Reporting

We’re hoping to gain insight into how other customers are forecasting handle time, particularly when using Intercom data. We've noticed discrepancies in the handle time metrics reported by Intercom that don’t align with the structure of our operations.Typically, I export the raw data and filter out outliers manually. However, we continue to see a significant number of conversations with unusually long handle times that lack a clear explanation. This inconsistency makes it difficult to trust the accuracy of the reports.We’re looking for a better approach—either a clearer understanding of how Intercom calculates handle time behind the scenes, or alternative methods for forecasting handle time more reliably using Intercom data.Thank you in advance for any guidance or shared experiences!

Hey folks! I’m trying to report what share (or at least total counts) of Solved conversations were closed after 1 reply, 2 replies, and ≥3 replies for a given date range. What’s the exact Intercom chart setup to do this?What I’ve tried:Replies to close a conversation with Median/Average/Sum. Workable but without counts option, it is difficult to reach the actual percentage of solved conversations based on the number of replies used. Closed on first contact rate. I believe this measures “one teammate replied,” not “closed in 1 reply.” so, not really something to rely on.Could someone share any finding, insights or experience calculating this? Or suggest the best workaround.Thanks!

Hello, We need to calculate total duration for which conversations are snoozed for analytical purposes. I went through the community forum and found this In the above topic, it’s mentioned that their isn't a direct method provided for generating a report specifically for snooze time in conversations.Although we have found out that we can derive snooze durations and it would be helpful if we can get a review from the Intercom community over this method, or if we can get some other attributes or methods (provided by the Intercom API) through which we can calculate snooze durations, this would be extremely useful for us.We are aiming to derive the snooze duration based on the following:Calculate snooze time by tracking conversation state transitions through conversation parts, which provide the most granular state information for each conversation. Each conversation_part has a status field that captures the conversation state (open, closed, or snoozed) and a created_at field which tracks

I have discoverd instances in conversation_parts where the conversation_part.status is in a snooze state even if the part_type is a comment type. How is this possible, because according to this article (https://www.intercom.com/help/en/articles/6564538-snooze-a-conversation) “If you send a reply message in a 'snoozed' conversation that's assigned to you, it will not automatically 'unsnooze'/reopen the conversation.”“If someone else sends a reply, adds a note, or updates an attribute in a 'snoozed' conversation assigned to you, it will be automatically 'unsnoozed'/reopened.” So How come a conversation remain in an snoozed state even after there is a comment from either admin or customer?A comment part type is defined as: Standard reply from customer or admin to a conversation (defined at: https://developers.intercom.com/docs/references/1.4/rest-api/conversations/conversation-part-model#conversation-part-types)  These are the part-types appearing with snoozed state1    comment2    conver

Hi all! Support team leaders, I’d love to know -What metric from reporting in Intercom do you use to inform a goal for your team monthly?Number of tickets closed? Number of tickets assigned? Number of replies sent? Or something else? I’m trying to zone in on how many tickets my support team members are handling individually each week.  Thanks!

I know the default survey uses emojis with hover-over text labels, but it seems like our customers might not be understanding the difference between each rating (see attached image). It’s starting to impact our metrics a bit.I’d really like to keep using Intercom’s native CSAT tool for reporting and widgets, but I’m wondering: Is there any way to customize the default emoji options (e.g., swap them out or show the text labels instead)? Or do we know if there’s already a product request or feature idea out there for that? Also related question, is it possible to tweak how CSAT is calculated in our instance? For example, could we have “OK” responses count as positive? A lot of our users are parents or older family members, and based on some past examples, “OK” seems to represent a satisfied response rather than a neutral or negative one.

I’m returning to Intercom after a long break, and I could use some advice from folks who’ve set up recent workflows.It looks like every customer interaction starts as a Conversation. And if it takes longer to resolve or needs extra structure, it can be turned into a Ticket. That all makes sense.But I’m trying to set things up in a way that keeps reporting clean and consistent.My goals:I want to capture the same core metadata for every interaction (things like feature area, source, etc.) I don’t want to double-count anything when pulling metrics I want to avoid having agents fill in fields twice, or inconsistently across conversations and ticketsMy questions:Do you only use tickets for escalations, or convert everything? Can you safely report on Conversations only, and still get reliable counts and resolution metrics? Are you adding custom fields to Conversations for universal stuff, and using ticket-only fields for more complex cases?Trying to figure out how to avoid having my metrics

Does anyone have a solid work around to export reports, including all the charts, as they appear in Intercom? I'd like to share these with internal stakeholders who don't have access to Intercom.Currently, only function is to download the underline data or screenshot the report 😅 - where is I really just want to send the report to stakeholders.

https://www.intercom.com/help/en/articles/5652381-conversational-support-report#h_c98526bc52Self Serve, Human & Proactive Support.  I am familiar with conversation ID’s, what are the other identifiers that make up Self Serve, Human & Proactive Support.   What differentiates them in the data?  I can’t seem to find a list of identifiers that would feed into those reports.

I know that you do not currently offer Automated reporting that users can subscribe to other than setting up the dataset exports to send daily.  Do you plan to add subscriptions for REPORTS so that we can receive them to our email?

One of the report metrics is to look at time spent on a ticket, and currently the available units are hours, days, years, but I really need the option of minutes. Can this be added or enabled?

Hey everyone,I’ve been looking into ways to improve user onboarding and updates for a little side project of mine. It’s a desktop launcher bloxstrap and I’m curious if Intercom could be useful in this setup.Couple things I’m wondering:Is it possible to trigger onboarding/help messages inside a desktop app, or is Intercom mainly limited to web and mobile?Would using Intercom’s email campaigns be the better route for sharing updates / new release notes with users?Has anyone here tried hooking Intercom into a custom software download flow?I’m not sure if Intercom is flexible enough for this kind of product, but it seems like it could be a nice way to keep people engaged without building everything from scratch.Appreciate any tips or experiences!

I'm setting a report filter by company tag. I've noticed when a conversation's primary contact (source.author) isn't connected to a company with that tag but another participant is, the conversation isn't populating on the report. How do I ensure this conversation or ticket will populate on the report for participating contacts with that company tag?

Device: Nexus 5XOS Version: Android 13App Version: 2.0.19 (82)Date: August 22, 2025, 15:22:48 GMT+03:00Issue Description:Our Flutter application is experiencing a ForegroundServiceDidNotStartInTimeException on Android 13 devices when using both Firebase Messaging and Intercom. This error specifically occurs when the Firebase Messaging background service (FlutterFirebaseMessagingBackgroundService) is initiated.Android expects a foreground service to call Service.startForeground() within 5 seconds after Context.startForegroundService() is invoked. In our case, this timeout is occurring, leading to an application crash.Key Detail from Logs:Upon reviewing the crash logs, a significant detail observed is the activity on a thread named DefaultDispatcher-worker-3, which is related to the Intercom SDK. This thread shows operations involving okhttp3.Cache and io.intercom.android.sdk.AppIdentityInjector.initializeEvents. DefaultDispatcher-worker-3:       at okhttp3.internal.io.FileSystem.<cli

We would like to request a new data point in the reports section for tickets: "Last Comment”. 

Hi Team,Where can I change the timestamp for the Dataset export to any other than the default one? I can simply switch that in other views, but not for Dataset export where I would need to do so. Thank you for any hint.Martin

Hi there, My team were wondering if it was possible to change customer ratings if this was deemed invalid from a supervisor POV?

Hi! We need to export, or be able to report on, conversation messages (the actual message text). We’ve read the documentation regarding API or Amazon S3 but we’re wondering – is there any 3rd party app that you’d recommend using? Maybe someone that is already using a 3rd party app for it can give us some advice. Thanks in advance!

Android devices always shows France language at the title of Help Center Collection although I have changed to English by updateUser, all content change to English correctly. 

It appears that the only opportunities for aggregation are Average, Median, Minimum, and Maximum but I want a Sum of all the time spent handling tickets for the last three months. How can I aggregate by Sum instead? How can I either remove the aggregation options so I can see a total sum, or get a full list of all the conversations and all of their handling time so I can sum it myself? Thanks!

Hello,When I query the conversation_tag_history table, I noticed there can be multiple conversation_updated_at values for the same tag_id and conversation_id.Usually the MIN(conversation_updated_at) value is accurate, but I wonder how this can be the case?It happens frequently so I don’t think it’s because someone is removing a tag and adding it again, but I can be mistaken.Thanks in advance!

Hello All,As I continue scaling tiered support, I’m working to improve how we monitor both real-time and historical trends. Specifically, I’m trying to track: Time from conversation start to first assignment (Time in Unassigned) Time from first assignment to reassignment (Time in Tier 1) Time from reassignment to first close (Time in Tier 2) I also want to make sure we’re capturing the entire lifespan of the ticket, including both open and snoozed time.Right now, inbox views are the only way to surface aging tickets—but they aren’t designed for this kind of analysis. They don’t function like reports, and they don’t allow us to layer in the conversation attributes we’ve added, such as product area or difficulty level.That makes it tough to proactively support aging tickets, coach the team in real time, or dig into specific themes during 1:1s—especially when trying to identify which product areas need more targeted coaching.I’d love any ideas on how we can bring better visibility to

How do I activate scheduled downloads to S3 for the following phone call metrics? Or can someone provide the formula to calculate call duration, call queue time, call talk time, and call answer time using the Fields gather from the conversation part API?

Hi Intercom Developers,  I would like to report an issue of the Intercom notification on Android platform. The issue here is when the user receives the notification, it’s showing not only the new message but also the other past messages. Our app expect it to only show the new message. Could you guys please help take a look at this issue? Thank you so much.Here is our implementation, we’re using react native 0.76.5 and @intercom/intercom-react-native: ^8.1.0Please let me know if I can help you guys provide any further information

Hello!This quarter we’re working on moving to a more proactive support approach, an element of this is building out our helpcentre/FAQs and utilising the AI bot.However I recognise the importance of studying our support queries to really understand what people are asking, learn what’s missing from the help centre etc.But how do people tend to do this?Does intercom have any functionality which reports on questions people are asking which the bot can’t answer? Or do people download conversation data and manually read through conversations?I’m uncertain atm the best approach within intercom.