Measuring the conversion rate of a specific page or the value it contributes to an overall conversion rate is both an art and a science. This article examines several scenarios ranging from a single page website to a complex website with multiple templates where conversions are not as likely to take place on the first visit.
Simple Landing Page and Single Page Website
Let start out simple and imagine that we have a single page website. It’s obviously very easy to calculate the conversion rate as the number of conversions per number of visitors. If visitors don’t convert its unlikely they will ever come back.
An E-Commerce Website with Multiple Landing Pages
In this case let us think of an actual e-commerce website which might consist of several templates including; homepage, product pages, category pages and perhaps a blog. Conversions happen when a user visits a product page and adds an item to cart. Some of the templates are non-converters, but it can be said they assist in the conversion as they draw in traffic and are part of the conversion path or funnel.
Most the time we see the “Funnel” concept sold to us as a process of discovery, need identification and so on leading to a conversion. The real question is how to make a funnel that effectively measures performance and can help you optimize a website. If want to optimize these non-converting templates we need to group them and put them into our funnel analysis.
We could group all of our content templates together or if we wanted to optimize at a more granular level we could break out the different templates and look at these individually. Google just started allowing this approach in the Shopping Behavior Analysis Report which you need to enable the enhanced e-commerce features to use.
Another way to look at this is by a Visitor Path analysis but it’s something I don’t find that helpful except to figure out where specific problems might exist in the funnels. The typical visitor path might only account for only a small fraction of possible paths so even if you optimize paths individually you won’t get the data you need to know if you are successful and it’s better to take a more holistic approach. However it is worth trying to figure if some specific pages in the different paths have a high drop-off rate and why.
In this scenario most of our visitors buy the first time they visit the website or not at all. Our next example will look at a scenario where visitors are more likely to buy on the 2nd or 3rd visit and how to deal with that. You can use Google Analytics to figure out how many time visitors are coming back before they purchase or what the average time delay is before making a purchase by looking at the “Time Lag” or “Path Length” data underneath Multi-Channel Funnels.
Consulting Website with Landing Page & Blog
Let’s imagine we write a blog on website optimization in order to drive traffic and generate leads for our web consultancy business. About a third our blog traffic is organic search, another third is direct traffic and the remaining third is from our Facebook advertising campaign. The aim behind driving traffic to the blog is to get visitors to click one of our banner advertisements directing them to our landing page in order to fill out a questionnaire. We also run a PPC campaign to direct visitors to this landing page. It’s fairly easy to measure the results of our PPC Campaign as visitors usually fill out or form our bounce and only rarely return. On the other hand it’s much more difficult to measure the effectiveness of our Facebook campaign since most visitors from this campaign rarely come to the blog and then directly convert on the Landing Page. In fact, it’s more likely that they follow our blog for a period of time and then convert at a later date. What this means is that if we want to measure the value of these visitors we have to differentiate them from the other channels and then track them overtime.
In this hypothetical visitor path we see that the visitor returned 3 times to the site before actually converting over the course of 20 days. The first time the visitor came it was from clicking on a Facebook advertisement, the second time was a direct visit and the third time was from organic search. Did the initial click on the Facebook Ad influence the decision to later click on the organic search result which resulted in the conversion? Or had the visitor completely forgotten about the Facebook ad that was clicked earlier. Understanding this is essential if you are trying to figure out your ROI on Facebook advertising vs. content development. One way to look at measuring the value of acquisition channels is via an Attribution Model.
A number of visits might occur before a conversion takes place on a website. An Attribution Model defines a set of rules on how to assign specific values to each step. Attribution models only come into play when multiple visits commonly occur before a conversion takes place and you need to assign values to the channels responsible for each of those conversions. If you are sending a lot of paid traffic to landing pages you likely don’t have any need for an attribution model but if you do a lot of inbound marketing and rely on a variety of channels it might be important to figure out the value of each channel.
The blog post is not going delve into how to choose the best Attribution Model but I will review the different types as they the concepts can be applied to how you think about conversion even on a single visit. You can build your own custom Attribution Model or you can one of several predefined models in Google analytics. Here is a look at some of the commonly used attribution models;
Last Touch Attribution Model – This model assign 100% credit to the last interactions and is used as the default by Google Analytics. In Scenario 2(above) this model would give 100% credit for the conversion to the organic search result.
First Touch Attribution Model – This model assign 100% credit to the first interaction a visitor has with the website. In Scenario 2(above) this model would give 100% credit for the conversion to the Facebook advertisement.
Linear Attribution Model – This model assign equal credit to each interaction and in Scenario 2(above) this model would give 33%% credit for the conversion to the Facebook Ad, 33% to the direct referral, and 33% to the organic search result.
Time Decay Attribution Models – This class of model makes some assumption about the value of time and the amount of credit an interaction deserves. If the initial Facebook Ad visit in the above scenario was separated by a large amount of time from the final touch it would be given less credit then if the amount of time was less.
Position Based Attribution Models – This class of models assign custom values to each of the interactions based on their positions. The standard default in Google Analytics is to give 40% credit to the first interaction, 20% credit to the middle interaction and 40% credit to the last.