How do you know which URLs are closest to conversion?

[Oscarne]

I want to know the URLs that are close to generate conversion for 3 specific products, how can I know in Adobe Analytics?

[Jennifer_Dungan · Accepted solution]

Hi, I want to make sure I understand the question, so I am going to ask a few things.

Now, when you say "Conversion" I am going to assume you mean an Order.

So in your website, I am going to assume you have multiple ways to "add to cart", and you are trying to figure out what pages those "add to carts" happened from? That is at least what I believe you are asking, but maybe I am wrong....

But if it is, this could be different for each of the items you are purchasing... And while you could try to do some sort of participation model, I think this would be really hard to get clarity and specifics...

However, you may want to use a Merchandising eVar on your "Cart Addition" action (giving it a long running expiry, especially if people have a propensity to Add to Cart in one Visit, and Checkout in another Visit).

So let's use a simple example:

When I add Product A to my cart from a search page:

s.products = ";product a;;;;eVar1=search";

s.events = "scAdd";

And when I add Product B to my cart from the product listing page:

s.products = ";product b;;;;eVar1=product listing";

s.events = "scAdd";

Now, later when I make my purchase:

s.products = ";product a;1;9.99,;product b;1;2.49";

s.events = "purchase";

(You will notice that I am not populating eVar1 here, instead, I am relying on Adobe's attribution for this user to maintain the values that were set during the Add to Cart step.)

Now, let's say a different user added Product A from "Recommended Content" then made a purchase:

s.products = ";product a;;;;eVar1=recommended";

s.events = "scAdd";

Then:

s.products = ";product a;1;9.99";

s.events = "purchase";

				Cart Additions	Orders
Products				3	3
	product a			2	2
		eVar1
			search	1	1
			recommended	1	1
	product b			1	1
		eVar1
			product listing	1	1

The eVar1 value would propagate to the Order event (so long as the order was made within the expiry of eVar1 - which is why you might want to maintain the value longer than Visit based on user behaviours. You may event want to create a custom event that is triggered when a purchase is made or when a product is removed from the cart... and use that as the eVar expiry...  you wouldn't so much use this event for reporting, but solely as a the "Expire After" trigger for eVar1.

If this isn't what you are asking, maybe you are trying to do a "path to conversion"? You might then instead try using a time decay custom attribution model that will scale the order attribution stronger for more recently viewed pages over pages farther back in the history... 

[Oscarne]

Hello Jennifer

I hope you are very well.

What I am trying to find out is at what point the users abandoned the shopping cart or came close to adding the product to the shopping cart.
For example: the user came to the home, then visited the refrigerators category, from there went to the televisions category and finally came to smartphones, added the smartphone to the shopping cart but did not finalize the purchase. I need this information only for organic traffic.
Why do I need this information? What you want to know is which URLs were close to generating a conversion (purchase) in order to add call to action to those URLs so that the user completes the purchase.
I hope my explanation was clear, and I thank you for your help. Thanks

[Jennifer_Dungan]

The problem with this type of granularity, is that every user is going to go through a completely different set of pages in their path to purchasing, or not purchasing...

Adobe is better for looking at general trends than in trying to get granular detail to each individual user's path.... 

I work in Newspapers, we have something similar called "Path to Subscription", looking at the last 5 articles read prior to a user subscribing... but we don't pull this report directly out of Adobe... we use Raw Data exports from Adobe to our Data Lake, and use advanced SQL to pull out the data and aggregate it.

Now, if you are good with a simplified model of where users are falling out, you could create a fallout for:

Home Page > Category Page (Any) > Add to Cart > Start Checkout > Order

This will show you the users who followed this path (could have repeated categories or adds to cart, etc) and where they fell out of this path.

Note however, that this is specifically this order, and if a user did NOT start or even visit the home page, they won't be included in this report.

So you might want to have several fallouts that are more general:

Category Page (Any) > Add to Cart > Start Checkout > Order

and a simple:

Add to Cart > Start Checkout > Order

But again, these will simply show you basic aggregate fallouts at certain parts... however, you can build segments based on steps and see if you can use a segment something like 

(Step C segment) and exclude (Step D segment)

and break that down by Page URL... I've never tried this, it may not work at all...

[rgaur]

Hey @Oscarne 

How about trying out Flow visualization to achieve your use case?

Try to make a new flow visualization with 'Ends with' as cart additions metric and choose the pathing dimension to be 'Page URL'

This will give you your top page URLs where users were present before the 'cart add' event was triggered. Additionally, you have the option to extend preceding pathways to capture the complete visitor journey, allowing for the analysis of popular visitor journeys that lead to cart addition event on your site. 

In case you wish to see fallouts that happened just before cart addition event, make use of fallout reports to analyze top pages against which users fell-out of your site before qualifying for cart add event.

Also don't forget to filter out the visualization by adding a segment to filter out the organic traffic on the panel!

[Oscarne]

Hello rgaur

I want to thank you for your answer, it was very helpful.
I would like to ask you if you know how I can know which are the keywords that generate more revenue.