How to export Heap data to Google Sheets for advanced analysis

If you’re using Heap to track product or website behavior and you’ve ever wanted to run your own formulas, build custom dashboards, or just see your data side-by-side with other stuff, exporting to Google Sheets is the way to go. But Heap (let’s be honest) isn’t exactly built for push-button Google Sheets integration. This guide is for product managers, analysts, or anyone who’s tired of clunky dashboards and wants to really get their hands on the data. I’ll walk you through the no-nonsense ways to get your Heap data into Google Sheets—what works, what’s a pain, and which hacks are worth your time.

Why bother? What Google Sheets does that Heap can’t

Heap’s built-in charts and dashboards are decent for quick checks, but Sheets gives you:

  • Custom calculations and formulas
  • More flexible charts and visuals
  • The ability to mash up Heap data with data from other sources
  • Easier sharing, especially for execs who just want a spreadsheet

If you’re just looking for a pretty chart, stick to Heap. But if you need to slice, dice, and experiment, exporting is the way to go.

Step 1: Figure out what you actually need

Before you start fiddling with exports, get clear about:

  • Do you need raw event-level data, or just summary metrics? Heap can export both, but the method changes.
  • How often does the data need to update? One-time analysis is simple. Automatic refreshes are trickier (and rarely seamless).
  • How big is your dataset? Google Sheets will choke if you try to move hundreds of thousands of rows.

Pro tip: Don’t overthink it. Most people only need summary data for Sheets. Exporting millions of raw events is usually overkill—and a quick way to break things.

Step 2: Export data from Heap

There are three main routes to get Heap data out. They all have tradeoffs.

2.1 Manual CSV export (Simple, works for most folks)

This is the method Heap actually supports out-of-the-box.

  1. Build your report, chart, or table inside Heap.
  2. Once you’re looking at the results, hit the “Export” or “Download CSV” button. (Usually in the corner or under “More Options.”)
  3. Save the CSV file to your computer.

What works:
- Dead simple. - Great for snapshots and quick projects. - You control exactly what to export.

What doesn’t:
- No automation. You have to do this every time you want new data. - Formatting can get weird, especially with dates or multi-value fields.

Ignore:
- Exporting huge raw event tables—Heap will let you request them, but Sheets can’t handle the volume.

2.2 Heap Connect (For the technically inclined, requires setup)

Heap Connect is a paid add-on that pipes your Heap data into a data warehouse (like BigQuery, Redshift, or Snowflake). If your company has this set up, you can use it as a middleman.

  1. Confirm with your data team if Heap Connect is enabled and which warehouse it’s using.
  2. Use a connector tool (like Google Sheets’ BigQuery connector) or a script to pull data from the warehouse into Sheets.

What works:
- Automates data flow; refreshes are possible. - Handles bigger datasets (up to Sheets’ limits). - You can join Heap data with other company data.

What doesn’t:
- Not for solo users—requires technical setup, access rights, and IT buy-in. - Can be overkill for simple needs.

Ignore:
- Setting this up just for one analysis. Use manual export unless you need regular, automated updates.

2.3 Heap API (If you love code and want repeatable exports)

Heap has a REST API you can use to query your data programmatically. You can write scripts to pull data and push it into Google Sheets via Apps Script or other tools.

  1. Read Heap’s API docs and generate an API token.
  2. Write a Google Apps Script or use a third-party tool (like API Connector) in Google Sheets to fetch data.
  3. You’ll need to handle authentication and pagination.
  4. Map the API response to your sheet columns.

What works:
- Fully automatable (schedule daily/weekly refreshes). - Customizable queries.

What doesn’t:
- APIs are technical—expect to spend time debugging. - Heap’s API isn’t lightning fast, and querying lots of data will hit limits. - Still subject to Google Sheets’ size and memory limits.

Ignore:
- Trying to rebuild dashboards in Sheets using API exports. Use this for targeted data pulls, not full product analytics.

Step 3: Bring your data into Google Sheets

Once you’ve got your CSV (or API data):

3.1 Import CSV manually

  1. Open Google Sheets and create a new sheet.
  2. Go to File → Import.
  3. Upload your CSV, and choose “Insert new sheet(s)” or “Replace data.”
  4. Clean up any weird formatting (date columns are notorious for coming in wrong).

Pro tip:
Save the original CSV. If you mess up the import, it’s easy to start over.

3.2 Automate imports (for recurring reports)

If you’re using Heap Connect or the API, set up:

  • BigQuery/Redshift connectors: Use Google Sheets’ built-in connectors or add-ons.
  • App Scripts: Write a script to pull the data on a schedule. This requires some JavaScript, but there are lots of templates online.

Heads up:
Sheets has a 10 million cell limit, and scripts can time out. If your data’s too big, sample or summarize it in Heap first.

Step 4: Analyze and visualize in Sheets

Here’s where Google Sheets shines:

  • Use formulas (SUMIF, QUERY, etc.) to slice and dice.
  • Build custom charts—way more flexible than Heap’s built-in options.
  • Combine with other data, like marketing spend or support tickets.

What to watch out for:

  • Data freshness: Unless you’ve automated, remember your data is a snapshot, not live.
  • Sheet performance: Lots of rows or formulas can slow things down. Archive old data if needed.
  • Formatting issues: Timestamps and user IDs can get funky—double-check before running calculations.

Common pitfalls (and how to dodge them)

  • Trying to analyze everything in Sheets.
    Sheets is great for analysis, not for storing millions of rows. Always aggregate in Heap when possible.

  • Forgetting to document your process.
    When you automate with scripts or connectors, note what’s running and when. Otherwise, you’ll forget in three months.

  • Assuming exports are always up-to-date.
    Manual exports go stale fast. Add a “last updated” cell so you (and your boss) don’t get confused.

  • Not cleaning up user IDs or event names.
    Heap’s friendly names can turn into ugly column headers. Rename fields as needed for clarity.

Pro tips for smoother workflows

  • Keep a “raw data” sheet and a separate “analysis” sheet.
    This makes it easier to redo the import without breaking your formulas.

  • Use Data Validation and Conditional Formatting.
    Highlight issues instantly—like negative values where they shouldn’t exist.

  • Template your Sheets.
    Once you’ve set up a process, copy the file and swap in new data instead of starting from scratch.

  • Set up email alerts for automated scripts.
    So you know if your data pull fails.

What to ignore (seriously)

  • Fancy “integrations” in the Google Marketplace.
    Most are wrappers for basic API calls, and add another failure point. If you’re not automating for dozens of people, stick to manual or script-based methods.

  • Overly complex data models.
    If you need to join 5+ tables or reconstruct every user journey, Sheets probably isn’t the tool. Use a real BI tool or database.

  • Exporting personal/user-identifiable data.
    Be careful—especially if you’re sharing Sheets. Mask or remove anything sensitive.

Wrapping up

Don’t get sucked into over-complicating things. Most of the time, a quick manual export from Heap and a bit of Sheet magic is more than enough to answer your questions. Start small, automate only what’s worth the effort, and keep your workflows simple. If you hit a wall, remember: Sheets is for analysis, not for warehousing. Stay nimble, iterate, and don’t be afraid to trash and start over when things get messy. Good luck!