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

The Problem With Getting Sheet Data In and Out of Datagma

You have a Google Sheet full of data — lead names, company domains, LinkedIn URLs, inbound phone numbers. You need Datagma to enrich it, find missing contact details, or check for job changes. And you need the results written back into the sheet so you can actually use them.

Datagma is good at returning accurate, structured contact intelligence at scale. But the path from "column of names" to "column of enriched data" runs straight through an API. The default flow is: export the data, make the API calls row by row, collect responses, clean the JSON, paste the fields back — and that's before you've handled any errors.

Below are the four ways teams deal with this. One of them scales.

Method 1: Manual Copy-Paste

The default move is to pull names or emails from your sheet, run them through Datagma's web UI one at a time, and paste the result — LinkedIn URL, job title, company size — back into the corresponding row.

For a handful of contacts, that's fine. It takes five minutes and you move on.

But your sheet doesn't stay at a handful. It grows to 80 contacts, then 200, then you get a fresh inbound list on Monday and your afternoon is gone. Every row is its own lookup, its own copy, its own paste. The data lands in different formats depending on which field you grabbed. And if someone touched column C between when you started and when you finished, you're reconciling by eye.

At some point, the time cost of manual enrichment starts outpacing the value of the data itself.

Method 2: Zapier or Make

Both platforms have a Datagma connector. You can set up a trigger on a new row, call Datagma's enrichment endpoint, and write the fields back.

Quick gut-check before you go further: do you know what a webhook trigger is? Have you mapped API response fields before? Do terms like "authentication token," "JSON path," and "field mapping" feel routine? If any of those made you pause, this method is going to cost you more time than it saves. Method 3 or 4 will be faster.

If you're still here: the Zap itself is achievable. You authenticate Datagma, pick the right endpoint for what you're enriching — person, company, or phone — set up the trigger, map each response field to its target column. It works.

The issue is what happens at the edges.

A trigger-per-row approach means one API call per contact. Three hundred leads means three hundred trigger fires, three hundred task logs to comb through when row 147 comes back empty and you're not sure if it was a bad email or a rate-limit hit.

You probably just need the LinkedIn URL and company size written into the columns next to each name. You probably have no idea which Datagma endpoint returns company headcount versus firmographics, and you shouldn't have to. So you either spend two hours reading the API docs, or you find the one person on your team who's built Zaps before and ask them to wire it up. And now you're waiting on a Slack reply instead of enriching your leads.

Once you need to filter rows by a condition — only enrich contacts where the email is present, skip anyone already enriched — you've added another layer. Zapier's filters work, but they add steps and billable tasks. The Zap that started simple is now four steps long and you're on the wrong pricing tier.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, teams that needed repeatable spreadsheet ↔ Datagma enrichment turned to a category of add-ons that let you configure column mappings, save templates, and run them on demand. Pick your range, tag your input columns, specify your output columns, save the config, click Run.

That was a genuine improvement over row-by-row manual work. The template was reusable. Outputs landed in consistent columns. Your team could hand the config to someone else and they'd get the same result.

What you were still responsible for: building the template yourself, defining the field mapping, deciding which fallback to use when an email wasn't found, handling the conditional logic that skipped already-enriched rows. The add-on ran the calls — the thinking was still entirely yours. And the first time someone added a column or renamed a header, the template broke and stayed broken until you fixed it by hand.

That's the previous generation. Reliable, but operator-heavy.

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 Datagma integration it can enrich contacts, find emails, detect job changes, or reverse-lookup phone numbers — in whatever columns your data already lives in. No template setup, no field mapping config, no export step. You just ask.

Example 1: Bulk enrich a lead list

For each row in my lead list (name in column A, company in column B), call Datagma to enrich the contact and write LinkedIn URL, job title, and company size into columns C, D, and E

SheetXAI reads the full range, sends each row to Datagma, and writes the enriched fields back — with errors surfaced inline where a lookup returned no result.

Example 2: Fall back to email when no LinkedIn URL is present

Enrich all 300 leads in my sheet using Datagma — use the LinkedIn URL in column C where available, otherwise fall back to the email in column D

The pattern: instead of building conditional logic into a Zap or filtering the sheet yourself, you describe the condition in the prompt. SheetXAI handles the fallback inline.

Try It

Get the 7-day free trial of SheetXAI and open any sheet with a contact or company list, then ask it to enrich the data using Datagma. The Datagma integration is included in every SheetXAI plan.