Let’s start with the hard truth. Assuming that users navigate through our website with the paths that we have designed for them is wishful thinking. In reality, our users do not use websites linearly. They open many tabs, they invent their own ways of using the solutions we propose to them and trying to impose on them a specific way to use our website it simply pointless.
The most important realization is that we should optimize customer paths based on the patterns of behavior that characterize them. Thanks to this, in addition to increasing sales, we will find ourselves on the right track to building a strong relationship with users, based on showing – so-called – digital empathy towards them. We provide understanding and support in those places that require it, in opposition to bombarding them with functionalities and options only because the competition offers something similar or the agency recommends this approach.
Where to start? Preferably from the beginning, i.e. from setting up our Waterfalls so that we can begin to analyze our customers’ behavior patterns on event-based data. How, you ask? This article will guide you through this process almost step by step.
First things first – determine events
Use the right mouse click to choose the “Inspect element” function. Then in the console that appears at the bottom of the screen, you will see the HTML code appropriate for this element.
What you need to find is the class or parameter ID of the item and copy the XPath – the path to this place. After pasting into the Waterfalls configurator, the application itself will prompt the element we want.
By defining as many events and steps as you need, you will create your own Waterfall.
Now you can observe where exactly are the drops within the sales path. You can finally see the numbers, but what you still need to be completely happy is the answer to the question: why do people drop off? To try to find out, just use the “View” option, and in the next view, you will get a list of visits in which users have not moved on to the next step.
All roads lead to… the basket
Remember that the sales path for your product is not necessarily the one you designed. If you want to sell more, find out how your customers want to buy.
Is there only one path leading to the destination? Highly unlikely. That is why it is best to check the entire context first and then take a closer look at the places where we see “drops”. Test different scenarios, don’t stop at just one. Your task is to narrow down the analysis to one goal and study individual places/ events in the project in detail.
Let’s see an example of a conversion scheme analysis.
/ main → / category → / product → / basket → / thank you for your purchase
We’re exploring the basic “classic” path. Disclaimer: this example is for the purpose of orientation and should not be the basis for drawing conclusions and recommending specific changes.
If you know that over 90% of your traffic comes from campaigns or marketplaces, you can skip this step and start from step 2.
/ product → adding to the cart → / basket → / thank you for your purchase
The path starts with the product, you check how customers arriving via the campaign or returning to specific products are consuming your website. You check how clients use the basket in your store – compare information on how many people add products to the basket, and compare it with information on how many actually make the purchase.
Most of the e-commerce customers come to us with a request to analyze why they have so many “abandoned” baskets. They focus on the analysis of the basket page and the finalization of the transaction because this stage is most important to them. In many cases, however, we are quickly able to verify whether we are dealing with abandoned baskets at all, or whether customers have never had the intention to buy the product in the first place, and use baskets only as a clipboard. Often, customers leave selected products for later (sometimes even several sessions later), choosing those that they want to compare with other products available online.
/ basket → placing an order → / thank you for a purchase
For the “placing an order” step, we want to have a look at the order form. If we have a simple, one-step form, we can check the “focus in” event for each field at this stage. If the form consists of many sections, you should first examine only the first fields in each of them to find out which section is the biggest problem and then go into a more specific analysis.
If you notice a significant drop in the form, you still need to deepen the analysis to determine events such as “Focus in”, “Change”, “Focus out”. If users do not fill in one of the fields, they most likely have objections related to privacy and data security. If in one of the fields we note an attempt to complete (“Change”), but the user hasn’t moved to the next step, this may mean that we have problems with validation or formatting. In that case you need to look at the form from its technical side.
Last, but not least.
To get the most important things to remember, below you will find a list of the most common mistakes made when analyzing customer behavior and some practical tips that will be useful during the event-based analysis.
The most common mistakes:
- Viewing recordings of individual sessions (not visits) and making conclusions of customer paths and behavior based on those individual sessions
- Assuming that users use the internet in a linear way – that they go through individual URLs without opening many tabs, without comparing products
- Concluding only on the basis of numbers, without an understanding of why this is happening
- Implementing solutions that have worked well for others – instead of observing your own users
- Analyzing only the last stage of the process – without an understanding of the drops along the funnel path.
- Assuming that the problem is always at the end of the funnel (e.g. abandoned baskets)
If you have any questions, doubts, would like to implement analytics or solve a problem on your website (or maybe you just want to say “hi” 😉) – drop me a line at firstname.lastname@example.org. We will be more than happy to hear from you ❤️
Categorised in: Analytics