You Ask, I Answer: Company-Level Amazon Ecommerce Datasets?

In today's episode, Steve asks, I'm looking for a data set of companies that are actively selling on Amazon. How would you, as a data scientist, approach this problem? Hmm. Well, um, that's an interesting question. To my knowledge, um, I don't know that there are any publicly available free data sets of this source that uh would do this sort of thing.

You'll probably end up building your own. Um by the way, if anyone knows of uh if you if you know of a comp a data set that is publicly available and free, or even if it's not and you it's available and it just costs money, uh, leave a link in the comments below if you would. Um for something like this, you're gonna have to do a bit of legwork. Uh you've got to create your own. And what you'll have to do is first look at if you know, if you have a known subset of companies that you know for sure are selling on Amazon, uh, then go to their websites and look for indicators uh that would help you classify those companies as Amazon sellers and then uh build a second data set of companies that you know are not that are not selling on Amazon.

And what you're gonna do is you're looking for some specific characteristics to try and identify uh something that in an automated fashion that indicates that yes, this company is an Amazon seller. Uh, there are really good tools um built with is one uh HubSpot, actually, HubSpot's uh free CRM is another that can analyze the most common technologies being used by uh a company's website and provide that information to you. In fact, let's let's bring this up here. So, this is what you see. This is inside of HubSpot.

Uh, this is a company, it's based in Los Angeles. You can see it has the time zone there. Um, and then it has a bot set at the bottom called Web Technologies, and you can see for this particular company on their website, they've got Microsoft Exchange for the email, YouTube, Google Tag Manager, Facebook Advertiser, Pixel, Office 365, oh, Adobe Analytics, Adobe DTM, we capture Google Analytics Ad Role and Outlook. So, this list of technologies here for this particular company. Now, this is not an Amazon resale, this is just some company I picked out of uh the pile randomly.

This company has this set of particular technologies, and these are good indicators of you know what their Martech stack looks like. So from a uh an analysis perspective, you're gonna want to create a data set, you know, 50 or 100 uh known Amazon sellers, and 50 or 100 known non-Amazon sellers, and you're gonna want to extract this data from HubSpot uh or from built with uh either either company's uh data is fine, and put it together in some sort of uh spreadsheet, or if you want to get more sophisticated and use some of the the more fancy tools like Python or R, you can certainly do that. But ultimately, what you want to do is you want to build a profile of what are the common technologies in use by an Amazon seller, what are the common technologies of a uh in use by non Amazon sellers, and what's the difference? Is there a particular technology that predicts pretty well, or a combination of technologies that predict pretty well that a company is an Amazon seller? Are there's certain things that are just dead giveaways.

Like that's what this this company does, or that's what this company has. Um for example, Amazon has tracking tags, right? There's tons of tracking tags uh that they offer for like affiliates. Are those are those the ones? Uh is that is that a good indicator, or are those tags so prevalent that it it's a it's a misleading signal.

You won't know until you do the data analysis, but once you have that, then you'll have the um the key essentially to being able to identify a list of companies, and then from there you load those companies into you know built with or HubSpot or whatever, uh just willy-nilly. And as you can see, one of the things that uh these tools will also do is give you a general sense, mostly for publicly traded companies, of what their annual revenue is, how many employees they have, etc. And that will uh really help identify and separate out these different types of companies. It is going to be a lot of work, it is a lot a lot of work, uh, and it's very manual work because you have to hunt down those companies on Amazon and then equally pull together a list of other uh e-commerce companies that are not on Amazon. But that training data set, and you're gonna want a good sample, you're gonna want you know 50 or 100 companies in either category, that will give you a robust enough data set to see the patterns in it, to see there's a certain you know, certain things that almost everybody on Amazon always uses on their websites.

There may not be a pattern. That is a risk with a project like this. There may not be a pattern, but then you know that you know that that is no longer something you can rely on, and you'll have to source the data some other way. That knowledge alone has value. That knowledge alone, even if there's n not a there there, that knowledge alone will tell you okay, we know that uh these web technologies or company size or number of employees or year they were founded or uh you know publicly traded or not are good or bad indicators of whether a company sells on Amazon or not as an e-commerce company.

Pull the data together, your best bet is going to store be to store it in a spreadsheet initially. And ideally, well what comes out of HubSpot is at least I know at least for the HubSpot API, is all the technologies come out in one big text string, and one of the things you have to do is you have to separate that out into different columns, which is not a lot of fun, but you it it is doable. Um then what I would suggest doing is turning though each of those into flags. So, for example, Google Analytics is uh one for yes, zero for no, and then you have essentially a spreadsheet with fifty or a hundred columns on it, and then for each company, you would have uh in a field indicator like Amazon seller yes no or one zero, and then you know Google Analytics one zero, Microsoft Exchange one zero, uh YouTube one or zero. That data format will let you do the analysis very quickly because you can start to add up and count the numbers of you know ones and zeros in each of the columns, and that will give you a much better, more robust analysis.

As I said, it's gonna take some time, but if you approach it with this methodology of the the 50 to 100 and the things they have in common, and then the 50 to 100 that are not in your target audience and the things they have in common, and looking for the intersections between the two, you will get an answer of some kind. If you don't get that answer, then you also know that there's a pretty good chance anyone selling a list, you would have to at least grill them really well. Okay, how did you get this information? What criteria? How did you scrape the information?

And if uh if they they say, well, you know, we looked at like their web technologies and you've already done your own analysis. You can say, Mmm, I did that too. I didn't find anything that was statistically relevant. And if they give you an answer, like, well, it's a proprietary blend of our own technologies and stuff, it's like no. Um but in talking to the people who are are providing these lists as vendors, uh, doing your own work first gives you much more depth to the questions you can ask them to qualify them as a vendor to say, yes, that sounds like something I hadn't tried, you might be on to something.

Or, you know, I did that and I I didn't see what you're seeing, so I'm not sure how reliable your data is. That way you can avoid spending a whole lot of money before uh without having any results to show for it. Uh if you have follow up questions about this, leave them in the comments box below. This is a a challenging data science question. That's not really a data science question, it's a data analysis question, although having the control and having the the experiment group does uh start to lean it towards a a scientific question.

It's an exploratory data analysis problem first. Is there even a there there before you can form a hypothesis? Um and that's what this information would would help you start to lean towards in terms of the data that you would need and things like that. Um again, questions leave them in the comments box below. Subscribe to the YouTube channel and the newsletter.

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