love your input on:What certifications would you actually want to see? Specific Fin features and capabilities, broader skills like conversation design or AI quality management, role-based tracks like support rep or manager, what would be most valuable to you/your team? What would make it worth your time? What would a cert need to include for you to feel it was genuinely valuable, to you, or to a hiring manager?Wou

Hi,We’re struggling with Fin’s behavior outside office hours, and at this point it’s becoming a critical issue for us. We’ve been in contact with support, they are looking into it. We’d really like to hear if anyone in the forum has tips or experience with the same issue. Expected behaviorFin should run 24/7 Answer what it can Not escalate when we are closed Inform the customer to return during opening hours if needed Continue the conversation and close it after inactivity What is happening1. Conversations are escalated anywayFin sends the away message But still creates an open “unassigned” conversation These pile up into a backlog we explicitly do not want 2. Fin stops respondingThe Fin step is skipped completely Customers often receive the away message twice The conversation just stopsThis creates a very poor and confusing experience. 3. Conversations never return to Fin Previously:Fin continued after the away message Conversations were handled and automatically closedNow:Conversatio

It appears new features are regularly deployed without testing against multilingual use cases, we now have Procedures, and Recommendations incompatible with our workspace, seemingly due to multilingual articles.Great example of "garbage in - garbage out"If your database isn't ready for AI, funny things will happen, and I suspect the Articles database has some poor architecture in the relation to parent pages against their multilingual pages.Fin AI poorly executes against multilingual, time and time again. I am losing confidence that Intercom's multilingual articles are being considered in the new deployments of products.Recommendations breaks with multilingual, Procedures break with multilingual. Hoping this is fixed soon!

Our confirmed resolution rate is between 6-7% usually and our assumed resolution rate is near 60%. Looking at the conversations, I would estimate that 1/3rd at least of the assumed resolutions represent a correct answer and our clients are just not clicking the “Yes that helped” button that would mark it as a confirmed resolution. My experience with bots is similar, I rarely give bots feedback, because I know its not human. Any tips or methods that you have tried that increase clients use of the confirmed resolution? 

We've been using Fin for about 6 months and genuinely love it — but the analytics drive me crazy.I can see our resolution rate is 71%. Great. But when my manager asks "why isn't it higher?" I have nothing. Which topics is Fin struggling with? Did that KB article I wrote last week actually help? No idea.We ended up exporting conversations manually and doing our own analysis in spreadsheets, which is... not ideal.How are other teams handling this? Are you just accepting the top-line number, or has anyone found a good way to actually dig into where Fin is failing?Would love to know if there's something I'm missing in the native reporting, or if everyone's just cobbling something together.

Curious how others are approaching Monitors and scorecards in Intercom since the release. What types of conversations are you flagging? How are you structuring your scorecards? What criteria are you using to evaluate performance? How are you using the results once you have them?What’s been genuinely useful for your team so far? 

Hi! I was wondering if anyone has insight into what a good resolution rate for Fin would be? I know the marketing site for Fin says “Resolve 50% of your support questions instantly” - but is 50% the average that we should resolution rate that we should be seeing? We only just started our trial but are preparing support goals for the quarter and I’m not finding any information about this in the Help Center. I would love to hear Intercom’s thoughts on what they see across their own Fin averages or an average across all companies who use Fin, but it would also be helpful to hear from anyone who may have insight into this for their own company. Thank you!

I want to be able to use the API to search for conversations with a certain CX score within a particular date range. Can you please add CX score to Accepted-Fields? https://developers.intercom.com/docs/references/rest-api/api.intercom.io/conversations/searchconversations#section/Accepted-Fields

How does your team measure Fin AI performance?We're trying to understand how different teams think about this. When you review Fin's performance, what signals do you actually look at?- Resolution rate vs. target- Handoff rate- Reopened conversations- CSAT on Fin interactions- Something else entirely?And when Fin underperforms, how do you diagnose why — do you have a process for that, or is it mostly manual conversation review?Would love to hear how others are approaching this.

Our team hit 71% resolution rate with Fin last month. Looked great on the surface — but when we dug in, we had no idea which topics were dragging it down or what to actually fix.Intercom shows you the overall number but nothing underneath it. No topic breakdown, no trend over time, no way to know if a KB update actually moved the needle.Curious how other teams handle this. Are you exporting conversation data and analyzing manually? Using a third-party tool? Or just accepting the top-line number and moving on?(I've been building something to address exactly this — happy to share early access if there's interest.)

Hey again! I noticed there is an option to collect Fin’s attributes data which is great, but is there any chance to be able to collect attributes’ data without Fin answering? Is this something that we can expect any time in the future?   

Hi All, What are the benchmarks for bot-resolved CSAT? Our team is looking for a baseline as we implement our first round of AI/Fin improvements. Thanks! 

How do you all tie your CSAT reporting together? We use Fin along with Zendesk and house data externally in Domo. It doesn’t appear possible to send Fin CSAT over in the Zendesk ticket that is created for tracking, and it looks like our only option is manually exporting CSAT CSV’s and then importing that into Domo. Is this correct?Are there any better solutions?

Whenever Fin receives a negative rating from a customer, I review the conversation and decide if perhaps it should be escalated to a human, or make improvements to our content. Are there any suggestions for automating this process? Or reports that tell us why there was a DSAT rating specifically (outside of the CX score)? Or perhaps a section in Optimize that will show us content suggestions specifically linked to a DSAT outcome? 

Hi everyone, I am Raghav Hinduja Swiss (Switzerland) based IT-professional. I’m trying to enable and customize chatbots in my system, but I’m not sure what the best approach is. I’d love to hear from anyone who has experience with chatbot setup and customization.

We use Fin’s inactivity auto close feature, initially this was set to a short 15 minutes but due to the nature of our clients industry it would not be uncommon for them to be away from the PC for 45 minutes or so. Last year the setting was changed to 3 hours and since then we’ve noticed our CSAT response rate has dwindled to a meagre 5%. I hypothesise this is largely due to the lengthy inactivity setting and customers then failing to interact and the interaction itself being largely forgotten. I’m curious other users inactivity setting and the CSAT response rate you receive. 

When I checked the Japanese inquiries in Fin AI's Performance, I found that all the content is displayed in English even though customers wrote in Japanese. Can you change it to as it was written?Also, could you add a feature to filter and select only Japanese customers?When I search in Japanese in the content from Knowledge, the relevant items do not appear in the results. As my customers use only Japanese, I would really appreciate if you could solve this issue. Thank you.

Today we noticed an issue with Fin failing to respond to tickets. There have been no changes to the workflow but for whatever reason it just hasn’t responded to queries. We noticed the increase in FRT and drop in involvement but looking back we are almost certain this has happened before. It appears to be intermittent and no real obvious answer but curious if anyone else has experienced this and how you resolved.