Hi everyone. My name is Erin and I’m going to give you an overview of some areas from web analytics that you can apply to your user experience work. My goal is that each of you will find at least one concept you can take back to the office and start exploring in your work.Let’s get started.
Bounce rate is one of those metrics that gets confused and misunderstood a lot. So if you’re sitting there thinking that you sort of understand what bounce rate is, but you’re not really sure, don’t worry.Bounce rate is NOT the same thing as exit rate. Bounce rate IS a measure of all the people who only view just one page on your website. So All People Who View One Page over All People Who Come to Your Site.
Exit rate is a different metric that applies to anyone who leaves a site, no matter how many pages they’ve visited. Bounce rate is often used as a quality measure for entry pages. If you drop a bunch of people onto a landing page, and they take a look, and then they leave without doing anything, you’re page might suck. You’d probably have a high bounce rate. If you monitor the bounce rates of important landing pages, you can watch for pages that might have an issue. Or you can pinpoint pages that might be just fine, but the wrong types of people are coming to them.So, check bounce rate when you’re interested in page quality.
Landing page stickiness. Landing page stickiness shows which pages do a good job of leading visitors further into the site.
Stickiness is the converse of bounce rate and is another measure you’d use if you are monitoring the quality of landing pages or site entry pages. Stickiness is a measure of the likeliness that your landing page will compel a visitor to take action and click on something or move on to another page within the site. Essentially, stickiness shows whether people stick around.The closer the stickiness percentage is to 100%, the better off your pages probably are. But if you have any pages where the stickiness percentage is less than 40%, you might want to flag those to evaluate for usability or technical issues.
Depending on how your analytics are set up, you probably have logs that are recording the search terms visitors type into your internal site search.
These search terms show the language that your visitors use to describe your products and services.
Internal site search is like a little taxonomy of terms generated by your users. You have records of all the terms and phrases people are looking for.
And then you can organize the terms by category or type.
Or you can separate them out by the type of user who searched that term, say customers and people aren’t customers, and look at the differences in their language and where searches overlap between the groups.
And you can grab the stats that go with the search terms, to see how often people are searching for a particular phrase, how many people buy something after making a search, or how many people leave your site after searching.With the numbers, you can look for trends and identify areas where you might have a navigation problem or a content problem.
Your web analytics data is going to be a lot more meaningful when user segments are applied.
Your users aren’t all identical. You already know that. But your analytics doesn’t know that so all the information is aggregated together.
But you can break that data out into different segments based on shared attributes.
So you can create groups of members and non-members and PC users with Windows 98 living in Reno, if those are the user groups important to you.Apply segmentation to all of your analyses. It will bring greater meaning to everything you do.
A lot of you have probably set up some kind of survey or feedback system for your customers.You can, in fact integrate some types of surveys into your web analytics.
We call it voice of customer data, and not every survey option integrates with analytics. But there are some that do, and they capture information about where your visitors came from and what pages they saw, along with their survey feedback.
With these two integrated, you can do things like look at how your users’ sentiment and tonality changes with amount of traffic to the site.
Or you can look at data from a specific page, and see how positive or negative comments vary over time for that page. Then, if you ever make a big design change, you can monitor the emotion levels from people who visited that page.
You may have designed a scenario based on steps people said they would expect to take when they are completing a task. It seems very logical and linear.And then you take a look at the click path data for that scenario, and find out that this is the path people are actually taking.
If your scenario is something that should have a clear progression, like a shopping cart checkout, examining the click path data could draw attention to usability problems, distracting designs, or missing information that your users are encountering.
Going along nicely with click path analyses are funnel visualizations.
Goal funnels can be created anytime there is a defined set of steps that people should be taking to accomplish something. A shopping cart checkout is a perfect example. Your ultimate goal here is that the people make it all the way to the confirmation page, because that means they just bought something. Every stage in that process can be listed as a step towards the ultimate goal. When that’s visualized, you get to see how many people make it through the entire scenario.Here, we’ve got 317 people who are leaving at the checkout confirmation stage. That’s almost half of everyone who started the process. Is this a problem?
It’s not good enough that people just visit our site once. We want people to come back multiple times. User loyalty shows how strongly attached visitors are to our sites.
Loyalty shows how many times people come back. You can combine loyalty with frequency - with what frequency they visit over the course of a week or a month – and recency – the last time they visited.Here we see that most people only come to these sites once. That’s pretty normal. But here you’ll notice that there’s a subset of people who have come back at least nine times. This content site doesn’t really have that same trend. You can isolate this group of people who have visited 9 times or more, and see what they do and what type of people they are. Then you can make changes to get more people into this group.
When most people talk about conversions, they’re talking about something big like a sale or a signup. But conversions don’t have to be just about sales and signups.
Customer successes are conversions for UX. And if you measure micro conversions, you measure customer successes.
Micro conversions are points where people took an appropriate action. Because we don’t all come to websites just to buy things. Sometimes we just want information or we want to do research.Your big conversions here are probably points like the sale of this book.But there are a number of micro conversions that could be tracked too. You’ve got people who clicked the “Look Inside” to see a sample, people who liked the book, people who wrote a review about it, people who searched for something, people who added the book to their wishlist, and people who sent a sample chapter to their Kindle. These are all micro conversions that let you know that your visitors are taking action.
Engagement. Everyone loves engagement. People want more engaging designs, they want more engaging marketing campaigns, and they need a better social media plan because they want to capture engagement from people who use Twitter and Facebook.
Everyone keeps talking about increasing their site’s engagement, but how do you know when you’ve increased it? There is no universal measure for engagement.
This is philly.com’s engagement calculation. It worked for them so if you really need to measure engagement, maybe you can modify their algorithm to come up with your own. They’ve got click index, duration index, recency index, loyalty index, brand index, interaction index, participation index. The next time someone asks you to design for engagement, you might ask what they’re really looking for. What if their engagement was 100%, but the sales stayed the same, the revenue stayed the same, and the number of customers stayed the same, would they still want engagement then?
These standard analytics dashboards that come with your analytics program are all you really need. You can get most of your insights just by looking at those charts and graphs.
These are generic. They’re not customized to fit what’s most meaningful for your customers and your business model. As a user experience person, the most valuable information you’ll get lies deeper inside than the top layer of dashboards.
10 Truths and a Lie: Answers You Need to Understand Web Analytics
10 Truths and a Lie<br />Answers You Need to Understand Web Analytics<br />Erin Jo Richey @erinjo<br />
There is no universal metric for user engagement.<br />
ENGAGEMENT<br />Σ(Ci + Di + Ri + Li + Bi + Ii + Pi)<br />CiClick Index: visits must have at least 6 pageviews, not counting photo galleries<br />DiDuration Index: visits must have spend a minimum of 5 minutes on the site<br />RiRecencyIndex: visits that return daily<br />LiLoyalty Index: visits that either are registered at the site or visit it at least three times a week<br />BiBrand Index: visits that come directly to the site by either bookmark or directly typing www.philly.com or come through search engines with keywords like “philly.com” or “inquirer”<br />IiInteraction Index: visits that interact with the site via commenting, forums, etc.<br />PiParticipation Index: visits that participate on the site via sharing, uploading pics, stories, videos, etc.<br />