CxFocus

Tag: google analytics reports

  • Let Your Customers Tell You What They Want: Paths of Desire

    Listen to your customers. Let them tell you what they want… Let them show you how to make your site better.

    Illustration showing confused stick man surrounded by question marks.

    Navigation is one of the biggest problems we all face when shopping online. It crops up again and again as a pain point in customer surveys. So why not let your customers help you make your navigation better?

    This series of articles explains how to use on-site search reports to improve your site and help shoppers find what they want.

    You’ll also discover how you can use this information to gain market insight which will be valuable offline as well.

    Traditional market researchers would love to get their hands on this kind of data. And it’s already sitting there waiting for you.

    The step-by-step instructions are based on Google Analytics, which has some excellent reports for this purpose. But the principles can be applied in any analytics system which allows you to see the search phrases people use and the pages where they use them.

    The title of this piece and the hints above are probably enough for you to get going. This technique is so simple and the results are often so clear that you’ll probably learn something if you dive into those reports right now.

    But if you’d like to learn a bit more, please read on. This post contains Part One

    1. Why Site Search is So Important
    2. Part Two: How To Use Site Search Reports in Google Analytics
    3. Part Three:

    The simplest way of finding out what your customers want is to ask them. And then pay attention to what they tell you. But that’s easier said than done even in a real ‘bricks and mortar’ store.

    We’re all familiar with this dead-end retail dialogue:

    “Can I help you?”
    “No, I’m just looking.”

    Online the situation is even more extreme. We don’t get to speak to our customers.

    So you should grab any chance you get to let the visitors to your site tell you what they want using their own words.

    I’m a great fan of using embedded surveys or feedback systems to listen to the voice of the customer. Give visitors a chance to tell you things and they will be very generous with their suggestions and advice. Any form of usability testing, whether it’s a formal lab-based study or a quick bit of “do it yourself” testing, is another great way of hearing visitors describe what they want to do on your site.

    Clickstream reporting tools, like Google Analytics and Yahoo!Web Analytics or Coremetrics, Omniture, Unica etc. are not normally considered any good for analysing visitor intent. They’re great for recording ‘what’ visitors did, but not ‘why’ they did it. But that’s not completely true, as I will explain.

    Search is the Language of Intent

    There are two places where analytics tools allow us a rare and valuable glimpse into the intentions of the visitor — the elusive ‘why’:

    • the external search keywords reports
    • the onsite search terms reports

    In the case of the of the external paid search you need to be sure that the report you are looking at is one which is showing the actual phrase typed by the visitor, and not just the keywords which were matched. But provided that is the case, the words you see there are ones your visitors chose.

    It’s as if that bricks and mortar dialogue had been:

    “Can I help you?”
    “Yes, please: I’m looking for…”

    That’s much better. Now we’re talking. We can do a lot with that information. For example, plenty of work has been done on ways of matching intentions, as expressed in external search phrases, with the content on the site’s landing pages in order to reduce bounce rates and smooth the path to the checkout. But that’s not what this article is about.

    Internal Search is the Voice of Frustrated Intent

    We worry a lot about getting people to our sites and perhaps not enough about helping them once they’ve arrived. So the internal search terms reports can easily be overlooked.

    There’s no pay per click budget at stake to focus attention on internal search. Meanwhile the volume of data is also smaller: typically less than 20% of visitors will use the internal site search. Most of us tend to use search to get to sites, but want to point and click when we get there.

    So navigation and information architecture seem vital matters to site-owners, while internal search is left on the sidelines. As a result, I suspect that more attention is paid to the path-analysis and site overlay reports in our web analytics tools compared to the internal site search reports. This is a mistake.

    Path-analysis is a time-bandit which can consume large chunks of your life for little gain. Often the more you learn, the less you know. On the other hand, internal search reports can often provide instant insights and even indicate what action to take. There are not many analytics reports which give such specific details about what you need to do.

    Internal Search Terms Tell You What Visitors Want — and Where to Put It

    Two wise men have encouraged me to treat internal search reports as a gold mine: Jared Spool and my Father-in-Law.

    My Father-in-Law’s contribution was a story from the days when he was doing his UK National Service in the RAF. He told me about the RAF approach to planning a new camp. He explained that they would put up all the buildings first, but lay down very few paths between them. They would then wait to see what happened.

    As people went about their daily routines in the camp, natural paths would start to emerge. As the paths wore down the grass, it would become clear which were the important routes. At that stage it was then possible to provide paved paths which matched the needs of the people using the place.

    You’ll hear different versions of this story from all over the world. Another example involves the building of a university campus. In fact there are books devoted to the subject:

    The phrase “desire paths” was coined by Gaston Bachelard in his book ‘The Poetics of Space’ to describe the paths people wear into the ground along the routes we actually want to use. We can see them all around us. There’s even a Desire Path Flickr group full of examples.

    Desire path cutting off corner

    There are desire paths on our web sites too. But how do we spot them? Not with ‘path analysis’, despite the name.

    This is where Jared Spool comes to the rescue. One of his standard pieces of advice is to use your internal search reports to look for clues as to where people wanted to go. In particular, look at the page where the search began. This is your visitor saying to you: “I got to this page, looking for ‘keyword’, and at this point I gave up using the navigation and tried search instead.”

    Once enough data has built up on your site you can often spot some patterns, like the emerging paths in those newly-built RAF camps.

    Seize those search terms. Treat them as the golden nuggets they are. This is the voice of your customers.

    Examine your Site Search Terms in Detail

    • Look closely at the pages where the search began and possibly the page most often preceding it (OK: path analysis has its uses, I concede)
    • See if the keyword deserves a place on that page, or in the navigation. Your visitors think it does
    • Put it there if you can
    • Help visitors turn into customers

    Coming Up: So How Do You Tap Into These Golden Insights?

    In the next part of the series you’ll learn how to put Google Analytics Site Search Reports to work for you. Let those reports show you what your visitors want, the language they use to describe it and where they’re having problems finding it. I’ll explain how to use to this information to improve the performance of your site, increase customer satisfaction and gain valuable market insight.

    After that I’ll give you a bonus tip which will allow you to look at those preceding pages without using the path analysis reports!

  • Let Google Analytics tell you when your visitors are having a bad experience

    Screen shot of Classic Facebook error messageIf you want to know what to do to make your site better for your visitors, you need to know what they find bad. And one of the best indicators that someone is having a bad time is if they keep seeing annoying error messages.

    Why error messages are a shortcut to improving customer experience

    Most error messages are triggered when someone is trying to do something, even if it’s only to click a link to a page which isn’t actually there.

    So these people are already starting to engage with your site. And then they get a slap in the face.

    Many error messages tell the visitor that they have done something wrong. It’s their fault.

    These people may start to feel frustrated and possibly annoyed by your site.

    That’s why it’s such a good idea to track error messages. They provide a very direct source of information about where your site is tripping people up and causing friction.

    I say “your site” because it is probably the site’s fault not theirs. If people are entering the wrong information, or skipping required fields it’s often because the language or signposting on the site is weak.

    OK: a typing mistake is a typing mistake — that’s the visitor’s fault, isn’t it? Fair enough. But if a lot of people are having trouble there, ask yourself: “do we really need that field, have we ever actually used that information”. Look at the exit rate from that error message and judge whether the information is worth it.

    So: error messages are a great source of actionable insight on the exact points of friction on your site. The action is simple: do what you can to reduce the errors. I’d actually set up goals and measure the conversion rate.

    The snag?

    Error messages are seldom page views, apart from the 404 “page not found” page.

    Error messages often take the form of text and highlighting which indicates the ‘bad’ entry in a form (good). Others are just ugly alert boxes which pop up in the face of the visitor, with a beep, telling them they’ve screwed up, not telling them where, and waiting to be dismissed (not so good). Neither of those would normally register as a page view, so a web analytics tool like Google Analytics will know nothing about them.

    What you need to do is add extra tracking code which is fired whenever the error message is shown. At the moment I favour using virtual page views for this in Google Analytics.

    Why use virtual page views to track errors

    There is an argument to say that errors should be tracked as ‘events’ not pageviews because they are an interaction on the site, not an actual page view. That’s debatable: the visitor certainly saw something!

    Actually, when you track these you may find that some errors are repeated again and again. This is classic evidence to suggest that the visitor might not even have noticed the error message — for example a list of form errors in small red text displayed at the top of the page, way above the submit button down at the bottom of the screen. The visitor’s eyes will be looking where they just clicked. Tip: compare the pageviews and unique page views for errors to identify errors which are seen several time by the same person:

    Screen shot from GA Content Drilldown report showing multiple views of same error message

    The real reason I favour virtual page views in GA is that, at the moment, only page views will appear in funnel reports as ‘abandon routes’ on the right side of the screen. Error messages in a checkout process would be a classic reason for abandoning a checkout. That’s something you definitely want to know about. So it’s best to have the information where you need it: in the checkout funnel report.

    [Update March 2011: The version of Google Analytics released in March 2011 introduced the ability to use ‘Events’ as goals. This may be more appropriate for many error messages. There’s a link to an article giving instructions on using Events at the end of this post. However Events do not appear as abandon routes on the right side of GA Funnel Visualization Reports so the argument for using a virtual page view in this context remains valid.

    Update May 2014: I’ve changed my advice on this over the years. I think that the case for ‘events’ is now stronger than ‘pageviews’. For example events can now be seen mixed in with pageviews in the Behaviour Flow report. The case for ‘pageviews’ depends on two things: the fact that there’s an ‘exit rate’ calculated metric available in the behaviour/content reports and because virtual pageviews will appear as abandon routes on the right side of the Funnel Visualisation report. If you want the best possible data, the solution is to use both!]

    Using either events or page views means that it is easy to configure the error messages as goals so that you can easily monitor the conversion rate. For example, I use a desktop client to monitor the conversion rates on the error goals across several sites. Each morning I can see at a glance if something is going wrong.

    How to choose a pageview structure to get the best from the content drilldown report

    I recommend structuring the page views carefully. I currently use a path for the virtual page view like:

    /errors/checkout/deliveryoptions/’the error message’
    /errors/checkout/payment/’the error message’
    /errors/emailsignup/’the error message’

    and so on.

    [Update: But see the very helpful comment from Rob Kingston below, who recommends starting all virtual pageviews with something like /virtual/errors/etc so that you can exclude them from some profiles.]

    This allows me to configure goals which consolidate groups of errors in a way which makes sense by matching the first part of the page name.

    This approach also means that the Google Analytics Content Drilldown Report works very well and becomes a power tool for digging into the errors.

    Configuring the last part of the page view as the actual text from the message makes things easier when coding but also has the added benefit that the reports show what we actually ‘said’ to the visitor. Simply seeing a list of all the unfriendly things we threw back at people, and how often, can be a wake up call.

    Once you have this information, the first thing to do straight away is to fix the error messages if they are a bit on the harsh side. And then get stuck into working out why people are seeing them in the first place.

    As usual, GA will help you prioritise. I would start by looking at the ones which happen most often and which are associated with higher exit rates.

    Switching to the Google Analtics ‘performance view’ of the data is a great way of comparing those exit rates. In this example, postcode problems stand out as a sore point:

    Screen shot showing Google Analytics performance view of exit rate from error

    Information like this is like gold dust when you’re trying to find exactly where to concentrate your efforts to give maximum benefit to your visitors.

    But please apply your knowledge of the site processes when you look at the abandon rate. The error message where people are leaving may turn out to be at the end of a series of other messages. Reducing the preceding errors would be the key to happier visitors. Custom segments might be useful here. Look at people who saw (or exited from) a particular error and see what other errors they also saw.

    As is always the case when working in web analytics, you need to have to be inquisitive and use your skill and experience to gain the real insights. But those error messages certainly provide a very clear starting point and possibly a wake up call.

    I’ll leave the question “How come all those people had a problem with their own last name?” hanging in the air for now!

    Direct Performance have taken this idea even further by coming up with a solution for using Google Analytics to track Javascript errors. Their article contains report examples, detailed instructions and sample code.

    This approach does involve making changes to your main tracking code, but it looks well worth the effort to me. Javascript errors can be a real problem for your visitors — the site simply does not do what you intend it to do and the visitor gets a crude message over which you have no control. So spotting those errors and fixing them would be a great contribution to user experience.

    http://www.directperformance.com.br/en/javascript-debug-simples-com-google-analytics

    I’ve written a separate blog post on Using Goals and GA Custom Alerts for automatic email alerts about errors.

    Updates

    March 2015:
    Mailchimp have a great blog post about their decision to remove social sign-in buttons which includes some very interesting data. In particular, they reveal that working with errors and copy had the biggest impact on reducing sign-in problems: [Opens in new tab] http://blog.mailchimp.com/social-login-buttons-arent-worth-it/

    May 2014: In January 2014 I gave a talk on this subject which included examples of some custom reports based on this data. One day I’ll add some of those here as they contain some compelling examples of lost orders from within checkouts which result from unusual errors.

    For now, here are the slides. They were created for an international audience so they include a lot of text for people who could not keep up with what I was saying, which means you can probably get a good understanding of the presentation now just from the slides.

    The slides also cover the ‘events’ vs ‘virtual pageviews’ question. These days I think the way to go is ‘errors’ but probably with duplicate tracking via virtual pageviews just to allow for inclusion in GA Funnel Visualisation reports and to get the oh-so-useful ‘exit rate’ calculated metric. Meanwhile the ability to create segments which track visitors across multiple sessions has opened up an interesting new area for studying the longer term effects of errors.

    This version skips right to the section showing example custom reports which are so useful for identifying the errors which are costing most in lost orders. In my experience you will often find there are some surprises in the list. And they may be fixable.

    You can import the checkout errors custom report shown in the slides from the GA Gallery here:
    https://www.google.com/analytics/gallery/#posts/search/%3F_.viewId%3DnHpEBtURS5-v1hUGfeh8xg

    November 2012: Peter O’Neill has written a great example explaining how to use GA Custom Variables for error tracking. He’s included a couple of Custom Reports to download too. Whether you use virtual pageviews, custom dimensions, or events, the main point is the same: work out where your visitors are having problems and try to make things better! http://www.l3analytics.com/2012/11/21/tracking-errors-with-web-analytics/ [Opens in new tab]

  • How to pick your top three pages and optimize for organic search

    Working out what to actually ‘do’ with Google Analytics is hard. We all need help deciding which aspects of our sites to work on and which GA reports to use to do it.

    That’s why I’m so keen on step by step instructions on how to use Google Analytics to narrow in on the most important work. It isn’t just a matter of choosing the reports, it’s a whole business process. You use the reports to prioritize the work and decide on the actions to take.

    I’ve just seen a really good example on the Future Now blog. Brendan Regan explains how to:

    Screenshot: Futur Now - How to optimize landing page for scent

    • use the traffic reports and one of the built-in default segments to
    • identify
    • and then isolate your key organic search of traffic
    • then use the landing page report to find the top 3 pages from that source
    • then report on the entrance keywords bringing those visitors to those pages

    Brendan then teaches you to make a list of those three top pages and the top three keywords for each. Then go to the pages and study them to see how well they would work for someone seeing the page for the first time having searched for the keywords.

    The article includes examples of the kind of thing you should be checking on if you want to make improvements.

    The whole point is to preserve the ‘scent’ of the original search. Whatever it was that the visitor was searching for needs to be prominent on the first page they see on your site. As the article points out, you can’t optimize the scent for every keyword that ever brings people to your site — that’s why you need this process to select the keyword/page combinations which deserve attention.

    It’s a great article.
    Read it here: http://www.grokdotcom.com/2010/07/19/fast-way-to-optimize-your-site-for-scent/

    [And if you like this stuff, here’s another example of a GA How-to from this site: Simple way to spot landing pages and keywords to fix]

  • Simple way to spot landing pages and keywords to fix

    Knowing where to start making improvements to your site can be difficult. But here’s a very simple technique which will make the trouble-spots stand out. You can use this trick in loads of Google Analytics reports. It will work for keywords, for landing pages, for referring sites. The technique is quick, easy and universal.

    Updates

    1. Using Google Analytics Weighted Sort [August 2010]
    2. Screenshot illustration of v5 Weighted Sort [September 2011]

    The golden metric is the bounce rate, of course. But the trick is to filter the data appearing in each report to focus on the trouble spots and the hot spots. Then draw your lessons and make your changes.

    The secret is the “advanced filter” link which appears next to the inline filter at the bottom of your reports.

    Where to find advanced inline filter in Google Analytics

    This simple text link opens up a powerful (almost hidden) form for filtering reports in multiple ways. It’s the kind of tiny feature which can be every bit as useful in real life as some of the big new ‘bells and whistles’ which Google make more fuss about.

    How to add an extra inline filter metric in Google Analytics

    You can probably figure out uses for this without me having to explain. This tip makes it much easier to narrow down a list of pages (or keywords, or whatever) in order to spot the problem areas and take action. Once you start using this you’ll find it will come in handy again and again.

    Please give it a try, it’s really simple and it often makes the insight almost jump out at you. The more I use this feature, the more I like it.

    If you’d like an example, please read on.

    The advanced inline filters interface is hidden behind a text link, so you might not notice it at first. But you may already be using the original inline filter to narrow down keywords, for example, to ones which contain or exclude brand terms. That technique is also good for grouping types of page, such as all the product pages or all the category pages on an ecommerce site. I would typically use that approach when looking at the product pages vs the category pages as landing pages.

    In the case of a set of category landing pages you might then click on the bounce rate column in order to sort them in descending order to spot the worst examples. But the snag with this is that the worst cases may (we hope!) turn out to be odd pages which have attracted a tiny level of traffic which happened to bounce. They’re not a priority.

    Landing page sorted by bounce rate in Google Analytics

    This is where the advanced inline filter comes in handy. You need to set a bottom threshold to the report. Just add an extra condition to your filter so that you only see the landing pages where a significant number of people entered the site:

    Add a threshold filter to grouped landing page report in GA

    Suddenly the report comes into focus and you can see the pages which matter: just the ones with significant traffic, with the ones with the highest bounce rate at the top. Details of the advanced filter can be seen at the top of the report, while the ‘edit’ and ‘clear’ filter links are at the bottom.

    Filtered view of Google Analytics landing pages report

    [Update: but please check the links in the resources section at the bottom of this post to see why Google Analytics ‘Weighted Sort’ fetaure, introduced after this post was written, provides a new and better way of doing this for most cases.]

    When you’re doing this kind of analysis, it’s also good to switch to the comparison view of the data.

    The comparison view makes it easy to judge the relative scale of the issues. This is one of the most useful views of data in Google Analytics. As with advanced inline filters, the interface does not draw much attention to a very powerful and simple tool. This is one of my favourite ways of letting GA help me spot where the problems lie. Just give that little icon up at the top right of the table a click and see for yourself!

    Comparison view in Google Analytics: a powerful way of spotting problems

    In the example above you can see that most of the bounce rate bars are green and to the right = good. This shows that most of the category pages have a lower bounce rate than the site average.

    In this case the data is from a blog, not an ecommerce site, but on a shopping site it is often the case that the category pages and the home page will both have lower than average bounce rates. Both are ‘routing’ pages: their function is to present options for further navigation and to route people to other pages. So ideally both should be better than average in earning at least one exploratory click.

    But two of those bars are red and to the left. So why are people so much more likely to bounce from those category pages? The next step is to look at the pages themselves and the sources of the visitors landing on them to see if there’s an explanation.

    In this example the really bad category page on this blog about skateboard history is a category called ‘skateboard competitions’. And the visitors are coming from search. So it’s not difficult to guess what is going on. These are almost certainly young skateboarders hoping to enter a skateboard competition of some kind. When they land on a page containing information about events which took place before they were born, they back out of there fast.

    You might find something similar on an ecommerce site: visitors landing on a category page after searching for something which seems to match in terms of pure search phrases, but is actually completely irrelevant. For example searches for red ballet shoes landing on your red shoes category page. But you sell trainers. If those are paid search clicks, go running to your negative keywords list. If it’s organic search, take a close look at your Title field and Meta Description field: can you make it more obvious what kind of shoes you sell?

    I’ve shown a landing page report in this example, but this approach works very well with keywords. In fact these functions are available throughout Google Analytics. The principle is the same:

    • Narrow the rows of data down so that you’re only looking at the traffic which matters and not drowning in numbers
    • Sort, or use the comparison view, so that you see what’s bad and what’s good
    • Examine the pages and the traffic source data to see if you can work out what’s happening
    • Then make changes

    That last bit’s harder than clicking buttons in an analytics tool of course. But that’s where the fun and the satisfaction really is.

    Acknowledgments: of course Avinash Kaushik was a strong influence on this! In this particular case I think it was a presentation at SES London. But if not there, somewhere else. And he’s certainly blogged about other great examples of using Advanced Filters.

    Update: August 2010 — Google Analytics Weighted Sort Provides a Better Way

    At the end of August 2010 Google updated the GA reporting interface to provide a built-in automated way of eliminating those insignificant visits and bringing what matters into prominence.

    Screenshot showing Google Ananlytics Weighted Sort