Analyze Fin

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.

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!

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!

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. 

Issues:Once you add a content to a missing one and create either a snippet or article from it- you can never go back or see an audit trail of what you have actually done.    There is no list anywhere where you can see which content gaps you have actually optimized. We would like to store them somewhere which them might be helpful for configuting another area   Once you add the content and hit save, it opens the next suggestion instead of taking you back to the Optimize page, this is quite confusing and unrelated from what you were doing.    No export buttons anywhere   There is a table where they give percentages of conversations, but you can not clic

I’ve learned from Intercom’s documentation that CX scores are calculated using three signals: Customer sentiment, Resolution status, and Service quality.When I want to understand why a conversation has a certain CX score, the single blended number doesn’t always give enough detail. For example, was it customer sentiment that drove the score down, or did resolution status carry more weight?Is there a way to see the score for each of the three signals individually, rather than just the overall CX score? Having visibility into the individual components would really help in analyzing conversations more deeply and understanding what’s driving the overall CX score.

I’m working on setting performance targets for FIN—does anyone have experience or data they could share around Resolution Rate or CX benchmarks?!--endfragment>!--startfragment>

Hi all,How is your team’s CX Score performing?Our Fin has scored 86% over the last 30 days while resolving 69% of conversations (90.9% and 74.3% respectively when limited to the Chat channel only).

Hey everyone!I’m curious to hear from other Fin users – what key metric are you focusing on to demonstrate Fin’s success to your organization? Personally, I’ve found that resolution rate isn’t always the best indicator since so many variables can impact it. For example, a customer might receive a perfect answer but still choose to speak to an agent, or Fin could close a conversation automatically due to spam, both of which can skew the resolution rate.I’m leaning toward deflection rate as a more telling metric – measuring the percentage of support handled by Fin versus what’s escalated to humans. But I realize this might be oversimplifying things. Would love to hear what others are tracking and why! 

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? 

Our deflection rate was 10-15 points below our average yesterday. It’s not the end of the world, but we’re typically very consistent.Was this a fluke or were there system bugs with Fin?

Hello! I’m working on integrating Fin AI to our queries handling.When conversations are handled by a teammate, we use a custom attribute “Subject Line” to ‘tag’ the query and report on volumes each week. Subject Lines would look like this:[Account] Login issue, [Account] Change Email Address[Billing] Refund Request[General] Query / Premium, [Feedback] Contentetc etc If I want Fin to handle tickets, how can I get it to tag the conversation based on its content? either via our Subject Line attribute or via tagsAt the moment, we have a workflow where users select what they want to talk about with buttons (“Question about my account”, “Help with my subscription”, “Share feedback”, “Something else”). Subscription queries go through Fin, the rests goes to a teammate. I’d like to open “Something else” to Fin AI too but I’d need to be able to tag the conversations properly to create usable data. What’s the b

Hi everyone!If you are using FIN as the chatbot, I’m curious about what is the current CSAT score of your FIN profile. Ours is currently ~50%, and we are not really sure if it’s a good one. Thanks!