How to Connect Remarkety to Excel (4 Methods Compared)

The Problem With Getting Workbook Data In and Out of Remarkety

You have an Excel workbook full of data — re-engagement lists, abandoned cart records, product catalogs pulled from your warehouse, customer exports you've been building for weeks. You need that data in Remarkety, or Remarkety's data back in the workbook, without losing a morning to CSV formatting and import errors.

Remarkety is good at behavior-based email and SMS automation for eCommerce. But the path between your Excel workbook and Remarkety's contact database, campaign records, and event API is not short. The typical flow is: save the workbook as a CSV, reformat columns to match Remarkety's import schema, run it through the upload wizard, debug the field mismatch errors, and start over when the data updates.

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

Method 1: Manual Export and Re-Import

The default for Excel users. You save a sheet as a CSV, clean up the headers to match what Remarkety expects, and drag the file through the import UI. For a one-time run with a clean dataset, that's manageable.

The trouble is that "clean dataset" is rarer than it sounds.

Remarkety's contact import expects specific field names. Your workbook uses whatever headers whoever built it felt like typing. So before you can upload anything, you rename columns, check for blank email rows, normalize phone number formats, strip leading zeros that Excel auto-formatted away. Then you upload — and Remarkety bounces fifteen rows for reasons you have to debug individually.

That's the flow for 200 contacts. For 600 contacts ahead of every win-back campaign, it stops being a task and starts being a tax on every single send.

Method 2: Power Automate

Power Automate has Remarkety connector options. You can wire a trigger on an Excel table update, call Remarkety's API with mapped fields, and write results back to the workbook — or the reverse.

Before going further: do you know what a flow trigger is? A dynamic content expression? A connector action with OAuth? A retry policy? If those words aren't already in your working vocabulary, this path leads somewhere that costs more time than it saves — skip to Method 3 or 4.

If you're still here: the flow works. You authenticate both sides, pick a trigger, map each column to the right Remarkety field, handle the format differences, test the run. When it's clean, it's clean.

But a row-per-trigger flow is not the same as a bulk operation.

Pushing 600 contacts through Power Automate means 600 individual API calls, 600 flow runs, and a run history that becomes impossible to parse when row 412 fails on a duplicate email and the surrounding rows silently skip.

You probably just need the contacts in Remarkety before the campaign launches. You probably have no idea how to build a Power Automate flow with error branches and retry logic — and that's a completely reasonable place to be. So you find whoever on your team handles automations and ask them to build it. Now you're waiting on them, and the campaign date isn't moving.

Conditional logic compounds the problem. Filter only lapsed customers, skip rows where total_spent is blank, dedup against existing contacts — each of those is another branch in the flow, another place for a silent failure.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable Excel ↔ Remarkety workflows was a category of add-ons that let you manually configure column mappings and saved import templates. You picked your range, tagged your fields, saved a config, ran it.

That was a real step up from the CSV roundtrip. Output was consistent, configs were reusable, and the team didn't have to redo field mapping on every run.

But the template design was still on you. The conditional logic about which rows to include was still on you. The schedule was still on you. The tool moved the data — the operator carried all the thinking. And the moment a column got renamed, the saved config broke until someone went back in and fixed it by hand.

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

The Easy Way: Using SheetXAI in Excel

There is a different way entirely. SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands what you are looking at, and through its built-in Remarkety integration it can push contacts, pull customer data, fetch campaigns, sync cart events, or export product catalogs for you. No CSV wizard, no Power Automate flow, no saved config to maintain. You just ask.

Example 1: Upload a re-engagement list as Remarkety contacts

Batch-import all contacts from this Excel table into Remarkety as contacts — use column A for email, column E for total_spent, and column F for last_purchase_date. Skip any rows where column A is empty.

SheetXAI reads the table, maps the columns to Remarkety's contact schema, skips empty-email rows, and bulk-imports the rest. Rows that Remarkety rejects come back as notes in the sidebar so you know exactly what needs fixing.

Example 2: Pull the full customer list sorted by spend

Fetch all Remarkety customers and write email, first name, last name, total spend, and number of orders into columns A through E. Sort by total spend descending.

The customer list lands in the workbook sorted, ready for RFM analysis or a manual review. No export wizard, no CSV formatting.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook with contact, cart, or catalog data, then ask it to push records to Remarkety or pull the data you need. The Remarkety integration is included in every SheetXAI plan.