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

The Problem With Getting Sheet Data In and Out of Chaser

You have a Google Sheet full of data — open invoices, customer IDs, payment amounts, due dates. You need it pushed into Chaser, or pulled back out, without spending an hour reformatting exports and copying rows by hand every time your AR team needs a fresh picture.

Chaser is good at automating the chase — sending payment reminders, tracking communication history, flagging at-risk accounts. But the moment you want to analyze its data in a spreadsheet, or load a bulk batch of invoices from an external system, you're on your own. The standard flow is opening a Chaser export, cleaning the date formatting, pasting into a sheet, sorting manually, then starting over the following week.

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

Method 1: Manual Copy-Paste

The default. You pull a CSV from Chaser's export interface, open it in Sheets, delete the columns you don't need, reformat the date fields, re-sort by amount or aging bucket, and start your calls.

That workflow might take fifteen minutes the first time. By the fourth week, when you realize the export drops trailing zeros on currency fields and that sorting by amount has been silently wrong for two months, the fifteen minutes becomes an argument. AR teams running this cycle weekly tend to accumulate a folder of slightly-wrong snapshots that nobody fully trusts.

Method 2: Zapier or Make

Both have Chaser connector options. You can set up a trigger that fires when an invoice status changes and writes a row into a sheet, or push new sheet rows into Chaser as invoice records.

Quick question before you build this — do you know what a webhook payload looks like? Can you map JSON fields to spreadsheet columns by hand? Are you comfortable setting up OAuth credentials and debugging a 422 response at midnight? If those questions feel unfamiliar, Method 3 or 4 will serve you better.

Assuming you're still here: yes, this works. A trigger-on-status-change Zap can keep a sheet reasonably current, and a new-row trigger can push invoices into Chaser without manual entry. Getting there requires picking the right event trigger, mapping every Chaser field to its spreadsheet counterpart, handling date format mismatches between the two systems, and being on the right Make or Zapier tier.

But a row-level trigger is not a bulk export.

Sending 80 overdue invoices through a Zap means 80 separate API calls — 80 chances for a 429 rate limit, 80 rows in your task history, and no clean way to know which ones silently failed when Chaser returns a validation error on row 34.

You probably just need a ranked list of overdue invoices to run your call block from. You probably have no idea how to build a webhook trigger that fans out across 80 records and writes results back to specific columns. So you either spend a week building it yourself, or you push it to whoever on your team does automations — and then you're waiting.

Once you need to filter by aging bucket, join against a second tab, or flag critical accounts inline, you've gone well past what a row-level Zap does cleanly.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best repeatable option for spreadsheet-to-Chaser data movement was a class of add-ons that let you configure column mappings, save a template, and run it on a schedule. You picked your Chaser object, you tagged your columns, you set up the schedule, and you ran it.

That was a genuine improvement over manual exports. The output was consistent run to run, configs were reusable, and your AR team didn't have to reformat the CSV every Monday morning.

But you were still responsible for the mapping logic, the filter conditions, the aging calculations, the column ordering. The add-on moved the data. The thinking was still entirely on you. And when Chaser updated its API response schema — which happens — your config would silently break until someone traced the empty column back to a renamed field.

This is the previous generation. It was useful, but it required someone to maintain 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 are looking at, and through its built-in Chaser integration it can push to or pull from Chaser for you — no template configuration, no automation plumbing, no manual export reformatting. You just ask.

Example 1: Pull all overdue invoices, ranked by exposure

Fetch all invoices from Chaser with an AUTHORISED status and a due date before today, calculate days overdue, and write invoice number, customer name, amount, due date, and days overdue into columns A through E sorted by amount descending.

SheetXAI calls Chaser, calculates the aging inline, sorts the results, and writes everything into the sheet. Your AR team opens the file and starts dialing.

Example 2: Bulk-create customers and opening invoices from a new client list

For each row in this sheet, bulk-upsert the customers into Chaser using company name in column A, email in column B, and external ID in column C — write the returned Chaser customer ID into column D. Then for each ID in column D, create an invoice in Chaser with the amount in column E, due date in column F, and description in column G — write the resulting invoice ID into column H.

The pattern: instead of logging into Chaser to create each client manually, you send the whole batch in one ask. SheetXAI handles the two-step sequence — customers first, then invoices against the returned IDs — and writes both sets of confirmations back inline.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet with invoice data or a list of customers you need in Chaser, then ask it to do one of the tasks above. The Chaser integration is included in every SheetXAI plan.