A practical guide to Intercom BigQuery export in 2025

You love Intercom for chatting with customers, right? It’s brilliant for those in-the-moment conversations. But when it comes to reporting, let's just say it can feel a little restrictive. It's like trying to paint a masterpiece with only three colors.

If you've ever wanted to analyze support trends from two years ago, or figure out how your support conversations connect to sales data, you know exactly what I mean. You hit a wall, and you hit it fast. That’s why so many teams decide to set up an Intercom BigQuery export. It's the standard way to get your valuable conversation data into a place where you can really analyze it.

This guide will walk you through why that's a popular move, the common ways to get it done, and a more modern, AI-first alternative that helps you act on your data instantly, not just analyze it next quarter.

What are Intercom and Google BigQuery?

First up, let’s make sure we’re on the same page. If one of these tools is new to you, here’s a quick rundown.

What is Intercom?

Intercom is a platform built to help businesses talk with their customers. Think live chat, email campaigns, and automated messages all in one place. It's all about managing customer interactions and building relationships in real time.

A screenshot of the Intercom Messenger, a key feature for an Intercom BigQuery export.

What is Google BigQuery?

Google BigQuery is Google's cloud data warehouse. The simplest way to think about it is as a gigantic, incredibly fast database in the cloud that’s built to handle absolutely enormous amounts of data. You can dump everything in there, and I mean everything, and use standard SQL to ask it complex questions. It's the kind of tool data teams use to find the real stories hidden in the numbers.

Why set up an Intercom BigQuery export?

While Intercom gives you decent day-to-day reports, an Intercom BigQuery export becomes necessary when you want to ask bigger questions. The native reporting inside Intercom has a few key limitations that growing businesses tend to bump up against.

• Your data has a short memory. Intercom is focused on the now, which means you’re often stuck looking at data from the last few months. If you want to see how your support trends have changed since you launched two years ago, you're pretty much out of luck.

• Your data lives in a bubble. It's incredibly difficult to combine your Intercom data with anything else. Sales data from your CRM, payment info from Stripe, product usage stats, they all live in separate houses. This makes getting a complete picture of your customer almost impossible.

• You can't ask your own questions. You're mostly limited to the pre-built reports Intercom provides. Forget about asking complex, custom questions like, "Which marketing campaign brought in customers who ended up submitting the most support tickets three months later?"

Getting your data into BigQuery solves these problems. Suddenly, you can:

• See the whole story. Combine conversation data with everything else to finally understand the full customer journey. You can see how support interactions actually impact churn, upgrades, and customer lifetime value.

• Analyze long-term trends. Store your support data forever. Track team performance, common customer questions, and seasonal spikes over years, not just quarters.

• Play with advanced AI and machine learning. BigQuery has built-in ML tools that let you do some seriously cool stuff, like forecasting next month's ticket volume or building a model that flags customers who might be at risk of churning.

• Build dashboards that actually matter. Connect BigQuery to tools like Looker Studio or Tableau to build reports that answer the questions your business cares about, not just the ones Intercom thinks you should ask.

How to set up your Intercom BigQuery export: Three common methods

So, you're sold on the idea. How do you actually get your data from Intercom to BigQuery? Most teams go down one of three paths. Let's look at the pros and cons of each, without getting lost in the technical weeds.

Method 1: The manual export via the Intercom API

This is the classic DIY approach. It means writing your own code (usually Python or Google Cloud Functions) to pull data from Intercom's API and load it into BigQuery.

On paper, this sounds great. Total control! You can build it exactly how you want. In practice? It’s a bit of a nightmare waiting to happen.

• It costs a fortune in engineering time. This isn't a weekend project. It requires a dedicated engineer to build the initial pipeline and, more importantly, to keep it running.

• It’s incredibly fragile. The moment Intercom changes its API (which happens!), your script breaks. You might not even notice for a few days, and suddenly you have a black hole in your data.

• It doesn't scale well. Handling API rate limits, pagination, and data transformations for a few thousand conversations is one thing. Doing it for millions is a serious engineering challenge that just gets more complicated over time.

Method 2: Using third-party ETL/ELT tools

A much more sensible option is to use a no-code data pipeline tool like HevoData, Airbyte, or Fivetran. These platforms act as a bridge, connecting to Intercom on one end and BigQuery on the other, automating the Intercom BigQuery export for you.

This is a huge improvement over wrestling with the API yourself, but it's not a silver bullet. These tools introduce a few new headaches.

• The costs can be unpredictable. Many of these tools charge based on usage or the number of rows you move. If you have a busy support month, you might get a nasty surprise on your next invoice.

• You still need a data team. The tool just moves the data from point A to point B. You still need data analysts or engineers to clean it, model it in BigQuery, and build the dashboards before anyone gets a single insight. The job is only half done.

• It’s all about looking backward. This entire setup is designed to help you analyze what already happened. It's reactive. While the insights can be valuable, it does nothing to help your support team deal with the flood of tickets they're facing right now.

The proactive alternative: Real-time AI automation

Okay, so we've talked about storing your data for analysis. But what if we're asking the wrong question? Instead of just figuring out how to look back at your data, what if you could use it to help your team today?

This is where a modern AI platform like eesel AI comes in. It offers a one-click integration with Intercom but uses your historical ticket data for something much more immediate than reports: it trains an AI agent that can understand and resolve customer issues automatically.

This completely changes the game. Your data goes from being a passive resource for quarterly reports to an active asset that drives automation. You stop just understanding trends and start acting on them.

Beyond analytics: An AI-first approach vs. a traditional Intercom BigQuery export

Let's make this more concrete. Here’s a side-by-side look at what you can expect from a traditional data warehouse project versus using an integrated AI solution.

From retroactive reports to real-time resolutions

Imagine you want to figure out your top ticket drivers.

• The BigQuery Scenario: A data analyst gets the request. They spend a few days pulling data, cleaning it, and building a dashboard. A week later, they present their findings: "20% of our tickets are about refund status!" Great. Now, a support manager has to create new saved replies and train the team on them. By the time a customer gets a faster answer, two weeks have passed, and a lot of money has been spent.

• The eesel AI Scenario: You connect your Intercom account. Within minutes, eesel AI analyzes your past tickets and learns exactly how your team has been answering "refund status" questions. The next day, it’s already automating a huge chunk of those conversations, freeing up your agents for trickier problems. The time to see a real impact? Less than 24 hours.

A dashboard showing historical data, which is what traditional Intercom BigQuery export methods provide.

Unified knowledge without the engineering overhead

A data warehouse is meant to be a central source of truth, but getting it there takes a ton of work. You need data engineers to build complex models just to join a few tables together and make the data usable.

eesel AI unifies your knowledge automatically. Think about where all your company's information lives. It's not just in Intercom. You've got detailed guides in Confluence and internal project specs in Google Docs. eesel AI connects to all of it, creating a single brain for your AI agent and your support team, with zero data modeling required.

A diagram showing how an AI like eesel can unify knowledge from multiple sources like Intercom, Confluence, and Google Docs, which is an alternative to a complex Intercom BigQuery export.

Simulate and deploy with confidence

This is a huge difference. When you kick off a BigQuery project, its value is purely theoretical until the reports are finally built. You’re flying blind for weeks, sometimes months, hoping it will pay off.

With eesel AI, you can use the simulation mode to test the AI on thousands of your own historical tickets before it ever speaks to a live customer. You get a precise, data-backed forecast of its automation rate and can see exactly how it would have responded to past conversations. All the guesswork is gone, giving you the confidence to turn it on.

Comparing costs: Intercom BigQuery export vs. AI automation

The way you pay for these two approaches couldn't be more different.

The hidden costs of an Intercom BigQuery export

If you build an in-house pipeline, you’re paying ongoing engineering salaries, which can easily climb into six figures.

And even with an ETL tool, the bills can be… surprising. Many charge you for every row of data you move. So a busy support month doesn't just mean a tired team; it means a bigger bill you didn't see coming. On top of that, you're still paying for the data team to actually make sense of it all. The total cost is often much higher than the price on the website.

Transparent and predictable pricing with eesel AI

eesel AI's pricing is designed to be simple and predictable. The tiers are clear, and the monthly plans are flexible (you can cancel anytime).

Most importantly, there are no per-resolution fees. Your bill won't suddenly jump because your customers need more help this month. The price scales with the value you get, not your ticket volume.

Plan	Monthly (bill monthly)	Key Unlocks
Team	$299	Train on website/docs; Copilot for help desk; Slack.
Business	$799	Everything in Team + train on past tickets; AI Actions; bulk simulation.
Custom	Contact Sales	Advanced actions; multi-agent orchestration; custom integrations.

Stop just analyzing your Intercom BigQuery export, start acting on it

Look, setting up an Intercom BigQuery export is a solid move if your main goal is deep-dive, historical analysis. It’s how you answer big questions about what happened last year.

But if you're looking to improve things today, to cut down response times, free up your agents, and make customers happier, you need to put your data to work, not just put it on a shelf. Instead of just analyzing the past, you can use that same data to automate the future. An AI-first approach closes the loop between insight and action instantly, turning your support history into your biggest asset.

Ready to see what your Intercom data can really do? Find out how eesel AI can start automating your support in minutes. Start your free trial or book a demo to see it in action.