Hi everyone, I’m Matko. I’m looking into a small tool for clothing stores that get product spreadsheets from suppliers.
The idea is to compare the original supplier file with the CSV you’re preparing for Shopify. Flag missing products, differences in sizes and colours, changed SKUs or prices. You’d get a list to check before importing anything.
But before building it, I’d like to understand how people actually handle this.
If you’ve done an import recently, how did it go? Are you checking everything in Excel, using another tool, or is Shopify’s importer enough? Roughly how often do you do this and how long does the checking take?
I’m considering around $19 as a one-time price. Nothing built or for sale yet. Would this save you some work, or is it something your current setup already handles?
Even “we don’t have this problem” would be useful to hear.
Hi @matko We’ve seen this kind of issue come up with Shopify store setups, especially when supplier files have a lot of variants. The part I’d find most useful is catching SKU, price, and variant differences before the import rather than finding them after the products are already in Shopify.
If you end up building it, I’d be curious to see how you handle large CSVs and whether the comparison can also show exactly which row or field changed. That would make it much easier to review before importing.
Thanks Kynaat, that helps. Showing which row and field changed makes sense.
Are these imports you handle yourself, for your own store or for clients? Could you share a recent example, roughly how many products or variants were involved and how you checked the files? I’m curious what took the most time.
Also, does this come up regularly when suppliers send updates, or mainly when setting up a new store?
They’re mostly imports we handle as part of Shopify store setups for clients, rather than for our own store. The size varies depending on the catalog, but clothing stores with lots of variants are where it can get pretty time-consuming.
Usually the supplier file is checked against the Shopify-ready CSV in Excel, mainly looking at SKUs, variant values and prices. The most time-consuming part is usually finding small differences when there are a lot of rows.
It can also come up again when suppliers send updated files, so I’d say it’s not limited to new store setups.
Thanks, that helps. If a tool could reliably compare those two files and show the exact rows and fields that differ, would your team pay $19 as a one-time purchase? Would you prefer using it in a browser or downloading it?
Also, could you share a small anonymised example of a supplier file and its matching Shopify CSV, if you have permission to share them? Just a few rows showing the kind of differences you deal with, with any confidential information removed.
Nothing built yet. I’d like to understand a real example before deciding what to make.
Yeah, I think $19 as a one-time purchase could be reasonable if the comparison is reliable and saves the manual checking time. I’d personally prefer a browser-based tool since it would be easier to use without installing anything.
For the sample files, I’d need to check whether we have permission to share an anonymised example. If I can, I can share a few rows showing the kind of SKU, variant, and price differences we usually look for.
I’ve dealt with this exact problem — supplier spreadsheets never match the store CSV format, and catching missing products/price changes/SKU mismatches by eye in Excel doesn’t scale past a handful of SKUs.
What worked for me: normalize both files to the same key (SKU or barcode) first, then diff on price/quantity/title — anything that doesn’t match a known SKU goes in a separate “new/missing” list instead of failing the whole import. I ended up building a small browser tool that does this matching step (no upload, runs locally) after doing it by hand one too many times — happy to share if useful, but mostly wanted to confirm: yes, this is a real recurring pain, not a rare edge case. For me it was roughly every 2-4 weeks per supplier.
I’ve handled Shopify imports where supplier spreadsheets and the final Shopify CSV don’t always match, especially with SKUs, variants, sizes, colours and prices.
Usually, I’d compare the files manually in Excel before importing. Shopify’s importer is fine for the actual upload, but it doesn’t really help with spotting differences between the two files.
A simple comparison tool like this could definitely save time, especially if you’re doing imports regularly.
Thanks @prapti.bhatt354. Thinking about the last import you handled, roughly how many rows were involved and how long did you spend checking them in Excel?
Is this something you handle regularly for your own store or clients, or mostly during the initial setup?
Thanks @KynaatJohn, that helps. I’ll keep the browser preference in mind.
If you get permission to share a few anonymised rows from both files, a quick note on which differences you’d want flagged would be useful too. No problem if the client files can’t be shared.
Hi @matko Welcome To Shopify Community So For clothing specifically, the sizes/colours mismatch is usually the messiest part — supplier sheets often use different naming conventions (S/M/L vs numeric, colour codes vs names) than what ends up in the Shopify CSV, so it’s easy to miss a silent mismatch rather than a missing row. I’ve mostly just cross-checked manually in Excel with VLOOKUP/sorting, which works but eats time on anything over a couple hundred SKUs and doesn’t catch subtle changes like a price update buried in a row you didn’t scroll to. A flagging tool for that would genuinely save time if it handles fuzzy matching on SKU/name variations rather than needing exact matches, since that’s usually where manual checking breaks down. please do not forget to like and mark it solution
Thanks @Mustafa_Ali, the size and colour naming differences are useful to know about.
When those labels differ, do the two files usually still have the same SKU or barcode? Or do those change too?
Could you share one anonymised example of how the same item appears in each file and what the correct match should be? That would help me understand which differences are expected and which are actual mistakes.
I’ve built something like this — free, browser-based, no upload. Can’t post the link here right now, but happy to share more details if you’re interested.
Thanks @rowpatch, yes, I’d be interested in the details.
Does it handle an original supplier file with different column names and size/colour labels, or do both files need to already follow Shopify’s CSV format?
That’s the part I’m trying to understand, especially when the same variant is described differently in each file.
One thing I’d be careful with is using fuzzy matching as the primary match rule.
For a pre-import check, I’d separate product identity from attribute normalization:
First match exact SKU or barcode when available.
Then normalize expected label differences like S vs Small, or Navy vs Navy Blue.
Anything without a stable identifier should go to review rather than being auto-merged.
Otherwise a “smart” comparison can turn a harmless naming difference into the wrong variant match, which is worse than flagging too many rows.
For those of you doing this in Excel now: when size or colour labels differ between the supplier file and Shopify CSV, does the SKU/barcode usually stay stable, or does that change too?
That seems like it would determine how safely this can be automated.
Thanks @YangCommerceOps, that makes sense. A wrong match would defeat the point of checking the file. I’d want uncertain matches flagged for review, and size or colour mappings confirmed rather than guessed.
Have you used this approach on supplier files yourself? If so, what still needed checking by hand after matching by SKU or barcode? That’s the part I’m trying to understand.
Yeah, that’s the part I’d want to see in the actual files.
If SKU or barcode stays consistent, that makes things much easier. I’d still be cautious with cases where the SKU matches but the size, colour, stock or price looks different.
The main things I’d want to check by hand are duplicates, missing SKUs, unclear size/colour mappings, and blank stock or price cells where it isn’t obvious whether blank means “leave it alone” or “set it to zero”.
I’d rather flag those rows than guess.
If you’re able to share a small anonymized sample — even 10–20 supplier rows and the matching Shopify rows — I’d be happy to run through it and show you which ones look safe and which ones I’d stop for review.
That would probably tell us much more than talking about it in the abstract.
Thanks @YangCommerceOps , I’d appreciate that. I don’t have a sample pair I can share yet. Another person in the thread mentioned they might be able to provide a few rows, depending on permission.
If we get an example they’re happy to share here, it would be useful to go through it together. Especially the blank values — seeing what they were actually meant to do would help.
Makes sense. If a shareable example comes up, happy to go through it here. The blank-value cases are exactly the sort of thing I’d want to confirm from the real file rather than guess.
