Data Panda brings your Intercom data together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your team uses every day.
Intercom started in 2011 as a messenger that let a web product talk to its own users, and has grown into a customer service platform covering conversations, tickets, help articles, bot flows and, more recently, the Fin AI agent. More than 25,000 paying customers use it, mostly software companies and subscription businesses where the support conversation is also a retention conversation.
The point of pulling Intercom into a warehouse is that support volume is one of the earliest signals a customer is about to churn, and Intercom's own dashboards do not see the account's billing health, pipeline stage or order history. Next to CRM, payments and commerce data, the conversation and ticket stream turns into a leading indicator, not a cost report.
Ticket volume, tag mix and response time in the same picture as revenue and churn.
Let the rest of the stack react to what support already heard this morning.
Use conversation history to score churn risk, classify intent and surface topics.
Internal tools on Intercom data that sales, product and CS would otherwise request as custom reports.
A list of concrete reports, automations and AI features we have built on Intercom data. Pick the one that matches your situation.
Which accounts are telling us they're about to leave?
Rising ticket volume and falling CSAT over eight weeks, on accounts with a renewal in the next ninety days. Ranks the accounts most likely to churn against their annual contract value, so customer success knows which ten calls to make, not fifty.
Which support issues are costing us the most revenue?
Ticket tags grouped by the revenue of the accounts that opened them, not by raw count. A bug filed by ten small accounts can read the same as a bug filed by one enterprise account until the numbers are tied to the books.
Is the bot resolving things or just delaying the human?
Fin-handled conversations split into resolved, escalated and reopened, with CSAT per path. The resolved-by-bot number only means something if it sits next to how often those same accounts come back the next week on the same issue.
Support cost per customer and per segment, tied to contract value. The CS budget defence stops being about headcount versus tickets and starts being about support cost against renewal likelihood.
Support history visible on the CRM account before a renewal call or an expansion conversation. The deal at risk because of two open P1s stops being news in the renewal meeting.
Queue load, first-response time and backlog aging per team in one picture, with trend. Staffing calls are made against how work flows, not against a gut feel about last week.
Intercom conversations, CSAT scores and tag changes push into HubSpot as timeline events on the matching contact and company. Sales and CS see the support context on the deal before a renewal call instead of hearing about it mid-meeting.
Open ticket count, age of the oldest open ticket and rolling CSAT land as fields on the Salesforce account. Forecast reviews show the accounts whose support load is rising, so the rep and CSM walk into the quarterly business review with the same picture.
For Pipedrive deals in the expansion or renewal pipeline, the Intercom conversation history and recent CSAT show up on the deal card. A stuck renewal reveals itself as 'three open tickets and two negative CSATs' before the rep sends another follow-up.
When a Shopify customer opens a conversation, their full order history, loyalty status and last shipment state show up in the Intercom inbox. Support agents stop asking for order numbers, and the business sees which product tickets are costing paid orders.
You keep the reporting tool you already have. We connect it to the warehouse where your Intercom data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Both are available. Metadata (ids, tags, timestamps, assignees, CSAT) always lands. Conversation bodies can be pulled in when topic analysis or AI scoring needs them, with PII handling agreed explicitly up front: redaction rules, retention and access controls are set before any message text enters the warehouse.
Fin-handled conversations are first-class in the warehouse, with resolved, escalated and reopened outcomes on each. That makes the bot's real resolution rate measurable against human-handled conversations on the same tag and severity, rather than against the marketing number.
We review your Intercom setup and the systems around it. Together we pick the first thing worth building.