I see many times people looking at the above report and when they spot a big bounce rate  they panic. They start screaming the page is good for nothing as only 17% of the people stay on the website while the others are leaving.

Sorry to tell you, but no, it’s not really like that.

First, bounce rate is not calculated in reference to page views but to visits. To look at the above report and say that 83.33% of  1681 people bounced is simply wrong. Just as it is to say that 83.33% of 1427 bounced (if you relate to unique views).

So, how to interpret the above report

If you are really interested to find out how many people bounced from this page we need to find out first how many people landed on it. Bounce rate is reported only to people (actually, visit sessions) who land on a page. People that visit it by coming from another page on the same website are not counted for bounce rate (they didn’t bounce in the first place).

So, go to “Top landing pages” and check the numbers for the page you are interested in. Here is what we get for the above page:

Just 5 people bounced. Really, no need to panic.

So, before doing radical changes on your website to improve bounce rate just make sure you got it right. Bounce rate can be a deceiving metric. If you really are interested in measuring it right, try implementing the real bounce rate in Google Analytics.

Here is another article on the same topic: Why Bounces is a Better Metric than Bounce Rate.

• How not to loose what we have gained so far
• How to make things better than before

Everybody is planing and working like crazy for the above objectives, then the new website gets launched and suddenly, all eyes are on you: the web analyst. After all, who else can answer those questions better than you?

It’s not a very good idea to wait till everything finishes and get the tracking going at the end… nope, not wise at all. You’d rather make sure you will be able to answer the above questions in detail and some more. Based on a project we have recently worked on, here’s our web analyst’s technical checklist for (re)launching a website:

Prepare for (advanced) web analytics

Start by finding out the expectations for the website of the other departments. Work with the dev team from day one to make sure that your web analytics tool will be able to report on the established KPIs.

Tip: Number one priority for the projects we worked on was to be sure that we can track both micro and macro conversions.

We use the following checklist:

• The back-end allows for global tracking settings. This way you make sure that each page has the tracking code placed properly.
• The global tracking can be overwritten on specific pages of the website directly from the backend
• All actions on the website have designated Thank You pages (makes conversions tracking a breeze)
• Error pages are being tracked as well (404, 503, etc.)
• Outgoing and download links can be tagged manually and/or automatically
• eCommerce data is accessible through JavaScript (we usually request a JS array containing the eCommerce data)
• Custom Variable data is available through JavaScript arrays (mostly used by advanced solutions like Omniture or WebTrends)

When all implementations are finished, use a tool like WASP to check that everything works like it is supposed to. The website should not go live unless everything is tracked accordingly. Having all that in place will offer an infrastructure that will make future tracking implementations easy.

Things don’t stop here though. Your job is not only to analyze but rather to help improve. So, the next step is to…

Prepare for A/B testing

Sooner or later, if you haven’t already, you are going to start A/B testing. Checkout the main tools out there for their technical requirements in order to do A/B or multivariate testing and see if the new platform will be able to handle them. Get your specs to the dev team before the work gets started as after might be too late. In case you will go with Google Website Optimizer, you will need the following:

• The possibility to add JavaScript snippets to any page’s footer and header;
• The possibility to add invalid JavaScript to the body of any page;
• The possibility to add JavaScript codes to Thank You pages or instances.

Have this and you’ll be able to roll out any A/B testing campaigns in minutes. Make no mistake about it: with any website redesign testing ideas pour in like nothing you’ve seen before.

Prepare for personalization

This is something that very few people actually prepare for. A/B testing is not the only method out there for increasing conversion rates. Personalizing the experience for different segments of traffic and behavioral targeting have great results for boosting up conversions. So, what are the basic things you can do for having a website platform that can customize itself based on user behavior:

• Make a list of the possible segments of traffic you might consider for targeting
• Ask your developers to create a framework in which you can populate cookies with data specific to those segments

What is important is just to have the framework in place… no need to populate the cookies with anything for now. However, when you will want to start behavioral targeting campaigns you should be able to do it easy and fast.

Ok, this is what we have been using so far. What about you. What do you have in your checklist for (re)launching a website?

Other great resources on the web about website (re)launch checklists, not necessarily related to analytics:

• Website Prelaunch Checklist
• The Ultimate Website Launch Checklist
• Checklist before (re)launching a website for the win!

However, one thing is 100% sure in any web analytics tool out there. When a user is being identified as a returning user, you can bet on it that that’s how it is. Don’t get me wrong here. There are returning visitors that your tool sees as new ones (they delete their cookies) but all identified as returning are definitely users that have previously visited your website.

No more accuracy excuses in this area. Time to get to do some analytics work. Segment only for returning visitors data and let’s dive in for some insights. Here are some questions most of the web analytics tools can answer:

How often do returning visitors come to my website?

Here is how a tool like Google Analytics presents us this information:

Recency Report in Google Analytics with "Returning Visitors" segment activated

Once you know how often they visit you, use this indicator to know how frequent to update the marketing campaigns to leverage a better retention. In the above image 40% of the traffic visited at least 1 day before the last visit. 10% of the visitors visited at least 8 days before.

The next thing would be segment the website traffic only for those visitors and see if they have a higher conversion rate. If yes, that’s the response for how often to update the blog or send a newsletter.

What’s the conversion rate for your returning visitors?

Do the returning visitors convert better than the new ones? If yes, a good strategy could be to boost up retention marketing campaigns.

When I say returning visitors I am not talking about visitors who have purchased something from you and now are coming to the website for support. Those visitors should be filtered out. Most of the analytics tools allow you to do that by using custom variables.

After finding the conversion rate for your returning visitors dive in to find out the following:

• how often does a visitor comes to your website before converting?
• how many days between the first visit and the visit when he became a customer?
• do returning visitors bounce more or spend less on the website?

What are the entry points of returning visitors?

Go to Top Landing Pages while having activated the "Returning Visitors" advanced segment

I guess homepage will be the biggest hit here, but look deeper. You might discover some low hanging fruits. It can be a good idea to recheck the copy of the landing pages that are main entry points for returning visitors.

Tackle homepage as well. Some little JavaScript hacks and you will be able to offer a different heading copy or call to action to your returning visitors. Test. Experiment. Write to us

One thing is for sure: returning visitors don’t have the same behavior as new ones. Don’t believe? Just compare the data in your analytics tool. Now, that we got this straight, don’t you think it’s not very efficient to offer the same website experience both to new users as to returning ones?

How can you act on it?

Well, if you just own a blog, there are at least 2 WordPress plugins you can use to differentiate between new visitors and returning ones:

• What would Seth Godin do? (love this plugin name)
• Published articles since last visit (I contributed to the development of this plugin as well)

If you own a complex website things can be easy as well. We are rolling out beta accounts to our behavioral targeting platform which you could use to do just that: offer a different user experience to returning visitors.

So, how would you like to treat your returning visitors?

Google Analytics changed the game of web analytics by making it accessible to everyone, but can it get you reports like the conversion rate of all the visitors that were once referred by a friend through a provided form on your website? What about the number of visitors that purchased on your website after downloading one of your whitepapers? No, it can’t, not out of the box. However, using custom variables and some little implementations on your website, the above reports are piece of cake.

What makes custom variables really good? It’s the fact that you can set them up to be persistent across sessions. So if a user downloads a pdf file today and comes and convert on your website next week, Google Analytics will be able to report that he is part of the segment of users who once downloaded a pdf. You will know his behavior across different visiting sessions.

How to start with custom variables

One starting point can be to tag the users that micro-convert on your website. Most of the times the micro conversions happen through a form and forms make custom variables really easy to implement.

Why micro-conversions? Because it already shows engagement from your visitors.

What to look for? How many of the people who micro-converted got to eventually macro-convert e.g. How many of the users who downloaded the trial came back to the website and bought it? How many days after? Yes, Google Analytics can give you this.

If the terms of micro conversions and macro conversions are still strangers to you, check out this great explanatory tutorial provided by Avinash Kaushik.

How to do it

Just forward to your dev team the following 2 resources and they’ll know how to continue from there:

• Custom Variables, Part II: The Code – LunaMetrics ninjaness
• Custom Variables Documentation

Attention: Custom variables are addictive. Once you start using them, you won’t be able to stop 

Most web analytics tools tend to track the outcomes of a visitor actions: the pages that load after users click a link or fill in a form. While for links I believe that is the right way to go, with forms things stay different. No matter how well optimized, there is always going to be an abandonment rate. With forms, things are a little bit more complex. So, how do we track them?

Tracking form errors

Tracking form filling errors is the first thing you wanna do when you want to know more about the performance of your forms. Make sure you have implemented a form validation method though. Check this tutorial on how to do it if you haven’t done it yet.

Each time a user gets an error make sure you trigger a JavaScript snippet as well that will send the data to your web analytics tool. If it is Google Analytics, use events to track the error. Here is how it could look like:

//use the following code inside your JavaScript validation function
pageTracker._trackEvent('Form Errors', 'Field Name', 'Field Error');
//replace Field Name & Field Errors with their respective values

Funnel vs last filled-in field

One of the pitfalls of tracking forms is that some analysts see forms as being funnels.Tracking them as such will get you a huge amount of data which will be not that easy to analyze. Wouldn’t it be easier to track only where users drop of, therefore the last filled-in field of the form? You’ll get less code to write (hint: onunload javascript function) within your forms and cleaner data to analyze. Double win.

Another trick can be to use a heat map tool for your form pages. Crazy Egg worked the best for me in the past.

Tracking and flagging success

A major issue with forms that use the POST method is that many times the “Thank you” page has the same URL as the form itself. Most web analytics tools track URL’s so you won’t be able to make the difference between a user loading up the form and a user completing the form. 2 things I suggest to do here:

• use custom tagging for successful form filling
• flag the visitors who have filled up a form with success so you can track their behavior from that point on

Here is one way to do it using Google Analytics on the page containing the form:

//the following example is based in php 
//can be replicated for any other scripting language 
<script type="text/javascript"> 
try { 
var pageTracker = _gat._getTracker("UA-xxxxx-x"); //use your own UA ID 
<?php if(formsuccess) 
{ 
   $pagename="form-end";
   echo ("pageTracker._setCustomVar(1, \"Form\", \"Submitted\", 2);"); //flag the visitors   
} else $pagename="form-start"; ?> 
pageTracker._trackPageview(<?php echo ($pagename) ?>); 
} 
catch(err) {}
</script>

Optimizing forms

When it comes to optimizing forms there is a great resource out there on the web. His name is Luke Wroblewski.

He has a great blog dedicated to forms and user interactions. When it comes to form design I highly recommend Luke’s book about Web form usability, visual design, and interaction design considerations: Web Form Design: Filling in the Blanks.

web ink

Try to answer the following question: how well is your website doing when it tries to engage visitors that have a clear focus? I am talking about those visitors that land on your website by searching for a specific keyword on search engines, unrelated to your brand.

1. Start by setting goals for each action you deem means engagement

The following video will offer you clear details on how to do exactly that:


Here are a bunch of examples of actions on your website that can define user engagement:

• buying a product
• filling up a form
• subscribing to newsletter or RSS
• sending you feedback
• commenting on an article
• recommending you to a friend
• tweeting your content

2. Define a non-brand keywords visits segment: that is, visitors that land on your website when searching with keywords unrelated to your brand

If you have never created segments before, check out this video tutorial.If you are familiar with segments, dive right just in and define one just like in the following image:

Replace "your-brand-name" with a keyword specific only to your brand

3. Check out for winners and losers

It is time to check out how well your website is doing when it comes to engaging your visitors. Apply the above segment to your Google Analytics data and dive in the following report: Traffic Sources > Keywords. Filter for non-paid traffic and look at the Goal statistics, just like in the image below:

Check out the conversion rate for each keyword

That’s about it. Now that you know, how do you plan to improve performance?

…or the scientific approach to actionable web analytics

Web analytics masters like Eric Peterson, Avinash Kaushik, Anil Batra and many others have been advocating since always for actionable web analytics. They have dedicated blogs and entire books on how not to be a reporting monkey but rather be an web analytics ninja.

The road to web analytics ninjaness ain’t easy. Whenever you think you get a big WOW from your web analytics data don’t just run to the IT department and tell them everything they did is plain wrong. Take a deep breath and try to answer some questions before.

How reliable is the source of your discovery?

This one gave me shame moments for a couple of times so far. On my dashboard I have a big widget for the conversion rate. One day, entering the web analytics solution it showed a huge drop. I got everybody in the company fired up, just to realize that nothing happened to the conversion rate. It was just a bug in our website that affected the way we measured a couple of our metrics.

Now, whenever I get WOW or OMG moments, the first thing I do is to do a WASP debugging of the pages are guilty for my WOW moment. I make sure everything is still measured correctly.

Have the claims been verified by other departments?

In the above scenario, when I got everybody fired up with my conversion rate drop, just before entering the CEO and giving the awful news,  the sales department told me that according to their reports, for the same day sales were up and not down. The marketing department told me that no special marketing campaigns are on so the traffic should be the same… All of them suggested me to calm down, drink some tea and go double check.

Since then, whenever I analyze revenue reports in my analytics application, I make sure to double check it with the other departments. If they confirm, now it’s the moment to get excited.

Is this how websites work?

This question actually helped me quite some times so far. Here is an example: A report is showing that a page has 100% bounce rate however people visiting that page bought a product in the same session. How is that possible? No, this is not how websites work. If a user bounces than he can’t convert: he is not on your website anymore. So there must be something wrong with this report

Actually, it isn’t. What is wrong is the way some people look at it. Bounce rate is calculated only for landing pages. Let’s say that page has been seen by 1000 visitors out of which 5 coming from search engines directly to the page and the others coming to it from other pages in the website. If all the 5 visitors coming from Google bounce, than we have 100% bounce rate. However, any of the other 995 users might of have bought something.

If things just don’t make sense, try to figure them out. If they don’t make sense to you, they won’t to your management as well.

What else could be explained with the data you get?

This is a classic one. When you see that the time spent on your website increases it doesn’t necessary means that users decided to look into more products of your website. It can also mean they don’t find what they are looking for. The same goes for increases or decreases of the pages/visit rate.

Whenever you are ready to take a decision based on the data, question yourself  if the same data can’t explain for other behaviors of your users as well.

Where does the other evidence point to?

The metrics you get for your website visitors are interconnected. When one metric is affected bet on it that other metrics will be affected as well. What does it mean when pages/visit increases but time spent on website doesn’t? What about the other way around? What if the drop in bounce rate has a very small impact on time on website? Does it mean people really stopped bouncing or might there be another explanation?

Whenever you want to act based on the web analytics data you have, make sure that any other metric you take to analyze, fits in your theory.

Are you taking it personal?

This is a tough one. As mastering the web analytics ninja way of life, your intuition becomes stronger as well; sometimes too eager to adopt new things. If during your experience the conversion rate increased only by working on the funnel, it doesn’t mean that’s the only way it can be increased.

Always question your personal beliefs and share them with seniors. Don’t take their word for it (they have their own personal beliefs) but rather use their arguments to see if your belief can still stand up.

Conclusions

Be scientific about it. Web analytics should not be treated as a walk in the park but again, it’s not rocket science for MIT graduates as well. Be skeptical.

What other questions/methods are you using to validate your web analytics actions?

This article idea came out while going through the Skeptical Magazine, which I recommend to any geek out there.

So, who subscribes to your newsletter?

In order to make a profile of the visitors who subscribe to your newsletter, first add a Goal to your Google Analytics implementation. A goal would be the URL of the page that says “Thank you for subscribing”. In case you use double opt-in, which I highly recommend, it is the URL address of the page that confirms the subscription.

Here is how your goal setup should look like:

Creating a Google Analytics Goal for newsletter subscribing

Define a new segment for newsletter subscribers

In case you are not familiar with user segments in Google Analytics, take a look at this video tutorial. Here is how your new segment should look like (that is if your Goal for newsletter subscribers is defined as Goal 1):

Defining a segment for newsletter subscribers

Now, all you need to do is get back to your Analytics data, apply the newly created segment and see what you find out about the people who subscribe to your newsletter. Try to answer the following questions:

• Are they mostly new or returning visitors (Dashboard > Visitors > New vs Returning)
• How many pages and how much time do they spend on the website before subscribing (Dashboard > Visitors > Visitor Trending)
• What are the pages that are most persuasive in convincing users to subscribe ($index metric in Dashboard > Content > Top content)
• What are the most common traffic sources for the visitors that decide to subscribe (Dashboard > Traffic Sources)

How much potential are you wasting?

After understanding your newsletter subscribers, the next step would be to see how many visitors of your website have the same behavior with your newsletter subscribers. In other words, how many people are most likely to become subscribers.

Back to the drawing board (that is the segment creating page). Generate a new segment that would define the behavior of your subscribed users. Let’s say the conclusion you get from the above study is that your newsletter subscribers are new users that come mostly from search engines and visit at least 4 pages a session. Create a segment for it and apply it to your website data.

Now you have the number of visitors that subscribe to your newsletter and the number of visitors that are most likely to subscribe. Check out the conversion rate from one to the other? Let’s improve on that.

The next best thing would be to act on it

In case you use MailChimp for delivering your newsletter, we have a free solution for you. Today we have just launched a public limited edition of our real time behavioral targeting platform, PadiAct. After defining your segment of visitors who are most likely to subscribe to your newsletter, using PadiAct, you can ask these visitors to subscribe to your newsletter. It can and it will skyrocket your subscribing rate. Here is how it has worked for one of our clients.

In case you don’t use MailChimp but you are still very interested in making this work for you as well, drop us a message and we’ll see what can be done.

First thing is first. As the owner of the website, make sure you own the analytics data for it. I’ve seen many cases where consultants create the Google Analytics profile on their username and share it with the owner of the website with read only rights. When the website owner wanted to switch the consultant guess what happened?

Yep, losing your analytics data is not cool at all. So, the owner of the website should be the one who creates the Google Analytics profile and shares it with as many consultants he wishes to. You can start by using your already existing Google account or create a new one.

So, here are the checklist chapters:

• Implement the right tracking code and make sure you track your whole website
• Accuracy in tracking traffic sources
• Setting up goals, funnels and ecommerce tracking
• Bonus tracking tips and hacks (filters, segments and others)

Here is the short version of the checklist:

Let me know your feedback on it or if you would like me to cover any other aspects as well.

The tracking code is customizable in order to fit the need of any website. It has 3 mandatory elements (the javascript file, the identification code and the tracker) and lots of optional elements. In order to make sure you’ll get accurate tracking when generating your tracking code you need to see what fits your website from the following:

• Your website uses subdomains;
• You use different domains for the same website (using this will also track subdomains);
• You want to track traffic from mobile devices as well (for advanced users).

Google Analytics offers a great wizard on how to manage all of the above scenarios. (click the Check Status link in your website settings inside Google Analytics)

Bonus: Google has pushed the boundaries of the tracking codes and developed what they call asynchronous tracking code which will make the loading time for code much faster. It is in beta, but if you are an advanced user and have no problems in understanding javascript implementation documentation I highly suggest to give it a try.

Tracking your whole website

Most of the times I diagnosed websites for the presence of the tracking code I found pages that simply weren’t tagged. This is one of the main reasons for which many website owners get their own domain as traffic source in the referring sites report in Google Analytics, but we will get back to this later.

If you have a small website you can use one of the following free services on the web: SiteScanGa by Epikone or GPablo (haven’t tried this one).

If you have a large website, things can get a little bit more complicated or more expensive. One of the options you have is to purchase a Market Research licence for WASP. If you can afford it, I recommend it especially if you use other analytics providers than Google Analytics as well.

For the free but  a little bit more technical way to do this, John Mueller has a great tutorial for it.

Troubleshooting

Having your own domain as referrer? This is the issue that gets to be very frustrating. Most of the times it happens, as I said earlier, because some pages in the website haven’t been tagged. All the visitors that continue browsing the website after entering through those pages will be considered as referred by your domain, which technically is correct. Here are other situations when you get your own website as a referrer:

• temporary redirects for entrance points in the website(302)
• faulty cross-domains tracking
• faulty encoding when passing referring sources data
• session expiration issues (visitors forgetting their browser open with your page for more than 30 minutes)

This article is part of the Google Analytics Implementation Checklist series. Here are the articles from the rest of the series:

• Accuracy in tracking traffic sources
• Setting up goals, funnels and ecommerce tracking
• Bonus tracking tips and hacks (filters, segments and others)