I’ve been building a lightweight Shopify app called Intent Flow, and I’m trying to validate whether this is actually useful for merchants or just 'another analytics tool'

The idea came from something I noticed repeatedly:

Most Shopify analytics apps tell you:

• what happened yesterday
• traffic numbers
• dashboards/charts

But as a store owner, what I REALLY wanted to know was:

"Which visitor is actually likely to buy right now?"

So Intent Flow focuses only on that.

Current features:

• Live visitor sessions
• Intent scoring based on behavior
• Scroll depth tracking
• Add-to-cart tracking
• Funnel insights
• Checkout tracking
• High-intent visitor identification

Example:
If someone:

• revisits the same product multiple times
• scrolls deeply
• spends more time
• adds to cart

The app increases their intent score automatically.

The goal is:
NOT more dashboards.

The goal is:
- helping merchants identify serious buyers before they leave.

What I’m intentionally trying to avoid:

• heavy bloated analytics
• complicated setup
• enterprise-style dashboards
• 100 useless charts

I want this to feel:

• lightweight
• real-time
• simple
• actionable

As a merchant, would something like this actually help you increase revenue?

For example:

• identifying high-intent traffic
• seeing where users drop
• understanding which products create buying intent
• spotting checkout leakage early

Also curious:
what would feel like fair pricing for this?

Would love brutally honest feedback:

• useful?
• pointless?
• already solved elsewhere?
• what feature would make this a "must pay for" app?

january through june 2024. same products. same prices. tracked by channel with separate UTMs, separate inventory allocations, and a rigorous attribution model that took two weeks to build and was worth every hour.

shopify direct: $44,200 average monthly revenue. CAC $58. repeat purchase rate 34 percent. average LTV at 6 months $218. margin after platform fees and fulfilment: 48 percent.

amazon: $28,400 average monthly revenue. CAC technically zero but fulfilment costs, amazon fees, and PPC spend produced an effective acquisition cost of $34 per customer. repeat purchase rate from amazon customers to our shopify store: 4 percent. margin: 31 percent after all fees. no customer data owned.

own website outside shopify: $8,100 average monthly revenue. attempted to test a custom-built storefront. conversion rate 0.8 percent versus shopify's 3.1 percent. checkout abandonment significantly higher. abandoned after month four. the shopify checkout infrastructure is worth more than most people account for when they consider building independently.

the insight from six months: amazon produced volume and margin compression and zero owned customer relationships. shopify produced lower volume, better margins, owned relationships, and LTV that compounds. our own infrastructure produced a lesson about why shopify's checkout is a moat that is genuinely difficult to replicate.

the decision: double down on shopify. run amazon for product discovery with explicit strategy to migrate customers to direct. abandon the custom infrastructure entirely.

the owned customer relationship on shopify is an asset that appears on no balance sheet and determines most of what matters long term.

I launched my Shopify store thinking the hard part was building the website. Turns out, that was the easiest part. The first 3 weeks? $0 in real sales. Maybe $80 from friends, but nothing organic.

I kept tweaking my homepage, changing colors, rewriting product descriptions like it would magically fix things. It didn’t.

What actually worked was embarrassingly simple. I started posting 3–4 TikToks a day. No editing, no strategy..just showing the product in real life. On day 9, one video hit around 90k views. That single video brought in $1,047 over the next 72 hours.

Same product, same store, just more attention.

That’s when it clicked..traffic isn’t about being perfect, it’s about being seen. My store didn’t suddenly become better. More people just saw it.

Since then, I’ve stopped obsessing over small design tweaks and focused more on volume and distribution. Because honestly, a “mid” store with high traffic will almost always outperform a perfect store that no one visits.

First $1k didn’t come from being smart. It came from finally doing enough.

https://preview.redd.it/pd2llzja2n0h1.png?width=969&format=png&auto=webp&s=0c3017ec30c9e1e28de23b9d0bb8bdcd38afe279

Shopify is now fully showing brands and products on Chatgpt, I was looking at the Agents storefront and realised we go a sale from that.
- Customer came in from Chatgpt, didn't buy
- Came back twice in 2 different days, didn't buy
- Came back a day later and completed a purchase

Chatgpt will surely become a leads channel - so all purchases will still be completed on your store.
Curious how much traffic/attributed sales you are seeing from Chatgpt and what you are doing to improve conversion from that channel

What do you think is the main reason why most ecommerce product images don’t stand out anymore?

Is it:
• poor visual branding?
• lack of emotional connection?
• generic mockups?
• low-quality lifestyle creatives?
• inconsistent product page layout?

And do you think a well-structured product page actually increases conversions, or are product images still the biggest factor in getting sales first?

Curious to hear everyone’s thoughts because lately I’ve noticed that even good products struggle when the visuals and page structure don’t create a strong first impression.

I'm a Shopify app developer and have a plan on releasing some YouTube videos regarding each app topic — but also I'd love to hear from people who've actually done it.

1. Do you get some engagement? For those who've tried it: did YouTube drive meaningful installs, or was it more of a brand/trust play that paid off indirectly?

2. What content did work? What titles were you trying? Curious what flopped too!
   Basically I am thinking of creating an app topic that is being searched by broader community, like for example: How can I create product bundles on my product pages Shopify? (not a bundles app, haha )

3. Tools & workflow? What are you using for scripting, recording, editing, thumbnails?
   Did you create the video then add a voiceover or were you doing it simultaneously and talking on the go?

Today spent around 4 hours creating my first vid and if was the first time doing a YouTube video, so took the time to install the apps for editing, screen recording & etc.

Video length currently is 1:45 minutes and took a lot of time and still the quality is mediocore at best. YouTube is really a great skill to know so would love to hear some tips, experience and knowledge regarding this!

Thanks in advance 🙏

My store does 1k a week but I am stuck as I am not growing. What should I do now? I am running CBO with around 20 ads. Should I increase the ads budget, add more ads or should I start scaling?

Spent the last few years in ecommerce / warehouse ops, and one thing I kept seeing over and over:

A package gets delayed, tracking stops updating, customer reaches out, and now support + ops are both manually digging through carrier updates, Shopify notes, spreadsheets, Slack, etc.

It’s weird because the shipment usually isn’t “lost” yet… it’s just sitting in limbo with no clear signal.

That made me start building a small Python tool after work to flag stalled shipments earlier and give support a faster heads-up before tickets pile up.

Not selling anything—still early and mostly researching.

For anyone in ecommerce ops/support:

What repetitive shipping or post-purchase issue wastes the most time on your team right now?

Disclaimer: this is a genuine research. It is not AI generated.
Disclaimer 2: This is purposefully thorough to cover everything found. There is a 'TL;DR' section for a quick summary at the end.

Out of a 47,420-store dataset, I found that:

• Paid themes are slower than free ones. 
• Only 0.6% of Shopify stores are fast enough by Google's standards.
• 12.5% of stores have a blog. 
• Apparel (clothing) is the most popular niche with 14,679 stores.

These are just a few out of the 40+ findings that you'll see in this post.

This project took me roughly 800h~ to complete. And this is not an exaggeration, I have actually documented it - of course not 800h of active work, but including the time the scraper was working, analyzing data, and so on.

I noticed many people were interested in the 10k stores research I did a few weeks ago, so I figured I'd do a new one including more data this time. 

I have decided to, yet again, focus on the performance side since I find it a critical aspect of ecom, though I do plan to expand these studies and collect more interesting data in the future (like profit per niche, average organic visits, countries, most sold products in a certain niche, etc). 

Why have I focused on performance, plus an important note on optimization scams

Firstly, speed is an overlooked, crucial pillar of ecom. And this comes from authoritative companies like Google, Amazon and Shopify itself. Especially in this day and age of SEO and GEO.

Second, there are endless scams of "speed optimization", especially on Fiverr, offering pseudo optimizations for $50-$100~ bucks. I want to bring attention to those and save people from wasting money. 

They guarantee high scores on tools like PageSpeed Insights (PSI) and GMetrix. But it's a script that manipulates these results, and this is how:

1. A scammer injects a hidden script into your theme, usually in theme.liquid
2. That script constantly checks: "is this PageSpeed Insights visiting me right now?"
3. When PSI visits, the script deletes all your store's code before PSI can measure it
4. PSI now sees a blank, empty page, which loads almost instantly.
5. PSI reports a perfect or near-perfect score
6. Your real visitors still get the original slow store

These scripts are usually loaded from an external server controlled by the scammer, which means they can modify what runs on your website at any moment without touching your theme again. Even after access is revoked.

If it is of public interest, I can make a post explaining this in detail and show examples.

Now, for the time being, let's take a look at some of the data that was found.

Methodology

This was actually a fairly complex project. If you're a dev of any sort, you know that web scraping is not too complex: it takes just a few hours to build something and fetch data. The complexity derives from performance, efficiency and managing thousands of stores.

I have written a separate technical post on how I coded the scraper, managed it and cleaned the data, but long story short: I fetched Shopify stores from publicWWW with 'myshopify.com' in it, and coded algorithms to find and clean everything I needed (themes, apps, etc) and then processed the data using Pandas. 

To find themes, I use Shopify's object "window.Shopify" via Javascript. To find apps it's a manual, more complex process. I need to fetch all <script> tags, check what is being injected and then create a selector for this. 

For example, maybe I can see a <script> from "Hulk Apps" in the store, but if they have 10 different apps, how do I know which is which? More often than not, these are not descriptive names like "app-that-does-x-thing.js", it's more like "axs.js" or whatever. So there is no workaround, it's a manual process. I have manually classified more than 400 apps for this.

Finally some data - baseline numbers

Let's start with the median speed score across all 47,024 scored stores, which is 53 out of 100 on mobile. The mean is 52.3. Very similar to my initial study. Half of all Shopify stores sit below that line.

• 41% of stores score below 50 on mobile
• 7% score below 30 - roughly 3,300 stores in a genuinely broken state
• 0.6% reach 90 or above (Google's "good" threshold) - around 282 stores out of 47,420

In my 10k study, 1.83% of stores reached 90+. At 47k stores the number is 0.6%. The larger and more representative the sample, the worse the picture looks.

Desktop is consistently much faster than mobile. The median desktop score is 71. The median mobile is 53. That is an 18-point gap driven almost entirely by how much JavaScript needs to execute on slower mobile hardware, not by server speed.

Speed metrics: a breakdown of every measurement

Main content load time (LCP)

This is unambiguously the biggest failure across the ecosystem. The median time until main content appears on mobile is 10.1 seconds. For reference, Google's good threshold is 2.5 seconds. The average Shopify store takes four times longer than it should for its main content to appear on a phone screen.

• 95.9% of stores are in the poor range (above 4 seconds)
• 0.3% achieve a good result (at or below 2.5 seconds)

Even the best-performing niche in this study - Media, Software and Digital - posts a median of 8.7 seconds for this metric. Every single niche is failing it, and failing it badly.

Page freeze time (TBT)

This measures how long your page is unresponsive to taps and clicks - it looks loaded, but nothing works. The median is 330ms against a 200ms good threshold. The mean is 616ms - nearly double the median - confirming a heavy tail of severely slow stores.

• 63% of stores have a freeze time above the good threshold
• 30.5% have a freeze time above 600ms
• 5.1% have a freeze time above 2 full seconds

Only about 1 in 3 stores achieves a good result here. This is the metric that most directly explains why pages feel slow even when they look loaded.

First visible content (FCP)

The median time until anything appears on screen for a mobile visitor is 3.4 seconds, against a good threshold of 1.8 seconds. 60.2% of stores are in the poor range (above 3 seconds). The mean is 3.9 seconds.

On desktop, the median is 0.8 seconds - well within the good zone. The 4x gap between mobile and desktop confirms this is a JavaScript problem on mobile hardware, not a server problem.

Time until fully usable (TTI)

The median time until a visitor can reliably interact with anything on a Shopify store on mobile is 18.2 seconds. The mean is 20.2 seconds. Google's good threshold is 3.8 seconds. The average store makes a first-time visitor on a phone wait nearly 20 seconds before any button, link, or add-to-cart action works reliably.

Visual fill speed (Speed Index)

The median time for the page to visually fill in on mobile is 6.6 seconds against a good threshold of 3.4 seconds. The mean is 7.6 seconds.

Layout jump (CLS)

This is the relative bright spot. The median layout shift score on mobile is 0.001 - very low, well inside the 0.1 good threshold. Only 20.3% of stores exceed it. Layout stability is the one metric the Shopify ecosystem has largely figured out.

Interestingly, desktop (mean 0.112) is actually worse than mobile (mean 0.088) for layout shift. Desktop loads more sidebar elements and carousels that shift after rendering.

Server response time (TTFB)

The median server response time is 7ms. Only 0.1% of stores exceed 600ms. Shopify's infrastructure is fast. The performance crisis is entirely client-side: too many scripts, too many apps, too much JavaScript executing after the server responds instantly.

Page weight and requests

The median home page weighs 3,746 KB on mobile. The mean is 5,383 KB. More than two-thirds of stores (67.6%) serve home pages heavier than 3MB - well above the general web recommendation of under 1MB.

The median number of separate network requests fired on a mobile home page is 200. The average is 223. Product pages are actually lighter in size (median 3,462 KB vs 3,746 KB for home pages) but fire more requests on average (251 vs 200), driven by review widgets, upsell scripts, and product-specific tracking pixels.

The fastest stores in this dataset (top 1%, median score 90+) average 132 requests and 2.7 MB. The slowest (bottom 1%, median score around 10) average 314 requests and 8.8 MB. The fastest stores fire fewer than half the requests and serve pages 3x lighter. There is no fast store with a heavy page in this dataset.

Apps and scripts

The app-count curve

The average store has 5.1 apps installed. The median is 4. App count is the single strongest predictor of poor mobile performance in the entire dataset - stronger than page size, stronger than script count, stronger than theme choice.

Apps installed	Median mobile score
0	65
1	62
2	60
3	57
4	55
5	52
6	50
7	48
8	45
9	43
10	42
11	39
15	35

Each additional app costs roughly 2 to 3 score points. Crossing 5 apps pushes the median below 50. Crossing 10 drops it to 38-39 - genuinely broken performance.

Script count

Every app, theme feature, and tracking tool injects JavaScript files called scripts. The average store loads 78.6 scripts per page visit. The median is 69. Most merchants have no idea this number is this high.

• 99.5% of stores load 30+ scripts
• 78.7% load 50+ scripts
• 22.1% load 100+ scripts
• 4.3% load 150+ scripts

Crossing 50 scripts is a clear performance cliff:

• Under 50 scripts: median score 62
• 50 to 99 scripts: median score 50
• 100+ scripts: median score 41
• 150+ scripts: median score 36

Of those roughly 69 median scripts per store, an estimated 15 to 25 come from Shopify's own platform, another 20 to 30 from the theme itself, and the remainder from apps. Even before you install a single app, your store is already loading 40 to 50 scripts.

Individual app impact

The table below shows the median speed score for stores using each app, compared to the overall baseline of 53. These are correlations - stores that install many apps tend to install heavy ones too - but the relative rankings are consistent and meaningful.

App	Score with app	Impact vs baseline	Stores using it
Microsoft Clarity	40	-13	3,855
Hotjar	41	-12	2,343
Google Tag Manager	42	-11	7,322
Microsoft Ads (Bing)	42	-11	2,961
Privy	44	-9	2,902
Klaviyo	45	-8	12,306
PageFly	45	-8	4,108
Segment	45	-8	3,465
Google Analytics (old version)	45	-8	2,532
Yotpo	46	-7	4,338
UpPromote	46	-7	2,858
Booster SEO	46	-7	2,580
Stamped.io	46	-7	2,313
Bold Subscriptions	47	-6	3,667
Form Builder by HulkApps	47	-6	1,974
Avada SEO Suite	47	-6	1,769
Judge.me	48	-5	8,000
Hextom Free Shipping Bar	48	-5	2,551
Nice Bundler	48	-5	1,794
Loox	48	-5	1,702
Facebook Pixel	49	-4	27,832
Hextom Announcement Bar	49	-4	2,630
Google Analytics 4	50	-3	31,653
Instafeed	50	-3	7,467
POWR	50	-3	3,631
Omnisend	50	-3	1,893
Customizery	51	-2	2,226
Mailchimp	53	0	11,028
Printful Product Customizer	54	+1	1,802

Mailchimp shows near-zero impact because it is an email tool that does not inject heavy JavaScript on the storefront. Printful Customizer is the only app in this dataset associated with a net positive - likely because stores using it tend to be smaller and leaner overall.

App categories: the biggest performance penalties

Tracking and analytics tools - the damage compounds with each one added:

• 0 analytics tools: median score 62
• 1 analytics tool: median score 57
• 2 analytics tools: median score 50
• 3+ analytics tools: median score 41

Moving from zero to 3+ analytics tools drops the median score by 21 points. The most common three-tool combination is Google Analytics 4 + Facebook Pixel + one session recorder (Hotjar or Microsoft Clarity). Google Analytics 4 alone is in 66.8% of all stores. Facebook Pixel is in 58.7%. Both are associated with a 10-point score drop compared to stores without them.

Google Tag Manager deserves its own mention. Present in 7,322 stores and associated with a 10-point drop (stores with it: 42, without: 60). Tag Manager loads additional scripts on top of itself - every tracking pixel fired through it adds more JavaScript overhead on top of the Tag Manager script itself.

Live chat apps (LiveChat, Tidio, Gorgias, Re:amaze, Zendesk, Intercom): stores with live chat score a median of 42 vs 54 without - a 12-point gap. Live chat widgets are particularly heavy because they maintain persistent connections, load large JavaScript bundles, and often inject floating frame content on every page.

Buy now pay later apps (Afterpay, Klarna, Sezzle): median 44 with vs 54 without - a 10-point gap across 3,103 stores.

Loyalty apps (Smile.io, LoyaltyLion, Growave): median 44 with vs 54 without - a 10-point gap across 4,598 stores.

Cookie consent / GDPR apps: median 45 with vs 54 without - a 9-point gap. Partly indirect: stores that need a consent app tend to already be running more analytics tools.

Page builder apps (PageFly, GemPages, Shogun): median 45 with vs 54 without - a 9-point gap across 3,926 stores.

jQuery

jQuery is an older JS  library that many themes still bundle by default. Absurdly useful back in the day, but just heavy and unnecessary  nowadays. Stores loading jQuery score a median of 50 vs 56 for stores without it - a 6-point gap. The themes with the highest jQuery usage rates:

• Flex: 97% of stores using it load jQuery
• Mr Parker: 66.7%
• Fashionopolism: 65.2%
• Icon: 64.9%
• Vantage: 63.9%
• Testament: 58.9%
• Blockshop: 56.5%
• Canopy: 56.1%
• Prestige: 42.5%
• Impulse: 41.9%

Themes that moved away from jQuery - Dawn, Craft, Sense, Refresh - consistently score higher. Dawn ships with 0% jQuery usage.

Themes

Most popular themes

The top 10 most used themes in the dataset:

1. Dawn - 4,362 stores (9.2% of all stores)
2. Debut - 2,363 stores
3. Impulse - 1,666 stores
4. Prestige - 1,644 stores
5. Turbo - 1,055 stores
6. Symmetry - 990 stores
7. Empire - 796 stores
8. Supply - 771 stores
9. Minimal - 752 stores
10. Pipeline - 738 stores

Dawn is the most popular theme in every niche except Media, Software and Digital (where Debut edges it out). About 64.6% of all stores run a theme distributed through the official Shopify theme store - free or paid.

Free vs paid: the counterintuitive finding

Free themes (Dawn, Debut, Craft, Sense, Refresh, etc.) have a median mobile score of 60. Paid official Shopify themes have a median of 51. Free themes also load fewer scripts: median 59 scripts vs 74 for paid themes.

The gap holds without exception across every niche:

Niche	Free themes	Paid themes	Gap
Apparel	60	50	10 pts
Health / Beauty	56	46	10 pts
Sporting Goods	60	47	13 pts
Arts, Crafts	61	50	11 pts
Furniture / Home Decor	61	50	11 pts
Business / Industrial	60	50	10 pts
Electronics	60	47	13 pts
Toys / Games	61	48	13 pts
Media / Software	64	54	10 pts
Vehicles / Automotive	60	47	13 pts

This is not because paid themes are worse by design. Merchants who invest in a paid theme tend to also install more apps and enable more built-in features. The theme becomes a proxy for overall store behavior.

Fastest themes (median mobile score, 50+ stores minimum)

1. Spotlight - 70 (197 stores)
2. Ride - 70 (177 stores)
3. Taste - 67 (174 stores)
4. Studio - 67 (280 stores)
5. Craft - 66.5 (466 stores)
6. Crave - 66 (100 stores)
7. Publisher - 66 (54 stores)
8. Simple - 65 (344 stores)
9. Origin - 64 (83 stores)
10. Sense - 63.5 (248 stores)
11. Trade - 62 (196 stores)
12. Atelier - 62 (78 stores)
13. Athens - 62 (68 stores)
14. Refresh - 62 (462 stores)
15. Baseline - 61 (98 stores)
16. Narrative - 61 (280 stores)
17. Debut - 60 (2,361 stores)
18. Boundless - 60 (182 stores)
19. Venture - 60 (719 stores)
20. Pop - 59.5 (106 stores)

Spotlight, Ride, Taste, Studio, Craft, Crave, Publisher, Sense, Refresh, and Origin are all built on Dawn's underlying codebase. Leaner by design, lower baseline script counts, no jQuery.

Slowest themes (median mobile score, 50+ stores minimum)

1. Startup - 39 (63 stores)
2. Testament - 40.5 (314 stores)
3. Empire - 41 (796 stores)
4. Wokiee - 41.5 (112 stores)
5. Providence - 42 (61 stores)
6. Superstore - 42 (111 stores)
7. Icon - 43 (242 stores)
8. Gecko - 43 (59 stores)
9. Retina - 43 (368 stores)
10. Vantage - 43 (156 stores)
11. Fashionopolism - 44 (207 stores)
12. Flex - 45 (397 stores)
13. Palo Alto - 46 (277 stores)
14. Ella - 46 (407 stores)
15. Turbo - 49.75 (1,055 stores)

Empire and Retina are the most concerning by install base. Both are older jQuery-dependent themes with feature-heavy architectures. Turbo, despite the name, consistently scores in the bottom third of the dataset. Flex's 97% jQuery rate goes a long way toward explaining its position.

Notable mentions:

• Prestige: median 55.95, 1,644 stores, avg 84 scripts, avg 6.31 apps - one of the highest average app counts of any major theme.
• Horizon: median 58.25, 314 stores, avg 108 scripts - one of the highest script counts relative to its score.
• Debut: median 60, 2,361 stores, avg 58 scripts - strong performance for a theme this popular.

Does updating your theme version help?

For Dawn specifically: the newest version (v15.4.1) has a median of 63. Older versions cluster between 58 and 60. The difference between the newest and oldest version is about 5 points. Theme version is not a meaningful performance lever. What you install on top of it is.

Most popular niches

Apparel & Accessories is by far the dominant niche in the dataset, accounting for nearly 1 in 3 stores. The top 5 niches alone cover 57.8% of all stores analyzed.

Rank	Niche	Stores	% of dataset
1	Apparel & Accessories	14,679	31.0%
2	Health, Beauty & Personal Care	4,052	8.5%
3	Sporting Goods & Outdoor	3,223	6.8%
4	Food, Beverages & Grocery	2,861	6.0%
5	Arts, Crafts & Hobbies	2,586	5.5%
6	Furniture & Home Decor	2,541	5.4%
7	Business & Industrial	2,011	4.2%
8	Animals & Pet Supplies	1,572	3.3%
9	Home & Garden	1,462	3.1%
10	Vehicles & Automotive	1,381	2.9%
11	Media, Software & Digital	1,096	2.3%
12	Hardware, Tools & Home Improvement	1,081	2.3%
13	Electronics & Tech	1,013	2.1%
14	Gifts & Gifting	745	1.6%
15	Toys & Games	706	1.5%
16	Other	571	1.2%
17	Baby & Toddler	484	1.0%
18	Intimacy & Adult	444	0.9%
19	CBD & Cannabis	290	0.6%
20	Luggage & Travel	240	0.5%

Interestingly, the two niches at opposite ends of the volume spectrum tell an interesting story when crossed with the performance data: Apparel, the most crowded niche by far, sits at a median score of 53 - right at the overall average.

Meanwhile Media, Software & Digital, one of the smallest niches, is the best performing of all at 59. Less competition for attention may mean less pressure to pile on apps.

Performance by niche

Niche	Median mobile score	% of stores below 50	% reaching 90+	Avg apps
Media, Software & Digital	59	27.0%	0.27%	3.41
Other	58	29.6%	0.88%	3.71
CBD & Cannabis	56	35.2%	0.69%	-
Gifts & Gifting	56	35.0%	0.81%	-
Arts, Crafts & Hobbies	56	35.3%	0.73%	3.97
Furniture & Home Decor	54	39.4%	0.43%	-
Toys & Games	54	39.4%	0.00%	-
Business & Industrial	54	39.3%	0.70%	-
Apparel & Accessories	53	41.4%	0.44%	5.26
Vehicles & Automotive	53	43.3%	0.51%	-
Food, Beverages & Grocery	53	42.8%	0.31%	5.32
Hardware / Tools	53	41.4%	0.46%	-
Home & Garden	53	42.8%	0.27%	-
Cameras & Photography	52	43.1%	0.00%	5.26
Animals & Pet Supplies	52	43.7%	0.76%	5.79
Sporting Goods & Outdoor	52	43.4%	0.59%	5.33
Electronics & Tech	52	45.0%	0.69%	-
Intimacy & Adult	51	43.5%	0.45%	5.96
Luggage & Travel	51	45.4%	0.00%	5.55
Baby & Toddler	50	49.8%	0.62%	6.11
Health, Beauty & Personal Care	50	49.0%	0.39%	6.55

The spread between best (59) and worst (50) is only 9 points. No niche is doing well. Every single niche has a main content load time above 8.7 seconds. Across all niches, less than 1% of stores reach a score of 90 - and three niches (Toys, Cameras, Luggage) have zero stores achieving it in this dataset.

Slowest main content load time by niche

• Baby & Toddler: 10.80 seconds
• Food, Beverages & Grocery: 10.60 seconds
• Luggage & Travel: 10.50 seconds
• Apparel & Accessories: 10.40 seconds
• Animals & Pet Supplies: 10.40 seconds
• Best niche - Media / Software: 8.70 seconds (still 6 seconds above Google's good threshold)

Longest page freeze time by niche

• Health, Beauty & Personal Care: 430ms
• Baby & Toddler: 425ms
• Luggage & Travel: 410ms
• Animals & Pet Supplies: 410ms
• Only niche below the good threshold - Media, Software & Digital: 190ms

Heaviest pages by niche (average mobile home page)

• Luggage & Travel: 7,019 KB
• Health, Beauty & Personal Care: 6,290 KB
• Baby & Toddler: 5,746 KB
• Intimacy & Adult: 5,684 KB
• Apparel & Accessories: 5,640 KB
• Lightest - Media, Software & Digital: 4,065 KB

Most scripts loaded by niche (average)

• Intimacy & Adult: 92.3 scripts
• Health, Beauty & Personal Care: 89.0 scripts
• Baby & Toddler: 86.2 scripts
• Animals & Pet Supplies: 86.1 scripts
• Fewest - Media, Software & Digital: 64.8 scripts

Health, Beauty and Personal Care

This niche is the single most underoptimized category in the dataset. 4,052 stores, median score 50, 49% scoring below 50, highest average app count (6.55 apps), second-highest script count (89), second-heaviest pages (6.3 MB), and the worst page freeze time of any niche (430ms). The competitive pressure in this category drives heavy app installations - reviews, quizzes, subscriptions, loyalty, upsells, live chat - and the performance cost is visible in every single metric.

Apparel and Accessories

The largest niche by volume: 14,679 stores, median 53, 41.4% below 50. Even a modest improvement across this category would affect more stores than any other niche in the dataset.

Blogs

12.5% of stores have a blog. Stores with blogs actually score lower (median 47) than stores without (median 54). This is not because blogging hurts performance. Larger, more established merchants who invest in content marketing also tend to install more apps and run heavier themes. The blog is a signal for store maturity and higher overall app density.

Currency and geography

64.9% of stores use USD. The next largest are GBP (7.9%), AUD (6.3%), CAD (5.8%), and EUR (5.0%). Speed score differences by currency are modest at the top of the table - USD, GBP, CAD, and EUR all cluster around 54. The sharpest drops are in emerging markets: India-based stores median 46, Brazil-based stores median 43 - an 11-point gap below the USD median.

Product catalog

The correlation between number of products and speed score is near zero (r = -0.075). A store with 30 products does not perform meaningfully differently from a store with 5. App stack and script count are far stronger predictors. Catalog size is essentially irrelevant to performance at the ranges in this dataset.

Images per product also show near-zero correlation with load times (r = 0.057). Luggage and Travel stores average 9.32 images per product - the highest of any niche - yet their load times are driven far more by script architecture than by image count.

The extremes

The highest-scoring store in the dataset achieved a perfect 100 out of 100. It runs one app: Google Analytics 4.

The lowest-scoring store achieved a 1 out of 100. It runs 14 apps: Klaviyo, Attentive, Yotpo, Zendesk, GA4, Google Tag Manager, Facebook Pixel, TikTok Pixel, Hotjar, Microsoft Clarity, Microsoft Ads, Rebuy, Swym Wishlist Plus, and AWIN. Four separate session recorders and five separate ad tracking pixels, all firing on every single page load.

The 99-point gap between these two stores is driven entirely by what was installed and how it was loaded.

TL;DR

Here is what this data means in simple terms, without any technical jargon:

• The average Shopify store scores 53 out of 100 on Google's PageSpeed Insight speed test on mobile. Google explicitly states that 90+ is considered good, below 89 needs work.
• On desktop, the average score is 71 out of 100 - almost 20 points higher than mobile.
• The homepage and the product page are essentially the same speed (53 home vs 52 product).
• The average store takes 10 seconds for its main content to appear on a phone. It should take under 2.5 seconds.
• After it appears to load, the average store is still frozen and unresponsive for another 330 milliseconds. For 30% of stores, that freeze lasts over half a second.
• The average home page weighs 3.7 MB and fires 200 separate requests every time someone visits.
• The average store loads 78 scripts per page, from apps, themes, and tracking tools combined.
• Only 0.6% of stores - around 1 in 167 - pass Google's speed threshold of 90+.
• 41% of stores score below 50 on mobile. 7% score below 30.
• Every app you add costs roughly 2 to 3 score points. At 5 apps, the average store is already below 50. At 10 apps, it is at 42.
• Free themes score a median of 60. Paid themes score a median of 51.
• Shopify's servers are fast - median response time is 7 milliseconds. The problem is everything loaded after that.

Now that was one long read! Thanks for your time, I hope it was useful. As you can see, the data points in the same direction as the 10k study. It's just sharper and across a more representative sample.

The bottleneck is not Shopify's infrastructure. It is everything stacked on top of it - and the compounding effect of each app, script, and tracking pixel added to the store. 

Happy to answer questions about the methodology or data in the comments.

I’ve noticed a lot of Shopify advice focuses on the same things over and over like ads, redesigns, or chasing viral products, but I’m curious about the smaller changes that actually moved the needle for people.

Was it:

• improving product pages?
• better SEO?
• adding blogs/content?
• fixing page speed?
• email flows?
• removing apps?
• something unexpected?

Would love to hear real examples from store owners because sometimes the biggest wins seem to come from things nobody talks about much.

march 2024. revenue had declined for three consecutive months. CAC was at $94. our average order value was $71. the math was producing a contribution that our accountant described as "a situation requiring attention."

had a serious conversation about whether to continue. the brand was eighteen months old. we had learned a lot. the market was real. the execution had gaps that were becoming expensive.

someone pulled a number we had not looked at in the discussion: cohort retention rate for customers who had been with us more than six months.

customers who had purchased four or more times: 67 percent retention rate at twelve months. meaning 67 percent of customers who had bought four times were still buying a year later.

the business had a customer quality problem disguised as a customer acquisition problem. we were acquiring customers at $94 CAC who had a 19 percent twelve month retention rate. within that same business was a segment of customers with 67 percent retention who were each worth $312 in annual revenue.

we weren't failing. we were acquiring the wrong customers through the wrong channels and measuring the wrong metrics.

spent the next quarter reducing paid social, increasing content and referral investment, and improving the post purchase experience for new customers.

month six after the near-shutdown conversation: CAC $52. repeat purchase rate 31 percent. monthly revenue $67,000 versus $28,000 at the low point.

the number that saved the business was not a revenue number. it was a retention cohort that we had never thought to look at until we were desperate enough to look at everything.

I never paid much attention to wishlist features before because I assumed most customers either buy or leave.

But after looking at store behavior more closely, I realized a lot of visitors are interested in products, they’re just not ready to purchase immediately.

Recently tested a wishlist club on a Shopify store and it was interesting seeing people come back later instead of disappearing completely.

It also seemed to help with:

• returning visitors
• back in stock interest
• repeat purchases
• products people keep checking multiple times

Curious if other merchants here are seeing the same thing or if wishlists are still underrated for Shopify stores?

At one point, I noticed refunds increasing around 12% of my orders.

That’s a huge hit.

The issue wasn’t the product quality. It was expectations.

My marketing made the product seem slightly better than it actually was.

So customers felt disappointed.

I adjusted my messaging..more realistic descriptions, clearer use cases.

Refund rate dropped to around 7% within a few weeks.

Revenue became more stable.

It taught me that overpromising might boost short-term sales, but it hurts long-term growth.

Trust is more valuable than quick wins.

Instead of just explaining why the symptoms happen, it makes dog owners feel the symptoms.

That emotional part matters a lot.

When an ad makes people feel guilt, fear, or urgency, they are more likely to stop scrolling and take action.

It’s not just selling the product. It’s making the problem feel real.

Getting to my first $500 felt like dragging a rock uphill. It took nearly a month of constant testing, random ideas, and a lot of second-guessing.

I kept thinking I needed a better product, a better website, better everything.

Then something changed.

One of my TikTok videos started performing slightly better than usual..not viral, just consistent. It drove about $120 in a day. Instead of moving on to the next idea, I doubled down. Same angle, same format, just repeated.

Within 9 days, I went from $500 total revenue to crossing $5,000.

Nothing revolutionary changed. I didn’t redesign my store or launch new products. I just stopped chasing new things and started scaling what was already working.

That’s when I realized that the hardest part isn’t making money, it’s finding something that works even a little. Once you have that, growth becomes a lot more predictable.

Most people quit right before that point. I almost did too.

Curious if anyone has used a tool like Tagboxx, Foursixty, Bazaarvoice etc..

Hoping to get some legitimate feedback redditors

Been going through a bunch of Shopify stores recently and noticed something weird:

Almost every store has bundles…
but most of them feel like:

• random product combos
• discount-first (10–20% off just to push it)
• buried somewhere customers barely see

But the few that actually work feel very different:

→ “starter kits”
→ “complete setup”
→ “add-ons that just make sense”

Not really about discounts — more about removing decision fatigue.

I saw one store mention ~15–20% of orders coming from bundles after keeping it simple, which makes me think the upside is real — just not executed well.

Curious from people actually running stores:

👉 What kind of bundles have actually worked for you?
👉 Where do you place them (PDP / cart / homepage)?
👉 Do customers respond better to discounts or “convenience bundles”?

Feels like most stores are leaving easy AOV on the table here.

How much are you all paying for ad creative? And how much are you spending on ads + agency to manage?