How to Merge 50 Shopify Order CSVs Without Crashing Excel
Shopify limits order exports to specific date ranges, so sellers with years of data end up with dozens of separate CSV files. This guide walks through merging them into a single master file while handling duplicate order IDs and inconsistent column orders across exports.
Why this matters?
Shopify's native export caps at ~30 days of orders per file. Sellers doing annual revenue analysis or migrating to a new analytics platform must combine months of data manually — a process that routinely crashes Excel when total rows exceed 100K.
The 3-Step Solution
Follow this streamlined workflow to transform your raw data export into a clean, analysis-ready dataset. Each step leverages our browser-based tools to ensure your sensitive data never leaves your device.
By following these three steps, you eliminate manual data wrangling, reduce human error, and maintain full GDPR compliance throughout the process.
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Recommended Tools
CSV Merger
Drag in 50 CSV files with mismatched columns and merge them into one unified table.
CSV Deduplicator
Remove duplicate rows based on single or multiple columns with fuzzy matching.
Shopify Order Normalizer
Collapse Shopify's multi-line order export into one row per order with aggregated totals.
Related Workflows
How to Clean Apollo Exported Leads Before Importing into Cold Email Software
Apollo.io exports contain duplicate contacts, generic role-based emails (info@, support@), invalid domains, and inconsistent company name formatting. This workflow shows how to clean all of these issues in one pass to keep your sender reputation above 95% deliverability.
Reconcile Facebook Ads Spend with Shopify Revenue
Attributing Facebook Ads spend to actual Shopify revenue is a nightmare because Shopify exports Name (e.g., #1042) and Order ID, but completely strips UTM parameters or Campaign IDs from the standard order CSV. Meanwhile, your Facebook Ads export groups spend by Campaign ID and Ad Set Name. You cannot directly VLOOKUP these two datasets. This workflow walks you through extracting UTM tags from Shopify's Note or Tags fields, parsing them with regex, and executing a memory-safe local VLOOKUP to bridge ad spend and realized revenue without touching a cloud server.
Sanitize Email Lists Before Klaviyo Import
Importing a dirty contact list into Klaviyo is the fastest way to torch your sending domain's reputation. Exported suppression lists from legacy ESPs are riddled with invisible zero-width spaces, malformed addresses like user@@domain.com, and role-based emails (info@, admin@) that trigger spam traps. If your hard bounce rate exceeds 2% on a single campaign, Klaviyo will throttle your account and your transactional emails start landing in Gmail's Promotions tab. This workflow cross-references your prospect list against historical bounce logs, strips non-printable Unicode characters, and validates RFC 5322 email formatting entirely in your browser.
Reconcile Stripe Disputes and Refunds Against Original Charges
Annual Stripe exports for mid-volume merchants routinely exceed 500,000 rows, mixing successful charges, partial refunds, full refunds, dispute creations, dispute wins, and dispute losses in one monolithic CSV. The Type column uses cryptic values like dispute, dispute_reversal, and refund without linking back to the original charge amount in the same row. You must trace each dispute or refund back to its Source ID to find the original charge row and calculate net revenue impact. This workflow filters the export to only dispute and refund event types, joins them back to original charge rows using Source ID as the foreign key, and computes the net realized revenue per order after all adjustments.
Sample Datasets
Shopify Standard Order Export CSV Sample
A realistic Shopify order export with 200 rows including multi-line items, refunded orders, and discount code fields. Includes both a 'dirty' version (raw export with duplicates and nested line items) and a 'clean' version showing the expected output after normalization. Use this to test the Shopify Normalizer and CSV Merger tools.
Apollo.io B2B Leads Export Sample
An Apollo.io lead list export with 300 contacts including duplicate emails, role-based addresses (info@, admin@), invalid domains, and inconsistent company name casing. Perfect for testing the Apollo Leads Cleaner and CSV Deduplicator on a realistic dirty dataset.
HubSpot Contacts Export Sample Dataset
This dataset contains 8,500 synthetic HubSpot contact records with exact production export columns: First Name, Last Name, Email, Lifecycle Stage, Associated Company, Lead Status, and HubSpot Score. It represents a mid-market B2B SaaS database with contacts spanning multiple lifecycle stages from subscriber to customer. The HubSpot-specific quirk is that multi-checkbox custom properties and system fields like Lead Status are exported as semicolon-separated strings (e.g., New;Qualified;SQL) rather than comma-separated values. Data engineers unfamiliar with this behavior often split these fields incorrectly during ETL, destroying the multi-select taxonomy. The dirty data inventory includes: semicolon-delimited strings in 2,100 Lead Status cells, trailing whitespace in 890 Email addresses that would cause duplicate contact creation on re-import, and 420 rows where Lifecycle Stage is completely blank due to API-synced contacts bypassing the form submission flow. After deduplication and whitespace trimming, this yields 8,340 unique, import-ready contacts. Ideal for: CRM migration testing, HubSpot import validation, data warehouse schema design, and reverse-ETL dry runs. Run this dataset through the csv-deduplicator tool to identify and merge the whitespace-polluted email duplicates.