Conversion Optimization: Do This First
Have you made the move beyond SEO to conversion optimization? Haven’t heard of it? According to Wikipedia, “Conversion optimization is the science and art of creating an experience for a website visitor with the goal of converting the visitor into a customer.”
While this seems to me to be a fairly accurate description of the process, it’s also important to note what conversion optimization isn’t.
- It isn’t making a website “better/look nicer/rank better”
- It isn’t concerned with current customers. In fact some of the processes can stand at odds with best practices for retaining current customers, and we should be mindful of this.
- Last but not least, it isn’t mindless chopping up of website content and structure “for the sake of it.”
The important thing to take into account when beginning a conversion optimization project is that the planning is at least if not more important than the test itself.
Before running a conversion optimization project we need to make sure we’re barking up the right tree. This first part looks how to decide which pages to think about optimizing. Next month we’ll look at some further cheap/free techniques to get more information from the behavior of your users.
Important metrics: bounces & exits
Starting with your excellently deployed analytics package, let’s look at which reports are useful to us in determining the focus of our conversion optimization process.
First, take a look at pages with high bounce rates/exit rates. That’s an easy one—pages with high bounce rates are bad and need to be optimized right? Well, maybe. I would suggest some segmentation here wouldn’t go amiss. Remember, we’re looking predominantly at new visitors here, the ones which haven’t been to your site before and need that extra helping hand to get them further involved. Try using advanced segmentation to convert more new users.
So, how do the high bounce rate pages look now? Select pages with bounce/exit rates which are significantly higher than the site average and then ask yourself:
- Do these pages have a significant proportion of website traffic?
- Are they important in the path to conversion?
- Are the traffic sources to these pages good quality and relevant to the content?
Pick no more than two distinct pages to follow up, look at the navigation report to understand where users are going to from this page. Once you’ve answered these questions you should have a couple of pages (or groups of pages) to analyse in more depth.
Advanced funnel analysis
Regardless of the pages you identified in the previous step, it’s almost certainly worth analyzing the pages which are directly involved in the conversion process, whether they be a lead generation conversion or an ecommerce sale. This involves tracking every page of the funnel, but also creating multiple funnels to analyse users coming from different areas of the website. You can do this by making required first steps of different pages, for example product or category pages.
This requires your analytics package to allow you to create multiple goals. I often find that there are subtle differences in the way users interact with different site categories or content types. Once you’ve got your funnels set up, collect data and analyse which funnel pages are causing users to exit the process. One word of caution here—keep in mind what a true funnel “exit” is here. To understand what I mean here, take a look at my colleague’s analysis of the true Google Analytics funnel post.
Listen to your users
Anecdote time: I recently carried out a project for a website which had at least 2 years worth of data. The site was receiving 12M+ pageviews per month and over 700,000 monthly searches. Yet the company was completely unaware that the site had 150,000 searches per month for “iphones” which resulted in an empty results page (and a huge search exit rate). If your website has a site search function, hopefully you are mining this data already. If not, get it set up!
At the risk of sounding cheesy, I always say it’s like having a conversation with your customers. If you listen carefully you’ll hear them. Sometimes it’s a whisper; sometimes it’s a sign that they’re confident of using a site search function and don’t mind typing. At other times though, they’re screaming at you. They’re angry; they took time to find your site, they invested time in browsing your navigation and come up dry. But, in searching again, they’re giving you a second chance. This time show them what they want to see. First concentrate on finding the screaming, angry users.
Which pages aren’t satisfying users simply with their content and navigation?
These pages are the ones which are most searched from. Once you’ve identified them, try to understand whether or not users are sticking with the searches or leaving the site. If there is a high search exit page from these pages then they should be a focus of your efforts. Some questions to ask:
- Do I need to add content to this page?
- Is the look and feel of this page incongruous to the content?
- Am I providing an obvious “next step” to my users?
- Are certain products/content less prominent than they should be?
- Do I need to make certain information more easily available?
Finally, after studying the stats, forget the stats. Look at each page under suspicion and think critically about what you should expect from this page. What is its purpose? Does it make sense for it to have relatively poor stats? Is there a logical reason or should you be concerned?
Take a decision
By now, you should have two or three pages or types of page which you have singled out for treatment. They should have the following attributes:
- Poor bounce/exit rate
- Low contribution to the conversion process
- Other signs such as high number of searches and search exits
- No reason, intuitively, to have this poor performance
The next step is to look at which aspects of these pages need improvement. That’s the focus of my post; brining the site search back into the arena along with some other interesting technologies in order to create priorities for optimization A/B & multivariate testing.
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