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GA Trick: Get data from the Upside Down of e-Commerce!

Thanks to the enhanced e-commerce functionalities, Google Analytics has become of the most powerful platforms that can help you analyze user behaviour across e-commerce websites and obtain a high-level detail of the demand of products and services.

 

However, if we are talking about websites that act as intermediaries, for every sale that is made through our website we can also think of it as the upside down of other type of users...the sellers.

 

stranger-things-lights.jpg

 

Nowadays, Google Analytics doesn't count with a built-in feature that lets us measure how sellers are publishing items in our website, but we all know that, in order to get transactions going, supply is as important as demand. This is why in Digodat we found a nice workaround that can let us get more control of which items are getting published by users, opening the posibilities for a set of really powerful insights.

 

 

 

Why you should be measuring what items sellers are publishing

We all know that an e-commerce website's growth is deeply linked to the sellers's growth, both in a quantitative and a qualitative way. This is why this companies dedicate a large amount of money and efforts in trying to get new sellers and, once they got them, in getting them to brake the inertia and publish their first products. Despite this part of operations is usually measured only in amount of sellers and amount of published items, we can be sure that a user that puts some few candies on sale doesn't provide the same value as another one that publishes computers or 3D printers for wholesale. In this article, we will unveil the secret of how to measure what items are being published by taking advantage of the full potential of the Google Analytics enhanced e-commerce plug-in...using it backwards!

 

In this article, we'll learn:

  • How to measure the seller's item publishing funnel. Do they get stuck anywhere?
  • How to measure which items are being published, adding details like category, name, price, stock and whatever we need to add. What are they publishing? How much "potential value" did our "Attract sellers" campaign generate?

 

OK...Let's go!

 

Step 1: Create a new property (without deleting the one you usually use!)

The main idea of this article is to use the Google Anaytics e-commerce plug-in, but it is originally meant for measuring transactions (not item publishing). This explains why Google Analytics platform only lets us install one instance at a property level. This means that if you are using your actual property to measure sales transactions (and you should), you won't be able to also use it to track published items. Because of this, we need to create a new property that we can call "Sellers Tracking", where the only difference with the original property we had will be the implementation of this new funnel. We will obtain a new GA ID which we have to add in our website (this is very easy to do if you use Google Tag Manager, because all you have to do is to duplicate the current GA tags you have and replace the old ID for the new one in the duplicated tags). For more information on how to install multiple GA codes, you can visit this link.

 

Step 2: Set-up the enhanced e-commerce code

This code's syntax will remain quite similar of how you would use it to describe your product's sales in most of the aspects: name, category, price, etc. Thus, there are some little details you have to be aware of: 

 

 

Little detail Nº 1: check-out funnel names 

This is an example of how I would modify the names of the funnel so I can get a description of how users behave in the different item publication steps: 

 

 

Reverse E-commerce in Google Analytics

 

In the previous caption, I took an example of an item publishing process that is divided in four steps: in the first one, the seller writes the product name, description and category. The second one is where he uploads pictures defines stock and price. The third step is about defining payment and shipping options. Finally, the fourth one is the "Yeah! your item has been published!" confirmation page.

 

 

Throughout this intuitive funnel, we can define segments afterwards in order to find out causes of losses in one of the steps. Per example, we could discover a mass drop-out in step 2 along Firefox users and get started with tests around some possible coding error.

 

One more thing: if your website has product publication unified in only one page, you can also apply this funnel by creating internal triggers within that same page.

 

 

Little detail Nº2: the stock

Perhaps, it would sound natural for us to replace the "quantity" field with the "stock" field a seller defines in its item, but I suggest you pay special attention to this because there may be some exceptions.

 

Some exception examples:

- If the website sells custom-made products, the "stock" field might not apply.

- If there are some users that import products, they might put a very high "stock" number that can distort all your statistics.

 

Because of this, I recommend you define a manual limit to the "quantity" field (not within your website inferface, but in the hit you send to Google Analytics) of 10 unities. This way, we will be able to differentiate one-item-in-stock sellers from many-items-in-stock ones.

 

Step 3: Optimize!

Now that we have the "transaction amount", that equals the monetary potential amount of published products, we can start using this for improving the performance of our marketing campaigns that are focused on attracting new sellers and product publishing generation. We can even import this transactions into our AdWords account and use ROI and ROAS bid types!

 

 

Final tip: the User ID

Applying the User ID feature to this seller property can generate many benefits! We could measure attribution in a very complete way and develop actions for getting sellers that may have gotten stuck in some point of the item publishing funnel to move on.

 

I hope you enjoy this article and get to work in your e-commerce clients!

 

about Alan Daitch

Founder of Digodat Analytics. Google Regional Trainer. Consultant and speaker. alan@digodat.com Skype: alan.daitch