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

The Problem With Getting Sheet Data In and Out of DeskTime

You have a Google Sheet full of data — employee IDs, project names, billing rates, expected hours. You need it synced with DeskTime, or you need DeskTime's time logs pulled into the sheet before a report goes out. In theory this is one API call. In practice it's an afternoon.

DeskTime is good at tracking exactly what employees do and for how long. But moving that data to wherever it actually gets used — a spreadsheet, a report, a client invoice — is entirely on you. The default move is to log into the DeskTime dashboard, filter to the date range you need, export a CSV, clean up the columns, paste it somewhere that doesn't break the existing formatting, and start over tomorrow.

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

Method 1: Manual Copy-Paste

Open DeskTime, navigate to the date range you need, export the CSV. Then open your sheet, figure out which columns map to which headers, delete the ones you don't need, rename the ones you do, and paste.

If you're comparing multiple employees, you do this once per person. If you want project-level data alongside productivity scores, you run two exports and merge them by hand. If someone's name changed in DeskTime last week, the lookup breaks and you find out after you've already sent the report.

Doing this once is annoying. Doing this every Monday morning, before the standup, before the stakeholder deck, before the client invoice — that's a different kind of pain. It's not that any single step is hard. It's that each one requires your attention, and the whole sequence has to be repeated from scratch every time.

Method 2: Zapier or Make

Both platforms have DeskTime connector options. You can wire up a trigger that fires on a schedule, calls the DeskTime API, and writes results into a sheet row.

Before you go any further — do you know what a webhook trigger is? A field mapping? An API authentication token? A task history limit? If those phrases aren't familiar, this path is going to cost you several hours before you even get a single row of real data. You're better off skipping to Method 3 or 4.

If you're still here: yes, the automation works. You pick a trigger, authenticate DeskTime, map which fields go into which columns, and schedule it. The setup takes longer than it should — the DeskTime connector documentation isn't always current, and you'll hit at least one field-mapping mismatch before you get a clean run.

But a scheduled trigger is not the same as a bulk pull.

Pulling yesterday's stats for 35 employees means 35 separate API calls, 35 trigger fires, and a task log that tells you nothing useful when employee 12 returns a null efficiency score and the rest of the batch completes silently.

You probably just need the team's productivity numbers in a sheet. You probably have no idea how to set up a multi-step Make scenario with error branches — and you shouldn't have to. So you either block off an afternoon to learn it, or you push it to whoever on your team handles integrations, and now you're waiting on them.

And once you need to filter by team, join project data with productivity data, or aggregate across multiple dates — you've run past what a standard connector can do.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable spreadsheet ↔ DeskTime workflows was a category of add-ons that let you configure column mappings and save templates for repeated use. You picked your range, you set your fields, you saved the config, you ran it on demand.

That was a real step up from CSV exports. The output was consistent, the mapping was reusable, and you weren't reformatting headers every run.

But you were still responsible for designing the template, mapping every field, writing conditional logic about which employees or projects to include, and managing what happened when DeskTime's field names shifted. The tool moved the data; the thinking was still yours. And the moment a new project type appeared in DeskTime or someone added a column to the sheet, the config needed manual repair before the next run.

This is the previous generation. It worked, but it demanded more operational attention than most teams had to give it.

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 DeskTime integration it can pull productivity data, project hours, or app usage reports directly into your sheet. No template setup, no automation plumbing, no manual field mapping. You just ask.

Example 1: Pull yesterday's productivity data for the whole team

Pull DeskTime productivity data for all employees for yesterday and put their name, online time, productive time, and efficiency score into this sheet

It fetches each employee's stats from DeskTime's API, matches them against the date range, and writes name, online time, productive hours, and efficiency percentage into the columns you specified — in one pass.

Example 2: Export active project hours for invoicing

List all DeskTime projects with hours tracked this month and paste them into the InvoiceData tab with project name, total hours, and project ID

The pattern: instead of running a DeskTime export, cleaning up the columns, and pasting manually — you ask for the cleaned output directly. SheetXAI handles the field selection and the write-back in the same step.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet with employee IDs or project names, then ask it to pull the corresponding DeskTime data. The DeskTime integration is included in every SheetXAI plan.