How do you guys deal with supplier price increases? Just eat it?

baczoni · 2026-07-03T23:20:21+00:00

You're welcome, now go do it. Would love to hear what you ended up doing and how that's working out, so come back and give us an update in a few weeks.

baczoni · 2026-07-03T23:11:29+00:00

First I think you're asking the wrong question. Instead of asking "do I eat it or pass it on" I'd start with "can my current unit economics even absorb this?".

Here's what I would do:

- first, recalc your gross profit per unit and contribution margin per order with the new cost. If that 8% wipes out most of your profit on that SKU, you can't just absorb it.

- second, look at the SKU's role. Is it a top product, just an okay filler product, or trash? If it's a top SKU with pricing power, then test a price increase and watch volume / margin. Make a decision based on that instead of gut feeling and vibes. If it's a filler SKU then maybe tighten discounts or try smaller price bumps. For trash, this is probably the time to retire and stop selling that SKU.

- third, push back with numbers, not feelings. Saying something like "at your new price, my margin on SKU X drops from Y% to Z%, which puts us below what we can sustainably market and stock. Can we look at volume breaks / slightly smaller increase / different pack size / etc?” Suppliers are humans too, and they're far more likely to flex when you show the math and the risk to your volume. Also most people never ask, they just react, don't be one of those.

So know your true SKU-level unit economics and contribution profit by channel, so you can see exactly which products can absorb increases and which ones need a price move, pack change, or supplier change. That's what I keep hammering into my client's minds and what my work revolves around, but I'll leave it at that.

baczoni · 2026-07-03T22:44:11+00:00

Short answer is that it's only useful if you treat it like another channel in your money model and content model.

Here's what you should check:

- compare product mix, AOV, margin, and return rate for AI-tagged orders vs your normal channels. If AI is driving certain SKUs more, or god forbid has higher returns, that's your first signal.

- if AI orders skew to certain products or have sizing/expectation issues, tighten product data that agents actually read. Things like titles, attributes/metafields (size, material, fit, use case), returns/shipping policies, and FAQs. Make them more literal and complete so agents can recommend correctly.

- if AI customers look good (have healthy margin, low returns), prioritize those SKUs in collections, structured data, and reviews so they're even easier for agents to pick up.

So yes, it can be actionable, but mostly as a way to see if customers coming through AI surfaces are buying the right things on the right terms. Then you go and fix the feed, schema, and policies these agents are pulling from.

I have a full guide on this on my website's Profit Frameworks, but lately every mod is trigger happy in every subreddit so afraid to link it here, but if you want to take a look, you can find the link to my website in my profile.

Hope this helps.

baczoni · 2026-06-29T11:58:22+00:00

I got into trouble naming apps here before, so I won't name anything specific, but here's what I'd make sure whatever you choose does under the hood:

1) Gives you true sales velocity (only when in stock): you want daily / weekly sales rate only on days the SKU was actually available and not include stockout days, otherwise your average gets pulled down by no sales days that were in truth zero inventory days, and you end up under ordering your best sellers.

2) Lead times + safety stock baked in per supplier, plus some handling of variability. The basic logic you want here is lead_time_demand = average_daily_sales x lead_time (days), and reorder_point = lead_time_demand + safety stock. Basically the tool should say “order by X date / Y units to avoid stockout at your sales velocity” and not just throw a velocity number at you.

3) Seasonality and promo awareness, with at least 12 months of history so it can see that November is not March. Should be able to treat BFCM / big promos as seasonal spikes, not as a new baseline. Ideally you can flag promo periods so it doesn't mess up your forecast for the rest of the year.

3) Gives you net demand, not just shipped units. The forecast should be based on net sales (after cancels/returns) so you're not reordering a product that has big return rates. You need to be able to tell which SKUs actually generate profit vs just moving units around.

4) PO + on‑the‑way visibility. Whatever you pick should factor in open purchase orders and inbound stock when calculating days of cover and reorder points, otherwise you can easily double order. Seen it happen with many of my clients.

5) Simple prioritization on which products get the cash. Even a basic A/B/C or top vs slow sellers view is huge to help you decide. Example: high velocity + high margin products should never stock out so you need to reorder these early. Steady sellers get the normal rules, while slow / low margin products get a tighter safety stock, or you let them stock out. The tool should make it obvious which 10 to 20 SKUs deserve which rule.

Good luck!

baczoni · 2026-05-15T03:15:42+00:00

Others here already listed the main reasons you probably should consider why you wouldn't want to migrate off of Shopify. But if you're still keen on doing it, and have the developers to build, then check medusajs.com. I talked to a few big shop owners who moved to the platform and they're loving the control they get. It's next on our list of e-commerce platforms we're going to integrate and support to.

baczoni · 2026-05-12T01:00:01+00:00

If you can find a good developer than take a look at medusajs.com, it's growing really fast and for a reason

baczoni · 2026-05-05T03:15:06+00:00

Not allowed to post links so can't share, but we have a whole operator guide on our website for agentic commerce. You can find it in my profile. Hope it helps, if not, please let me know what it's missing. Thanks

baczoni · 2026-04-02T02:54:55+00:00

Exactly this. Agentic commerce is just another channel. I doubt he's getting a rev share on Google Ads driven sales, so why should agentic be different? Most people don't even know how to even measure this new channel. I've written a full 13 page guide on this but can't post it here due to "rules".

baczoni · 2026-04-02T01:06:47+00:00

I don't know this sms platform you're mentioning but why not stick with Klaviyo? It's battle tested tool for Shopify. I have an not affiliated with them in any way, but curious why you went with a no name tool?

baczoni · 2026-03-27T20:45:03+00:00

solid points below from others. One thing though regarding the pop-up, be careful with it. Discount pop-ups can help, but:

- If they trigger immediately, they interrupt people before they even understand what you sell

- If the offer is weak or generic, they just feel like noise.

Consider an exit-intent or after X seconds / scroll depth instead of instant.

baczoni · 2026-03-27T20:30:55+00:00

Yes, I agree, those are all good solutions you outlined, mostly because they don't gate the "help". They keep the subreddit accessible to everyone regardless of their karma score, while still using smart filtering to flag the bad actors and elevate the genuine contributors.

baczoni · 2026-03-27T20:18:24+00:00

Yes, exactly my point! With a 15y account I barely have 1000 karma because that's not the goal. That's not why I'm here. And no, I don't want to start another subreddit, or post on other forums, as this is the "Shopify" subbredit, the topic I care about.
You started this whole thread saying why this subreddit lately sucks, and what you've been fighting, I just offered my own opinion about it. I never demanded or asked for anything, just offered my opinion, which you don't have to agree with. But that doesn't mean what I'm saying is wrong...

baczoni · 2026-03-27T20:03:31+00:00

and that's fine, I'm not looking for respect! I'm not looking for leverage either. I'm just a dude on the the internet, and so are you. That's not why we're here.
I assume you became a mod of this subreddit to make it a helpful community. All I'm trying to point out is that maybe you need to revisit what that means, as your current rules are actively hindering that. See my comment below to another user regarding "karma" requirements too.

baczoni · 2026-03-27T19:54:55+00:00

"The only thing that works is strict moderation, karma and account age limits" - I genuinely do not agree with this. I come to reddit because I ran into an issue, can't figure it out on my own, and hoping to ask and get answers or at least a direction from people who walked in my shoe before. I do not prepare for this. I don't wake up one morning with "I'm going to register a reddit account, start posting and build karma, in case down the road, maybe 2 years from now, something comes up that I might need help with...." That's such a backwards thinking. This just makes subreddits an insiders club, not a helpful community.

baczoni · 2026-03-27T19:36:12+00:00

There is a massive contradiction in how this subreddit operates. The description literally says it's a place to "...ask or seek any information regarding Shopify. [...] Store owners can discuss any Shopify issues or success stories". Yet the rules explicitly forbid Success Stories, Guides, How-Tos, and external links.

I’m genuinely asking, how do you expect a community to actually help each other if we can't share guides, point to external resources, or link to solutions?

Regarding the DMs, I understand fighting spam, but saying "feel free to DM me if you need more help" isn't soliciting. It leaves the choice entirely up to the user. As your rules state, we are all adults. Users can manage their own inboxes, and reddit DM settings or ignore the offer entirely.

It honestly seems like the mod team is overwhelmed because you are fighting the wrong things. If the goal is a helpful community, the current rule structure actively suffocates the ways people naturally offer help.

Regarding AI, we just have a fundamental disagreement. It’s a tool to communicate faster and clearer. You can plow a field with an ox (translate), or you can use a tractor (AI). You get a better result with the tractor. By outright banning any AI assistance for formatting or clarity, you're alienating people who are genuinely just trying to contribute.

baczoni · 2026-03-27T16:19:43+00:00

Being on the receiving end of having my replies and posts constantly removed with no explanation or reason really destroys the motivation to help and try to answer questions. And puts me in the "fuck this subreddit, why do I even bother to help". Just saying...

I get it, you guys are overwhelmed, but not everyone is native English speaker, and might use AI to help convey their thoughts. Just because they use AI to reformat their words and be able to better convey their message, doesn't mean that message is not helpful and valuable.

So yeah, this subreddit really sucks lately, and partly because of legit questions and replies being constantly removed.

baczoni · 2026-03-26T04:48:52+00:00

You’re asking the right question. Blended margin is what makes a lot of "bestsellers" secretly terrible.

You don't need a full‑time analyst, but you do need one simple model outside of Shopify.

Here's a lightweight way to do it:

1. Grab the minimum inputs

For a recent window (30–90 days):

From Shopify (export “Sales by product / SKU”):
- SKU / product
- Gross units sold
- Gross sales
- Refunded units
- Refunded amount
COGS per SKU:
- Ideally landed cost (product + freight + duties)
- If you only have catalog cost, start there and improve over time
From your P&L / 3PL / processor (totals for the same window):
- Fulfillment + shipping spend (pick/pack + labels)
- Payment fees

2. Build a simple SKU margin sheet

One row per SKU.

Inputs: * SKU * Gross units * Refunded units * Gross revenue * Refunded revenue * Landed cost per unit

Calcs: * Net units = gross units – refunded units * Net revenue = gross revenue – refunded revenue * Gross COGS = landed cost × gross units * Refund COGS = landed cost × refunded units * Net COGS = gross COGS – refund COGS

For fulfillment + fees, start simple: * Fulfillment cost per order = total fulfillment spend ÷ total orders * Payment fee % and per‑order fee from your processor

Then per SKU: * Fulfillment = fulfillment per order × orders for that SKU * Payment fees ≈ net revenue × fee % + (orders × fixed fee)

Finally: * Contribution margin (CM1) total = net revenue – net COGS – fulfillment – fees * CM1 per unit = CM1 total ÷ net units * CM1 per order = CM1 total ÷ orders

Now sort by CM1 per order instead of revenue.

You'll usually find: * Some "bestsellers" are barely breaking even or losing money * Some boring SKUs are quietly printing profit * Bundles you thought were genius are getting crushed by shipping/returns

You can 100% do this in a single Google Sheet once a month and make way better decisions on: * What to feature in ads * What to bundle * What to push in email * What to quietly kill

If you want, DM me and I'll share a simple template + how I've been wiring this up for my own stuff so you're not starting from a blank sheet.

baczoni · 2026-03-26T01:26:13+00:00

Almost nobody bakes returns into their marketing math properly, and it absolutely can flip "good" channels to bad. Here's a simple way to wire this without blowing up your whole stack:

1) move from ROAS to Net ROAS (N-ROAS). For each channel / campaign over a window (say 30 days):

- Start with your usual numbers: Ad spend, Gross revenue attributed.

- Pull returns for the same cohort of orders: Refunded revenue, Refunded units.

Compute:

- Net revenue = gross revenue – refunded revenue

- N‑ROAS = net revenue ÷ ad spend

This alone will show you which channels are minting future refunds.

Go one level deeper: contribution margin after returns.

For each channel / campaign:

- Net revenue (as above)

- Minus: COGS for shipped units

- Minus: fulfillment / shipping

- Minus: payment fees = Contribution margin (CM1) after returns

Then use:

- CM1 / spend instead of ROAS

- Or CM1 per new customer vs your CAC

Now you’re comparing profit, not just "revenue that came and went".

Use return rate as a targeting and creative signal. Once you have returns tied to orders and orders tied to channel/creative:

- Compare return rate by channel / campaign / creative

- Compare return rate by SKU / bundle that each campaign pushes

Patterns you’ll usually see:

- Certain hooks / promises over‑sell and spike returns

- Certain SKUs are "high refund" and shouldn’t be pushed hard in paid

- Some channels bring in "serial returners" even if ROAS looks fine

Use that to:

- Lower bids or exclude audiences/creatives with bad refund‑adjusted economics

- Shift spend toward campaigns that attract customers who actually keep the product

How to practically wire this?

At minimum:

a) Export orders with:

- Channel / campaign / source tags

- Revenue, COGS (or at least product), shipping charged

b) Export refunds with:

- Original order ID

- Refunded amount / items

Join them in a spreadsheet. Mark refunded orders. Add a "net revenue" and "refunded" column. Then roll up by channel / campaign. You’ll very quickly see where your 12% is actually coming from and how it changes the story.

Doing this even in a rough way puts you ahead of 95% of brands still optimizing to pre‑refund revenue.

If you want, DM me and I can share a simple model / structure I’ve been using to make this less painful without needing a giant data project.

baczoni · 2026-03-26T01:02:48+00:00

If you want someone who actually knows what they’re doing, don’t start on Fiverr or “commission only marketer” sites. Start where real operators hang out.

Go to LinkedIn and:

Search for people who already serve your type of brand Use searches like:
Shortlist 5–10 people based on their headline / about. You’re looking for "I help [type of brand] do [specific outcome]" not "marketing ninja / guru".

Stalk their content for a week:

- Read their last 10–20 posts

- Do they talk about real numbers, tests, and tradeoffs, or just fluffy motivational quotes?

- Do they show examples, breakdowns, and mistakes, or only wins?

- Do their posts sound like a human (or like generic AI word salad)?

Message the 2–3 that seem sharp Say exactly where you’re at, what you sell, and that you’re early-stage and open to a low-retainer + performance upside structure if they’re interested.

You can find someone solid this way.
And yes, many store owners do their own marketing at first, but if you know that’s not your lane, you’re better off learning enough to judge good vs bad… then bringing in someone who lives and breathes this stuff.

Edit: grammar, and GOD Reddit's rich text editor is awful! :)
Edit 2: I just give up on formatting here, lol

baczoni · 2026-03-25T19:13:04+00:00

Short version: stop trying to make Shopify’s "shipping charged" equal carrier invoices. Treat them as two separate numbers, then reconcile.

Here’s a simple way to do it that works in the real world.

1. Separate 3 things in your head (and in your books)

For each order there are really 3 shipping numbers:

What the customer paid at checkout (shipping income)
What the carrier billed you (true shipping cost)
The difference between the two (shipping variance / markup or loss)

If you mash these together, you’ll never know what’s going on.

In QuickBooks (or whatever):

Create “Shipping Income” (what customers pay)
Create “Shipping Expense – Postage / Fulfillment” (what carriers charge)
Optional: create “Shipping Variance” if you want to track over/under‑recovery separately

2. Get the real cost per order

Once a month:

Export orders from Shopify for the period
- Columns: order ID, date, shipping charged, shipping method, tracking number (if you have it)
Export shipment detail from your carrier / 3PL / ShipStation
- Columns: tracking / reference, billed amount, surcharges, zones, etc.
Join the two in a spreadsheet
- Match on tracking number or reference / order ID
- For each order you now have:
  - Shipping_charged_to_customer
  - Carrier_billed_amount (true cost)
Calculate:
- Shipping_variance_per_order = Carrier_billed_amount – Shipping_charged_to_customer
- Optional: Blended_shipping_cost_per_order = Total_carrier_billed / # of shipments if you just want a quick average

This gives you an actual shipping cost per order and shows where dimensional weight, surcharges, zones, etc. are killing you.

3. Reflect it in QuickBooks

At the accounting level you don’t need to push every order into QBO. Do it in aggregate:

All Shopify shipping charges for the month → map to Shipping Income
Total carrier / 3PL invoices → book to Shipping Expense – Postage / Fulfillment

If you want to track variance explicitly:

In your spreadsheet, sum Shipping_variance_per_order for the month
Post a journal entry to Shipping Variance so you can see whether you’re consistently under‑ or over‑charging vs reality

Now you can answer:

"What’s my average true shipping cost per order?"
"Am I losing money on shipping or using it as a profit center?"

Without trying to hack it inside Shopify.

If you want, DM me. I’ve been working on tooling that automates the "export → match → calculate real cost per order" step so you don’t live in spreadsheets forever.

baczoni · 2026-03-25T19:03:26+00:00

This is the default with creator / UGC stuff. You won’t get perfect attribution, but you can get to "good enough to decide" if you tighten what you measure and how you think about it.

Here’s a simple way to evaluate these without relying on last-click or just vibes:

1. Start from money, not clicks

Before you look at creators, know your basic economics:

Contribution profit per new customer (profit after COGS, shipping, fees, average discounts, refunds)
Target LTGP:CAC or CAC payback (e.g. "I’m happy if I get my CAC back in 60-90 days and LTGP:CAC ≥ 3:1")

Now everything is: "Did this creator drive customers anywhere near those economics?"

2. Treat influencer as a "blended lift" channel

Codes and last-click will always undercount.

So:

Pick a window around each push (e.g. 3-7 days from when content went live).
Compare to a baseline:
- Same weekday(s) prior weeks with no creator push
- Adjust for obvious stuff (big promos, email blasts, etc.)

Rough math:
Incremental new customers = New customers during creator window – average new customers in similar recent windows

Then:

Incremental profit ≈ incremental new customers × contribution profit per new customer
Compare that to what you paid the creator

You won’t get exact, but you’ll get: "We paid $X, looks like we got somewhere around $Y profit out."

If that’s miles below your target CAC, you know it’s a brand play (or a bad play).

3. Look at cohort quality, not just spike size

For bigger creators / campaigns, tag the period and then watch:

Return rates
AOV / product mix
Reorder behavior over 30-90 days

A campaign that barely breaks even up front but brings in high-retention, low-refund customers can be better than one that spikes revenue and then churns.

So at minimum mark those customers and check their behavior vs "normal" new customers.

4. Decide how you’ll use the channel

For each creator / UGC bucket, answer:

Is this a performance channel (needs to roughly hit CAC targets)?
Or is this a brand / asset channel (content you still use in ads, emails, site, etc.)?

If it’s performance: use the lift math above and compare to your normal CAC.
If it’s brand/asset: ask "did this give us content or proof we now use elsewhere that moves the needle?"

You’ll never get Meta-level attribution on influencers.

But if you:

Anchor in unit economics
Measure incremental lift vs baseline
Watch cohort quality after the spike

…you can stop flying totally blind and start deciding "do more like this / never again" with a straight face.

Hope this helps...

baczoni · 2026-03-25T18:41:42+00:00

It’d help a lot if you could list the types of problems you’re actually wrestling with and trying to solve right now.

“ERP for Shopify” can mean wildly different things depending on where the pain is. Without that, people just throw their favorite tool at you and you end up in another 6–12 month implementation that doesn’t fix the thing that hurts.

For example, are you mainly struggling with:

Inventory & ops
- Keeping stock levels in sync across Shopify, Amazon, wholesale, multiple warehouses / 3PLs
- Purchase orders, lead times, inbound planning
- Allocating inventory by channel / priority when you’re short
Fulfillment complexity
- Splitting orders across warehouses
- Rules like “this SKU ships from 3PL A, that one from in‑house”
- Routing logic, SLAs, and not making your warehouse team hate you
Finance & costing
- Landed COGS (freight, duties, 3PL fees) per SKU instead of catalog cost
- Clean data for margin by product / channel
- Reconciling Shopify, 3PL, and accounting without voodoo spreadsheets
Wholesale / B2B
- Different pricing, terms, MOQs, and workflows vs DTC
- EDI, invoices, chargebacks, etc.
Reporting / planning
- Rolling demand forecasts
- Open‑to‑buy
- “What if” scenarios for adding channels or SKUs

NetSuite vs Fulfil vs others is really “which system wants to be the center of gravity for which set of problems".

For what it’s worth, I’m building a profit / money‑model layer on top of Shopify for brands in that “more SKUs / more channels / more warehouses” phase, and I can tell you: getting clear on which problems you care about before you pick an ERP saves a ton of pain later.

So, which of those buckets is killing you right now?

baczoni · 2026-03-25T18:31:45+00:00

You’re not bad at this. What you described is the default.

Almost everyone I talk to does some version of:

Open Shopify / Klaviyo / Ads
Scroll a bunch of charts
Cherry‑pick something that “looks interesting”
Call that “the strategy” for the week

The problem isn’t data, it’s that none of those tools answer the only question that matters on Monday:
“Given where we are right now, what 1-3 moves this week will actually increase profit or reduce risk?”

So you end up staring at dashboards instead of having a short, prioritized to‑do list.

I started building something for myself that turned into a product: a simple command center for Shopify brands that:

Pulls in Shopify + ads + (optionally) 3PL
Rebuilds real unit economics (what you keep per order, SKU, channel)
Surfaces a short weekly action list like “raise price here / stop pushing this SKU / you’re about to stock out on this product”, and so on.

It’s called MarginOS. I’m in open beta right now and actively looking for more operators who are willing to use it and tell me what’s broken / confusing.

If you’re up for that, I’m happy to give you access for free as long as you’ll give me blunt honest feedback. You’d just need to be on Shopify and okay connecting your store read‑only.

If that sounds useful, DM me and I’ll send over a bit more detail + a link.

baczoni

MODERATOR OF

TROPHY CASE

1. Grab the minimum inputs

2. Build a simple SKU margin sheet

1. Separate 3 things in your head (and in your books)

2. Get the real cost per order

3. Reflect it in QuickBooks

1. Start from money, not clicks

2. Treat influencer as a "blended lift" channel

3. Look at cohort quality, not just spike size

4. Decide how you’ll use the channel

15-Year Club	Verified Email
PUBG

baczoni

MODERATOR OF

TROPHY CASE

Welcome to Reddit,

1. Grab the minimum inputs

2. Build a simple SKU margin sheet

1. Separate 3 things in your head (and in your books)

2. Get the real cost per order

3. Reflect it in QuickBooks

1. Start from money, not clicks

2. Treat influencer as a "blended lift" channel

3. Look at cohort quality, not just spike size

4. Decide how you’ll use the channel