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Made with 💖 for founders and marketeers by Louis Vieira.

I made this personal database while reverse-engineering the paid growth methods top Shopify stores were using to scale and exponentiate their sales.

After scraping all this data, I realized this database was just too interesting to leave for myself - so I released it for free.

As for the paid growth methods, I launched the best one of them as a PDF guide - where I explain the most profitable video targeting strategy that these stores are using today to sell millions, using Social Media ads.

If you feel this profitable strategy can benefit your business, please visit this download area ⬅️

Now, down below you'll find exactly how I scraped/calculated all the data to build this database. Enjoy 🙂

This project all begun when I realized Shopify hosts all stores hosted under the same IP: 23.227.38.32

This made it very possible to find the top stores - I just went to Myip.ms and searched for Shopify's IP under “Whois Lookup”.

Now, this returned over 150,000 websites - so I took the top 500 most visited websites (by Myips.ms calculations), ordered them by Alexa Rank, and finally, listed the top 100 of those for my database.

To bulk check all the 500 Alexa Ranks I used this tool: https://www.bulkseotools.com/bulk-check-alexa-ranking.php

This rank, in my opinion, is also the most precise of all traffic ranks that are available online - it uses a combination of average daily visitors and pageviews over the past month to rank websites.

To add up, the reliability of this rank for this particular website list is stronger than average - given that it is mostly composed by some of the biggest websites in the world, there's a really low chance of incomplete data.

Easy peasy first one - it consisted of basically taking off the "https://" prefix, and in most cases the ".com" suffix.

This one had to be mostly human reviewed, so I visited all the websites to label them - also, I used Amazon's main product category names to help label the niches.

Thanks Jeff!

I used an online tool which gave traffic figures that were the closest to the proven data that I could find about some of the top websites: https://www.seoreviewtools.com/website-traffic-checker/

This data point also gave the database a second decisive rank, one I could count to make sure the Alexa Rank wasn't being gamed, or just accidentally miscalculated.

This way, "Alexa Rank" was used to cut off the top 100 websites, and then "Monthly Traffic" was used to rank them on the default database view.

Every Shopify store has a URL enabled feature that allows users to find the current most sold products in the store.

All that needs to be done is adding this URL bit to any Shopify store link: "/collections/all?sort_by=best-selling"

For example: https://thepihut.com/collections/all?sort_by=best-selling

The first product shown there will correspond to the most sold product in the large majority of stores.

However, in some stores, I had to pick between men's best selling or women's best selling - in these cases, the best selling product will come from the gender the brand most communicates to.

Also, in stores that have free shipping as the best selling product, I picked the next-selling product that isn't user-experience/satisfaction based.

Manual scraping of the 100 URL's I got from the method above.

This one was a projection metric, calculated with believable figures, but consider these results to be in low-end of true revenue figures.

Assuming these top e-commerce stores maintain the worldwide average conversion rate of 2.86% (Source), I first multiplied the Monthly Traffic Estimates with the Price of Best Sold Product from each store - which I then multiplied those results with 0.0286.

The end result would be the Estimated Monthly Revenue in a dollar figure ($).

To put it in a visual formula: (Monthly Traffic Estimates x Price Of Best Sold Product) x 0.0286 = Estimated Monthly Revenue ($)

Now, as you've seen, the way this category is calculated cannot account for: unknown Average Order Values, paid marketing campaigns to sell higher-value products (essentially increasing revenues by improving and stimulating conversion rates), and the brand loyalty factor that brings return customers for 0$ and higher average order values.

Also, in the rare cases where there was a big price discrepancy between Best Sold vs Overall Most Sold Products, I averaged out all the prices from Overall Most Sold Products.

I explained this above, but I'll paste it here again:

Every Shopify store has a URL enabled feature that allows users to find the current most sold products in the store.

In most cases, all that had to be done is adding this URL suffix to any Shopify store homepage link: "/collections/all?sort_by=best-selling"

For example: https://thepihut.com/collections/all?sort_by=best-selling

So, for this specific category, I added this URL suffix to all of the 100 homepage URL's - although in some cases I did had to manually search for this feature inside some stores sitemaps.

Did a quick hack in this one.

First, I removed the "https://" prefix in the URL list, while also removing the domain extension suffix on all the URL's.

This resulted in a list of 100 raw names of the stores, without any spacing.

I then added the "https://instagram.com/" prefix, which got 80+ exact profile matches of the URL list.

The last 20 had to be human-searched (and by human-searched, I mean me-searched).

Also, in those 20, there were a lot of personal profiles with exact brand usernames - which makes me wonder if any of those 20 million-dollar stores ever tried to bid their brand's Instagram username to random (mostly foreign) users from around the world.

Simple scraping of the 100 URL's after I scrubbed the list and made sure every username was real and belonged to the stores.

The same exact method of the Instagram Profile.

Got 80+ exact username matches from the stores on Facebook - but had to manually search and fix the remaining 20 stores.

Again, the same as I did for the Instagram Followers data.

Simple scraping of the 100 URL's after I scrubbed the list plus making sure every username was real and belonged to the stores.

This metric counts the number of times any base URL has been shared or commented on Facebook.

It helps to give an idea of where the brand strengths are - some are very viral by nature.

For this category, I used this tool: https://www.sharedcount.com/

It allowed for only 10 URL's at the time - however, the end result keeps adding up, so a fast delete of the already counted URL's made this a quick and easy task.

For this, I found a really cool tool online that allowed me to bulk test 100 URL's at the same time, using Google Page Insights API to get reliable results - I present to you: https://batchspeed.com/

Now, I used First Meaningful Paint as values for speed, because those were the closest ones to reality.

Google was very mean to some websites, saying they took 20 seconds to fully load - which would totally tank sales if it was true.

I found that in 90% of the cases, First Meaningful Paint was the true speed result.

I also used Homepage URL's as the overall speed indicator for the website, so keep that in mind when you're seeing this data point.

Exact same method as the one above.

Also used First Meaningful Paint, and also used Homepage URL's to test for speed.

This one I also had to think indirectly, because there was no bulk tool that could help me check all 100 URL's super fast.

So, I used Facebook's official Pixel helper tool: https://chrome.google.com/webstore/detail/facebook-pixel-helper/fdgfkebogiimcoedlicjlajpkdmockpc?hl=en

I then started bulk opening the website list, to check which ones weren't using Facebook ads.

For this, I took this general Facebook Ads library URL: "https://www.facebook.com/ads/library/?activestatus=all&adtype=all&country=ALL&impressionsearchfield=hasimpressionslifetime&viewallpage_id=", and added a suffix with the Facebook Page ID of each store page.

I found those same page ID's by using a bulk tool: https://seotoolstation.com/bulk-facebook-id-finder

Given we are discussing the top 100 websites, at the top e-commerce hosting platform - the question wouldn't really be to find out what websites were running Google ads, the question would really be to find out those that weren't.

So, I had to use some indirect thinking in this one.

There were no bulk tools that I could use to quickly check this, plus, Google's Tag Assistant chrome extension was just not running.

So I used Ghostery, a Chrome extension that is used to block ads and trackers, which also provides a list of every single tracking/advertising platform used by any website.

This was perfect because I needed to know which websites were remarketing to me using Google Ads.

So, I just bulk opened sites, and hand-checked all the 100 URL's.

It was a lot faster than I expected because all I had to find out was which one's weren't using any of Google's Advertising platforms.

The quick on-screen UI of Ghostery also helped for this task to be completed in (I think) less than 10 minutes.

So, here, have a backlink Ghostery: https://www.ghostery.com/

I explained this method in the intro, but basically, I used: https://www.bulkseotools.com/bulk-check-alexa-ranking.php

It allowed me to bulk check the Alexa Rank of over 500 URL's, at once.

For this one, I used: https://www.bulkseotools.com/bulk-check-domain-age.php

This tool allowed me to see when all of the 100 URL's were first registered, which also matched the opening of their e-commerce store in +95% of the cases in this database.

This one was tricky because a lot of it had to be human-reviewed.

Yes, some websites used the domain extension for their countries (and thanks a lot for that, I must say), but others just used ".com" because that's the standard on the web and by extension, on e-commerce.

Some had non-English names, which made it easier to spot them. I also marked a few URL's while I was doing the main niche labeling.

I also went full Sherlock in the niche labeling process and uncovered a couple of foreign drop-shipping websites that were hidden in plain sight.

This involved checking Whois records, using forbidden black magic arts, and doing some advanced googling.
So, nothing major.