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- Eric Fettman

Rather than a Single-Page Visit, Think of Bounce as a Single-Hit Session

Bounce Rateview at full size

Bounce is not defined by pageviews only.

While we may be tempted to define a bounce as a single-page visit, a much better definition is a single-hit session.

Let’s first examine hit. In Google Analytics, a hit corresponds to any data that is sent from your website (or app) to the Google Analytics servers, including:

• physical pageview (or screen view)

• virtual pageview

• event

• Ecommerce transaction

• tracked social interaction

If three users arrive on your home page and respectively play a video, download a PDF, and click through to your Twitter page, and you have those actions tracked as event, virtual pageview, and social, those sessions will not count as bounces, even if the users do not view any other physical pages.

As a note, hit in the context of Google Analytics doesn’t correspond to the broader meaning of hit in Web server parlance, which means any file request, such as HTML, image, JavaScript, or CSS. (As website optimizers, we want to remain aware of these types of hits as well – and minimize them to reduce download time for mobile sites – but this consideration is separate from Web analytics.)

Now, why session instead of visit?

If a user arrives on your site, views one page, speaks on the phone for 31 minutes, and then accesses another two pages, Google Analytics records two sessions for that same user, with the first session counting as a bounce. The visit consisted of three pageviews, but in the first session, there was only one pageview.

For these reasons, in defining bounce, hit is more accurate than page, and session is more accurate than visit.

It’s just one definition, but it encapsulates two important concepts in Google Analytics.

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View Traffic by Day of the Week

Day of the weekview at full size

You can display session by day of the week in a custom report.

Over the last six months, which day of the week saw the most visits to your site?

You won’t find an answer, at least not in aggregated numbers, within the standard Google Analytics reports, but we can easily display this data in a custom report.

First step is to create a custom report in Explorer format, with Day of Week Name defined as the primary dimension and Session (as well as other metrics, such as Bounce Rate, as needed) as the metric.

Once we display the custom report, we can apply Day of Week (as the numbers 0-6) as a secondary dimension so we can sort the report from first day of the week to last (Sunday-Friday). We can also apply alternate displays, such as Percentage or Performance, instead of the default Data display.

Don’t forget to take advantage of custom reports. They’re easy to use and available individually to each user, and they often allow you to display dimensions and metrics in ways that are not possible in the standard reports.

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Table Filters are Interpreted as Regular Expressions, So You Must “Escape” Question Marks

Table Filter Interpreted as Regular Expressionview at full size

Table filters are interpreted as regular expressions.

Though it’s not indicated anywhere in the interface, the table filter field in Google Analytics reports is interpreted as a regular expression (or “regex” for short).

In regex notation, there are two types of characters: literal characters and metacharacters. If you’re trying to filter your Pages report to display pages that contain article.aspx?id=, you must “escape” the question mark with a \ character so the ? acts as a literal character. Otherwise, the ? will be interpreted as metacharacter – specifically, a quantifier meaning zero or one of the previous character.

To further illustrate, if you enter article.aspx?id= into the filter field, you’ll match any pages that contain article.aspid= or article.aspxid=, but not article.aspx?id=, since the question mark is interpreted as a regular expression metacharacter and not as a literal. If you enter article.aspx\?id= into the filter field, you’ll match any page that contains article.aspx?id= since the question mark is now interpreted literally.

By escaping the question mark, we “escape” interpretation of the character following the \ as a regex metacharacter and allow it to act as a literal character.

Note as well that in proper regex notation, all literal . characters are also escaped so they’re not interpreted as the wildcard metacharacter that matches any single literal character. The filter article.aspx\?id= would also match pages that contained articlesaspx?id= and article-aspx?id= because the . is acting as a metacharacter. To restrict the match to article.aspx\?id=, you’d need to also escape the . as in article\.aspx\?id= and thereby force the . to be interpreted literally.

Don’t feel that you need to memorize regular expression notations. Know the principles, and download a cheat sheet at: http://www.e-nor.com/blog/google-analytics/google-analytics-regex-and-keyboard-shortcuts-desktop-backgrounds

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Focus on Assists to Understand Where Your Conversions Are Really Coming From

Conversion Assistsview at full size

Assists can provide a much more complete understanding of conversion sources.

In a recent post about Multi-Channel Funnels, we examined the Top Conversion Paths report. Assisted Conversions, another report that Google Analytics provides under Multi-Channel Funnels, specifically indicates how often a channel is participating in Ecommerce or goal conversion assists vs. acting as the final channel for conversion.

In the Assisted / Last Click or Direct Conversions column, a value greater than 1 indicates that a channel is stronger with assists than with closes, and a value greater than one indicates a stronger closer.

Assists and closes are both good things, but if you were to review the Source/Medium list in the Goal Overview report, you would know only which channels were closing the deal.

As additional options within the Assisted Conversions report, you can:

• switch from Assisting Interactions Analysis to First Interaction Analysis

• change the lookback window from the 30-day default to as long as 90 days

• narrow your analysis to a single goal or Ecommerce transactions only

In any case, make sure that you’re referring to the Multi-Channel Funnel reports to understand where your most valuable traffic is really coming from.

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Chart Visits from Specific Traffic Sources Using the Plot Rows Feature

Plot rowsview at full size

Two dimension values are charted using Plot Rows.

By default, the main “over-time” graphic in Google Analytics reports displays an aggregate metric for all dimension values, but you can also use the Plot Rows feature to chart up to six specific dimension values.

Plot Rows is simple and easy to overlook, but it can be a quick and powerful way to break down aggregate trends into more specific insights.

Also keep in mind that you can select metrics other than the default to display in the chart. For instance, in the All Traffic report, we can display Ecommerce Revenue instead of the default Sessions metric.

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Round-Trip PDF Tracking with Virtual Pageviews and Campaign Tags

Pages reportview at full size

All virtual pageviews are integrated into the Pages report.

For the simple reason that PDFs do not contain the Google Analytics tracking code, links to PDFs do not, by default, generate data in Google Analytics.

So what we can do? We can generate a virtual pageview on the click action as in the code below, before the PDF even loads.

<a href="/catalog.pdf" onclick="ga('send','pageview','virtual-catalog-pdf')">Download our catalog</a>

In this way, we can generate virtual pageviews that will appear integrated into the Pages report and will act is the equivalent of a virtual page for all other purposes in Google Analytics, such as goals and funnel steps.

But how do we track links back to our website from a PDF? By default, a link back to your site from a PDF would count as direct traffic since there would be no referring website that GA could record. To make these clickbacks trackable, you can add campaign parameters, also called campaign “tags”, to your inbound links.

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Understand the Migration to Google Analytics Universal: Flowchart and Video

Flowchart for Google Analytics Universal Migrationview at full size

The flowchart maps out the steps required for Google Analytics Universal migration.

Google announced yesterday that Google Analytics Universal has launched out of beta. What does this mean?

For most Google Analytics users, this means that you can now enable the Demographics and Interests reports and use remarketing segments, functionalities heretofore reserved for Google Analytics Classic.

More generally, it means that you need to start thinking about your migration to Google Analytics Universal if you haven’t already. While Google Analytics Classic (ga.js tracking code) is supposed to be supported for up to two more years, new features will be available only in Universal (analytics.js tracking code), so don’t delay in planning the switch from the version of Google Analytics that is now officially deprecated.

That said, much of the discussion around Universal has centered around new features such as the Measurement Protocol and cross-device tracking. While these are welcome additions, you first need to focus on the basics of transferring your properties to Universal and making the necessary coding changes. To this end, the E-Nor team has put together a flowchart and video to help with your migration.

The switch to Universal doesn’t have to be complicated. The flowchart and video should help to clarify the overall process and highlight the special technical considerations.

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Conversion Rate Down? – Check the Path Length Report

Path Length reportview at full size

The Path Length report can provide another perspective on your conversion rates.

A lower goal or Ecommerce conversion rate relative to the same period last year may not necessarily indicate that your site’s performance has slipped: it may just mean that visitors are visiting a greater number of times before converting.

As with the other Multi-Channel Funnel reports, the Path Length report provides an additional perspective on your conversions. In the case of Path Length, you can see how many visits are required for visitors to convert.

Because conversion rate is based on visits and not unique visitors, a greater number of visits before conversion would lower conversion rate, perhaps even if the overall number of conversions has increased. The opposite could also be true: if visitors are converting in fewer visits, but fewer conversions are occurring overall, conversion rate could actually increase, since there would be fewer total visits before conversion.

Thus, when we’re looking at conversion rates, it’s helpful to also consider path length and, of course, total conversions.

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Apply Advanced Segments to Goal Reports for More Meaningful (and Higher) Conversion Rates

Goal report with advanced segment appliedview at full size

With an advanced segment applied, goal and Ecommerce conversion rates are more meaningful (and higher).

If you only expect one type of visitor to your site – such as a visitor who has or has not logged in, a visitor from a specific geographical area, or a visitor who has accessed a certain page – to complete a certain goal or an Ecommerce transaction, you should apply an advanced segment to your goal reports for more meaningful (and higher) conversion rates.

For example, let’s say that your website consists of two main sections: news and content on the one hand, and an online store on the other. If you don’t necessarily expect visitors on the news side to complete transactions in the store, you should create an advanced segment based on visitors who have viewed at least one page in the store and apply that segment to your goal and Ecommerce reports.

As is illustrated in the screen shot, the conversion rate for the segment will be higher (and more meaningful) than for all traffic, and variations in conversion rate over time will have greater amplitude and therefore be easier to detect.

This illustrates a general principle in Web analytics: the more specificity you apply to your analytics in terms visitor segmentation, the more you’ll be able to make meaningful observations and take concrete action to improve your website.

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Goal Conversion Rate Is Based on Total Visits, Not Unique Visitors

Goal Overview reportview at full size

Conversion Rate can be somewhat ambiguous as a metric.

A conversion rate of 2.62% for the main goal of your website does not indicate that 2.62% of people converted. Rather, it means that 2.62% of total visits included a goal completion.

The implication is therefore that your conversion rate could be decreasing while the actual performance of your website for that goal is improving. If more visits are required for a goal completion but the goal completion is ultimately taking place, your website is doing its job.

A less ambiguous metric is goal completions. This is an absolute number that requires little interpretation (although we always need to keep in mind that a significant number of website visits could result in phone and brick-and-mortar conversions).

Does this mean that we should ignore conversion rate? Surely not, but we do need to remember how it’s calculated.

In future posts, we’ll look at ways you can further analyze and better understand your goal conversion rates.

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