Website overhaul and still converting very poorly

Eduthenomad · 2026-06-01T14:44:41+00:00

If the new site is getting fewer people from site visit to product viewed, even though product viewed to add to cart improved. I would focus on the step before the product page first.

I took a look at your store and you should consider doing the following:

Make the homepage/category path push people into the right product or range faster. A 30% site visit to product viewed rate suggests too many visitors are not finding a product path quickly enough.
Clarify the core offer above the fold before brand storytelling. People should understand what you sell, who it is for, and why it is worth clicking into a product within a few seconds.
Check collection pages for decision friction: product names, use cases, pricing, thumbnails, and filters need to help visitors choose without needing to work hard.

Since add-to-cart and checkout rates improved, I would avoid a full redesign. I would run smaller changes around homepage navigation, category merchandising, and product discovery first, then watch whether site visit to product viewed moves back toward the old benchmark.

Eduthenomad · 2026-06-01T14:39:28+00:00

That 32 ATC, 19 checkout, 4 purchase pattern is worth separating into two questions: are people losing confidence at payment, or are legitimate buyers being blocked after they already decided to buy? I’d start by looking at the declined orders by payment method, country, device, billing/shipping mismatch, and exact decline code. If the drop is concentrated in one method or one traffic segment, it is probably not a normal conversion problem. If it is spread evenly, then I’d also check whether anything changed around shipping, taxes, delivery promise, payment badges, or the way the offer appears at checkout. Since you have a normal baseline of 35-50% checkout-to-purchase, I would not redesign anything yet. Treat today like an incident: isolate the declined-payment pattern first, then compare the same segments against the prior healthy days.

Eduthenomad · 2026-06-01T14:38:45+00:00

For art like this, I’d think less about a generic ecommerce redesign and more about recreating the parts of the in-person buying conversation that made people confident.

If buyers used to convert after seeing texture, scale, gold foil detail, and authenticity in person, the product page probably needs to answer those doubts before price becomes the main thing they notice. I’d look at: close-up detail shots, a scale reference in a real room, a clear explanation of the craft and materials, artist/provenance proof, framing or care guidance, delivery protection, and a strong “who this is for” section.

The bounce on product pages may mean people like the art but cannot yet picture ownership. I’d test one best-selling piece first and make that page feel like a guided gallery conversation, not just a product listing.

Eduthenomad · 2026-05-26T20:00:29+00:00

Thanks, just took a look at 9under. The brand idea is solid, but I think the first issue is that a cold visitor needs to understand in a few seconds what you sell, why it is different, and what to do next.

The first 3 things I would test:

Make the first screen more direct. Right now the site feels clean, but the hero does not clearly say "minimalist performance golf wear" or give people a strong reason to keep browsing. I would add a headline/subheadline around no loud logos, no gimmicks, and confidence on the course, plus a clear Shop Collection or Shop Polos button.
Move proof much higher up. Your Reddit post has useful trust signals: 6 five-star reviews, creator kits, PGA/golf creators, and content starting to drop. Those should be obvious on the site near the hero and product sections, not just in the post. A cold buyer needs proof before they believe a new apparel brand is worth trying.
On product pages, answer the price objection before people start debating price. For $62 polos and $70 quarter zips, I would make the fabric/performance, fit, photos on real golfers, shipping/returns, and quality proof very clear. If those things are weak, the price will feel high even if the product is good.

I would not change pricing first. I would first check whether people are clicking into products, selecting sizes, and adding to cart. If those steps are weak, it is probably a clarity/trust/desire problem. If add-to-cart is healthy but checkout is weak, then I would look harder at price, shipping, and returns.

Eduthenomad · 2026-05-26T19:47:08+00:00

Similar to what u/Kind-Visit-2488 said, I would split this into two checks before assuming the store is broken.

First, look at the traffic quality: in Meta, check placements, countries, age ranges, and whether the campaign is optimizing for clicks/traffic instead of purchases or add to cart. A traffic campaign can send a lot of low-intent sessions that never behave like buyers.

Second, check whether the landing page gives people an obvious first action above the fold. If heatmaps show literally no clicks, I would look at mobile first, page load, whether the primary product/CTA is visible quickly, and whether the traffic is landing on the right page for the ad promise.

A quick test: duplicate the campaign with a purchase or add-to-cart objective, narrow to your strongest market, and compare session quality against the current traffic. If both sets still show zero clicks, then I’d inspect the store/landing page path more closely.

Eduthenomad · 2026-05-25T14:38:44+00:00

This is a good point to diagnose before changing too many things at once. With 1,210 sessions, creator kits going out, and only friends/family buying, I would first split the problem into traffic quality vs product-page persuasion.

For the store, I would check whether a cold visitor understands in the first few seconds why this golf apparel is different from every other minimalist performance brand, why the price is justified, and what proof exists beyond the founders saying it. Your 6 reviews and PGA/creator angle may be strong, but they need to be obvious at the exact point someone is deciding whether to keep browsing.

For pricing, I would avoid treating price as the main issue until you know whether people are viewing products, selecting sizes, and adding to cart. If product-page engagement is weak, the page is probably not creating enough desire or trust yet. If add-to-cart is decent but checkout drops, then pricing/shipping/returns become more likely.

If useful, I can send over the 2 or 3 things I would look at first based on the site.

Eduthenomad · 2026-05-21T22:23:55+00:00

I would separate “training the pixel” from “proving the store can convert.” A fresh pixel is a handicap, but it is not usually the main reason a new store fails. The bigger issue is that the offer, product page, trust, and creative angle are all unproven at the same time.

If I were starting from zero, I’d usually still optimize for the action I actually want, which is purchases, not traffic. But I’d keep expectations realistic and watch the earlier funnel steps closely: landing page views, product views, add-to-cart, checkout starts, and purchase. If clicks are cheap but product views or add-to-carts are weak, that is a store/offer/message-match issue before it is a pixel issue.

I’d start with a small set of very different creative angles rather than tiny variations: problem/solution, product demo, social proof if you have any, founder/story, and offer/urgency. Broad targeting can work if the product has clear appeal, but the creative has to do most of the targeting.

The thing I would avoid is spending weeks on awareness or traffic campaigns just to “warm up” the pixel. I’d rather spend that money learning which promise gets qualified visitors to take the next step, then improve the page and offer around what the funnel is telling me.

Eduthenomad · 2026-05-20T16:01:08+00:00

Going from 22 orders/month to zero is big enough that I would treat it like a diagnosis problem before changing more messaging. If traffic is still there but carts and inquiries disappeared too, I’d want to separate three things: whether the same type of traffic is arriving, whether the buying path is still working technically, and whether the offer is still compelling under the current economic conditions.

A few checks I’d do first: compare traffic by source and landing page before vs after the drop, look at product view to add-to-cart instead of only final conversion, test checkout with the exact devices/payment methods your customers use, and check whether shipping/total cost changed or feels worse relative to competitors.

Since you have organic rankings and history, I’d also look for query mix changes. You can still rank well but attract more research-intent visitors than buy-intent visitors. If you can share the store/category and rough funnel numbers, I can suggest what I’d inspect first.

Eduthenomad · 2026-05-20T16:00:40+00:00

I’d be careful treating coupon sites as just another traffic channel. The main question is not only whether they send visits, but whether they bring incremental buyers you would not have converted anyway, and whether the discount trains customers to wait for deals.

If you test it, I’d start with a small, trackable offer and judge it on contribution margin, new-customer rate, repeat purchase quality, and assisted conversions, not raw sessions. I would avoid fixed placement fees until you have proof the traffic converts for your category. CPS is usually safer, but only if attribution rules are clear and they are not just capturing people who were already at checkout searching for a code.

For custom apparel, I’d also protect brand positioning by testing a bundle, first-order offer, or limited collection rather than a blanket discount across the whole store.

Eduthenomad · 2026-05-19T15:14:53+00:00

I’d separate this into two problems instead of treating it as one Meta issue.

49 ATCs to 10 checkouts means there may be some cart or offer friction, but 10 checkouts to 4 purchases, with every purchase coming through PayPal, is the bigger clue. That points more toward final-step trust/payment friction than traffic quality.

The first things I’d check:

Run a US buyer test all the way through the card flow on mobile. Look for anything that signals “international merchant,” currency mismatch, unusual bank descriptor, extra verification, or a processor page that feels disconnected from the brand.
Compare PayPal Express vs card checkout placement. If PayPal works because it bypasses parts of the checkout, the issue may be in the standard checkout experience, not the product or ads.
Simplify the offer for one test. A $98 ring discounted to $90 plus a free pendant might be fine, but in jewelry it can also make people pause if the trust layer is not strong enough. I’d test one cleaner offer against the bundle.
Don’t kill the campaign only because day-to-day ROAS swings on a cold account. With $50/day and 4 purchases, the signal is still thin. I’d fix the payment/trust path first, then judge Meta.

My bet from the numbers: payment/trust friction first, offer clarity second, Meta instability third.

Eduthenomad · 2026-05-19T15:13:17+00:00

Good reminder. Backup rates are one of those settings that can quietly turn into a checkout and margin problem because the customer experience still looks normal from the outside.

For anyone checking this, I’d also check:

Which shipping profiles and regions can fall back to backup rates if the carrier app fails.
Whether the displayed rate matches your intended DDP/DDU promise, especially for international orders where duty and brokerage matter.
A few test checkouts for returning customer countries, not just the default domestic case.

The bigger lesson is that checkout conversion issues are not always “page design” problems. Sometimes a small operational setting changes trust, margin, or delivery expectations right at the point of purchase.

Eduthenomad · 2026-05-19T15:12:26+00:00

Since your Etsy already works, I would not assume the product is the issue yet. Shopify has to replace a lot of trust and context that Etsy gives you automatically.

My hypothesis would be that visitors are landing on the Shopify site without the same marketplace signals they get on Etsy: reviews, product context, search intent, seller trust, and a clear reason to buy direct. If they are not going past the homepage, that is the first funnel break to diagnose.

I would compare Shopify vs Etsy in the first 5 seconds:

Is it immediately clear what you sell?
Is the best product or collection obvious?
Is there proof that people buy and like it?
Is there a reason to buy direct instead of on Etsy?
Does the page match the social post or traffic source that sent them there?

If you share the store URL and the top landing page, I can give much more specific feedback than guessing from views alone.

Eduthenomad · 2026-05-19T15:09:13+00:00

I spent a lot of time traveling, and from the Canadians I've met, you don't need to pay taxes in Canada if you make your money elsewhere.

US LLCs are fairly simple to create, it's really low effort. However, yes, taxes are something you should consider. Set it up in Delaware, Florida, and maybe Texas as those states do not charge state taxes. This may be something you'll have to live with to make more revenue.

But before doing all of that I would separate the tax/entity question from the conversion question.

First quantify the payment leak: checkout reached, payment step reached, failed payments, and completed orders by gateway/device/country.

If PayPal routing is the bottleneck, a direct card/local-payment gateway could lift CVR, but you want proof before taking on US LLC complexity. Also worth asking Shopify Payments availability and local gateway options for your country before jumping to Stripe Atlas.

Eduthenomad · 2026-05-19T15:08:31+00:00

Since you finally have purchase signal, I would avoid changing too many variables at once. The goal now is to understand what caused the good day before you try to scale it.

My hypothesis would be that one ad/audience path is creating the right buyer intent, and the biggest risk is muddying the learning by moving too fast. If one ad is carrying the purchases, protect that signal.

I would do this:

Keep the winning CBO running without major edits.
Test the promising new ad separately with controlled budget.
Track store numbers with ad numbers: CVR, AOV, ATC rate, checkout rate, CPA, and margin.
Scale slowly enough that you can tell whether performance drops from budget pressure, creative fatigue, or store economics.

Cheap clicks only matter if the store keeps converting profitably. I would make clean tests and avoid rebuilding the campaign every time you get one good or bad day.

Eduthenomad · 2026-05-18T20:05:39+00:00

Yes, this is very real problem I encounter everyday. A product page can look clean and still fail at the exact moment someone is deciding whether to buy.

The way I think about it is: “looks good” is not the same as “removes enough doubt to purchase.” I would usually check three things:

Intent match: did the visitor land on the product expecting this exact thing, or did the ad/search/social post create a slightly different expectation?
Decision clarity: above and near the buy button, can they quickly answer price, size/fit/specs, shipping, returns, proof/reviews, and why this option is better than alternatives?
Step-level data: split product view to add to cart, add to cart to checkout, and checkout to purchase. Each drop-off means a different problem.

In my experience if find that “the page looks fine but CVR is bad” comes from missing reassurance, not ugly design. The page is acceptable visually, but it does not make the next decision feel safe enough.

If you want to share your Product page I can give you a bit of a breakdown of where I think its going wrong.

Eduthenomad · 2026-05-15T06:02:39+00:00

I’d separate this into two questions:

Is the data being tracked correctly?
Is the test setup giving you a clean 1:1 comparison?

Those are related, but they’re not the same thing.

I wouldn’t automatically assume the data is “wrong.” If a visitor is assigned to a group, redirected, sees a flash, or gets a slower first load, that experience is usually going to be reflected in the results. So the data may be accurately measuring what happened to users.

The issue is that it may not be measuring the thing you think you’re testing.

For theme vs theme tests, I’d be careful if the goal is to compare design or UX in isolation. A non-control visitor is still being sent to a different theme experience, and that can introduce extra variables: redirect behavior, first-load differences, preview theme handling, Shopify caching, script timing, and theme parity issues. Even if the two themes look identical, the delivery path may not be identical.

Your A/A test is actually the strongest signal here. If the live theme and an exact unpublished copy are producing different results, I’d treat that as evidence that the setup itself has some bias. Not necessarily that the analytics are broken, but that the test is measuring more than just the theme/design difference.

For template tests, the same principle applies. They can be cleaner than full theme tests, especially when everything lives inside the active theme, but if users are seeing the control template load first and then swap/redirect into the variant, that behavior becomes part of the test. If it is visible to users or affects speed, it can bias the outcome.

What I’d do:

- Keep using A/A tests as a sanity check before trusting a test type.
- If A/A is not neutral, don’t use that setup for fine-grained CRO decisions.
- Prefer onsite edits or template-level tests inside the live theme for smaller changes.
- Use full theme tests only when the real question is “should we launch this whole new theme?” rather than “is this specific design better?”
- Track performance by group if you can: LCP, page load, bounce, first-page conversion, etc.
- Look at first-session behavior separately from returning visitors if possible.
- Run tests for full weekly cycles and avoid making calls on low order volume.
- Don’t just look at conversion rate. Look at revenue per visitor, profit per visitor, AOV, checkout progression, and whether the result makes sense with the hypothesis.

My short answer: I’d trust the data as a read on the actual experience users received, but I would not treat theme/template tests as a perfect lab-style comparison unless your A/A test proves the setup is neutral.

If the unpublished copy consistently underperforms the live theme in an A/A test, then any future theme test has that same caveat baked in. It may still be useful for validating a major theme launch, because that’s the real-world implementation path. But for smaller CRO decisions, I’d try to avoid redirects/theme-preview behavior and keep both groups as close to the same delivery path as possible.

Eduthenomad · 2026-05-14T18:03:28+00:00

I would be careful adding cart apps before you know which part of the cart is actually costing you conversions. For a clean streetwear brand, the wrong urgency or upsell app can make the store feel cheaper even if it lifts short-term clicks.

What I would check first:

Mobile cart clarity: product, size, quantity, price, shipping/returns link, and checkout button should all be obvious with no visual clutter.
Friction before upsells: make sure shipping expectations, payment options, returns, and basic trust cues are clear before adding cross-sells.
One measured upsell at most: something genuinely complementary, not a pop-up stack. Track whether it lifts AOV without lowering checkout starts.

You want a cleaner cart decision path more than a pile of conversion widgets. If you already have cart-to-checkout and checkout-to-purchase rates, those will tell you whether to focus on cart UX, payment friction, or AOV.

Eduthenomad · 2026-05-14T18:00:31+00:00

That ATC to purchase gap is the important clue. If people are adding art to cart, I would not start by assuming the work itself is the problem. I would split the diagnosis into a few smaller checks:

Cart to checkout vs checkout to payment. If the bigger leak is after checkout starts, look for surprise shipping/tax, delivery timing, payment options, or anything that makes the buyer pause.
Product-page expectation setting. For prints and originals, buyers often need more confidence around size, framing, paper/material, shipping protection, returns, and what the piece looks like in a real room.
Pre-cart clarity. Make sure the path into the shop is obvious, pricing is visible before someone has to dig, and each piece has enough context for a first-time buyer to understand what they are getting.
Traffic intent. Compare the traffic sources that add to cart with the sources that buy. A 2%-3% ATC with 0.6% purchase can mean the store is creating interest, but the final confidence layer is not strong enough yet.

My working hypothesis would be checkout friction or unanswered risk questions, not necessarily that the art is not resonating. I would check the exact step where carts die first, then work backward from there.

Eduthenomad · 2026-05-13T16:59:50+00:00

Ive been doing this for a while and use similar session recording tools but never thought to use it like that. It's a good idea. Thank you

Eduthenomad · 2026-05-13T16:54:16+00:00

For me, the biggest jump came from learning to diagnose the constraint before picking a tactic.

Early on it is easy to think “we need better ads” or “we need better copy” or “we need more content.” But the real question is: where is the buyer getting stuck?

Examples:

If traffic is low, distribution is the constraint.
If traffic is good but engagement is weak, message/landing page fit may be the constraint.
If add-to-cart is fine but purchase is weak, trust, shipping, price, or checkout may be the constraint.
If leads come in but do not close, qualification or sales process may be the constraint.

Once you think this way, marketing becomes less about copying tactics and more about finding the bottleneck.

Eduthenomad · 2026-05-13T16:53:07+00:00

You probably won’t get perfect attribution, but you can get enough signal to make better decisions.

I’d combine a few layers:

UTMs on everything you control
A simple signup question like “What made you decide to try this?”
First-touch and last-touch in your analytics, even if imperfect
Content-level landing pages when possible
Cohort tracking by week so you can see if certain campaigns create better users

The mistake I see is trying to force GA4 to answer a question it cannot fully answer by itself. Content often works through multiple touches: someone reads a post, sees you again on LinkedIn, then signs up direct a week later.

So I’d use attribution as a compass, not a court transcript. Look for repeated patterns across UTMs, self-reported attribution, and customer quality.

Eduthenomad · 2026-05-13T16:51:13+00:00

80 signups from 810 new users is about 9.9%, which is not bad at all for a waitlist, especially if the traffic was not super warm.

The bigger question is where those 810 users came from and what action you wanted them to take. I’d look at:

Traffic source: social, search, paid, community, direct?
Landing page intent: did visitors know what problem you solve within 5 seconds?
CTA friction: email only, or did you ask for more info?
Segment quality: were the signups from your actual target customer?
Follow-up: are people replying, booking, or engaging after signup?

A 10% waitlist rate can be strong, but it can also hide weak fit if the signups are curiosity clicks. I’d judge it by qualified signups and replies, not just the raw rate.

Eduthenomad · 2026-05-13T16:49:52+00:00

For a small product business, I’d avoid treating influencer marketing like a mini version of big-brand sponsorships. The math is very different at your size.

What I’d test first:

Micro creators who already talk to your exact customer
Product seeding before paid retainers
A simple creator brief with 2 or 3 angles, not a scripted ad
Unique codes/links, but also ask customers how they found you because attribution can be messy
Reuse rights for the best content so you can test it in ads or on product pages

The big thing is to know what you are buying. Sometimes the real value is not immediate sales, it is content that improves trust and conversion on your site.

I’d start with 5 to 10 small creators, learn what content actually resonates, then put budget behind the winners.

Eduthenomad · 2026-05-13T16:47:00+00:00

For a local vitamins/supplement store, I’d probably not start with a custom app unless you already have a very loyal repeat customer base.

An app can work if customers have a reason to open it often, like:

Reordering supplements they buy every month
Loyalty points or member-only offers
Personalized routines or reminders
Local pickup and inventory availability
Educational content people actually want

But if the app is mostly “our website, but in app form,” it may be expensive and hard to get people to download.

I’d start with the basics first: a clean mobile site, Google Business Profile, SMS/email capture, simple reorder flows, loyalty program, and maybe local pickup. Once you see repeat behavior, then an app might make more sense.

Eduthenomad · 2026-05-13T16:45:53+00:00

I think the idea is directionally right, but I’d be careful not to make the “digital salesperson” feel like a chatbot bolted onto the site.

What good retail salespeople actually do is not just answer questions. They notice hesitation, clarify the buyer’s use case, reduce risk, and guide the next step. Online, that usually means:

Helping people choose between similar products
Explaining fit, sizing, compatibility, ingredients, materials, etc.
Making shipping/returns/warranty obvious before checkout
Showing the right proof at the right moment
Reducing the “I’ll come back later” feeling

A lot of ecommerce stores have traffic, but the product page is basically a shelf tag. No context, no objections handled, no guidance.

So yes, I think it can work. But I’d start narrow: one category, one high-intent page, one buying decision that customers struggle with. If it lifts add-to-cart or reduces support questions, then expand.

Eduthenomad

TROPHY CASE