r/ecommerce

So I've been noticing more non human traffic actually getting to checkout, not just browsing and leaving. some of it looks like real ai agents doing legit shopping for someone, but some of it is probably just bots trying to grab limited stock or abuse discount codes.

Problem is i cant tell the difference. Like whats stopping someone from spinning up 50 bots that just look like "shopping agents" and cleaning out my inventory during a sale. I dont have any way to check if theres an actual accountable person behind the thing thats buying, or if its just a script pretending to be an agent.

Been reading a bit and apparently this is a known gap right now, theres this "human backed AI agent" idea forming, basically kyc but for the agent instead of the customer. From what i found tools like AgentKit and skyfire are both trying to solve this, letting the agent carry some kind of proof that a real verified person is actually behind it, without me needing to know who that person actually is. So i just get a yes/no on "is someone real accountable for this" instead of full identity info.

Still feels pretty early tho and i dont think theres one agreed standard yet, and idk if any of this is even easy to plug into an existing store setup

anyone else run into this? how are you guys checking if its a real person behind the agent or just treating all agent traffic as suspicious until proven otherwise.

So i've launched my first experimental ad campaigns on meta ads and i'm really not sure how to gauge what's "good" or not.

I've had two campaigns so far, in which i collect leads through a basic insta form of "first name" and "email address":

Campaign 1: generated 18 leads for like $109.00 in spend. Yikes--yes, i realize that's crazy expensive. Like $6.00+ / lead.

Campaign 2: Launched a day or so after the first campaign with better creatives and that's generated leads at a rate of about 50 / day; costing about $0.70 each.

Obviously campaign 2 is performing better than campaign 1, but is it considered "good" yet? After all, these are leads requiring absolute minimal personal investment, so it's unclear as to the quality of these leads yet.

The category of my product is in apparel and accessories. AI seems to think this is good, but AI also tends to be a 'yes' man and has a limited scope of insight into reality.

Thoughts?

We sell a SaaS tool for Shopify stores. When customers hit a bug or can't figure something out, they email us "it's not working." Then starts the endless back and forth. By email #8 we finally understand the problem and fix it in 2 minutes. The actual issue resolution takes no time. The diagnosis takes forever because we can't see what they're seeing.Is there a support tool where you can just see the customer's screen during a live chat without asking them to download anything?

Anyone have any good use cases for Claude or other AI when using Shopify?

I’ve been digging into cross-border checkout lately and there’s one scenario I’m curious how actual store operators diagnose. Say traffic quality looks roughly normal across markets. People are viewing products, adding to cart and starting checkout at a reasonable rate, but completed purchases are noticeably worse in a few countries.

At that point there are so many possible causes that it feels easy to optimize the wrong thing, like unexpected shipping/tax costs, currency or pricing friction, missing local payment methods, 3DS/authentication drop-off or issuer declines and whatever else possible at that point.

For anyone selling internationally, what order do you actually investigate these in?

Do you start with the checkout UX because it’s easiest to control, or do you look at payment acceptance/decline data early on?

Also curious if anyone has ever spent ages optimizing the front end only to find out the real problem was happening at the payment step.

Not looking for payment-provider recommendations, more interested in how people actually troubleshoot this.

Hey! I’d love to get some honest opinions on this website: https://thesupernova.store.
My fiance has been working on it for a while and we have looked at it so much that we honestly can’t tell anymore if it looks good or not 😭.
If you have a minute to check it out, he would really appreciate your first impression whether it looks trustworthy, if you’d actually consider buying something, and anything you think could be improved. Feel free to be brutally honest, he is actually looking for criticism lol.

Got a clean product shot and needed a 5-10 sec lifestyle clip for TikTok, didn't wanna set up another whole shoot. Tried an image-to-video AI workflow in Framia.

Honestly, for one clip I'd probably just use my phone. But if u're doing like 10-20 variations for testing? That's where this starts making more sense.

What are you guys using rn, reference motion or just shooting the thing for real?

Hey all - new to the e-commerce space and would appreciate any insights :-)

I'm trying to tighten up our listing process before Q4 and I'm realizing we are very messy keeping a same product listing accurate across Amazon, Shopify, TikTok Shop, eBay, and sometimes Temu.

The little differences add up like title length, image rules, variant naming, category quirks, shipping language, return policy wording, and platform-specific keywords. One SKU quickly becomes five different projects.

I've been looking at tools to address this and Accio Work caught my eye. Their pitch is “one product brief turning into marketplace-ready listings”. That sounds useful, but I am curious how this community thinks about this or approaches this problem to solve.

Do you still prefer writing each marketplace listing manually, or would you trust an ops tool to generate the first version and then have a human review after?

Let me start by giving you a bit of context.

I'm a young man (27) who recently started a brand in strapless bra's for woman. I'm naturally not really good at making content, and having to make content for a female audience is totally making an error in my head.

The reason I started this business.

I started this brand because I noticed a problem with my girlfriend. She would always take off her bra the first thing when she would get home. She said her bra is constricting and sometimes painful. When I was looking for products to sell I found 'nipple covers' after which I dug into the rabbit hole of strapless lingerie and started a brand.

I now realise that these products needs a lot of social proof, lots of video proof and building this brand requires constant social media presence.

I have the feeling that I cannot make content for it myself because it would feel as 'mansplaining' and very unnatural for women to trust a man on such a delicate subject. I don't have the budget to run 5 ugc video's from different creators every week because these would drain the low amount of cashflow I have at the moment.

The goal is to ultimately in the long term build a big social media presence, and in the short term gain sales traction to increase cashflow to reinvest in the business.

Do you have any ideas on how I can tackle this problem? All tips, tricks, systems, ... are all welcome, I'm really open to any solutions.

I've been running my business full time for about 5 years, and there seems to be a cycle of great weeks, doing above average days, lots of orders coming in, followed by a very slow string of days, before eventually getting back to what I like to call normal. Nothing in particular has changed, traffic is the same, people just aren't buying.

Does anyone else experience this? Do you have anything that you do to stir up sales during those times when you see them happening?

I’m offering 5 free ecommerce UX/conversion audits in exchange for honest feedback.

I’m a UX designer testing a new audit framework for small ecommerce stores. I’m looking for stores that already have some traffic/sales, so I can study real friction points and improve the process.

Best fit:
Stores having:
- 1,000+ visits/month
- 10+ orders/month
- Clear conversion goal: product purchase, add to cart, checkout completion, etc.

- You’re willing to share basic numbers privately: traffic source, conversion rate if known, AOV, and biggest drop-off point

What I’ll review:
-Homepage clarity
-Product page friction
-Mobile shopping experience
-Add-to-cart and checkout flow
-shipping/returns clarity
-CTA placement
AOV/bundle opportunities
Top 5 fixes I’d prioritize

What you’ll get:
-Annotated screenshots with severity ratings & usability issues
-Top 5 conversion friction points
-Recommended fixes

A short priority list: fix now / fix next / nice to have

In return, I’d just ask for feedback on whether the audit was useful.

If interested, comment or DM with:
Store URL
Platform
Monthly visits
Monthly orders
Main traffic source: organic, paid, social, email, etc.
Mobile vs desktop split, if known
Biggest issue you want help with
I’ll pick 5 stores where there’s enough data to make the audit useful.

Most "must have features" lists are just a description of the default theme. Here are the eight that actually move numbers, and what happens when each one is missing.

1. Product pages that answer the objection, not just describe the product
   Sizing, materials, what it does not do, cautions where they apply. Every unanswered question becomes a return or an abandoned cart.

2. Filtering that matches how people decide
   Customers do not shop your category tree. They shop by goal, constraint, budget or dietary need. If your filters mirror your admin structure, your search is doing nothing for you.

3. Value clarity at the moment of comparison
   Price per unit, what is in the bundle, what shipping will cost. Ambiguity at comparison time is where the session ends.

4. Reviews and Q&A on the page, not behind a tab nobody opens
   Q&A does double duty. It converts, and it tells you exactly which product page copy is failing.

5. Accounts and reorder that actually work
   Order history, saved details, one-tap repeat purchase. For consumables this is most of your repeat revenue, and it is usually the most broken thing on the site.

6. Speed on a real phone on a real network
   Not a lab score. Mid-tier Android, cellular connection, cold cache.

7. Content your team can change without a developer
   Banners, guides, FAQs, seasonal sets. If a promo needs a developer ticket, you will quietly stop running promos.

8. Analytics you trust before you optimise anything
   Clean events across search, product page, cart and checkout. Optimising on broken tracking is worse than not optimising at all.

One nuance most lists miss: the weighting is not universal. A high-value, one-of-a-kind catalog inverts it. GetDevDone built the store for The Satice, an antique jewellery brand where every piece is unique, from Victorian bangles to individual sapphire rings. There, brand identity and visual hierarchy carried the work, because giving each piece room and building the page around detailed provenance is what turns a browser into a buyer. Filtering and reorder matter far less when there is exactly one of each item.

So before you build against any checklist, decide which of your products the site has to sell hardest. Then weight accordingly.

I have the usual order confirmation/shipping/delivery emails, but wondering if things like cross-sells etc. are actually worth adding or just fatigue

I’m posting this because I genuinely don’t want another freelancer or contractor ending up in the same position I’m in.

I’m currently £6,000 out of pocket from unpaid invoices relating to work for Calashock, and this has now been dragging on for around two years.

Throughout that time, I was given excuse after excuse about why payment hadn’t arrived. I was told invoices were just slightly delayed, then there was apparently an issue with the invoice number, then another reason, then another. Every time it sounded like payment was supposedly just around the corner.

It never came.

Eventually, after getting nowhere privately, I started speaking publicly about my experience with the company.

Only then did things suddenly become urgent.

I was told that I would be paid if I removed the negative things I had posted about them online. Wanting the situation over with, I agreed and removed the posts.

Guess what happened next? I still wasn’t paid.

What makes this even more concerning is that, from conversations I’ve had, I understand I’m not the only person connected with Calashock who has experienced problems getting paid. I’m not going to speak on anyone else’s behalf, but my situation does not appear to be an isolated one.

I’m sharing this as a warning to other developers, designers, project managers, freelancers and contractors who might be considering doing work for Calashock.

My advice, based entirely on my own experience: avoid working with them.

I gave them plenty of opportunities to resolve this privately. Two years is more than enough.

Disclosure up front: I build fraud detection software for Shopify stores. I'm not linking it and I'm not naming it. I spent the last year cataloguing how this actually works, and the list is more useful to you than a sales pitch is to me.

Most merchants know three or four of these. There are 35. Almost none of them look like fraud on a single order, which is the whole problem. One wardrobing return is a woman who changed her mind. Six of them in a year, always the week after a holiday, is a business model.

Here's the full list, grouped by where the money leaves. For each one I've put the signal that separates it from an honest customer doing the same thing, because that distinction is the only part that matters.

Fraud that goes through the bank

Friendly fraud. Customer buys, receives, then disputes the charge with their bank instead of asking you for a refund. Roughly one in five disputes, per Mastercard and Javelin research. Tell: prior chargeback history on the same customer, address or card hash, and disputes filed despite tracked delivery. Repeat offenders are most of the volume.

Item not received (INR). They got the parcel, they say they didn't. Tell: claim timing against the delivery scan, repeat INR history per address, and order value skew. Riskified's claims data (1M+ claims across 3 major retailers, 2024) found INR claims 25% more likely to be abusive than missing-item claims, orders above $1,000 drawing 33% more abusive claims, and claims filed within 7 days of delivery 20% more likely abusive.

Double-dip. Return the item, get refunded, then file a chargeback on the same order anyway. Tell: a dispute landing on an order that already has a completed refund record. This one is trivially detectable and shockingly common, because most stores never check.

Account takeover refunds. Someone else's account, new device, address changed, high-value order, refund redirected. Tell: dormancy break plus behaviour change plus a delivery address edit in the same session.

Triangulation. They sell your product on a marketplace, take the buyer's money, then order from you with a stolen card and ship it to the buyer. The buyer is innocent and the cardholder never knew. Tell: cardholder and shipping identity mismatch at volume, fresh addresses with one order each, and chargeback autopsies that surface a confused cardholder.

Stolen card fraud. The classic one, and the one every store already screens for. Tell: AVS and CVV mismatch, IP versus card origin, velocity across cards and devices.

Worth knowing if you rely on Shopify Protect: it covers "fraudulent" and "unrecognized" chargebacks only. It explicitly does not cover item not received or not as described, and it needs a US merchant on Shop Pay checkout. If you're in the EU or UK, or you take payments any other way, you are not covered for the category that is growing fastest.

Fraud in what comes back in the box

Wardrobing. Bought for the wedding, worn once, returned with the tags tucked back in. Tell: the weekend-order to Monday-return cycle, non-defective reason on a fast return, and event clustering per customer. Any single instance is innocent. The calendar is the evidence.

Empty box. The return arrives, it weighs nothing. Then it becomes an argument about whether your warehouse lost it. Tell: inbound parcel weight against shipped weight, anything over about 10% mismatch, plus repeat "your warehouse lost my return" claims from one customer.

Item switch. Your new item goes out, their broken old one comes back. Tell: serial or IMEI mismatch at inspection, weight and dimension deltas, and high-value SKU plus fast return together.

False damage claims. Photos of damage that didn't happen, or didn't happen to your item. Tell: reused or edited image hashes, EXIF anomalies, and claim text that reads identically across unrelated customers. AI-generated damage photos are now a real category, and several large retailers went public about it this year.

Missing item claims. "The box arrived but the jacket wasn't in it." Tell: claim frequency per address, claim values clustering just under whatever your no-questions-asked approval threshold is, and pack weight against the claimed-missing item's weight. If your threshold is $50, look at how many claims land at $47.

Cross-retailer returns. They buy from you at full price, buy the same thing cheaper elsewhere, and return the cheap one to you. Tell: serial or batch mismatch at receiving, and unit condition that doesn't match the order age.

Abuse of your policy, at volume

Bracketing. Order five sizes, keep one, return four. Not fraud exactly, but it can quietly eat a category's margin. Tell: same-SKU multi-variant orders and lifetime keep rate. Watch net AOV after returns, not gross.

Serial returning. A customer whose personal return rate is four times your store baseline, forever. Tell: return rate versus store baseline, returns landing at 80 to 100% of your policy window, and category hopping.

Price-drop repurchase. Return at full price, rebuy on sale, pocket the difference. Tell: returns filed just after a price drop on that exact SKU, and the same customer reordering the same size days later.

Returnless refund farming. You tell them to keep it because return shipping costs more than the item. They learn that, and they industrialise it. Tell: refund-without-return frequency per identity cluster, and claim values sitting just under your return-shipping threshold.

Reseller and bulk-buy abuse. Bots buy the limited drop, the resale price disappoints, the units come back to you. Tell: multi-unit limited SKU purchases, return timing that tracks the resale market, and bot fingerprints at checkout.

Fraud in the logistics and the paperwork

FTID (fake tracking ID). They ship an empty envelope on a real label to your ZIP code but not your street, so the carrier scans "delivered" and your warehouse never receives anything. Then they show you the tracking. Tell: delivery scan geolocation matching at ZIP level but not street level, and no warehouse receiving scan despite a carrier delivered status. This one is widely taught in paid communities and most merchants have never heard of it.

BORIS and channel hopping. Refunded online, then walks into the store and returns the same order again. Tell: purchase channel against return channel, and prior online refunds on the same order.

Receipt and e-receipt fraud. Forged or reused proof of purchase. Tell: duplicate return attempts against one transaction, and receipt amount and SKU that don't reconcile.

Warranty and replacement claims. Claim frequency per serial number and per address, and claims that always land just inside warranty expiry.

Stolen goods returns. Shoplifted or fraudulently bought goods returned for clean money. Tell: the refund-method switch, original tender out, different card or gift card in. That switch is one of the highest-signal events in the whole list.

Identity and program abuse

Multi-account. One person, six accounts, evading your per-customer limits. Tell: shared address, payment method or device across accounts, and email pattern analysis (dot variants, plus addressing, disposable domains).

Discount and promo abuse. One-time codes redeemed repeatedly by one identity cluster, and refund amounts that don't match what was actually paid after the code.

Referral abuse. Referrer and referee are the same person. Tell: device, address and payment overlap, plus disposable-email density in a referral cohort.

Loyalty and points fraud. Points earned on purchases that get returned. Tell: points earned against net kept value, and the return rate of your heaviest redeemers.

Gift card cash-out. Buy a gift card, refund it to a different method, and dirty money comes out clean. Tell: short gift-card-to-refund cycles and refund-method switches on gift card orders.

Subscription and trial abuse. New identity every first box. Tell: identity clustering on trial redemptions and cancel timing that is always post-delivery, pre-renewal.

BNPL abuse. High value, new account, buy now, pay never. Tell: BNPL tender plus new account plus high value together, and claim timing against the installment schedule.

Digital goods refunds. Full consumption, then a refund request at the edge of the guarantee window. Tell: consumption depth (progress, downloads, activations) against the claim.

Organized and assisted

Fraud rings. Not individuals. Shared addresses, shared payment instruments, shared drop points, operating across many stores at once. Tell: identifier graphs rather than account-level rules. Velocity measured at the cluster, not the customer.

Refund as a service. Professional refunders who charge the "customer" 10 to 25% of the order value to run the claim for them. Labels sell for $20 to $50, mentorships for thousands. The DOJ has prosecuted this. Tell: coached claim language with identical narrative structure, sudden claim-type concentration shifts, and new account plus high value plus immediate claim.

Coordinated return waves. A SKU's return volume spikes against its own 90-day baseline, with copy-paste reason text and batch-created accounts. Tell: the timing synchronisation. Honest customers don't return in formation.

GenAI-assisted claims. Consumers now use ChatGPT to draft refund demands and dispute letters, which strips out the bad grammar and hesitancy that support teams used to read as suspicious. Tell: stop reading the text. History and identity signals are decoupled from writing quality. Escalation velocity, denial straight to a formal dispute letter in minutes, is more informative than anything in the prose.

Employee and insider fraud. Refunds issued with no return and no ticket. Tell: refunds per agent against peer baseline, off-hours timing, and repeated beneficiary accounts.

One thing before anyone says it

Most of your returns are honest, and the fastest way to lose money on this is to start treating ordinary customers like suspects. Porch piracy is real, parcels genuinely do go missing, and someone with a 40% return rate might just be a woman buying clothes online in a world where sizing is a lie.

Every signal above is only meaningful as a pattern across time or across identities. Single-order rules generate false positives, false positives generate refunds you'd have given anyway plus a customer who now hates you. The scale reference, for what it's worth, is Appriss Retail's 2026 benchmark: about $100B in preventable loss against $706B of US returns, roughly 14%. That's a new methodology so it isn't comparable to their older numbers, but the shape is right. Most returns are fine. A small slice isn't, and that slice repeats.

Happy to go deeper on any single one of these in the comments. FTID and the refund-method switch are the two I'd look at first if you've never looked at any of this, because they're both cheap to check and neither requires any software.

At my current store If i wanna use BNPL from Klarna, they want quite high % fee per transaction

So rn my store don't have BNPL.

I'd love to hear people who have BNPL as a payment option, how is your sales going?

Most ecommerce analytics give you a pretty good picture of what happens before and during a purchase - acquisition, conversion, etc.

But once the order is delivered, there seems to be a big blind spot.

Did the customer actually like the product?
Did it meet their expectations?
What almost made them return it?
Would they buy it again?

Reviews and support tickets give you some of this, but they're usually reactive and disconnected from the actual order/product.

Curious how other ecommerce teams handle this today. What signals do you look at after a purchase to understand whether a customer is actually happy?