How to Connect FullEnrich to Google Sheets (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of FullEnrich

You have a Google Sheet full of prospect data — LinkedIn-scraped names and company domains, inbound form submissions, a raw export from your CRM — and none of it has contact information yet. FullEnrich can find the emails and phone numbers, but the default workflow is to export your list, upload it to the platform, run the enrichment, wait, download the results, and paste them back into the original sheet. If your list lives in a sheet, that process runs against every instinct you have about where work is supposed to happen.

FullEnrich is good at aggregating contact data across 15+ vendors in a single waterfall lookup. But the connective tissue between it and your spreadsheet is entirely manual. Every enrichment run means a round-trip out of the sheet and back.

Below are the four common ways teams handle this. Only the last one scales.

Method 1: Manual Copy-Paste

The default flow looks like this: you export the rows you want enriched as a CSV, upload that file into FullEnrich's bulk enrichment tool, configure which columns map to which fields, kick off the job, download the result file when it's done, and then import those results back into your sheet — making sure the row order still matches, merging the new columns into the right positions, and cleaning up the duplicates the download format introduced.

Once is fine. Once is just a data task.

The third time you're doing this in a single campaign sprint — different segment, same sheet, same column-matching ceremony — you start to recognize it for what it is: a tax you're paying on your own toolchain.

Method 2: Zapier or Make

Both platforms have FullEnrich connector options. You can wire up a trigger on a new row in the sheet, call the FullEnrich enrichment endpoint, and write the returned fields back into the right columns.

Before describing what that setup actually involves — a quick check. Do you know what a webhook payload looks like? What field mapping means in an automation context? How to handle a 429 rate-limit error from a third-party API inside a Zap? If those words don't feel like yours, skip to Method 3 or 4. This path is genuinely not designed for non-builders.

For everyone still here: the flow works. FullEnrich's API is documented. Zapier's connector has the key endpoints. But setup means picking the right trigger (new row, or a scheduled batch?), mapping every input field by name, deciding how to handle enrichment failures, and testing against a small slice of live data before you trust it on 500 rows.

One row at a time is the structural ceiling of a Zap.

A trigger-per-row automation means one API call, one enrichment result, one writeback — per contact. If you need to enrich 400 prospects before Monday, that's 400 trigger fires. And when row 182 comes back with a null phone number and the Zap silently moves on, you find out at the end when 40 rows are empty.

You probably just need the emails and phone numbers from this list. You probably have no idea how to build a multi-step enrichment Zap that handles partial results gracefully — and you shouldn't have to learn that to run an outbound campaign. So you hand this to whoever on the team knows automations, and now you're waiting on a Slack reply to find out if they have time this week.

Cost compounds fast once you start chaining steps.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable spreadsheet ↔ enrichment workflows was a category of add-ons that let you define a column mapping template: these input fields, those output columns, save the config, run on demand.

That was a real improvement over manual exports. You could rerun the same enrichment setup each week without rebuilding it from scratch. Results landed in the same columns every time.

But you were still responsible for every piece of the configuration — which fields to pass, which columns to write into, whether to skip already-enriched rows, how to handle failures. The add-on moved the data; the thinking was still yours. And if FullEnrich changed a response field name or you added a column to the sheet, the config broke until someone went back and patched it.

This is the previous generation. It worked, but it asked a lot of the operator.

The Easy Way: Using SheetXAI in Google Sheets

There is a different way entirely. SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands what you're looking at, and through its built-in FullEnrich integration it can run bulk enrichments, reverse-email lookups, and people searches for you — writing results back into the columns you name. No export, no import, no template configuration. You just ask.

Example 1: Bulk-enrich 500 prospects with verified email and phone

Enrich all rows in the Prospects tab using the First Name, Last Name, and Domain columns — run a FullEnrich waterfall lookup and write the verified work email and direct-dial phone number into columns E and F

SheetXAI runs the enrichment across every row in one pass, writes email into column E and phone into column F, and flags rows where FullEnrich returned no result so you know exactly what's missing before the campaign goes out.

Example 2: Reverse-enrich inbound leads from email addresses alone

For every email in column A of this sheet, run a FullEnrich reverse email lookup and write the returned first name, last name, company name, and job title back into columns B through E — skip any row that already has a value in column C

The pattern: instead of cleaning the data first and then moving it, you ask for both in one prompt. SheetXAI handles the skip-logic inline.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet with prospect data, then ask it to run a FullEnrich enrichment on the rows. The FullEnrich integration is included in every SheetXAI plan.