See if anything is wrong with your checkout at a glance with this Google Analytics Custom Dashboard.

If you have an ecommerce web site you know that the performance of the checkout is critical. If anything goes wrong with the checkout it will be costing you money.

That’s why first thing every morning, before I even look at the sales figures, I look at my clients ‘Checkout’ custom dashboards.

It gives me an instant health check. I can clearly see if something has gone wrong with any of the stages in the checkout, such as the payment gateway or the address lookup system. Or if something is wrong with a promotional offer codes and people who use it are unable to buy…

All I have to do is to take a quick look at this one-stop dashboard to see if any of the ‘sparkline’ charts has an unusual looking spike. I can instantly see if something is out of line, and exactly which part of the checkout has a problem. I really like reports like these. They’re what I call ‘read and react‘ reports.

Build a report like this and you’ll find it’s very powerful. You may already have read enough to know what to do, but I’ve explained how to do it below. Follow these instructions and you will be able to spot if there’s anything odd happening with your checkout by looking at one clear report.

Which means that… You can jump in and fix the problem before too much money has been lost.

How To Make Your Read and React Checkout Dashboard

The secret of this technique is to set up a sequence of Google Analytics funnels, one for each stage, and then build a dashboard showing the abandon rate for each of those funnels.

The idea of using a series of separate funnels has been around since 2010.

The technique is called building a ‘horizontal funnel’ and is mostly used to allow you to use segments with funnel abandon data. This idea was recommended in a Lunametrics article by John Henson: http://www.lunametrics.com/blog/2010/06/04/segment-goal-funnel-google-analytics/ [link opens in a new tab]

The original concept involved using a Google Analytics Custom Report to present the information in a way which made all the important numbers easy to see, but it did not include any visualisation of the abandon rate from the different stages.

But now we can use GA Custom Dashboards to show all the individual abandon rates, trended over time, on a single screen.

Being able to spot the changes in the trend compared to normal performance adds the final touch which transforms ‘detailed analysis’ data into a ‘read and react’ operational report. You also get a bonus of being reminded of the relative abandon rates from the different stages so you can see where the friction points are.

Each time you look at this dashboard you can see what’s going on in a split second. You can instantly tell if you need to fix anything.

At peak periods it’s a good idea to check back later and set the date range to include ‘today’ so you can keep an eye on things.

Once you’ve tried this technique you can apply it in lots of ways. In the example here, I’ve extended the concept to show some extra very useful data which is relevant to checkout problems.

In the first column I’ve got the ‘alarm bell’ step by step abandon rates. These are all ‘metric’ widgets which include the sparklines trend charts which are the key to this technique. I’ve arranged the widgets in order of the likelihood of trouble, not the real life order of the stages. You can just drag and drop them to suit your preference.

The middle column contains a ‘table’ widget which might also provide evidence of something odd, or at least interesting, going on with the checkout. It shows the overall checkout abandon rate by source/medium of the visit. I find it useful to be constantly aware of how some channels bring visitors who are far more motivated to make it all the way through the checkout than others.

In the third column I’ve got some ‘line chart’ widgets because they allow me to compare two metrics. In this example they are comparing the abandon rate from just the cart page with the abandon rate for the rest of the checkout. Or the abandon rate from just the cart page with the abandon rate of the checkout as a whole.

You could extend the idea behind the middle ‘table’ widget further. For example, on many sites it would be useful to have another widget showing the abandon rates from just the cart page, broken down by source. If your cart page contains an offer code box, for example, a sudden spike in the cart page abandon rate from one source is a strong indication that there’s probably a ‘bad’ code being promoted there. In that situation you can either create an extra version of the code which matches the one which people are trying to use, or try to promote the correct code via the same source.

In the video below you’ll see me building another variation on this layout. Once again the first column contains the ‘alarm bell’ sparklines, but the table widgets in the middle can be used to gain a wide range of operational insights which you can use to modify or correct promotional campaigns straight away.

You can include up to 12 widgets on each dashboard. What you choose to show in the tables should be decided on the basis of what you’re actually putting your resources into now. This is a living dashboard: you should change it to align with what matters now and what you can still change.

Can you see the theme there? This dashboard is not just a read and react tool for spotting technical errors. It’s also a very useful tool for getting an understanding of the motivation of the visitors from different channels. The biggest variations in checkout performance, in my experience, have very little to do with the technicalities of the site and everything to do with the strength of people’s desire to buy. Persuasion and motivation can have more influence on abandon rates than the position or wording of the ‘checkout now’ button.

The original Lunametrics post gives you excellent instructions on how to set up the sequence of funnels.

And this video shows how to use those funnels in a custom dashboard like the one above:

The example shows the abandon rates from a Magento one page checkout, as reported using the GoogleAnalyticsPlus Magento plugin from Fooman.

Updates on Checkout Abandon Rate Dashboards in Google Analytics

[January 2012] Someone has just pointed out that it can be extremely useful to look at checkout abandon rates by browser for each stage. At the moment there’s no easy way to do this in the dashboard itself. You can’t use Advanced Segments in dashboards. And even if you could, you really need to be able to segment by browser version, not browser, as this screenshot shows:

Having to configure each segment first and only being able to see four browser versions at a time would make using segments tedious, even if it was possible.

So the way I do this is to configure the widget to link through to a Google Analytics custom report containing a tab which allows me to see the abandon rate broken down by browser and then by browser version. I also have a tab showing a source/medium breakdown, which is the one I use more often.

You can import an example of such a report into your own GA profiles by logging into GA and then clicking this link:

https://www.google.com/analytics/web/permalink?type=custom_report&uid=pmQVC9ZyRj6SYJ565Eu9kA

This example shows the rates for two goals (14 and 15 in this case) so that it can be used as the link from two widgets. That suits the way I work. This is intended as an example to get you started: you must edit the report to show the goals which you have configured in the relevant profiles.

Resources for Horizontal Funnels and Custom Dashboards

• The original Lunametrics post on horizontal funnels: http://www.lunametrics.com/blog/2010/06/04/segment-goal-funnel-google-analytics/ [link opens in a new tab]
• Official Google Analytics introduction to custom dashboards [link opens in a new tab]

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Get an Instant Checkout Health-check With This One-stop Report

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Get an Instant Checkout Health-check With This One-stop Report

Multi-channel Funnel Reports in Google Analytics bring us a whole new depth of understanding to how people come to web sites over the course of several visits.

And there’s a bonus: we can also start working with our SEO keywords and other sources of traffic with a level of control which was previously only available for paid advertising and other campaigns.

In real time. Retrospectively. With no pre-configuration effort (so not like campaigns, then).

The most stunning of these reports, in my opinion, is the Top Conversion Paths report. For the first time ever it shows the history of a series of visits.

What’s more, it groups them by channel so that you can see the most common sequence of routes to the site. For example: social, followed by search, followed by email.

What’s even more, you can create your own custom groupings which means that you can start working with your organic search keywords in groups, like ‘head terms’ and ‘long tail’ or ‘brand’ and ‘generic’, just as you would with paid search. Or you could group your social sites according to different types, or separate different classes of referring sites, and so on.

The standard groupings include things like:

• ‘Paid advertising’ — PPC advertising, banners and so on
• ‘Organic search’ — unpaid search visits
• ‘Social’ — Facebook, Twitter and a long list of other social sites (take a look)
• ‘Referral’ — referred visits
• ‘Email’ – visits where the medium has been tagged as ‘email’

This allows for a very powerful summarised view of the pattern of visits like this:

But this default view only scratches the surface of what you can do with these reports.

The real power lies in the fact that you can create your own custom groups. The interface for doing this is great and you can (should!) use the basic groupings as a template to get you started.

The first thing I would do is to split those ‘Paid Advertising’ and ‘Organic Search’ groups down so that you can separate searches for your brand keywords from generic searches for more general terms. Those are two hugely different types of search.

In order to get new customers you need to be able to clearly understand the steps which someone takes from doing a generic search for something you supply through to the point where they know about you and your brand.

At this point I should pause and thank Matt Trimmer at iVantage who pointed out that Custom Channel Groupings has the facility for doing exactly what we need.

Here’s an example of what such a report looks like. This is much more revealing than the default version.

Suddenly it’s obvious that most of the search visits are from people who already know about the brand. Generic search doesn’t appear until much lower down the list.

The Custom Channel Groupings interface makes it very easy to copy and then edit the basic group configuration.

The slowest part of the process is working out what your brand keyword variations are. There are full instructions in an earlier article on How to Start Working with Brand Keywords in Google Analytics

Once you have your list of keywords it’s all point and click from then on. This 6 minute video shows you what to do:

Separating brand from generic search is the first thing I would do with these reports. But the potential to go further is huge. This approach means that you can start working with groups of unpaid keywords in the same way as you do with paid keywords. For example, you can start breaking those generic search terms down into ‘head’ and ‘long tail’ groups.

Things to Watch Out For with Multi-channel Funnels

• You have to have goals or ecommerce configured for these reports to work
• The default view takes all goals into consideration. I think it’s better to be selective about which goals you are reporting on, particularly if you have many micro-conversion goals set up. For most ecommerce sites, I would tend to consider the ecommerce transactions on their own and then look at key micro-conversions separately. It stands to reason that you might get a series of micro-conversion visits before a final transaction visit, so you need to keep them separate.
• These reports only work with goals configured and they only show visits with goals. But goals could include non-bounce visits if you so wished.
• Experiment with the drop down menu which allows you to choose whether to see all the visits, even if they only have one step, or more than 2, and so on. The ‘more than’ options are the most interesting when you are looking for patterns. But don’t treat those as the default or you risk missing the fact that only some visits have any history at all.

That last point is an absolutely critical one. The history of multiple visits is based on cookies. People now use multiple devices to visit sites and some systems also delete cookies, which means that many real-life returning visitors are counted as new visits by Google Analytics and all cookie-based systems. So what we have here is a sample. These reports provide much more insight than we have had up to this point. We can now see the history of a series of visits. But it’s not the complete picture: this is great for assessing relative numbers  and trends, but the actual numbers should only be treated as indications.

Resources Relating to Multi-channel Funnels

Here’s a beginner’s introduction video from Google:

There’s a more serious one here: http://youtu.be/rZ2RbGsuy3U [Opens in new tab]

• Christopher Penn has a great article on how to use MCF to discover if email is worth more to you than you think. I love specific examples like this of how to actually use this data. Multi-Channel Funnels Show the Power of Email+Social [Opens in new tab]
• The official GA Multi-Channel Funnels Usage Guide [Opens in new tab]
• Michael Harrison has a great post on Customising Channel Groupings, with step by step screenshots (clearer than my video!) and interesting suggestions such as using this with affiliate sites (I wish we could group on the basis of parameters in the landing page URI request): http://www.lunametrics.com/blog/2011/08/25/custom-channel-groupings-google-analytics/ [Opens in new tab]
• Naturally Avinash Kaushik has written an extremely good post on Multi Channel Funnel reports (and more!) which should be a vital read for everyone. In particular, the video embedded in his article contains a very clear explanation of why the changes to the calculation of visits have been introduced as well as a great introduction to the benefits:
  http://www.kaushik.net/avinash/google-analytics-visits-metric-change-implications-opportunities/ [Opens in new tab]
• Charles Farina from e-Nor offers another thoughtful set of examples of ways in which you can customise your groupings here: http://www.e-nor.com/blog/index.php/web-analytics/channel-grouping-in-google-analytics-multi-channel-funnels/ [Opens in new tab]
• As soon as I read this excellent introduction from Dara Fitzgerald at Fresh Egg I realised that I need to explore the Conversion Segments facility more and the ‘Custom Conversion Segments’ aspect in particular: http://www.freshegg.com/blog/multi-channel-funnels-conversion-optimisation-google-analytics_8146 [Opens in new tab]

[Other good sources are appearing all over the place at the moment, so I will continue to update this list.]

Updates

• [November 2011] The introduction of secure search by Google means that a growing proportion of organic search visits do not have any keyword information. These will show as ‘Not Provided’ in your keyword reports. Since you do not know whether these searches were for ‘Brand’ or ‘Generic / Non-Brand’ terms, it now makes sense to add a third organic search Channel Group for ‘Not Provided’.

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How Multi-channel Funnels Give You More Insight Than Ever Before

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How Multi-channel Funnels Give You More Insight Than Ever Before

Here’s a great custom report for you to try. There’s a link below to import the report straight into GA, but first let me explain why I think this is so exciting.

Retailers and other web sites with strong weekly visitor patterns need to be able to compare Mondays with Mondays and Saturdays with Saturdays.

Comparing the same day of each week over a long period is also very useful for any site planning emails or any other form of on-line campaigns.

So I was really excited when a comment on Linked In from Mike Sullivan of Nextanalytics provided me with the key to doing this.

Although I’m a huge fan of Custom Reports, for some reason I had not noticed that ‘Day of Week‘ is available as one of the dimensions.

In v1 of Google Analytics you used to be able to select all the ‘Mondays’ in a month by clicking on the top of the column in the calendar. But you could only do one month at a time and even that ability disappeared in later versions. It became necessary to do this kind of reporting outside of GA using Excel or a dashboard client.

Now you can do it all in a custom report, so I’ve made an example report which you can download using a link at the end of this article.

Using Custom Reports is so much better and allows you to experiment with using secondary dimensions at the top level of the report. ‘Dimensions’ is the GA word for ‘what the data is that you’re looking at the numbers for’ — the information in the first column of the table. Dates or Days in this case. So you could add the source/medium for each day next to it in a second column, for example.

But first take a look at this standard version:

But my favourite view at the moment is to use ‘Day of Week’ as the top level dimension, and then drill down to ‘Date’. By using Custom Reports you can add a new tab to view the data that way round:

I’ve added the names of the days to the screenshot so you can see what’s happening. The report just shows the number of the day: 0 for the first day of the week and 6 for the last day.

This version of the report means you can see the average figures for all 7 days of the week compared to each other over an extended period. You can then drill down to see how each of the ‘Tuesdays’ compare with each other. That’s the insight which interests me most. The chart also updates to match, so you can choose whether to leave it with a single spike for one day each week, or change to ‘Graph by week’ view to see a continuous line.

These reports work really well in ‘Comparison View’ and ‘Performance View’, so be sure to experiment with those. But I’ve discovered that you can’t sort by the day of the week when you’re using that view, so you’ll need to be careful. There have been changes to the sorting functions during the last few weeks (weighted sort appeared and then disappeared), so this situation may well change.

How to Make a Days of the Week Custom Report

The simple clue that ‘Day of Week’ is available as a dimension will have been enough for some people.

If you’d prefer to see how it’s done, I’ve made a 5 minute video showing each step.

Or if you’d prefer to save some time, use the link below the video to import a copy of the report and then edit it to match your business needs.

You’ll see in the video that I ran up against a problem when I went for overkill and tried to include all 20 goals and a load of extra ecommerce metrics. I was doing that because I thought it would be helpful to make a report which would work for everyone no matter which goals they use.

The final report does not include the final few goals, but you can download it into your own GA and see for yourself. Have a play with it and edit it to suit.

How to Import the Days of Week Google Ananlytics Custom Report

1. Log into GA
2. Click this link: http://goo.gl/EluNF [opens in new tab] to import the report
3. Don’t forget to scroll down to choose your profiles and then click save!

Things to Watch Out For

When you’re doing this kind of comparison it makes sense to use a date range which includes full weeks

If you’re using the ‘View by Day of Week’ approach it is vital to choose full weeks

The default sort order in GA is based on the first column of metrics in the data. I always repeat ‘visits’ in each metric group because it’s important to always be aware of the size/significance of the group of visits you’re looking at. But in these reports you will almost certainly want to sort by the dimension, not the metrics (the date or the day of the week). Don’t forget to click at the top of the dimensions in the table to change the sort order.

In the screenshots I’ve added the names of the days of the week. GA just gives you the numbers! 0 is your first day of the week, 6 the last.

Resources Relating to Custom Reports and Days of Week

• Kate Morris wrote a great post about Dayparting in Google Analytics back in 2009. I wish I’d seen it then!
• Avinash Kaushik has several hugely important posts on the subject of custom reports with more download links, notably:
  • 3 Awesome, Downloadable, Custom Web Analytics Reports
  • Produce Actionable Insights: Mate Custom Reports With Adv Segments!
• This same approach can be used to get at the ‘by hour’ data. Robert Kingston has posted a really great example of how to combine that with Excel’s handy conditional formatting feature to make the interesting data really sing out. For example, to visualise your best converting hours of each week [Opens in new tab]. The post includes an Excel template.
• Here’s the import link again: http://goo.gl/EluNF [opens in new tab]

Before you go, please give one of these sharing buttons a click:

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Download Custom Report to Compare Days of the Week in Google Analytics

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Download Custom Report to Compare Days of the Week in Google Analytics

Here’s a quick way to get Google Analytics to shine a light on problems on your site and help you sort them out. You can also use this tip to spot promising sources of new customers and other good news like that.

In the practical example you’ll learn how easy it is to set GA to alert you when new visitors are landing on a 404 (“Page Not Found”) error page and give you a one-click route to the source of the problem so you can head over there and fix it.

That’s powerful stuff. If someone comes to your site and all they get is an error they may not give you a second chance.

To adapt a well-known analytics phrase: “They came, you slapped them in the face, they left”. So a quick fix for these problems is very valuable indeed. Using this approach, GA gives you a great way of sorting things out fast.

Once you’ve tried this very practical example, you’ll probably find that you want to apply the same method to other key events on your site, including good ones. It’s and easy but powerful technique — let Google Analytics do some of the tricky work for you!

The secret is to combine micro-conversion goals with GA Custom Alerts. The hidden super-power of this combination is that it uses the cleverness of Google to give you clues as to ‘why’ something unusual has happened, not just to flag up the fact that it ‘has’ happened.

First I’d like to thank @mattycurry for really driving this point home in a @boagworld podcast I heard a while back.

Example: How to Spot and Fix Broken Links from External Web Sites

Use this simple technique to make a “Read and React” report for spotting accidentally broken links from external web sites. In the age of user-generated-content there’s a lot of this about. I often see it with links being posted in forums on voucher or coupon code sites.

Using Custom Alerts I can not only see that there’s been a sudden increase in 404 pages:

But GA also provides me with an instant analysis of the source of these visits:

And, since these visitors are being referred from another site, there’s also a handy button to take me to the referring page with the dodgy link. So I can click straight over there and try to sort things out.

In short: GA tells me something’s wrong, suggests what the cause may be and even helps me on my way to providing a fix. Nice.

How to Set Up a Custom Alert Based on a Goal

The first thing you need to do is to make sure that your 404 page is being tracked in Google Analytics. I’ve written about this already in another post, which also contains an interesting discussion in the comments about how to get the best out of error tracking if you’re starting from scratch. But this time round I’ll assume that you can identify your 404 page as either a pageview or an event in Google Analytics.

Once you know how your 404 page is being tracked, you need to set up a Goal for it in Google Analytics.

The next thing to do is to set up a Custom Alert based on that Goal. It’s true that GA will show you automatic alerts for unusual Goal data without you needing to configure anything. But I currently recommend using a Custom Alert for two reasons:

1. Custom Alerts allow you to fine tune the timing and scale of the change which will trigger the alert
2. Custom Alerts generate the oh-so-helpful ‘Major Contributors’ report

Here’s a video showing you how to do those two stages:

And that’s it.

Now all you need to do is check the Google Analytics Intelligence reports each day and watch for those alerts. When you spot one, look at the “Major Contributors” then click through to the source and try to fix the problem. If it’s something someone has posted in a forum you’ve got a golden opportunity to sort out the problem and generally demonstrate your willingness to listen and to help.

I’ve seen discussions with broken links to a promotional offer start to turn hostile because people assume that the offer has been suddenly withdrawn. This situation is a classic example of where you can turn the impression of your brand all the way from negative, through neutral, and right round to positive just by showing up and being helpful. You can rescue thousands of visits by using this technique if you move fast and handle things well.

Final point on ‘what to do’ — don’t forget to add a Google Analytics Annotation to note what happened:

Why You Shouldn’t Rely Just on Email Alerts

To repeat one point from the video: at the moment I think you need to check the reports rather than rely on the automatic email and SMS alert options.

The Intelligence processing takes time and (in my UK time-zone) those emails don’t arrive until more than a day after the trouble began. With this particular alert you need to react as quickly as possible.

In fact I also add the 404 Page Goal Conversion Rate to my morning dashboard so that I can spot anything nasty as soon as I start work. I use a desktop dashboard client (Trakkboard) which is configured to include the current day as well as the last last month so that I can spot what’s happening in almost real time. That’s good in case something really bad is happening. But then I need to do some manual work right away to track down the source because I don’t have access to that handy one-click route via the “Major Contributors” report.

Use the Custom Alerts and Goals Combination Again and Again

The example I’ve given above is a very specific and practical one which should be relevant to many sites and which can help you take action to fix something which is broken.

You can also use the same technique to tell you when something good is happening and suggest why.

For example: you may well already have a Goal set up for the “Add To Cart” micro-conversion. This approach could point you at the source of an unexpected increase in purchases.

Better still, if you have a goal which matches the start of a “new customer” checkout, the “Major Contributors” report could alert you to a promising new referral source of business. This is the kind of thing you might not normally see buried in all the campaign activity to recruit new customers.

Google Analytics Intelligence is a really powerful tool for spotting things that you may not be actively monitoring. It shows you things you weren’t looking for. You can take a look at that unexpected source and see if there’s anything you can do to get more of those people interested in you. In the age of social networking, simply showing up and showing you care can bring big rewards. Intelligence Alerts can help you take an interest in things you would not normally have noticed. Give it a try!

Resources Related to Using Google Analytics Custom Alerts and Goals

• Previous article about Why Error Messages are a Shortcut to Improving Customer Experience
• Occam’s Razor by Avinash Kaushik: huge list of great custom alert ideas (be sure to read comments)
• Official GA Video On Intelligence Alerts:

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Learn the Secret Power of Google Analytics Custom Alerts

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Learn the Secret Power of Google Analytics Custom Alerts

You’re almost certainly getting a better return on your social marketing that you think. It’s just not showing up where you’re looking for it.

The recent announcement of Google +1 has turned the volume of excitement over social marketing all the way up to 11. But for some time now the people who count numbers will have been looking at the figures for visits to their web sites from social networks and scratching their heads.

“If social is so ‘big’ why can’t I see those visits in my site’s analytics?”

There is a big elephant in the web-analytics room. We are less likely to accurately track visits referred from social networks than we are visits from more traditional sources. ‘Less’ likely because the rise of those sources of traffic has coincided with (or helped cause) the rise in use of mobile apps or desktop clients at the expense of traditional web browsers.

So the visits you see as being reported as referred from one of the social networks are only the tip of the iceberg.

If your calculations of the return on your efforts in social marketing are based on those referred visits, plus the visits from campaigns which you started and tagged yourself, you’re only seeing a small part of the story.

How small?

Thomas Baekdal posted some interesting estimates of the effect of incorrect social referrers on his data a year ago. The situation will probably have become more extreme since then as mobile continues to surge ahead.

[Update: There's also a superb detailed discussion of the subject here: http://blog.awe.sm/2011/07/14/twitter-drives-4-times-as-much-traffic-as-you-think-it-does/ from one awe.sm -- who offer a solution for tracking the social activity which you started for yourself. ]

I recently had a chance to see this effect for myself. One of the advantages is having some very low-profile personal web sites is that you can sometimes observe things which might be lost in the general noise of a busy commercial site.

A few weeks ago Avinash Kaushik was kind enough to Tweet about one of my blog posts. The effect of having an industry leader like that post a link is instant and dramatic. I also know the exact source of the spike in visits.

Here’s the spike:

And this is where Google Analytics thinks they came from (date range changed to show just the day of the Tweet):

To break that down (who knows whether you can see the images wherever you’re reading this).

• The Tweet caused a clear spike of c170 visits (I really do believe we can agree that ’caused’ is ok here)
• 30% were counted as referrals by GA
• 60% were counted as direct by GA

Within the referrers (in v5 the Overview reports are now good for this kind of thing) we can see Twitter in top spot with 39 visits, followed by Hootsuite with 10 visits. Mobile.twitter.com sent 1 visit, by the way.

Avinash’s followers are tech savvy and the spike began as soon as he tweeted. The chances are the majority of those people were using some kind of app on their desktop or, more likely, a mobile device. Google Analytics had no referrer information and no campaign tag to tell it what to do, so it dumped the visits in the great bucket which should be called “unattributed” or “I haven’t a clue” but is know for historical reasons as ‘direct’. This does not just apply to Google Analytics, it will be true of any system which is using the referral data from the web browser to track the source of the visit. The ‘direct’ bucket is one of the standard features of web analytics.

To make matters worse, this kind of visit is the most critical aspect of social marketing.

Blasting out nicely tagged-up campaign Tweets or Facebook links back to your site is only really a slight variation on the traditional outbound direct promotion. The real strength of the social web is when your customers refer you to their friends and the word spreads from person to person simply on the basis of the quality of your products or services. That amplification of the reward for providing good service, products and value is the power of the social web. And it’s the part which is least likely to show up in your numbers.

[Update: At the risk of repetition, much of the discussion on this subject is revolving around the inability to track the return from social campaigns which companies have initiated themselves. There are solutions for that aspect already. But that kind of social marketing is very close to traditional on and off line promotion.

The aspect of on-line social activity which I think may be more significant is the ability for these new channels to amplify unsolicited mentions and praise of your products or service. Since you didn't begin those conversations, you can't tag them and you can't easily see the benefit in your site's data. We just don't know.]

The announcement of the new social reports in Google Analytics is extremely welcome. These will provide a great way for getting insight into how well people are reacting to what your are offering them. That’s hugely valuable. It seems a much better measure than calculations of engagement based on page views, time on site or viewing specific micro-conversions, because it is based on a specific indication of liking something or wanting to share it. There’s an element of intent involved in such an action.

But that data is still about what’s happening on your site. The great promise of the social web is that it will be a means for new people to learn about your products or services through the amplification of referrals. Unless I’ve got this wrong, we’re still largely in the dark about the scale of customer acquisition through social marketing.

Common sense says social marketing matters. I’ll go with that for now. But I’d like my numbers too. Any ideas where I can find them?

Updates on Tracking Social Media in Google Analytics

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If Social Marketing is So Big, Where Are All the New Visitors?

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If Social Marketing is So Big, Where Are All the New Visitors?

In the first two parts of this series I explained why on-site search is such a powerful tool for learning about the intentions of your visitors, and showed how you can use Google Analytics Site Search Reports to learn about your market and improve customer experience.

Now I’m going to wrap up the series with a couple of extra tips.

I’ll point out a short cut which will give you quick insight into site search information wherever you are in GA. Then I’ll show you where you can see in one glance the path which led someone to search, without using Path Analysis reports. Finally, there’s a list of links to further sources of information on site search.

Series Navigation Links

1. Start: Why Site Search is So Important
2. Previous Post: How To Use Site Search Reports in Google Analytics
3. This Post: Extras
• Tip: Configure Site Search as a Goal
• Tip: Use the Google Analytics Reverse Goal Path Report to Fix Navigation
• On-site Search Resources

Tip: Configure Site Search as a Goal

I recommend that you configure your search results page as a url-based goal in GA. Goal details are available almost everywhere within Google Analytics, so this means that you can quickly see the level of use of search wherever you happen to be.

More to the point, GA will automatically calculate a figure which allows you to compare the likelihood of different pages, sources of visits or whatever, to being involved in a ‘visit with search” — the conversion rate for your goal. So you can easily get an idea of the relative performance of those pages in terms of site search. This is a case where a lower conversion rate is good.

An example which might give you an idea of how useful this is would be to consider the ‘All Traffic Sources’ report. Being able to see the percentage of people who use search (the goal conversion rate) could be extremely useful. By doing this you could spot sources of traffic which seem to result in more on-site searches or where the rate of use of search suddenly changes. You can then ask why those people are more likely to use search and do something about it.

It’s true that you could get a similar effect using a Custom Segment based on ‘Site Search Status’ matching ‘Visits With Site Search’ (watch out, it’s case sensitive). But I favour using a micro-conversion goal because it brings all the other benefits of Google Analytics Goals reporting.

Tip: Use the Google Analytics Reverse Goal Path Report to Fix Navigation

Using a goal for the search results page also allows you to use one of the specialist goals reports: the ‘Reverse Goal Path’ report. This report will show you the three pages which were viewed prior to the search results page. One of those you already know about: the page before the search results page is the ‘start page’ shown in all the dedicated site search reports. But using this report you can see the two pages before that. This can be very useful information for getting an idea of the last stages in the trail of frustration which led to the search.

For example you may see high use of the same sequence of ‘Some Particular Category Page’, ‘Another Particular Category Page’, ‘A Third Particular Category Page’ (where the visitor gave up and entered a search term’ and then ‘Search Result Page’. This gives you real detail on how confusing your navigation is.

This would be a great example where putting the extra link on ‘Category Page 1′ would be a much better fix than adding the link to the search start page (‘Category Page 1′). The Reverse Goal path gives you strong evidence to think that the visitor was trying to find that product on those earlier pages and that the first category page was where they expected it to be. If you see high numbers of people following that particular path, you now know where to move the product or add an extra link.

This kind of analysis can eat up a lot of time, so be careful. But it is much more efficient than standard path analysis. In the previous version of GA there was even a nice bar graph ‘performance’ view to show you whether any particular path was really prominent. Right now you have to eyeball the numbers or export the data and work with it in Excel. Eyeballing it should be fine here: you only need a rough idea of what matters and what doesn’t.

I’ll close by repeating the sanity check: you DO need to make that judgement yourself on what matters and what doesn’t. Beware making a change which benefits a few but makes the site worse for most users. But provided you stop and think before making the final changes, the Site Search Reports are one of the most powerful secret tools in the Google Analytics toolbox.

On-site Search Resources

• The official instructions on how to configure on-site search reports in Google Analytics. There’s also an official GA IQ video on the subject (note: it shows the previous version of the GA interface):

• Avinash Kaushik wrote about Site Search when Google Analytics introduced these reports in 2007 and included links to his earlier posts on internal site search: Kick Butt with Internal Search Analytics. There’s also a Google video from 2007 of the man himself talking about the subject:

• Jared Spool is one of the original pioneers of website usability and has published a great deal of material on this subject. In 2010 he wrote a very detailed piece on the importance of site-search reports and in the past he has published great advice on how to design good search results page and classic research on whether there are some people who prefer to use search for navigation (No, that turns out to be a myth).
• Justin Cutroni wrote an excellent guide to setting up and using Google Analytics Site Search reports.
• Lou Rosenfeld is author and publisher of Site Search Analytics: Conversations with Your Customers (Rosenfeld Media, 2011). There’s a great interview with him on Boagworld: http://boagworld.com/usability/lou-rosenfeld and an article with some powerful advice on analysing site search reports on the Clicktale site.
• Get Elastic have published a list of links to useful advice on making improvements to the actual results (good idea!): 61 Tips for Improving Site Search

Here Are the Series Navigation Links Again

1. Start: Why Site Search is So Important
2. Previous Post: How To Use Site Search Reports in Google Analytics
3. This Post: Extras
• Tip: Configure Site Search as a Goal
• Tip: Use the Google Analytics Reverse Goal Path Report to Fix Navigation
• On-site Search Resources

Post from: CxFocus - making Customer experience count

Bonus Google Analytics Site Search Tips

Post from: CxFocus - making Customer experience count

Bonus Google Analytics Site Search Tips

In part one of this series I explained why site search reports are so valuable. These reports contain the words actually typed by your visitors, so they give you valuable insight into the intentions, needs and language of your market.

The site search reports do a lot of the heavy lifting for you: they give you lists of words you should be adding to your site and they show you where you should put them. The search terms also provide quantifiable information about the level of interest in new products and items which have been discontinued too early.

You can use this information to

• Attract people to your site by using the words they use themselves
• Make it easier for them to find what they want when they’re on the site
• Gain valuable insight into demand for new products and other market intelligence

In this post I’ll explain the specifics of how to do all that.

This post is part two of a series:

1. Previous Post: Why Site Search is So Important
2. This Post: How To Use Site Search Reports in Google Analytics
• Search Terms
• Search Start Pages Report
• Internal Search As Part of Your Routine
3. Coming up: Extras
• Bonus Google Analytics Site Search Tips
• On-site Search Resources

How To Use Site Search Reports in Google Analytics

I’ll use Google Analytics for my example because it is widely used and because its Site Search reports work very well in this context.

The two reports which are most useful are “Site Search: Search Terms” and “Site Search: Start Pages”.

Site Search: Search Terms

I recommend that you begin with this report because it gives you an overview of what visitors are searching for on your site. When you’re new to this process, the biggest gains will probably be found here.

The moment you look at this report you may instantly see some surprising and useful information.

Examples of things to watch out for include:

• Terms which are different words from the ones which you use on the site
• Words which are on the site, but people are using search to find them
• Searches for new products which you don’t actually stock (but might)
• Searches for discontinued items

In the next sections I’ll show you some of the things you can do with the clues you’ve found.

Your Customers Use Different Words

It’s very common for manufacturers and retailers to use industry terms and jargon which are very different from the language used by shoppers in our stores or on our web sites. Those industry concepts affect our classification of products and the category navigation of our sites.

• We design our sites to suit industry insiders, not the people using the site
• But on-site search reports teach us the words our visitors actually use

External search reports can do this too, but to a more limited extent — they only show the keywords which did result in a visit to the site. Which means that the relevant words probably are already on the site. These reports don’t give you information on those other words, the ones people are using which are not the ones on your site and which are not bringing visitors. Those potential customers don’t even get to your site. (By the way don’t forget: Google Insights for Search can provide some very useful on information on such phrases and the volume of people who’re searching for them.)

The fix for this is obvious but it may be easier said than done.

You need to change the language used on the site to match the words your customers use. Those trigger words are the ones they are scanning for when trying to find what they want to buy. These search clues may turn out to be the tip of an iceberg of lost sales.

Many other people may have scanned for those words and decided that you don’t stock what they want. Search is the navigation method of last resort: those who actually searched were giving you one last chance. Other people would have gone back to their Google results and on to your rivals.

• Changing the language in your product copy may be hard in between catalogues or seasons. But you need to do it
• Changing the language used for categorisation and navigation is probably even more of an upheaval. But you need to do it

As ever, the secret is to test. Choose the search term with the highest volume of searches. If possible configure a GA goal to match the URI of the relevant destination pages (regular expressions make it possible to match several different pages). This way you can establish a benchmark conversion rate for this micro-conversion.

Make the change: add these words to your navigation, product names and description as appropriate. Then see if the volume of search for that term goes down and, more to the point, the traffic to the relevant pages via navigation goes up. Keep an eye on your organic search traffic as well. Since you’ve added a popular search term to your navigation structure, as well as including it in the copy, you should pick up some extra external search benefit as well. This could well start to form a virtuous circle, attracting more people who will see the trigger words prominently on the site and will be more likely to buy.

Your Site Navigation and Copy Use The Same Words, So Why Do People Search?

If you spot a large volume of searches using words which are the same ones as you use on the site, you need to find out why people are needing to search for them.

By ‘large volume’ I mean anything which stands out as having an unusual share of search when compared to the spread of other terms in the report, or compared to your expectations of the market. You can use the GA ‘performance view’ of the data table if you would like some percentages calculated for you — but it’s usually enough to eyeball the standard table to spot any interesting cases.

Next you need to find out where those searches were taking place. Click on the search word to drill down to next level of detail. The default view will show the ‘Destination Page’ as the dimension in the table rows. For our purposes we need to see the ‘Start Page’ instead. You’ll find it within the ‘Other’ dimensions menu, which is easier to show than to describe:

On the left of this report you can see a list of the pages where people searched for that term. Where were people when they decided to search for whatever-it-is.

[At this point I'll mention something that you may find puzzling. It is not unusual to find that many searches start with an 'entrance'. That means that the first page view recorded in the session was the search results page. A common reason for this would be if someone has used a link to a particular search results page on another site. Sometimes you may even choose to use such an approach for PPC advertising so that you land people on a specific dynamic set of products without needing to build a special landing page or category page for them. These pre-built searches do not involve the visitor personally starting a search, so they are not relevant here. If you discover that the term you are looking is one of these pre-configured searches, ignore it for now.]

Next go to the page on your site where the term you are interested in was used. Ask yourself why someone looking at that page would decide to enter that term into search. Later you might need to get into the details of where the people who searched had come from, because that might have an influence on why they would expect to see ‘that term’ here. But it’s often enough to just accept the simple fact that a load of people were on this page and typed what they did into the search box. They were on the page. They wanted to get to something related to ‘that term’ and they could see nothing to click.

In this case, where people are searching for words which are already on the site, you know what those visitors should have done to get to the right place. But it wasn’t obvious to them.

The solution here is to make the route to the relevant page more prominent. That may not be easy to do if it would require making changes to the permanent navigation of the site. So it would be a good idea to do a test first in order to measure whether you can get an improvement and whether the return on investment would be justified.

For the purposes of this test you need to create some form of manual change to the start page to add a prominent extra link. If it’s the home page or a category page perhaps you might have content-managed promotional areas where you could fit in some form of banner-type link. If people are ending up on a product page and then consistently searching for something else, then you’ve probably got a cast-iron case for using one of the “you may also like” slots, if they’re a feature of the site!

Somehow or other you need to manually add that prominent route to whatever it is people on that page are searching for. Then monitor to see whether there is a drop in the level of search for that term and whether the micro-conversion rate for people reaching the relevant page goes up. If you set up a GA goal for the relevant page you could even use the “Reverse Goal Path” to see whether people were coming from the page where you added the manual link. And you need to track the increase in revenue from the relevant products to see whether the investment is justified.

Market Intelligence

Those points about “New products you don’t actually stock” and “Discontinued items” are important too. They’re another classic example of how web analytics can provide market intelligence. This work is not just about improving your website: the website should be a valuable source of information for the whole business. These search reports can give you information about the demand for different products.

Site Search: Start Pages Report

The value of the start pages report is that it matches the way many of us see our own sites. The structure is based on pages which is often the perspective of someone working behind the scenes. This can make the Start Pages Report a very practical tool for actually working on the site.

The first thing to do on this page is to switch the view of the data to ‘Performance’ so that you can see the start pages listed in descending order with Total Unique Searches expressed as percentage share as well as actual numbers. You’re mostly interested in the share, but keep an eye on the numbers too. Search is not heavily used on most sites, other than ones in markets like books, music and technology which are particularly suited for searching, so you need to be aware of when to cut off and avoid wasting time on items which affect only tiny numbers of people.

It’s likely that the homepage will be the top search-start page. If there’s a very steep drop-off, so that all the other pages have low single-digit shares of internal search, then that suggests that people are resorting to search at various times in various places. That’s generally a healthy sign. What you’re looking for are examples of other pages which are getting a higher share of search than similar pages on the site. That’s not happening in the example below, where the start pages are spread very widely throughout the site, with only the home page standing out as prominent.

Keep an eye out for pages in the top positions with a relatively large share of search.

Category pages are worth a careful look. Even if the highest category page in the list does not stand out as having a particularly high rate of searches, it might be worth digging into this subject just in case. Use the inline search filter (now located above the data table in GA v5) to include only the category pages. That way you will be able to compare the share of search between category pages to see whether any are particularly bad. In order to do this, you’ll need to work out what pattern in your site’s URI appears in all the category pages. If you can’t do that, you may not be able to filter the report and will just have to study the results as best you can.

Category pages deserve this special attention because they are ‘routing’ pages. Their purpose is to help people find things via browsing navigation. So if people are switching to search on a category page, it suggests that browsing navigation has failed and the customer is becoming frustrated.

You now need to perform the reverse of the process described in the ‘Search Terms” section above. This time you know where they started the search, so you need to drill down into that page in the report to see the internal search terms people used.

Then you need to go and look at that page on the site and ask yourself why people on that page would search for that term, and what you can do to help them.

In the case of category pages it could be that the customers do not have the same expectations about how different types of product should be grouped. This is often the case in my experience. The Search Start Page report can throw light on this by highlighting cases where a significant number of people expect a particular item to be in a category only to discover that it’s not there.

Please treat these reports as powerful clues to navigation problems, not definitive answers. Moving a product from one category to another might please one group of customers but make life hard for those who have had no problem finding it in the original location.

Why Do People Search For Things Which Are Already There?

There’s another kind of category page issue which might be more surprising, but is less troublesome to fix. You may find that people on a category page are searching for items which are already displayed on that page. For some reason they have navigated to that page, or landed on it from outside the site, expecting to see some ‘search terms’ but they haven’t spotted them, so they search instead.

Product names can be a problem here. If the product name does not include what the product ‘is’ and the category page only displays the name, then the word the customer is looking for is nowhere on the page. (You’ve also thrown away a Search Engine Optimisation opportunity as well, but we’ll concentrate on the needs of real people, not search engine spiders, for now.) You might have some short persuasive copy for each item, which can be a great sales tool — but that’s more text and less likely to be read, even if the relevant word is in there. You need to fix the name itself.

If the name doesn’t include the trigger words, you’re depending on the customer recognising the product in a quick scan of the pictures. Which in turn depends on the customer knowing what the item looks like, which is not something you should assume in all markets. Even if the customer has an idea what they’re looking for, their expectation might not match the way you display your products. Perhaps you show them as part of groups, perhaps those groups are in a context such as a ‘lifestyle image’?

People scan very rapidly when they are browsing through a site. The headlines and the images get a brief glance. The smaller text scarcely registers. There are loads of reasons why something which is obvious to you might not be obvious to your customers. The start pages report is telling you: “they came, they didn’t see, they searched…”

As ever, the solution is to test. Select a problem page and see whether you can make a difference. I would start with the words, because they’re almost certainly the easiest to change. But if you can manage to include different types of product picture in your early tests, then you will learn lessons which you can apply to all product photography next time round.

Internal Search As Part of Your Routine

Once you’ve been working with this data for a while you will become familiar with the internal search terms used by your visitors. Ideally you will have taken action to make it easier to find things, by adding those words to your navigation or as on-page trigger words.

But the search terms report has a continuing use once the first round of improvements is in place. You should monitor the report for increases in searches for new terms.

Watch out for the sudden “out of the blue” appearance of a new term. This usually means that something is driving people to the site with an expectation that they are going to find something to do with that word. And they’re not finding it.

A classic example would be a promotion you have launched, with a particular name or perhaps an offer code attached to it. If something goes wrong and the landing page for the campaign does not carry matching material and trigger words (the ‘scent’ of the promotion), then you may well spot this in the internal search keywords report.

Another example would be people starting to search for seasonal products earlier that you would have expected. This could provide evidence that you should be adding your seasonal ranges to the site earlier and maybe launching some of your seasonal campaigns earlier.

Perhaps at some point Google’s ‘Automatic Alerts’ and ‘Custom Alerts’ will start working with external and internal search keywords? It would be great to know about sudden increases in the volume of searches for various terms. But for the time being it’s useful to keep an eye on the internal search terms report to try to catch this happening.

When you spot the sudden appearance of a search term like that, drill down into the search terms report and then use the ‘viewing’ option above the data table to change to look at the medium, source or other campaign data to find out where these people are coming from.

If, for example, you find something like ‘email’ is bringing visitors to the page but they are then searching for some promotional term, then the chances are that your email is promoting that term but the relevant trigger words are not on the landing page. Fix the page. ‘Email’ is just one example. Affiliate voucher and coupon campaigns are also prone to this kind of mistake. It even happens with paid search.

Examples like the ones above can be expensive mistakes. So checking your site search terms each day can be a wise idea. These days that’s a doddle to do. The latest version of GA allows you to add Widgets to your dashboard, so you can have a list of your top site search terms right there for you to glance at each day.

That’s it for now. This long article comes down to one simple point. Listen to the language your visitors are using. They’re probably telling you things you ought to know.

In the final part of this series I’ll give a few more tips and some further sources of information and guidance.

Meanwhile, here’s that navigation menu again:

1. Previous Post: Why Site Search is So Important
2. This Post: How To Use Site Search Reports in Google Analytics
• Search Terms
• Search Start Pages Report
• Internal Search As Part of Your Routine
3. Coming up: Extras
• Bonus Google Analytics Site Search Tips
• On-site Search Resources

Post from: CxFocus - making Customer experience count

How You Can Put Google Analytics Site Search to Work for You

Post from: CxFocus - making Customer experience count

How You Can Put Google Analytics Site Search to Work for You

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

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:
   • Bonus Google Analytics Site Search Tips
   • On-site Search Resources

Why Site Search is So Important

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.

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!

Post from: CxFocus - making Customer experience count

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

Post from: CxFocus - making Customer experience count

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

Separating your ‘Brand’ searches from ‘Generic’ searches will help you measure and improve your customer acquisition and retention programs.

Consistency is important here if you are to make and track changes over time. So this article explains the starting point for using this information in a systematic way in Google Analytics.

As a bonus, the process itself may also reveal a hidden ‘long tail’ of Brand keyword variations which you did not know you had.

• What are ‘Brand’ Keywords
• Why Brand Keywords are different
• Which are better, ‘Brand’ or ‘Generic’ visits
• How to use inline filters to build a Brand Keyword list
• Ideas for using your Brand Keyword list
• Resources related to using your Brand Keyword list
• Update: August 2011. Brand keywords in Custom Groups for Google Analytics Multi Channel Funnels

What are ‘Brand’ Keywords

Brand keywords are the words which people use to search on Google (or other search engines) when they are specifically trying to find your company’s site or products.

The obvious examples are the name of your company, your brand, or the actual address of your web site.

Plenty of people enter web site addresses into Google instead of typing the address into their browser. This is perfectly sensible behaviour. Google will automatically take care of typing mistakes. And these days Google’s “Instant Search” will probably bring up the correct result after you’ve typed the first few letters. So it’s a quick and easy way of getting to a site.

This form of searching is often called “navigational search”.

The less obvious examples are when someone searches for a product name or other word which is so closely associated with you that it is as specific to you as your actual brand or company name.

Why Brand Keywords are different

When someone uses a brand keyword to search for your site it is obvious that they already at least know your name.

Their intention is also fairly clear. It’s reasonable to assume that they wanted to find your site: they searched for your brand and clicked on a link to your site.

You cannot assume that all these visitors have been to your site before (unless GA shows them as a returning visitor). Their awareness may have been created by off-line experiences or promotions or other forms of on-line activity.

But it’s also common for Brand searches to be used by people who are relatively new to you, but have conducted some earlier Generic searches while in reseach mode. Brand search can indicate someone who is well down the bigger sales funnel and is close to becoming a customer, as well as existing and loyal customers.

The key point about brand keywords is that they indicate that the visitor already knew of your company to some extent. You would expect a different pattern of behavior compared to visits which began with a non-brand or ‘generic’ search.

A very specific example of the difference is that you would expect to see much stronger performance from paid search and also better ecommerce conversion rates from both paid and organic Brand search. The paid search click through rates, cost per click and return on advertising spend are likely to be much better than Generic search and so they should be managed and reported on separately.

Which are better, ‘Brand’ or ‘Generic’ visits?

This is a classic “it depends” question!

If you get a lot of visits starting with a Brand search then you could take this as an indication that people are conscious of your brand. That’s wonderful: your name is in people’s minds and they are searching for you. This is a great indication of ‘Brand Awareness’ and you may well want to track this. This is very valuable information if you’re doing a lot of off-line promotion or on-line social marketing, both of which may be building awareness without actual clicks to the site.

If you get a lot of visits starting with a Generic search then that indicates that your site is probably ranking well in the search engines for those terms and that your site’s title fields and descriptions are attractive enough for people to click through to your site. That’s also wonderful: it means you might be able to acquire new customers who have not yet heard of your brand.

So the answer is, indeed, “it depends”. More of both types of visit would be very welcome, of course.

In my experience of working with medium sized ecommerce sites operated by multi-channel retailers with significant catalogue and high street businesses, it’s common to see a lot of Brand search and not so much Generic search. So in these cases I think it’s more encouraging to see an increase in Generic search.

But if your brand is not so well established, and you do not have such powerful off-line promotional channels, you might be more interested in seeing the level of Brand search going up.

How to use inline filters to build a Brand Keyword list

The starting place for this process is the normal Google Analytics ‘Keywords’ report in the ‘Traffic Sources’ area.

When you first load this report, the chances are that you will see variations of your obvious brand terms filling the top part of the table.

But the aim of this process is to also identify the many variations on your brand terms which may be in use alongside the obvious ones.

So I recommend increasing the date range to make sure you have a decent representative sample. It’s also a good idea to increase the number of rows being displayed in the table so that you are able to scan a good number of keywords.

You need to build a list of words which can be used in a filter to match all your brand terms. The best way of doing this is to use the ‘inline filter’ at the bottom of the keyword table.

At the time of writing, the ‘Advanced’ inline filter function only allows you to build up filter patterns which contain a series of ‘and’ matches. This is unlike the similar interface for ‘Advanced Segments’ which allows for ‘or’ matches as well. So, although it’s reasonable to bet that at some point the Advanced Inline filter will also allow for ‘or’, right now you have to use the old method.

The old (current) method is to build up lists using the ‘pipe’ symbol: ‘|’, which is treated as ‘or’ by the system. This symbol appears as the shifted ‘\’ key on most keyboards, but its location varies. It’s bottom left on my PC and over on the right near the ‘return’ on my Mac.

The process is to start with the first most obvious keyword variations such as

‘brand’ and, for example, things like ‘mybrand’, or ‘my-unique-product-name’

which would be represented by

brand|mybrand|my-unique-product-name

• Start by filtering the keywords so that they ‘include’ only words which match
• Check that nothing which shouldn’t appear is in there
• Then switch to ‘exclude’ those words
• Look through the new list and find other examples which are actually brand terms
• Add them to the list with | in between each one
• Switch back to ‘include’ view
• Check that nothing which shouldn’t appear is in there
• Toggle back again
• Repeat until you feel you’ve got the main list

This process is much easier to show than to describe in print. So this video may make it easier for you to understand:

Once you understand the process you will probably find that you can shorten your list by editing it down to versions of the keywords which match more variations in one go. But be careful when doing this in case you ‘catch’ more than you want.

Once you’ve build up the list, make a permanent copy of it which you can cut and paste whenever you need it. It’s a very important list and you will be able to use it again and again in many different ways.

Ideas for using your Brand Keyword list

Once you’ve created your list of Brand Keywords you can use it to gain a much better and consistent understanding of these two key groups of search visitors: those who already know of you and the generic searchers.

Examples of places where you can use this list in Google Analytics include:

• Custom Segments: one for Brand search, the other for Generic Search
• Segmented Profiles: use ‘Include’ filters based on your list to create profiles showing only Brand search visits and only Generic search visits. (Hint: this last one can be a real eye-opener and provide a frightening glimpse of how your site performs for people who are not already familiar with your company.)
• Rolled-up Reports: use advanced filters in an extra ‘Rolled Up’ or ‘Summarized’ profile to overwrite all the actual keywords which match your Brand keyword list with something like ‘a Brand Search’. This can allow you to keep the volume of Brand search in mind but also pay more attention to the detail of Generic search. Without this kind of rolling-up, the Generic terms can easily get lost in the crowd of Brand keywords.
• Multi-Channel Funnel Custom Channel Groups: I’ve written a specific article about how to work with your brand keywords in Google Analytics Multi-channel Reports.

Resources Relating to Using Brand Keywords

• L3 Analytics have a great article on how to use GA filters for Search Term Categorisation which gives you a great example of how you can you your list in a very clever way. It also has a series of other links to some more related resources.
• [UPDATE IMPORTANT: August 2011] Google have just introduced ‘Multi-channel Funnel Reports’ which have a remarkably powerful new facility for working with customised groups of keywords. This is extremely relevant to the approach I have discussed in this article. Full details of how to use Brand Keywords in MCF are explained in my article here: http://www.cxfocus.com/index.php/google-analytics-tips/multichannel-funnel-reports-group-brand-generic-search/

Post from: CxFocus - making Customer experience count

How to start working with brand keywords in Google Analytics

Post from: CxFocus - making Customer experience count

How to start working with brand keywords in Google Analytics

If 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
• Why use virtual page views to track errors
• How to choose a pageview structure to get the best from the content drilldown report
• Resources related to tracking error messages in Google Analytics

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:

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 new 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. However I will need to conduct some tests before I am clear on whether an 'Event' would appear as the abandon route from a checkout funnel stage. It seems unlikely to me that this would happen. In which case the argument for using a virtual page view in this context remains valid.]

Using 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 Trakkboard Google Analytics dashboard 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:

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!

Resources related to tracking error messages in Google Analytics

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

Post from: CxFocus - making Customer experience count

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

Post from: CxFocus - making Customer experience count

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