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8
Goal and Ecommerce Tracking

As first discussed in Chapter 2, “Google Analytics Reporting Overview,” Google Analytics (GA) does not populate the Conversions reports at all until you take either or both of the following steps:

tell GA which existing pageview or event actions constitute a goal conversion
provide additional Ecommerce data beyond pageview and event tracking

In either case, you’re enabling GA to measure success according to the specific key performance indicators (KPIs) that you identified for your website or mobile app as discussed in Chapter 3, “Measurement Strategy.”

It’s critical to configure goals and/or Ecommerce so your analytics can clearly tell you if your users are achieving these objectives.

Goal Tracking

In the following example, we set up a goal for a health tips email newsletter signup on a health insurance provider website—this is a primary action that the company executives and website designers want visitors to take, so we’re going to track it as a goal.

As we configure our goals, we need to consider four options:

goal type
match type (for Destination goals only)
funnel or no funnel (for Destination goals only)
goal value

We discuss each of these considerations in the next sections.

Configuring a Goal

Since goal configuration changes the underlying report data (by calculating conversion rate and related metrics) for everyone who accesses the corresponding GA view, you need Edit access to the view to set up a goal. We’ll discuss access rights in Chapter 9, “View Settings, View Filters, and Access Rights.”

Note

Create Your Goals in a Test View First

In Chapter 9, we discuss the best practice of maintaining at least three views for each GA property: a raw backup view, a test view, and one or more working views for your reporting. You should create goals in a test view first, let them run for a few days or a week to verify that your goal setup is working, and then recreate the goal in a working view, either manually or, preferably, by sharing the goal with yourself from the Share Assets panel as discussed in Chapter 11, “Dashboards, Custom Reports, and Intelligence Alerts,” which you can also access from the View Admin screen.

On a related note, you cannot actually delete a goal once it’s created; you can only edit it or turn off recording. We thus have another incentive for verifying goal configuration in a test view before creating the goal in a working view.

Twenty Goals per View

GA allows us to create 20 goals per view. This limit should suffice for most websites or mobile apps, since we don’t want to track every user action as a goal, only those that indicate a central or supporting success relative to our KPIs.

If, however, you do need additional goal slots, you can always create a new view, apply the same view filters and settings, and set up more goals.

Goal Sets

As you create goals, GA by default populates the five goal slots in four goal sets in sequence. It’s perfectly acceptable to stick with the default sequencing, but you could opt to group related goals within a goal set, which would allow you, for example, to choose Goal Set 2 as the metric group in the Source/Medium report and display the conversion rate per Source/Medium for each goal within the goal set.

As just stated, however, it’s fine to stick with default goal sequencing. In most cases, you should analyze goals individually, so the initial grouping is not critical in most implementations.

Goal Type

Let’s start by clicking Goals in the view column on the admin screen, and then clicking New Goal. An initial screen displaying goal template options such as Place an Order or Media Play can be helpful to provide additional ideas for goal tracking, but select Custom and click Continue to access the goal setup screen shown in Figure 8.1.

Screenshot shows a window titled goal description with text fields for name, dropdown for goal slot ID and radio buttons under type; the radio buttons are destination, duration, pages/screens per session and event. — **Figure 8.1** As the first step of goal setup, specify the goal name and type. If you choose, you can also specify a goal slot other than the default.

Note that we have chosen Destination as the goal type. Destination is the most common goal type, since, in many cases, it’s a specific pageview or screen view that signifies the goal completion.

You can also create a goal based on any action that you have tracked as an event, such as a video play or a click on a specific offsite link. As another goal option that we will not be discussing, Smart Goals are designed to be used for AdWords when you don't have actual GA goals or Ecommerce in place.

GUEST SPOT Seven Tips for an Effective Call to Action

Tim Ash

Tim Ash is the CEO of the strategic conversion rate optimization agency SiteTuners, author of the bestselling books on Landing Page Optimization, and the founder and chair of the international Conversion Conference.

If you work in marketing or digital operations, the call to action (CTA) is something of a rock star. We test it. We revere it. We understand that a small lift it generates in clickthroughs can mean significant gains for the business.

However, despite the increased attention towards CTAs, compared to, say, 10 years ago, we could still be thinking about them and improving them quite a bit more.

The tips below for optimizing and testing your CTAs apply to lead-generation landing pages and more broadly to any Web page that has a call to action (as pretty much all Web pages should).

Increase contrast: as a general principle, it’s important to have a mostly color-neutral presentation. Benign and boring works quite well for most sites, and you’ll have more leeway to make the CTA more distinctive when using a “bland” page.
Limit choices: for your CTAs to be found and acted upon, you need to limit choices. Limit the potential actions of your visitors. One is ideal, and two potential actions can be forgiven if you absolutely need the second choice. Any more than that and you’re just confusing your visitor. Remember: if you prioritize everything, you prioritize nothing.
Use affordances: visual affordances provide clues about what your visitors can do with screen elements.
- Shape: rounded objects call a little more attention and convey a little more interactivity.
- Size: greater sizes typically help give buttons more attention and hierarchical “weight.”
- Position: negative space around buttons help visitors understand that the area can be interacted with.
Match intent: Don’t underestimate the importance of matching intent. You might be surprised to find a difference in clickthrough rates between the passive “Free Trial” and action-oriented, “Try it Free,” or the generic “Submit” and more emotional “Help Tornado Survivors.”
Use graphics to make CTA explicit: make sure the picture in the background does not dominate the scene, and that the form is not so bland against the color graphic. The CTA should naturally arise from the Zen-stillness of your page.
Direct towards CTA with faces: Visitors will always look at faces because they’re a rich source of information about the environment. We have a general object recognition system, and near our emotional midbrain, we have a separate part for recognizing faces and people. That allows us to judge someone’s attitude or aggression towards us, and this is all critical for social and survival reasons. On your page, the direction of the gaze of the face matters. Generally, rather than looking past your form or off the page, you want the face to signal something interesting on the page: your CTA.
Keep it boring and short: In the real world, you wouldn’t want to be rude or too forward, so don’t ask for too much information too early in the process when there’s no obvious benefit for the customer to give up that information. To echo the tips above, it’s critical that other graphic elements of your page don’t compete with your CTA for attention, so visitors know right off the bat what they’re supposed to do. (See Figure 8.2.)

Figure 8.2 MR Insurance Consultants’ photo of a woman gazing towards the form directs the visitor’s attention to the CTA area.

Engagement Goals

Because they’re based on a threshold rather than a specific action, Duration and Pages/Screens per Session goals are referred to as engagement goals. Keep in mind that your Duration goals depend directly on hit timestamps. For example, if a visitor lands on a long blog page and spends 10 minutes scrolling and reading the blog post, but you haven’t set up any event or virtual pageview tracking for scrolling (as described in Chapter 6, “Events, Virtual Pageviews, Social Actions, and Errors”), that session will not count toward a 5-minute Duration goal.

The Pages/Screens per Session goal type, for its part, depends only on pageviews (physical or virtual) or screen views and does not consider time on page/screen or session duration.

Match Type

Because we chose Destination as the goal type in the previous step, we must now specify the text string for the matching page(s) or screen(s) as well as the match type. The page or screen text string that you specify is compared to the Page or Screen dimension values as listed in the Pages or Screens report. The match type defaults to Equals To, but you can change this to Begins With or Regular Expression, as shown in Figure 8.3, so more than one Page or Screen value would count as a conversion.

Screenshot shows a window titled destination with a dropdown menu listing equals to, begins with and regular expression, a text field and a tick box for case sensitive — **Figure 8.3** For a Destination goal, you can choose one of three match types.

Match Type Begins With

Let’s say that you manage a consumer credit website. The primary KPI for the website is the number of sessions in which a credit report is purchased. The user’s selection of delivery method for the credit report—by mail, or as PDF attachment in an email—determines the confirmation URL that the user is directed to /confirmation-mail or /confirmation-email. In this case, we could easily configure a Destination Goal with Begins With as the match type and /confirmation as the Page field, since these two confirmation pages should satisfy the same goal.

Match Type Regular Expression

Begins With would not be suitable, however, if there were other pages that began with /confirmation, such as /confirmation-contact-us but that should not count as a goal match, or if the two URLs for the credit report confirmation appeared as /mail-confirmation and /email-confirmation. In these cases, we’d need the specificity that only a regular expression could provide.

In the second case, we could select Regular Expression as the Match Type and specify the Page field as follows:

/(mail|email)-confirmation

The regular expression above uses the pipe metacharacter to signify or. At the beginning of the regular expression, we “escaped” the forward slash with a backslash to ensure that the forward slash is interpreted as a literal character.

Too much duplication between mail and email in the regular expression? We could rewrite the regular expression more efficiently using the ? quantifier metacharacter to signify zero or one occurrences of the preceding character:

/e?mail-confirmation

Chapter 9 includes an overview of regular expressions.

Goal Value

Though not obligatory, Goal Value is very useful—especially if you do not have Ecommerce tracking enabled—since it allows GA to calculate metrics such as Page Value, which we discuss later in the chapter, and Per-Session Goal Value.

Goal values are not dynamic; the static value that you configure is used in each goal completion. (Goals based on events can pull in varying event values as the goal value, but event value is not normally specified as part of event tracking.) For purchases of products and services at varying price points, Ecommerce tracking, discussed later in this chapter, offers greater flexibility than goal tracking.

There is not necessarily a single correct approach, but some recommendations follow.

Single goal, actual value. If you’re configuring a goal to track the sale of only one product or service, populate the purchase price as the goal value.
Single goal, estimated value. If you’re configuring a goal to track a lead submission for £1,000 financial consulting engagement, and you close 10% of leads, populate £100 as the goal value.
Single goal, nonmonetized. If you’re tracking a goal that’s not in any way monetized, populate an arbitrary value: €1 or €100 or €1,000 (as in Figure 8.4). The relative Page Value and Per Session Goal Value metrics will still be fully valid for comparison purposes.

Multiple goals, actual or estimated values. Set up the goals and populate individually with the actual or estimated values. You can also opt to duplicate any goal in a separate view to report on Page Value and Per-Session Goal Value based on Goal Value only for a single goal only.
Multiple goals, nonmonetized. Set proportional goal values for each goal. If, for instance, you consider your Contact Us goal to be twice as important as your Mailing List Signup goal, you can assign respective Goal Values of $2 and $1. You can also duplicate the goals in separate views, again, for separate Page Value or Per-Session Goal Value metrics by goal.
Ecommerce and goals. This is probably the trickiest scenario. If you’re tracking Ecommerce and goals in the same main view, you may want to consider duplicating your goal in a separate view and specifying Goal Value for that goal in that separate view. This will allow GA to calculate Page Value separately for Ecommerce transactions (in the main view) and for goal completions (in the separate view). There’s no need to refer to the separate view, however, for Ecommerce- and goal-specific metrics, such as Revenue and Per-Session Value for Ecommerce and Per-Session Goal Value for goal completions; GA will calculate these separately in the main view.

Screenshot shows a window with headings value and optional, an ON and OFF button and a text field with suffix dollar USD. — **Figure 8.4** Goal value is optional but usually recommended. Even in the case of nonmonetized goals, populating an arbitrary goal value such as US$1 or €100 will allow GA to calculate Page Value and Per Session Goal Value.

Goal Funnel

Going back to the health tips newsletter signup, our Web designers envisioned the conversion process to consist of three pages:

health tips page, with a link to the newsletter signup form
newsletter signup form page, with delivery and subject selections
signup confirmation page

We have already set the final step in the funnel as the Destination field in the previous example: the bottom of the funnel appears at the top of the goal configuration. We now need to just specify the first two funnel steps as in Figure 8.5.

Note that, by default, the Funnel Visualization Report will display entries at any funnel step, not just the first. If you want to restrict the funnel to top-step entries only, you can set the Required? toggle for the first step to Yes. You could also create two goals with identical funnel steps but different first-step Required settings. (Instead of manually re-creating the goal, you can use the Share Asset feature to share the goal with yourself as previously mentioned and then just change the first-step Required setting in the new copy.) This would provide two different ways to view entries and completions in the same funnel for the same goal.

Funnel Is Recommended but Not Required

A funnel is not required to calculate Goal Conversion Rate. It is, however, required for the following reporting elements, so it is therefore recommended:

Funnel Visualization report.
Funnel Conversion Rate (appears at the bottom of the Funnel Visualization report).
Goal Abandonment Rate (appears in the Goal Overview report).

While a funnel is recommended, and is required to populate the Funnel Visualization report and also to calculate the Goal Abandonment Rate and the Funnel Conversion Rate (which will be shown in Figure 8.9), it’s not required to calculate the Goal Conversion Rate.

Screenshot shows the metrics values of health tips in goal overview report; the values are goal 1 completions 1421, goal 1 Value dollar 1421, goal 1 conversion rate 2.42 percentage and goal 1 abandonment rate 44.82 percentage. — **Figure 8.6** Metrics in the Goal Overview report.

A goal funnel is not required to populate the Goal Flow report (Figure 8.10) for a given goal, but it makes the report more useful.

If you’re unsure of your funnel steps, omit them at first, and proceed with your goal setup. Once the goal has been running for a period of time, you can view the Reverse Goal Path report (which will be shown in Figure 8.11) to understand the actual paths that visitors are taking to your goal completion pages or screens, and you can then map the most common path(s) as funnel steps in the original goal or in a new goal and thereby measure drop-off between the steps leading to conversion.

Note

You Can Use Virtual Pageviews in a Funnel, but You Can’t Use Events

In Chapter 6, we reviewed many types of user actions that are not tracked by default but that you can track as events or virtual pageviews. While either option is valid, it’s important to remember that you cannot use an event at any point in a goal funnel. If you were tracking a PDF download as an event, you could define a goal based on the event, but you could not build a funnel on top of the goal, and you could not use the PDF download as a funnel step leading to another destination page.

Since virtual pageviews are treated identically to physical pageviews once recorded in GA, you can specify a virtual pageview as a funnel step or as the page field in Destination goal on which you configure other pages as the funnel steps.

If you tracked each step (after the first physical pageview) in the multistep AJAX process shown in Figure 6.26 as a virtual pageview, you’d be able to define a corresponding goal and funnel.

Similarly, if the health tips signup form submission did not redirect to another page and therefore generate a physical pageview by default, you could generate a virtual pageview with the help of a GTM Form Submission trigger as listed in Table 6.1.

Goal and Funnel Reports

Once you have configured a goal, GA will start calculating goal-related metrics and populating the Goal Reports. We will discuss the goal reports as well as goal metrics that appear within other reports.

Goal Overview

It’s usually advisable to analyze goals individually, so if you have multiple goals configured in the view, select a single goal for display as in Figure 8.6.

Conversion Rate: Perhaps Not What You Think

In the context of goals, a conversion is a completion. Although the two terms are used in different metrics, they represent the same concept. Let’s continue by clarifying two potentially confusing points about goal conversions and conversion rate:

For a given goal, a single user cannot convert more than once during a session. If you have two newsletter signups that lead to the same thank-you page, GA will record only one conversion for a session in which a user signs up for both newsletters. Conversely, one user can convert multiple times on the same goal, but only in separate sessions.
Goals metrics are based on sessions, not users. Because conversion rates are based on sessions and not users, they can sometimes appear bleaker than they are in reality. If you see a 1% conversion rate for application submission on a mortgage website that you’re analyzing, it does not mean that 1% of people are converting; it means that 1% of sessions included a conversion.

Especially for costly or complex propositions, it may take several return sessions for a user to convert, as we’ll be able to see in the Multi-Channel Funnel reports discussed later. If you’re presenting your conversion data at a company meeting and someone asks why only 1% of people are converting, that’s your cue to briefly explain the concept of a session and clarify the definition of conversion rate, as shown in Figure 8.7.

Diagram shows the two different methods of conversion rate; 50 goal completions by 1000 users giving 5 percent conversion rate and 50 goal completions by 5000 sessions giving 1 percent conversion rate. — **Figure 8.7** Since conversion rate is defined as conversions per session, it appears lower in all instances than if defined as conversions per user.

Why doesn’t GA base conversion rate on users rather than sessions? While this would be useful, unique users as a metric is much more difficult to determine than sessions (because of multiple-device usage, cookie deletion, etc.), so conversion rate based on users would suffer a much greater margin of error. That said, we can, if we choose, create our own calculated metric for user-based conversion rate, as described in Chapter 12.

GUEST SPOT The Buyer Legend Precedes the Funnel

Bryan and Jeffrey Eisenberg

Bryan and Jeffrey Eisenberg are the cofounders of training company BuyerLegends.com.

You can make money being average. We hope you aspire to be more than average.

In 2015, the average conversion rate of the nation’s top online retailers was 3.32%.

In 2001, we wrote:

Conversion rates are a measure of your ability to persuade visitors to take the action you want them to take. They’re also a reflection of your effectiveness at satisfying customers, because for you to achieve your goals, visitors must first achieve theirs.

The top 25% of online retailers convert at 5.31% and the top 10% of online retailers convert at 11.45%. Amazon Prime members convert 74% of the time on Amazon.com, according to a 2015 study from Millward Brown Digital, compared to 13% for non-Prime members.

Companies that plan and optimize persuasive momentum usually convert 2 to 4 times better than their industry average.

The Difference between Average and Excellent

The companies that truly excel at conversion rate optimization have evolved a culture of customer-centricity; not at the manager or director level, but in the C suite. The best companies absorb CRO learnings and incorporate them into strategy and operational changes. That makes them superior, not just at fixing, but at creating relevant customer experiences that delight their customers.

They think about the sales funnel differently.

Conversion Funnels: Fixing a Flawed Metaphor

You’ve planned the campaign or the test.

You’ve designed the checkout or registration.

Now your customers come along and enter your funnel.

Let’s get real: Your customer isn’t truly in a funnel. There’s no gravity compelling them through your experience like there is in a real funnel. There is only the customer’s motivation and your understanding of that motivation to create persuasive momentum.

Your customers’ journeys are their stories, not funnels. They could tell you the stories, just try asking them. And those stories don’t always have happy endings.

Your customers’ stories end happily when they are delighted. And for them that may mean buying from you or from a competitor. It’s simply a matter of perspective.

Now you come along and interrogate your analytics to find out what your customers did.

Is this process so very different from what you do?

The most successful companies start with the story from the customer’s perspective. They anticipate what needs to be measured in order for the analysts to understand the customer’s experience.

Analysts must tell stories and businesspeople must measure accountability. If not their strategy isn’t truly aligned with customers’ needs.

It’s time to perfect your concept of a funnel.

Start with a Story

Buyer Legends are measurable and accountable by design. A Buyer Legend is not a feel-good story; it’s about your business. A Buyer Legend should describe in significant detail what actions you expect your customer to take, many of which are measurable.

Measure and Optimize Your Persuasive System

Whether or not you are aware, your business has created a de facto persuasive system. Buyer Legends is a process for creating a persuasive system that is intentional, measurable, and optimizable. That is why it is important for you to track both the micro and macro actions so that you are not just optimizing the final conversion but all the steps in between, where you can spot breakdowns in the system and fix them.

What Is Persuasive Momentum?

In our book Waiting for Your Cat to Bark?, we defined persuasive momentum as “the progressive decision-making process that aligns the customers’ goals with our own business goals.” Whether intentional or not, your business is operating a persuasive system.

Funnel visualizations are certainly a useful tool for analysis and optimization, but marketers who are stuck on the sales/conversion funnel metaphor, without considering user stories or persuasive momentum, work too hard for too little return. They pour more customers into the top of the funnel hoping more come out at the end.

Micro actions vs. Macro actions

Typically your conversion goals are the macro actions. It is dangerous to ignore micro actions; all the required smaller actions customers need to take to before they can take a macro action. Without persuasive momentum customers stop moving forward in their buying journey.

The Three Elements of Persuasive Momentum

No matter if the action is micro or macro, there is a simple formula that will help you identify persuasive momentum or the lack thereof.

Relevance. Are you relevant to my wants/needs/desires (search query)?
Value. Do I know why you are the right solution for me? Have you explained your value proposition/offer well?
Call to action. Is it obvious what I need to do next? Have you given me the confidence to take that action?

Ask these questions of every touchpoint, and you will quickly find if your touch points are missing one, two, or all three of these components. We also call this “The Conversion Trinity,” shown in Figure 8.8.

Figure 8.8 The Conversion Trinity: Relevance, Value, Call to Action.

A Legend for Your Customer’s Buying Journey

Your Buyer Legend is a map of your customer’s journey, and to read the map properly you need to have a legend.

Your hero is on a journey. You tell his or her story. Every successful customer journey needs a map and every map needs a legend. The journey’s legend is the key to navigating the map. The components of a journey legend are described below.

Hero This is the protagonist of your legend. All legends are told from the point of view of the hero.

Catalyst This is the point at which the customer first identifies your company, product, and/or service as a potential solution.

First Measurable Step Here is where your customer enters the measurable portion of the journey.

Road Signs These are points in the customer’s path that are critical to their completion of the journey. These include information that, if not available, will most likely prevent the customer from completing the journey.

Detours These are pathways that marketers must construct as solutions to forks in the road. They often go off the path in search of answers to concerns, alternative solutions, or just plain curiosity. So meet the customer along those wrong turns and guide them back onto the proper path.

Measurable Step This is any step along the way that can be measured.

Fork in the Road These are decision points in the person’s path where a specific need or curiosity can take them off the ideal path in search of answers to a specific need, curiosity, question, or concern.

Destination This is the final measurable step where the customer converts into a lead or sale.

Understanding the Value of Quantitative vs. Qualitative

As humans, and certainly as website visitors, our actions can be measured. This creates quantitative data that tells you what your customers are doing, and qualitative data can provide insight into why your customers are doing what they do. Quantitative data needs to make sense in the context of qualitative data: business needs both types of research to see the whole picture. So do not discount the value of focus groups, surveys, customer interviews, and even customer comments and reviews as you begin to craft your Buyer Legend.

With a combination of qualitative inputs and quantitative validation, you can start to build meaningful, user-focused Persuasive Momentum that delivers better experiences to your website visitors, generates greater success for your organization, and drives towards real, measurable excellence.

Note

Conversion Rate Optimization: When 1% Can Be 50%

Even with the understanding that conversion rates appear lower because they’re based on sessions and not users, our job as analysts and optimizers is to never be satisfied with current goal and Ecommerce conversion rates. There is always lots of room for improvement, particularly if your organization has not yet made a focused effort in this regard.

In this book, we learn from Brian Clifton that U.S. merchants report a purchase conversion rate between 1.0% and 2.9% and from Bryan and Jeffrey Eisenberg that the top 10% of U.S. online retailers convert at 11.45%.

If you’re already converting at 5% for a goal or Ecommerce, and, through a concerted effort on analytics and optimization, you increase your conversion rate to 6%, how much of an improvement does this represent? It’s not 1%; it’s 20%. If instead you started at a 2% conversion rate, that 1% increase would represent a 50% improvement.

Also keep in mind that the margin in that additional 1% is likely greater than in the first 2% or 5%: you’ve invested more person-hours in analytics and optimization, but you haven’t increased your marketing spend, your website hosting fees are the same, and it’s not costing any more to keep the lights, heat, and water cooler going at your office. The 1% conversion rate gain represents a profound performance improvement and potentially a critical competitive advantage for your business.

Abandonment Rate

In Figure 8.6 (previously shown), why isn’t abandonment rate the inverse of conversion rate? While goal conversion rate is based on all sessions and is not influenced by a funnel, goal abandonment rate is based only on the number of sessions in which the funnel was entered (but the goal was not completed). If you do not configure a funnel for a goal, GA will not calculate an abandonment rate for that goal.

Note

Abandonment Does Not Respect “First Step Required” in Funnel Setup

If you have selected First Step Required in your funnel setup, the Abandonment Rate in the Goal Overview report will not correspond accurately with the Funnel Visualization report shown in Figure 8.9. The Funnel Visualization report, including the Funnel Conversion Rate that appears at the bottom, does respect First Step Required, while Abandonment Rate does not, such that two funnel definitions that are identical except for First Step Required would display identical Abandonment Rates.

Figure 8.9 The Funnel Visualization report shows drop-off between steps and provides the overall funnel conversion rate.

If you have selected First Step Required in your funnel setup, you can calculate Abandonment Rate as the inverse of the Funnel Conversion Rate (e.g., 100% – 67.97% Funnel Conversion Rate = 32.03% Abandonment Rate).

Funnel Visualization

GA offers two reports for goal funnels: Funnel Visualization and Goal Flow. These reports are very useful, but, as described below, some aspects of these reports are not readily apparent, particularly for Funnel Visualization.

Note that the Custom Funnel feature available in Analytics 360 and discussed in Chapter 18, “Analytics 360,” is somewhat similar to goal funnels but is not necessarily related to goal completions and also, importantly, applies retroactively. Furthermore, the backfilling issue described in the sections below does not affect Custom Funnels, and you can also configure a Custom Funnel to include funnel completions during a single session or over multiple sessions.

Funnel Conversion Rate Is Based on Funnel Completions—Goal Conversion Rate Is Not

It may be surprising that GA calculates goal conversion rate without considering any funnel that you may have configured. If you have created two goals with the same destination page but funnels containing different steps, the abandonment rates for the two goals will certainly differ, but the conversion rates would be identical.

Funnel Step Completions Correspond to Unique Pageviews

As we discussed in Chapter 2, the Unique Pageviews metric represents the number of sessions in which a page was viewed at least one time. Funnel step counts are based on Unique Pageviews: if a user views a funnel page 1 time or 10 times before conversion during a session, the count for the funnel step will increase by one only.

Funnel Steps Don’t Need to Be Completed Sequentially

Funnel steps don’t need to be completed sequentially to count. If a user views other pages in between funnel steps, the step counts in the Funnel Visualization are still incremented normally. In fact, the funnel steps don’t even need to be completed in the order you specified: if a user happens to view your third funnel step before your first funnel step, both steps are still incremented normally.

This might lead you to ask: what if step 3 actually does experience more pageviews than step 2? In this case, something strange and somewhat inexplicable called backfilling occurs in the Funnel Visualization report: step 2 will appear with a marginally higher unique pageview count than step 3 (minus the entries directly at step 3 if you have not made the first step required).

The good news is that backfilling does not occur in most funnels. The two following scenarios are the most susceptible to backfilling:

an optional step that many users skip in a process
a more specific page (such as an article category page) that leads to a more general page (such as newsletter signup page for all article categories)

How can you tell if your funnel is a victim of backfilling? You can check your funnel step counts against the Unique Pageview counts in the All Pages report: if a funnel step count is greatly inflated relative to Unique Pageviews in the All Pages report, backfilling is occurring in the Funnel Visualization report. Even quicker: check the Goal Flow report, described later, which is not subject to backfilling.

You Can’t Apply Segments to Funnel Visualizations

Unlike most other GA reports, Funnel Visualization does not allow you to apply the segments discussed in Chapter 10, so you cannot therefore dynamically segment your funnel for visits from Chile and Argentina or organic clickthroughs from Bing, just as two examples.

Since you can apply segments to Goal Flow shown in Figure 8.10, you can refer to this report instead of the Funnel Visualization if you do need to segment. If, however, it is important for you to display the drop-off percentages of the Funnel Visualization report itself for a specific traffic segment such as those mentioned above, you can create a dedicated, hard-filtered view as described in Chapter 9, thereby ensuring that every report in the view, including Funnel Visualization, reflects only the subset of sessions after filtering.

Goal Flow

While the Goal Flow report populates for goals that don’t have funnels configured, it’s more useful for goals that do have funnels and serves as a good complement to the Funnel Visualization report in the following ways:

Segments: unlike the Funnel Visualization report, you can apply segments to Goal Flow.
No backfill: the backfill issue does not affect Goal Flow. (The First Step Required setting in the funnel setup also does not apply to Goal Flow.)
Level of detail: you can apply a more aggregated or more granular level of detail to Goal Flow, which can help to illustrate skipping of funnel steps and even looping back to previous funnel steps.

With these advantages of Goal Flow over Funnel Visualization, what’s the downside? The Goal Flow overall requires more interaction and interpretation, as shown in Figure 8.10. It’s worthwhile for your own analysis but may be somewhat less suitable than the Funnel Visualization for a general audience.

Note

Funnel Visualization and Abandonment: Simple, Visual, and Emotional

Though the Funnel Visualization has some drawbacks, it possesses a special power of demonstrating visitor progress and drop-off toward your goals—for yourself, for clients, and for any stakeholder in your organization. If you need support (in terms of development resources or usability testing to help determine the reasons for drop-off at specific steps) for your conversion optimization efforts, the Funnel Visualization report can help you communicate your message and get others on board.

Similarly, Abandonment Rate is a metric that we can all connect with viscerally and that everyone within a business can recognize as a negative indicator that you should work to minimize.

You, as an analyst, can drill drown to finer and more actionable detail, but make sure to first present analytics data (and conversion problems) to other stakeholders in clear and impactful terms.

Reverse Goal Path

The Reverse Goal Path report (Figure 8.11) shows the most frequent series of three pageviews or screen views that lead to a goal. You might wonder: if I have this report, why do I need the Funnel Visualization (or Goal Flow)? Notice that the Reverse Goal Path indicates only the most popular paths but does not show page-to-page drop-off (and also that it goes back three pages only). The Reverse Goal Path provides the best ideas for funnels if you have previously defined goals without them: set up a funnel for the most popular paths as reported view drop-off in the Funnel Visualization and Goal Flow reports, and begin to optimize for goal completions.

Screenshot shows a table with columns goal completion location, goal previous step - 1, goal previous step - 2, goal previous step - 3 and practice test completed. — **Figure 8.11** Reverse Goal Path report shows how visitors are actually getting to your goal pages.

GUEST SPOT Fixing the Biggest Blind Spot in Web Analytics: Phone Calls

Irv Shapiro

Irv Shapiro is the CEO of DialogTech.

Let’s talk about phone calls. In today’s digital world, the roles that phone calls play in marketing and sales success have never been bigger. Inbound calls have reemerged as the most effective channel for generating high-quality leads and closing business.

Over 76 billion calls were made to businesses in the United States from search, social, and display advertising alone in 2014, and that number is expected to reach 162 billion by 2016, according to BIA/Kelsey. The surge in smartphone adoption and the continued growth in mobile advertising are partly responsible, driving a huge influx of sales calls into businesses. A 2013 study by Google found that 70% of mobile searchers call a business directly from search results. It makes sense: tapping a click-to-call link on your smartphone is much easier and more natural than trying to fill out a Web form on your device’s tiny screen.

That same Google study also found that 61% say it’s important that businesses give them a phone number to call, and 33% would be less likely to use and refer brands that don’t. Consumers see phone numbers as a sign that a business is trustworthy.

The data on calls continues to mount. According to BIA/Kelsey, 66% of businesses consider inbound phone calls to be excellent leads, more than any other type. It makes sense, since inbound calls convert to revenue on average 10 to 15 times more frequently than Web leads. It’s why marketers are spending $68 billion annually on ads to generate those sought-after inbound sales calls: Phone calls mean revenue.

The resurgence of the phone call is great news for marketers trying to drive quality leads to their sales teams, but it’s also a problem if you are using GA to track engagement from your marketing. Let me explain.

“Help! I Don’t See Phone Call Data in Google Analytics!”

For years marketers have been using GA to better understand activity on their website. In 2013, Google took their analytics platform to the next level with the release of Universal Analytics, which gives businesses the ability to view not just Web activity, but also data on offline interactions, mobile app traffic, and a wide range of custom dimensions and metrics—all in the same interface.

Being able to view online and offline data together can lead to truly amazing insights into how your online, mobile, and offline marketing channels generate leads and the path customers take in the buyer journey. But it only works if you are able to able to collect and pass that offline data to GA, and that’s where the problems begin. GA can’t collect data on phone calls by default, and if you aren’t including call data in your marketing analytics, you could be missing one of the biggest drivers of quality leads, new business and revenue.

So how can you solve this problem? There is a technology called call tracking that can help. It eliminates the blind spot in Web analytics and conversion optimization by collecting detailed data on phone calls from your website and marketing campaigns and passing that data to GA. Taking advantage the GA Measurement Protocol discussed in Chapter 17, “Data Import and Measurement Protocol,“ call tracking technology can record data into GA for calls that occur on your website and even for marketing campaigns in which the target audience does not necessarily visit your website.

What Is Call Tracking?

Call tracking is a form of lead attribution and marketing analytics technology. Marketers use it to track inbound phone calls back to the specific marketing source that originated them and then follow those leads through the sales cycle to revenue.

Call tracking enables marketers to understand what marketing sources—including offline, online, and mobile ads, campaigns, keyword searches, Web pages, pieces of content, direct mail or email blasts, and social media sites—generate phone leads and revenue, and which don’t. They can then use this data to make more intelligent decisions on everything from where and how to advertise, what content to create, what messaging resonates, and what search engine optimization (SEO) terms to target. Plus, the data enables marketers to more accurately prove to their executives (or to their clients, for agency marketers) how their work is driving leads and revenue.

How Does Call Tracking Work?

Call tracking technology works by giving you clean, trackable phone numbers (local, toll-free, or vanity) that you insert into your marketing material. When someone calls one of those numbers, call tracking knows exactly what source they’re calling from and pins that source data with the lead information. If that lead goes on to become an opportunity or a customer, you can tie those events and revenue back to the original source, so you can understand and prove how your marketing is impacting lead generation and sales.

For offline advertising (sources like print ads, direct mail campaigns, TV or radio spots, billboards, or trade show materials), content marketing (eBooks, webinars, brochures, and videos) or email blasts, call tracking is a fairly basic concept. You simply:

Insert a trackable phone number obtained from the call tracking software into your marketing materials.
Attribute those numbers in the software to their respective source.
Tell the software where to route the call (more on those options later), and you’re off. All calls will still come to you, and the callers won’t know the difference, but you will be able to tie each call back to the specific source that originated it.
Use call tracking software to generate reports on the calls each source generates.

If you are using Google call extensions (the “Click” buttons that appear in your mobile paid search ads), you can also insert call tracking numbers in your ads to attribute calls from your call extensions back to the keyword search that generated them. Setting it up is either a manual, one-to-one process, or you can also use a call tracking provider (like DialogTech) to automate the entire process and save yourself a lot of time and effort.

DNI: Dynamic Number Insertion

For online and mobile sources such as search, social, or display ads where people visit your Web page first before calling, call tracking works a bit differently by using a technique called dynamic number insertion (DNI). DNI automatically displays a unique call tracking phone number (taken from a pool of numbers exclusive to your business) on your Web pages based on the visitor’s specific referral source (site, keyword search, etc.). The visitor sees this special phone number the entire time they’re on your site, and when they call you, the call tracking software will populate an event or custom metric into GA and thus tie that call to the proper referring source, including the specific keyword search they used to find you (if the visitor came from any paid search campaign or from an organic result on a search engine that shares that data).

Here’s a summary of how DNI works:

First, you should include a phone number prominently on your Web pages for people to call.
You then add a small snippet of JavaScript code, provided by the call tracking software, to the page to enable the dynamic insertion over the phone number in HTML. JavaScript code can sometimes be inserted via a tag manager such Google Tag Manager, Ensighten, or Tealium with some call tracking providers (like DialogTech).
The code then automatically displays a unique phone number from a pool of numbers on your web pages for each visitor based on how they got there (i.e., Google or Bing keyword search, pay-per-click [PPC] ad, banner ad, referring website, social media post, etc.).
When the visitor calls the number, the phone lead is attributed in call tracking reports to the right referral source, including the exact keyword search.
After a certain period of time, the call tracking software returns that number to your pool so it can be assigned to another visitor. This way, you don’t need (or have to pay for) thousands of unique numbers, making keyword-level call tracking both effective and affordable, even for big businesses and larger marketing agencies with hundreds of clients.

Integrating Call Tracking with Google Analytics

While not every call tracking technology integrates with GA, a few (like DialogTech) do, enabling you to add the critical phone data piece to Google’s marketing analytics puzzle. By integrating your call tracking data with GA, you can view and analyze call data alongside Web traffic data to better understand how Web visitors and callers find your business and better measure campaign ROI.

GA users can view rich, context-specific call tracking data alongside your other online and offline marketing analytics, so you can get insight into things like:

Which channels, ads, search keywords and marketing programs are driving Web visits and phone calls to your business.
Which of your Web pages or videos visitors viewed before and after calling you.
Which of your content they downloaded or items they purchased before and after calling you.
Where people are calling from.

Figure 8.12 shows an example of a GA report. This is for a Pricing page. It shows all the traffic to that page for a given time frame, and what the users did next.

Figure 8.12 With the calls recorded in GA as events, the Behavior Flow report can indicate pages that were viewed (and other events that were generated) before and after the call.

Google Analytics Custom Reports with Call Tracking Data

As mentioned above, DNI normally integrates into GA by generating event hits or, in the case of DialogTech, a custom metric. Figures 8.13 and 8.14 are two custom reports that display our call metric by traffic source and by U.S. state.

Figure 8.13 This custom report displays calls by Default Channel Grouping. You could create similar reports for calls by search engine or by new versus returning visitor.

Figure 8.14 This custom Map Overlay report shows where website visitors and callers are located. This report is invaluable in helping you measure the success of your marketing at the local level. Understanding which geographies are driving the most calls can help you allocate budget to the right programs in the right areas.

This first report includes calls as a metric alongside website visits based on the channel that drove the website visit and call. It gives you a high-level understanding of which ones are driving the most overall engagement.

What to Consider When Evaluating Call Tracking Technologies

Not all call tracking technologies are created equal. In order to select the right call tracking vendor, here are some questions to consider asking:

How Clean Are Your Call Tracking Numbers, Really?

Many call tracking vendors sell phone numbers that are “dirty,” meaning they receive calls from past advertising campaigns with other businesses. If you use one of these dirty numbers to track marketing, it can ruin the validity of the data you pass to GA. Ask the call tracking vendor how they clean their numbers. Do they put their phone numbers through rigorous call cleansing processes before they give them to you, ensuring the accuracy of your data? If so, how?
Can Your Call Tracking Software Automatically Route Calls to the Right Call Center, Store, Dealer, or Agent?

Generating a higher volume of inbound calls is fantastic, but as a marketer, what you really want to do is drive opportunities and revenue. That means you need to be sure inbound calls are getting sent to the best call center, store, dealer or agent to convert them. Few call tracking vendors offer intelligent call routing options, so be sure to ask what they provide.
Will You Have the Proper Support to Help You Along the Way?

It’s important to ask potential vendors what kind of support they offer, how much it costs, and if you are able to get them on the phone and get help when you need it. When working with a call tracking solution, proper implementation is key to getting the most value. This is true especially when you are tracking digital campaign sources like AdWords ads, as they can be complex and require specific expertise to configure. A vendor who has Google-certified call tracking specialists on staff and available to take your call will be a big benefit to you and to the overall value you receive from implementing call tracking.
Some Other Questions to Consider Asking Vendors
- Can you track calls from Google call extensions at the keyword level?
- Can I list more than one call tracking number on a single Web page for pages that list multiple business locations?
- Do you offer “whisper” messages to announce how each caller found you to your agents before they accept the call?

Call Tracking and Google Analytics: A Match Made in Return on Investment (ROI) Heaven

GA is an amazing asset to marketers. Being able to view online and offline data together can lead to truly amazing insights into how your marketing is driving engagement, but only if you are able to able to collect and pass that offline call data to GA. By picking the right call tracking solution, you can reap the benefits of having complete, accurate call data in your GA reports. It really is a match made in ROI analytics heaven.

Ecommerce Tracking

Goal tracking, while extremely useful, is not ideally suited for Ecommerce transactions that entail different products, quantities, and price points. To record the detail for these more varied types of transactions, Ecommerce tracking offers the needed flexibility.

We’ll consider two types of Ecommerce tracking: basic Ecommerce, and Enhanced Ecommerce. As you might expect, Enhanced Ecommerce requires more setup than basic Ecommerce, but they both differ from goal tracking in this fundamental way: you configure goals within the view admin screen and based on hits that are already captured as pageviews or events, while both types of Ecommerce tracking normally require you to work with your developers to push additional data into GA specifically about the Ecommerce transaction.

As a note, while Google Tag Manager (GTM) facilitates event tracking that might otherwise require code-level changes, GTM normally cannot help to automate Ecommerce tracking or avoid all code-level changes, since, in most implementations, you still need to populate Ecommerce-specific variables from the back end into the data layer as discussed below.

Configuring Basic Ecommerce Tracking

Setup for basic Ecommerce requires three steps:

Work with your developers to populate the data layer with Ecommerce transaction and product variables, normally on the purchase confirmation page.
In GTM, create a GA transaction tag and a corresponding trigger.
Enable Ecommerce Tracking in one or more views.

Each of these steps is detailed below.

Populate the Data Layer with Ecommerce Variables

This step represents most of the work in Ecommerce configuration. You’ll need to work with your developers to populate your transaction confirmation page with specific back-end data for the transaction and one or more specific products.

You can use five variables for the transaction data:

transactionId (required)
transactionTotal (required)
transactionProducts (optional, but used routinely)
transactionShipping (optional)
transactionTax (optional)

As noted above, transactionProducts is optional but is usually populated. Unlike the other transaction variables, which require a single value, transactionProducts requires one or more product objects that can take the following variables:

name (required)
sku (required)
price (required)
quantity (required)
category (optional)

You could populate the data layer as in Listing 8.1. Note that the listing displays the output of server-side coding; you can work with your developers to actually pull the Ecommerce values from the back end and format as demonstrated.

Listing 8.1: Populating the data layer with Ecommerce variables.

dataLayer = window.dataLayer  || []; //this is the data layer initializer
dataLayer.push({
   'transactionId': '12345678',
   'transactionAffiliation': 'Natural Hair Care',
   'transactionTotal': 43.24,
   'transactionTax': 3.29,
   'transactionShipping': 7.98,
   'transactionProducts': [{
       'sku': '486GOU',
       'name': 'Aloe Wash',
       'category': 'Soap',
       'price': 11.99,
       'quantity': 1
   },{
       'sku': '574PRV',
       'name': 'Coconut Shell Scalp Scrub',
       'category': 'Shampoo',
       'price': 9.99,
       'quantity': 2
   }]
});

Create a Transaction Tag in GTM

In Chapter 4, “Account Creation and Tracking Code Installation,” we discussed the concept of a hit as basically a packet of dimension values sent to GA. The primary hit types that we have examined thus far are pageviews and events, and we also discussed social hits in Chapter 6. We now get to work with another kind of GA hit: Ecommerce transaction.

To generate a transaction hit through GTM:

Create a GA tag.
Set the Tracking ID to the same value that we’re using for pageviews and events (ideally by using the GA Tracking ID variable that we first created in Figure 5.5).
Set the Track Type to Transaction as shown in Figure 8.15.

Image described by surrounding text and caption. — **Figure 8.15** A GA tag with Track Type set to Transaction automatically reads Ecommerce variables that you have populated into the data layer.

You do not need to create GTM Data Layer variables: a GA tag with Track Type set to Transaction will automatically read in the Ecommerce variables named in the data layer as indicated above.

Populate the Data Layer Before the Transaction Tag Fires

To make sure that the Ecommerce variables are populated into the data layer as shown in Listing 8.1 when the GA Transaction tag fires, we can take either of the two following options:

Populate the data layer with Ecommerce variables before the GTM container code occurs in the page; in this case, you could apply a basic Page View trigger to the Transaction tag, for example, {{Page Path}} equals /receipt
Apply a DOM Ready trigger (first discussed in Chapter 5) to the GA Transaction tag so the page has been fully parsed when the tag looks for the Ecommerce variables in the data layer; an example trigger would be Page View – DOM Ready – {{Page Path}} equals /receipt

Reusing Existing Ecommerce Variables

If the variables that you need for GA Ecommerce tracking are already present on the page as different variable names, or if your environment blocks the dataLayer object, you can instead use an Enhanced Ecommerce purchase action with a Custom JavaScript variable (instead of using a GA Transaction tag). See Reading Enhanced Ecommerce Data from Your Own Object Variable below.

Enable Ecommerce Setting

As the final (and certainly the easiest) step for Ecommerce configuration, turn on the Enable Ecommerce toggle in the Ecommerce Settings for one or more views within the property for which you have performed the two previous steps.

Ecommerce Reporting

The variables listed above that you populate in Ecommerce tracking become available as dimensions and metrics within the several built-in Ecommerce reports that GA provides. Some of the metrics in the Ecommerce Overview report shown in Figure 8.16 require explanation.

Screenshot shows the ecommerce overview report listing the metrics values of ecommerce conversion rate 0.84 percentage, transactions 19737, revenue dollar 928126.91, average order value dollar 47.02, unique purchases 66814 and quantity 73612. — **Figure 8.16** Metrics in the Ecommerce Overview report.

If you went to a hiking website that had GA Ecommerce enabled and you purchased one pair of hiking boots and two pairs of the same hiking socks, Transactions would increment by one, Unique Purchases would increment by two (two products included at least one time in the transaction), and Quantity would increment by three (three product units total in the transaction).

Similarly to goal conversions, a visitor can convert for Ecommerce one time only in a given session, meaning that Ecommerce Conversion Rate only considers if there is at least one Ecommerce transaction in the session. Additional transactions in the same session would not increase Ecommerce Conversion Rate but would increase other Ecommerce metrics (such as Revenue). In this way, GA recognizes a conversion for a given goal or for Ecommerce once per session only, but it will record multiple Ecommerce transactions.

Transactions with the same Transaction ID within the same session, however, are not double counted; to record additional transactions within a session, you must make sure to use different Transaction IDs.

In the Product Performance report, you can switch the primary dimension to view metrics by Product, Product SKU, or (if populated) Product Category. The Sales Performance and Transactions reports break down the metrics by date and transaction ID, respectively.

The Ecommerce Time to Purchase report shows the time lapse between the last nondirect session and the purchase. For further lookback, see the Time Lag report shown later in Figure 8.17.

Graph shows shopping behavior report representing all sessions, sessions with product views, sessions with add to cart, sessions with check-out, sessions with transactions, no shopping activity, no cart addition, cart abandonment and checkout abandonment. — **Figure 8.17** The Shopping Behavior report shows progress and drop-off from site entry through transaction.

Terminology

Value and Revenue Terminology

GA displays several metrics related to goal value and Ecommerce revenue:

Goal Value. The static monetary value per goal that we optionally specify in the goal configuration. The corresponding Goal Value metric in the reports corresponds to all goals in a view, whereas Goal 1 Value corresponds only to that goal.
Revenue. The monetary value of Ecommerce transactions.
Product Revenue. The Revenue metric minus tax and shipping.
Per Session Value. Average Revenue per session; this does not appear as Per-Session Revenue.
Average Order Value. Revenue per Ecommerce transaction.
Per Session Goal Value. Average Goal Value (for all goals in the view) per session.
Page Value. As discussed below, the total Revenue and Goal Value accrued after a given page was viewed at least once, divided by the number of unique pageviews for the page.

There are also several value-related terms in the AdWords reports, as discussed in Chapter 7, “Acquisition Reports.”

Enhanced Ecommerce

While basic Ecommerce tracking provides much greater detail than goal tracking, it only measures the transaction itself. It still doesn’t track the several steps that usually must precede an Ecommerce transaction: essentially, the Ecommerce funnel.

You can implement GA Enhanced Ecommerce tracking to populate a range of additional Ecommerce reports and metrics in GA; let’s review the reports and metrics before examining the corresponding code requirements.

Shopping Behavior: displays a five-stage funnel from session start to purchase (Figure 8.17).
Checkout Behavior: displays up to eight funnel steps corresponding to the checkout process (Figure 8.18).
Product Performance: to this report, Enhanced Ecommerce adds the Cart-to-Detail Rate and the Buy-to-Detail Rate as performance metrics after visitors have viewed a product details page (Figure 8.19).
Product List Performance: shows performance from impression through revenue by product, product list, and product list position (Figure 8.20).
Internal Promotion: shows clicks and views for internal promotions (such as a banner) and performance metrics following the click (Figure 8.21).
Order Coupon, Product Coupon: transactions by order-based coupons (e.g., 20% off your order) and product-based coupons (e.g., 20% off a specific item) submitted during checkout.
Affiliate Code: revenue, transactions, and average order value for clickthroughs from affiliate programs.

Graph shows checkout behavior report representing cart page, login page or guest checkout, shipping details, shipping options, payment details, cart page dropoff, login page or guest checkout, shipping details dropoff, shipping options dropoff and payment details dropoff. — **Figure 8.18** You can configure the Checkout Behavior report to display the specific steps within checkout.

Screenshot shows a table with columns product and shopping behavior with sub columns cart to detail rate and buy to detail rate; the items listed under product are sony xperia Z3 plus, samsung galaxy S6 edge and apple iphone 6 plus. — **Figure 8.19** With Enhanced Ecommerce implemented, the Product Performance report lists the Cart-to-Detail and Buy-to-Detail.

Screenshot shows a table with columns product list name, product list views, product list clicks, product list CTR, product adds to cart, product checkouts, unique purchases and product revenue; the items under product list name are phones, cases and headsets. — **Figure 8.20** Product List report displays performance in terms of views, clicks, purchases and other metrics, broken down by the list parameters that you provide in the Enhanced Ecommerce code.

Screenshot shows a table with columns internal promotion name, shopping behavior and conversions. The sub columns are internal promotion views, internal promotion clicks and internal promotion CTR and the sub columns of conversions are transactions, revenue and transaction per internal promotion click. — **Figure 8.21** Internal Promotion Report shows click and purchase metrics for internal promotional banners and text links.

Implementing Enhanced Ecommerce Tracking

Enhanced Ecommerce implementation is similar to basic Ecommerce in that you typically populate the data layer with back-end data that a GA tag in GTM will read in and record. If you currently track basic Ecommerce, you’ll be able to migrate to Enhanced Ecommerce without losing the Ecommerce data that you’ve already captured.

As we’ll soon see, somewhat surprisingly, you don’t implement Enhanced Ecommerce using a GA tag with Track Type set to Transaction. Instead, the Enhanced Ecommerce variables are read from the data layer by a Page View or Event tag that you have enabled for Enhanced Ecommerce.

As we saw in the preceding section, basic Ecommerce implementation is more difficult than goal configuration since it normally requires your developers to populate the GTM data layer with additional data from the back end. Enhanced Ecommerce, for its part, is significantly more complex than basic Ecommerce and is undoubtedly the most challenging code-level aspect of GA implementation.

To make this topic more approachable, we review a high-level outline as well as specific code examples. For reference, we’ll be using the very helpful and interactive Google Analytics “Enhanced Ecommerce” demo at https://ga-dev-tools.appspot.com/enhanced-ecommerce/. please be sure to access this demo directly as we proceed through the examples.

Also refer to the Google Tag Manager “Enhanced Ecommerce (UA) Developer Guide ” at https://developers.google.com/tag-manager/enhanced-ecommerce for additional detail on the Enhanced Ecommerce data objects.

What Data Do You Track for Enhanced Ecommerce?

From an implementation standpoint, we can consider Enhanced Ecommerce as tracking four types of occurrences:

Product Impressions: the user was exposed to a product listing without accessing information about that product specifically. (If the user does click through to a product page, you’d capture product click and product detail actions separately from the impression, as discussed below.) You often record several product impressions (for all products on a page or screen) as an array at one time.
Product Actions: for the many types of actions that the user can perform on a product or an array of products, you specify the following elements:
- Action—specific action that the user takes or experiences. Actions include:
  - Click
  - Detail
  - Add (to cart)
  - Remove (from cart)
  - Checkout
  - Checkout_option (discussed below)
  - Purchase
  - Refund
- ActionField—additional data that you can embed in some types of actions, such as checkout step for a checkout action or tax and shipping for a purchase action.
- Products—the one or more products that the action applies to. For instance, you’d normally apply an add (to cart) action to a single product only but a checkout action to an array of all products in the shopping cart.
- CurrencyCode—optional field to designate a currency from which the Ecommerce needs to be converted to match the currency setting in your GA view; especially useful if you process transactions in multiple currencies.
Promotion Impressions: you can capture promotion impressions to which the user was exposed.
Promotion Clicks: you can also capture clickthroughs on a promotion impression.

The sections below detail the tracking for these occurrences. Keep in mind that you are not required to implement all aspects of Enhanced Ecommerce at one time. You could start, for example, by tracking only product detail pageviews, adds to cart, and purchases, and later add impressions, promotions, and checkout steps. That said, it’s advisable right from the start, while you’re making the effort, to + implement tracking that will provide an actionable level of data for your Ecommerce analysis.

Pageview or Event Tag for Enhanced Ecommerce

As demonstrated in Figure 8.15, we set the Track Type in the GA tag for basic Ecommerce tracking to Transaction; for basic Ecommerce, it’s the Transaction tag that reads the Ecommerce data from the data layer and sends it to GA. As mentioned previously, however, you use a GA tag with Track Type set to either Page View (or App View in mobile app tracking) or Event to pass Enhanced Ecommerce data.

So which do you choose, pageview or event? In most cases, that depends on whether the user executed a specific step or if the user was just exposed to the product or promotion as part of the page (or screen) load.

Occurrences to Track with a Pageview Tag

You’d normally track product impressions and promotion impressions in a pageview tag. You’d also normally track a product detail action in a pageview tag. You can also use a pageview tag for product add to cart, remove from cart, checkout, and purchase if these occur on separate pages.

Figure 8.22 shows a Page View tag enabled to read Enhanced Ecommerce variables from the data layer.

Screenshot shows tracktype page with pageview dropdown menu along with vertical menu for more settings and ecommerce features; the main under ecommerce features are enable enhanced ecommerce features and use data layer. — **Figure 8.22** You can enable a Page View tag, as shown in this figure, or an Event tag to read Enhanced Ecommerce variables from the data layer.

Occurrences to Track with an Event Tag

If any of the actions mentioned previously occur on the original page without a new page load—common in AJAX-based checkout scenarios as demonstrated in Figure 6.26—you can track them instead with events.

You can also use an event tag instead of a pageview tag for product or promotion impressions that are not initially rendered on the page but are instead generated through additional user interaction—typically scrolling.

You’d normally track product actions that don’t coincide with a pageview, and also promotion clicks, in an event tag.

Product and Promotion Impressions

As you use the Google Analytics Ecommerce demo, you can click the icons to view the code that executes on pageview or click and populates the data layer with Enhanced Ecommerce variables, as illustrated in Figure 8.2.

In Listing 8.2, we see that multiple product impressions (just two shown in the listing for brevity) and also the Back to School promo impression are pushed to the data layer. Since these impressions occur as soon as the page loads, and without further interaction on the part of the user, you can enable the GA pageview tag in GTM to read in this Enhanced Ecommerce data from the data layer. You should place the code in Listing 8.2 before the GTM container code (or set the pageview tag to fire on DOM ready instead of the default page load).

Screenshot shows a google analytics page with header Ibby's T-shirt Shop and subheading All T-shirts. The image of 6 t-shirts along with the price and product details, your cart and checkout buttons is also represented. — **Figure 8.23** You can enable a Page View tag, as shown in this figure, or an Event tag to read Enhanced Ecommerce variables from the data layer.

LISTING 8.2: Populating the data layer with product and promotion impressions.

dataLayer = window.dataLayer
|| []; //initializer not shown in online example
dataLayer.push({
  "ecommerce": {
    "currencyCode":"USD",
    "impressions": [{
      "id":"9bdd2",
      "name":"Compton T-Shirt",
      "price":"44.00",
      "brand":"Compton",
      "category":"T-Shirts",
      "position": 0,
      "list":"homepage"
    },
    {
      "id":"6d9b0",
      "name":"Poyo T-Shirt",
      "price":"62.00",
      "brand":"Poyo",
      "category":"T-Shirts",
      "position": 3,
      "list":"shirts you may like"
    }], //see full product array in online demo
    "promoView": {
      "promotions": [{
        "id":"bts",
        "name":"Back To School",
        "creative":"HOME banner",
        "position":"right sidebar"
      }]
    }
  }
});

Product Click

Listing 8.3 shows the code that is executed when a shirt is clicked. Note that it populates an optional list field.

Also note that the event field in the second line of the listing corresponds to a Custom Event trigger that you can apply to any tag, as first demonstrated in Figure 6.16. In this example, there would be a Custom Event trigger listening for"event":"productClick" in the data layer, at which point it would fire a GA event tag to which that trigger was applied that was enabled to read Enhanced Ecommerce data from the data layer.

Instead of setting up a new tag and trigger in GTM for each type of click action as in the online demo, we could reuse the “catch-all” event tag, data layer variables, and trigger that we implemented as demonstrated in Figures 6.32, 6.33, and 6.34. To take advantage of this existing infrastructure, replace"event":"productClick", with:

"event":"eventTracker","eventCat":"ecommerce","eventAct":"click","eventLbl":"product",

Note that the specific event category, action, and label values aren’t that critical in themselves; we’re just using the event tag to convey the Enhanced Ecommerce data, which will be the focus of our analysis rather than the event data.

LISTING 8.3: Populating the data layer with Ecommerce product click data, as well as the designated event variable that can fire a Custom Event trigger.

dataLayer.push({
  "event":"productClick",
  "ecommerce": {
    "click": {
      "actionField": {
        "list":"homepage"
      },
      "products": [{
        "id":"f6be8",
        "name":"Comverges T-Shirt",
        "price":"33.00",
        "brand":"Comverges",
        "category":"T-Shirts",
        "position":"2"
      }]
    }
  }
});

Promotion Click

If we click the small icon for the Back to School banner, we see the code in Listing 8.4. As in Listing 8.3, we can replace"event":"promotionClick" with four data layer variables that our existing catch-all event tag will use.

LISTING 8.4: Populating the data layer with Ecommerce promotion click data.

dataLayer.push({
  "event":"promotionClick",
  "ecommerce": {
    "promoClick": {
      "promotions": [{
        "id":"bts",
        "name":"Back To School",
        "creative":"HOME banner",
        "position":"right sidebar"
      }]
    }
  }
});

Note that the Enhanced Ecommerce promotion impressions and promotion clicks provide a safe way to track internal campaigns. In Chapter 7, we warned against campaign parameters for internal campaigns because they spawn a new session and break the attribution of subsequent actions to the rightful traffic source, so make sure to squelch any temptation to use campaign parameters in parallel with Enhanced Ecommerce promotions.

Product Detail

When the product detail page pictured in Figure 8.24 loads, the GA pageview tag will read the product detail, product impression (for the shirts listed on the bottom), and promotion impression data that we wrote to the data layer.

Screenshot shows a webpage with header fuelworks T-shirt where the image of a T-shirt and the price along with radio buttons for choosing a trim color and size; four other T-shirts with price and product details are represented. — **Figure 8.24** Product detail, product impression, and promotion impression data is recorded when this product description page loads.

Product click and product detail may seem redundant, since one should lead to the other, but product click actions are used specifically to calculate the Product List Clicks metric that appears in the Product List report.

Add and Remove (To and From Cart)

The code that is populating the add-to-cart action for the main shirt on the page appears in Listing 8.5. Note that the code for the remove action is identical, except add is replaced with remove. There is no reference maintained for the product that was previously added to the cart, so we must again specify all the product fields.

Also note that"event":"addToCart" and"event":"removeFromCart" differ in the online demo, but, again, the event variable in the data layer is just used to trigger a GTM tag, and we could instead push the four values used in our event catch-all tag, as in previous examples.

LISTING 8.5: Populating the data layer with add (to cart) action.

dataLayer.push({
  "event":"addToCart",
  "ecommerce": {
    "currencyCode":"USD",
    "add": {
      "products": [{
        "id":"bc823",
        "name":"Fuelworks T-Shirt",
        "price":"92.00",
        "brand":"Fuelworks",
        "category":"T-Shirts",
        "variant":"green",
        "dimension1":"M",
        "position": 0,
        "quantity": 1
      }]
    }
  }
});

Note

Custom Dimensions and Custom Metrics for Ecommerce Products

In Chapter 12, we’ll learn how to record our own dimensions and metrics to capture data that GA would not record as a built-in dimension or metric. In Enhanced Ecommerce, we can create custom dimensions and metrics at product scope, for example, the M custom dimension value for men’s clothing in Listing 8.4. We could also create a custom metric to capture a discount, surcharge, or extra tax for a product. Custom dimensions and metrics do not appear in any of the built-in reports, but you can use them in custom reports, which we discuss in Chapter 13.

You only have to record custom dimensions or metrics once per product within an Enhanced Ecommerce transaction, but it’s not harmful to record them with multiple actions as is done in the Enhanced Ecommerce demo that we’re exploring online.

Checkout Steps

We need to discuss checkout action somewhat separately from the other Enhanced Ecommerce actions because we’re going to configure and populate multiple checkout steps that will appear in the Checkout Behavior funnel.

Let’s first complete the required admin configuration. (As a note, even if we are not populating the Checkout Behavior funnel, we need to enable the Enhanced Ecommerce toggle as shown in Figure 8.25.)

Screenshot shows a webpage with header enhanced ecommerce settings with an ON and OFF button along with the funnel steps cart page, login page or guest checkout, shipping details, shipping options, payment details and confirmation page. — **Figure 8.25** The Checkout Behavior steps appear as you have named them in the Enhanced Ecommerce Settings.

If we do want to populate the Checkout Behavior funnel, we must begin by adding the steps: note the correspondence with the names of the checkout steps in Figure 8.18. For the Ecommerce Demo, we would instead name steps that correspond to each of the tabs that represent different checkout steps. In this way, we can configure Checkout Behavior report to match our checkout steps as they occur in our own checkout process.

With our checkout steps set up, we can begin receiving checkout data, as shown in Listing 8.6 for the Shipping tab of the online demo. Note that for each of our checkout steps and our purchase action, we’d include all products in the cart. (We have only one product in the cart for our example.)

LISTING 8.6: Populating the data layer with the checkout action and checkout step 2 specified in the actionField.

dataLayer.push({
  "event":"checkout",
  "ecommerce": {
    "checkout": {
      "actionField": {
        "step": 2
      },
      "products": [{
        "id":"bc823",
        "name":"Fuelworks T-Shirt",
        "price":"92.00",
        "brand":"Fuelworks",
        "category":"T-Shirts",
        "variant":"red",
        "dimension1":"M",
        "position": 0,
        "quantity": 1
      }]
    }
  }
});

Checkout Options

You can record additional user input in the checkout process, such as the credit card selection that appears in Figure 8.26. As indicated in Listing 8.7, we don’t need to include product information with a checkout_option action; we just need to associate it with a checkout action for which you have specified the same step number: step 3, in the example.

Screenshot shows a page with header credit card, radio buttons for visa, mastercard and AmEx, text field go back to customer info and a next button along with an icon to view checkout option data. — **Figure 8.26** You can capture selections in the checkout process such as credit card as Enhanced Ecommerce checkout options.

Because the checkout_option check step corresponds to a checkout step that you’ve already recorded, you typically record a check_option in a separate GA event or potentially at the time that the following checkout step is recorded. (In this way, you’d record checkout_option for step 2 at the same time as checkout step 3.)

LISTING 8.7: Populating a checkout option.

dataLayer.push({
  "event":"checkoutOption",
  "ecommerce": {
    "checkout_option": {
      "actionField": {
        "step": 3,
        "option":"Mastercard"
      }
    }
  }
});

Purchase

The purchase action is similar to the checkout action in that you apply it to all products that are in the cart. You use the actionField to specify additional detail about the purchase.

Coupon

When recording a purchase, you have the option of applying a coupon value at the transaction or product level, both shown in Listing 8.8.

LISTING 8.8: Purchase data, with optional coupon details, populated into the data layer.

dataLayer.push({
  "event":"transaction",
  "ecommerce": {
    "purchase": {
      "actionField": {
        "id":"6172c619-a62f-40c0-bba8-aeff7f7a6c54",
        "affiliation":"Online Store",
        "revenue": 102,
        "tax": 5,
        "shipping": 5,
        "coupon":"SummerSale"
      },
      "products": [{
        "id":"bc823",
        "name":"Fuelworks T-Shirt",
        "price":"92.00",
        "brand":"Fuelworks",
        "category":"T-Shirts",
        "variant":"red",
        "dimension1":"M",
        "position": 0,
        "quantity": 1
        "coupon":"RedShirtPromo"
      }]
    }
  }
});

Record Confirmation Step with Purchase

Don’t forget to record a confirmation step as the last step of your checkout process (so it appears in the Checkout Analysis report). You can send a confirmation checkout step along with your purchase when your thank-you page loads.

The online demo takes a different approach: it records the purchase when the user clicks the Purchase button on the Review Cart tab and then records the confirmation checkout step by itself when the Confirmation tab loads. Either approach is good, but there’s a slight risk with the latter of recording a few Purchase clicks as purchases even when the purchase does not complete because of a credit card decline, etc. (To mitigate this risk, you can select the Check Validation option in a Form or Click - Just Links trigger in GTM—this will block the trigger from firing if the form submit or link click returns false.)

Reading Enhanced Ecommerce Data from Your Own Object Variable

If your Ecommerce data is already present on the page, or if for any reason you cannot use the data layer object, Figure 8.27 demonstrates a Custom JavaScript variable that you can set up to populate a JavaScript object with the same name/value pairs that you would otherwise populate into the data layer. Figure 8.28 illustrates a Page View or Event tag that uses the Custom JavaScript variable instead of the data layer to read Enhanced Ecommerce data.

Figure 8.27 This custom JavaScript variable rewrites existing Ecommerce data from the page into the format that GTM can consume.

Figure 8.28 We can configure a Page View or Event tag to read Enhanced Ecommerce data from a Custom JavaScript variable instead of from the data layer.

As a related note mentioned in Chapter 6, you can rename the two instances of dataLayer in the GTM container code if this variable name is already in use for another purpose. If, for example, you changed the name in the container snippet to gtmDataLayer, you’d use that variable name to populate the GTM data layer for Enhanced Ecommerce and so forth.

Enhanced Ecommerce in Testing and Live Environments

In Chapter 5, we learned about the Environments feature in GTM that allows you to publish a single container into development, staging, and live environments.

Because Enhanced Ecommerce (as well as basic Ecommerce) relies both on code-level changes within the page and on tag changes within the container, you’ll need to coordinate your development-to-testing and especially your testing-to-live updates so you’re pushing code at the same time as you’re publishing to the Live environment in GTM.

As detailed in the Chapter 5 discussion, you can use a GTM Custom JavaScript or Lookup Table variable to dynamically track your Enhanced Ecommerce transactions to a dedicated development and/or test GA property from a development/test hostname (e.g., test.mysite.com) and to your main/live GA property from your live hostname.

Ecommerce and Enhanced Ecommerce Tracking in Mobile Apps

As, we’ll learn in Chapter 13, the two basic GA tracking options for mobile apps are the GA SDK or the GTM SDK (either for iOS or Android in both cases). If you’re using a GTM SDK, the process is similar to Ecommerce or Enhanced Ecommerce tracking for a website as detailed above.

For basic Ecommerce tracking through GTM:

Use a GA tag with Track Type set to Transaction.
Populate the data layer with the designated Ecommerce variables.

For Enhanced Ecommerce tracking through GTM:

Use one or more tags with Track Type set to App View (meaning screen view) or Event.
Select the Enable Enhanced Ecommerce Features setting and Use Data Layer settings, as shown previously in Figure 8.22.
Populate the data layer with the designated Ecommerce variables.

For code examples, see:

Google Tag Manager for Android—Enhanced Ecommerce (https://developers.google.com/tag-manager/android/v4/enhanced-ecommerce)
Google Tag Manager for iOS—Enhanced Ecommerce (https://developers.google.com/tag-manager/ios/v3/enhanced-ecommerce)

For Ecommerce and Enhanced Ecommerce tracking through the GA SDKs, see:
Enhanced Ecommerce Tracking—Android SDKv4 (https://developers.google.com/analytics/devguides/collection/android/v4/enhanced-ecommerce)
Enhanced Ecommerce Tracking—iOS SDK (https://developers.google.com/analytics/devguides/collection/ios/v3/enhanced-ecommerce)

This documentation is current at the time of writing; you can check for any updates. You can use the demonstrated syntax to write to the data layer for basic or Enhanced Ecommerce tracking.

If you’re using the third-party cart for in-app purchases, ask your provider the questions listed in Integrating with “Third-Party Shopping Carts” below.

GUEST SPOT The Last Concession: Advanced Technique for Tracking Build & Price Leads with Google Analytics Enhanced Ecommerce

Stéphane Hamel

Stéphane Hamel is a distinguished thought leader in the field of digital analytics.

Some time ago, after implementing analytics on a complex build-and-price workflow for an automotive website (navigation shown in Figure 8.29), I was tasked with analysis. The workflow involved typical model selection/transmission/colors/accessories/extended warranty, and ultimately enticed the visitor to stay in touch by getting some financing, finding a dealer, printing a nice personalized brochure, or saving their dream car configuration for later.

Figure 8.29 Which are the last accessories that car purchasers sacrifice?

While digging into the data, I had one of those “aha!” moments.

Think of the above scenario—what is your behavior? If you are like me and most people I have watched using this type of tool over the years, you start with your dream car and quickly realize you will have to make some concessions.

Analyzing what gets added and removed from a build and price tool can be very time consuming and complex, and can lead to analysis paralysis. However, the last item, option, or feature the user conceded is worth a lot of money! If you can uncover a pattern based on this information, you’ll know exactly how to subtly tap into a new segment of previously hidden opportunities—and ultimately grow revenue.

At the aggregate level, this gained knowledge can inform new marketing campaigns and promotions. At the individual level, this invaluable piece of information can be leveraged to know exactly what to upsell to the client! Note that this technique isn’t against GA’s Personally Identifiable Information restriction. The only thing we’re adding is a fake product to the enhanced Ecommerce purchase. The one-to-one tie-in with a specific prospect/client happens in whichever sales or CRM system the organization is using on the back end.

Build and price tools aren’t limited to automotive websites. Think of vacation packages, computers, phone and cable services, and more.

Solution

The concept builds upon the Enhanced Ecommerce tracking presented in this chapter, except that instead of actual orders, we track “leads.” The main differences are:

Track add/remove from cart for each option, feature, or item.
Persist the information about the last item removed from the cart—you can use a cookie or HTML5 localStorage to do that. You might also want to keep this item in a custom dimension, and its value in a custom metric, both with a session scope—see the Conceded Accessory dimension and the Conceded Value metric in Figure 8.32.
When the user converts (the Enhanced Ecommerce purchase action), you will also include a very special product to your purchase: the last concession item you kept in your back pocket! You will change two things: the price will be 0, and the Enhanced Ecommerce variant will be concession.

Caveat

Lead tracking: anytime you look at Ecommerce reports, you will have to remember those are not actual sales! You have a couple of options here:

Configuration value. If you are OK seeing the extra Ecommerce revenue reported (which, for a car manufacturer website, would tally several millions of dollars!), leave it as is.
Lead value. If you consider a lead is worth $100, use this as the purchase value of the main item, and set all other items to be free. Also set a custom metric for each item with their actual value.
Fractional value. If you consider a configuration is worth something but the exact value depends on the selected options, use 1/10 or even 1/100 of each cart item value.

Analysis

You now have detailed data about the build-and-price flow and selected configurations in your Ecommerce reports. Following are some examples of the analysis you can perform.

Most Conceded Items

Under the Product Performance report, you will be able to view which configuration items are most popular (regardless of package). By adding a secondary dimension for Product Variant, as in Figure 8.30, you will see how many times a specific item was conceded. The next step is to understand why: Why is this popular item not making the cut? Is it too expensive? Not compatible with another item? Replaced with a lower-cost item?

Figure 8.30 Product Performance report, filtered for concession as the Product Variant secondary dimension value.

Lost Opportunity/Share of Wallet

What’s the value of items being abandoned? How much more money could you make if those items were included? If you had to discount those items, what would be the tipping point where the customer would keep it in the build?

Custom Funnel

You could use the Custom Funnels feature (included in Analytics 360 and outlined in Chapter 18) to mimic an Enhanced Ecommerce shopping or checkout funnel, but for the build-and-price process.

Lead Generation

Last but not least, pass the “last concession item” down to your CRM or lead management platform and use it to upsell or simply better serve your customers! (To enable the data integration, you could store the back-end lead number in GA as a custom dimension, as shown in Figure 8.32. Integrating GA and CRM data is discussed in Chapters 15 and 17.)

Figure 8.31 Custom funnel steps configured for the build-and-price process.

Figure 8.32 Custom report containing lead number imported from CRM, Conceded Accessory custom dimension, and Conceded Value custom metric.

Related Items

You can activate the Related Products feature under Ecommerce Settings in the View Admin. Related Product data then becomes accessible only through the reporting API after 30 days, and provided there are enough products and transactions recorded. (See https://support.google.com/analytics/answer/6223409?hl=en.)

Once enabled, you will gain a unique view of the most popular configuration packages, as demonstrated through the GA Query Explorer in Figures 8.33 and 8.34. You will find out which model/color/accessories work well together. You will also have the ability to see which accessories were considered as part of those popular packages, but later conceded (by looking into the Product Variant dimension).

Figure 8.33 This query configuration in the Google Analytics Query Explorer includes the relatedProductName dimension as well as the relatedProductQuantity and correlationScore metrics.

Figure 8.34 The Query Explorer results list both car models and accessories as the primary dimension and the most often coinciding accessory as secondary dimension, with the Correlation Score indicating the frequency of the car/accessory or accessory/accessory combination. The correlation score ranges between 0 and 1, the higher value meaning that the correlation is stronger.

Segmenting and Remarketing Based on Shopping and Checkout Behavior Funnels

In Chapter 10, “Segments,” we’ll see how segments can narrow the audience we’re analyzing to highlight significant points in the data. As a preview, let’s consider segmentation scenarios for the Shopping Behavior and Checkout Behavior funnels:

Create a segment based on abandonment (or continuation) at a funnel step. We can create a segment from any point in either of the funnel and then apply it to other reports. In Figure 8.35, we’re creating a segment for all shopping cart abandoners. We could also create a counterpart segment: visitors who continued to checkout. If we applied both of these segments to the Mobile Devices report, for example, we could see which devices experience the most abandonments.
Create a segment based on other report data and apply to the Enhanced Ecommerce reports. As a complementary approach, we would create a segment based on a mobile device (Android tablets, for example) and apply this segment to the Shopping or Checkout Behavior funnels, or any of the other Enhanced Ecommerce reports.

Screenshot shows a window representing the car abandonment value of 7276 and check-out abandonment value of 129,533 along with a down arrow button used to create an ecommerce segment. — **Figure 8.35** You can easily create custom segments or remarketing audiences from any continuation or abandonment point in the Shopping Analysis or Checkout Analysis funnels.

As we learn in Chapter 14, GA remarketing audiences for AdWords are defined essentially the same way as segments, and dynamic remarketing will allow us to display specific product details in our remarketing ads.

Multi-Channel Funnel Reports

Goal and Ecommerce metrics also appear outside of the conversion reports, as in the Source/Medium report shown in Figure 8.36. The conversion data in this report seems quite straightforward, and we might be tempted to say that Google organic traffic drove 4,766 lead submissions.

Screenshot shows a table with columns source or medium, acquisition, behavior and conversions. The sub columns under acquisition are sessions, percentage new sessions and new users, under behavior are bounce rate, pages or sessions and average sessions duration and under conversions are three lead submission columns. — **Figure 8.36** last-click attribution, goal and Ecommerce data in the Source/Medium report does not apportion credit among multiple channels for the conversions.

Before feeling confident in this observation, however, we need to recognize that some or many of these 4,766 conversions should be attributed, at least in part, to other traffic sources.

Last-Click Attribution

Most of the GA reports display conversion data according to last-click attribution: the source that drove the converting visit receives credit—all credit—for the conversion, even if one or more previous sessions were driven by a different channel. (The exception is a returning direct visit, which pulls in the more specific previous source and medium values, as demonstrated in Figures 7.22 and 7.23.)

As an example, for the 4,766 lead submissions attributed to Google/organic, many of the converting sessions could have been preceded by a clickthrough from a paid campaign or an email. When we remember from Chapter 7 that much of the traffic reported as organic in GA is branded organic, and therefore not truly organic but navigational and very similar in essence to direct traffic, it’s even more of an inaccuracy to give Google/ organic full credit for the 4,766 lead submissions.

Multi-Channel Funnel Reports

As discussed below, the Multi-Channel Funnel (MCF) reports provide a more complete picture of your visitors’ journeys to conversion. In fact, you should devote a large part of your conversion analysis to the MCF reports, especially if you are responsible for driving traffic to a website.

Assisted Conversions

The Assisted Conversions report shown in Figure 8.37 is indispensable for demonstrating the traffic sources that drove initial and assisting sessions for visitors who ultimately converted in a returning session, in many cases from another traffic source.

As shown in Figure 8.38, the top portion of the MCF reports provide many configurable options:

Conversion: MCF reports default to all goals and Ecommerce transactions, but you can narrow focus to a smaller conversion selection.
All/AdWords: You can display the MCF reports for AdWords (so that the Assisted Conversion report, for example, defaults to AdWords campaign as the primary dimension).
Lookback Window: the default is a 30-day lookback, but it can be useful to extend the lookback to the maximum 90 days for a more complete attribution story. (If one of your visitors converts on an organic clickthrough today, wouldn’t you want to know if that visitor clicked through from your email campaign 89 days ago but did not convert at that time?)
First Interaction Analysis: the MCF reports default to Assisting Conversion Analysis, but you can change the display to First Interaction Analysis. (First Click Conversions are a subset of Assisted Conversions, not separate from Assisted Conversions.)
Conversion Segments: you cannot apply the same segments to the MCF reports that you can to other GA reports. You can, however, apply built-in and custom Conversion segments, such as Last Interaction Is Organic Search. (As stated earlier for Funnel Visualization, if you need to view the MCF reports for a specific portion of your traffic, such as a country or a source, you can set up a dedicated filtered view, as described in Chapter 9.)

Screenshot shows a window with header conversion segments, a dropdown export and header shortcut along with the data under conversion, type and lookback window. — **Figure 8.38** You can configure several options at the top of the MCF reports.

Top Conversion Paths

The Top Conversion Paths report does not relate to the pages viewed before a conversion; path in this context refers to the channels that drove the sessions leading to and including conversion as shown in Figure 8.39. Note that in the Top Conversion Paths report as well as the other MCF reports, Direct has the same precedence as the other channels; it is not overwritten by a more specific channel from a previous session as in the Acquisition reports.

You can display the Top Conversion Paths report with other primary dimensions selected, such as Source/Medium, as illustrated in Figure 8.40, or Campaign.

Screenshot shows a window with two rows representing menus labeled google or cpc, direct or none, google or cpc and promo or email. — **Figure 8.40** You can set the primary dimension in the MCF reports to Source/Medium, as above, or to Campaign.

Time Lag

The Time Lag report, shown in Figure 8.41, indicates how long it takes for the conversions to occur relative to the first session.

Note that the Multi-Channel Funnel reports don’t exist in GA mobile app views, but if you have Ecommerce or Enhanced Ecommerce tracking configured for a mobile app, the Ecommerce ˃ Time to Purchase report can provide similar data to Time Lag (but for Ecommerce transactions only, not for goal completions).

Path Length

As in Top Conversion Paths shown above, the path in the Path Length report, shown in Figure 8.42, refers to the channels that drove one or more sessions that (eventually) led to a conversion.

Attribution Model Comparison Tool

Attribution modeling is the art/science/practice of experimenting with different ways to assign conversion credit to marketing channels based on factors including position in the conversion path, attributes of user interactions, and engagement metrics to better show the value of marketing investments across the conversion funnel.

You can use the model comparison tool to compare channel performance according to different built-in (or custom) models. In Figure 8.43, we’re comparing the number of conversions that would be attributed to each channel according to different attribution models:

Last Interaction: all conversion credit to the last interaction that drove the converting session.
First Interaction: all conversion credit to the first interaction that drove an immediate conversion or preceded a conversion on a return session.
Linear: conversion credit divided equally by each session that led to a conversion.

Screenshot shows a table with columns MFC channel grouping, spend and conversion and CPA. The sub columns for conversion and CPA are last interaction, first interaction and linear. The items under MFC channel grouping are direct, organic search, display, email and paid search. — **Figure 8.43** In this example we’re using the Model Comparison Tool to compare channel performance using First Interaction, Last Interaction, and Linear models.

Figure 8.44 shows all available built-in models. Note that you can create your own models or customize any of the built-in models based on session recency before conversion, level of user engagement during the session, and many other factors.

Screenshot shows a window with vertical menu for data-driven models and default models. The item under data-driven models is data driven and items under default models are last interaction, last non-direct click, last adwords click, first interaction, linear, time decay and position based. — **Figure 8.44** Built-in attribution models.

Note also that the Data-Driven Attribution model, discussed in Chapter 18, is available in Analytics 360 only.

Note

Attribution Modeling and Assumptions

The built-in and custom attribution models basically represent assumptions (e.g., if I gave all conversion credit to the first interaction, how many conversions could I attribute to email?). There are some assumptions, however, that are better than others. If you run multi-channel, integrated marketing programs then you have at least two (and likely many more) channels driving ad impressions and site interactions leading to conversion activity. If that’s the case, we can definitely say that the assumptions built into single touchpoint conversion models are not as good as models that potentially give credit to all touchpoints that contributed to a conversion.

As an exception, the Data-Driven Attribution model (available only in Analytics 360 and discussed further in Chapter 18) bases conversion credit on algorithms rather than assumption.

The Assisted Conversion report discussed earlier does not assume that one model is more valid than the other—instead, it just displays all channels that it can objectively demonstrate, through GA data collection, to have driven a session that led to an eventual conversion. For more on attribution modeling, see www.e-nor.com/gabook.

Custom Channel Groupings

Page Value

The Page Value metric that appears as the last column in the All Pages report is perhaps the most misunderstood (and underappreciated) metric in GA. In general, we can say that Page Value indicates how much support a page is providing to goal (Destination and Event type, primarily) and Ecommerce conversions.

For each page, Page Value is calculated as:

As we remember from Chapter 4, the Unique Pageviews metric represents the number of sessions in which the page was viewed at least one time. When calculating Page Value, a page counts in the denominator of our equation only once, even if it was viewed multiple times in a session. Similarly, Goal Value and Ecommerce Revenue is added to the numerator only once, even if the page was viewed multiple times before the conversion took place, and the full Goal Value or Ecommerce Revenue is credited regardless of how many pageviews interceded between the given page and the conversion—as long as the conversion was completed after at least one of the pageviews occurred.

Figures 8.45 and 8.46 illustrate Page Value respectively for Ecommerce and for a nonmonetized lead generation goal to which we have assigned a goal value of $1. Note that even based on an arbitrary goal value in the second example, Page Value helps to identify pages that are—and are not—supporting the conversion process.

Screenshot shows the tables for ecommerce - camping equipment, page, ecommerce revenue contributed, total sessions and page value. — **Figure 8.45** With Page Value based on actual Ecommerce revenue, we see that the Free Shipping page seems to be helping Conversions more than Privacy Policy.

Screenshot shows the tables for lead generation - financial planners, page, goal value contributed, total sessions and page value. — **Figure 8.46** Even with Page Value based on an arbitrary goal value of $1, we can see which pages are likely helping lead submissions.

Does Page Value provide an absolutely objective comparison of your pages’ conversion impact? Not completely, of course. Visitors who viewed the Privacy Policy page in Figure 8.43 may have already been experiencing some anxiety and have therefore been less likely to convert, while those viewing the Free Shipping page may have been seeking affirmation on a purchase that they had already nearly decided on.

Nonetheless, Page Value can provide an important aid to conversion analysis and optimization. If your Advisory Panel page has the highest Page Value as in Figure 8.44, you can revise your home page variation to more prominently display or link to Advisory Panel information, or—even better—you can set up an A/B test variation with these changes and measure the difference in conversion rate.

Conversely, if a page that you expected to help conversions is displaying a low Page Value, review the page for potential improvements or consider directing traffic away from that page and towards a page that is achieving higher Page Value.

Distinguishing between Goal Value and Ecommerce Revenue within Page Value

As shown above, Page Value calculation is based on all Goal Value and Ecommerce Revenue accrued in the given sessions, so how can you display Page Value based on one goal or Ecommerce revenue?

There are two approaches: segments or separate views. Let’s say specifically that within the same view, we have:

Ecommerce tracking
one goal configured with a £10 Goal Value

If you wanted to display Page Value based on either Ecommerce transactions or Goal completions alone, you could apply custom segments as shown in Figures 8.47 and 8.48 to remove sessions in which the goal conversion or the Ecommerce transaction occurred. (We discuss segments in Chapter 10.)

Screenshot shows a window with header filter and vertical menu for sessions and excludes. The dropdowns thank you (goal 1 completions), per sessions, equal to along with hyphen, OR and AND buttons. — **Figure 8.47** To display Page Value based only on Ecommerce Revenue, we can apply a filter to exclude sessions in which the goal conversion occurred.

Image described by caption. — **Figure 8.48** To display Page Value based only on the Goal Value that we configured, we can apply a filter to exclude sessions in which Revenue (i.e., Ecommerce revenue) is greater than 0.

You may have noticed in the segments above that we would be inadvertently excluding, in both cases, those sessions in which both the goal and an Ecommerce transaction took place. Though this would probably not significantly skew our analysis, we always have the option (with Edit rights to the property) to create two additional views: one to track the goal only, and one to track the Ecommerce transactions only. In this way, Page Value as reported in these dedicated views would be based on all Ecommerce transactions or all completions of the £10 goal.

Integrating with Third-Party Shopping Carts

If you’re working with a third-party Ecommerce platform (or planning to), you’ll want to make sure to verify the level of GA support that the platform offers, for tracking in general and Ecommerce specifically. For hosted platforms such as Shopify, BigCommerce, and Volusion, cross-domain support is also a consideration, since you’ll want to make sure to maintain the session between your own domain and the hosted cart domain. (Cross-domain tracking is discussed in Chapter 12.) For self-hosted platforms such as Magento, cross-domain tracking does not pertain, since users won’t traverse domains for the ecommerce functionality.

GUEST SPOT GA Tracking Questions to Ask Your Ecommerce Provider

Vanessa Sabino

Vanessa Sabino is Data Analysis Lead for Shopify.

Using a hosted checkout/shopping platform to power your Ecommerce is a great way to build a site to promote your offerings and generate sales online without having to worry about infrastructure. In some cases, this also means a big part of the GA implementation will be done for you.

Here are a few items that you should verify to ensure your online store is collecting high-quality data that will help you understand your customers.

Does Your Platform Offer Any Kind of GA Integration?

If there is a place to enter your GA Property ID, the platform will probably track at least the pageviews for your site. But check which version of the tracking code is being used: if they don’t support Universal Analytics (analytics.js), there are many new features that will be unavailable to you.

Is There Ecommerce Tracking?

If you want to see your sales in GA, you’ll need to enable Ecommerce tracking, which can be done from the Admin in your GA account. You also need to ensure that data is being sent from the order confirmation page, which includes the transaction ID, products that were sold, quantity, price, and so on.

Ideally, your checkout platform provider should automate the Ecommerce coding and data population. If, however, the platform provider does not automatically configure GA Ecommerce for you, do they provide a way for you to add this code manually? In this case, you’ll need to edit the HTML of the page to insert JavaScript code. You’ll also need to have variables (such as product ID and tax) available that contain all the required information to create a transaction in GA.

Is There Support for Enhanced Ecommerce?

Even better than the standard Ecommerce tracking is the new Enhanced Ecommerce option for Universal Analytics. This collects Ecommerce-related data for the entire session, not just the final purchase. This data includes product views, items added to cart, and different checkout steps.

Does the Checkout Happen in a Different Domain from Your Storefront?

In order to transmit information securely during checkout, the URL needs to have a valid secure sockets layer (SSL) certificate. Depending on the platform or subscription plan that you select, this may be achieved by having the checkout flow in a shared domain that belongs to the platform (such as tshirtstore.platform.com) instead of your own store domain (such as www.tshirtstore.com) (Figure 8.49). In this case, make sure the cross-domain tracking is working properly. When you first go from your own domain to the checkout domain, you should see a _ga parameter in the end of the URL containing a set of numbers. This will enable GA to recreate the cookie in the new domain in a way that you are still identified as the same user. Otherwise, your GA reports will show one user ending their visit in the previous page and a new user starting a new visit on the checkout page, which would make you completely miss the original traffic source of a sale. (For further details on cross-domain tracking, see Chapter 12, “Implementation Customizations.”)

Figure 8.49 In some checkout configurations, your shopping cart resides on your main storefront domain while checkout (including payment) and confirmation reside on a separate checkout domain.

Is Your Payment Gateway Under a Domain Separate from Your Checkout Platform?

If the customer is taken to a completely different site to enter their payment information, such as PayPal, there isn’t a lot you can do to track what is happening there. But make sure everything is set up to take the customer back to your thank-you page on the hosted platform after the payment is completed, as this is the ideal place to trigger the Ecommerce tracking (Figure 8.50). In some cases, you have to set an Auto Return URL on the configuration page of the payment gateway as in Figure 8.51, but depending on the integration between your platform and payment gateway, this may not be necessary, as the return URL could be generated automatically for you.

Figure 8.50 Another possible configuration is the payment gateway that resides on a third domain, separate from the checkout domain.

Figure 8.51 For some payment gateways, you need to configure the URL that the user will be redirected to on the secure checkout site after payment on the payment domain.

Also make sure you add your payment gateway domain to the Referral Exclusion List under Tracking Info in your GA property settings.

Ask the Right Questions

Whichever checkout and payment configuration you’re considering, make sure to ask about GA Ecommerce and cross-domain tracking. With the right configurations, you’ll be able to fully understand the performance of your online store and drive ongoing improvement.

Key Takeaways

Goals and Ecommerce tracking are not retroactive. Don’t delay in setting up a goal for any action or engagement level that represents a success in terms of your KPIs, and Ecommerce (or Enhanced Ecommerce) for transactions that include a variety of products, price points, and so on.

Four goal types. You can create goals based on a pageview or screen view (physical or virtual), an event, or threshold of engagement (that is, pages/screens per session, or session duration).

Three match types for Destination goals. You can use the Equals To match type to match a single Page or Screen dimension value only, or you can use Begins With or Regular Expression for multiple pages or screens to count as a completion of the same goal.

Goal funnels are recommended but not required. If your conversion process is designed to include a series of pages or screens, create a Destination goal with a funnel.

Conversion rate is based on sessions. Conversion rate is calculated as conversions per session and therefore appears lower than if it were calculated as conversions per user.

Funnels do not affect conversion rate. Funnels populate the Funnel Visualization report and determine Abandonment Rate and Funnel Conversion Rate, but they do not affect Goal Conversion Rate. The only factor for calculating conversion rate for a Destination goal is the number of sessions in which the Destination page or screen was viewed.

Unlike goal tracking, Ecommerce tracking requires additional code. While goal tracking depends only on an Admin configuration and the page or event data that is already being recorded, the greater detail in Ecommerce tracking usually depends on additional back-end transaction data written to the data layer. For this reason, you normally need to work your developers to implement Ecommerce tracking.

Enhanced Ecommerce provides reporting on actions leading to the transaction. While Ecommerce reporting is much more detailed than goal reporting in terms of transaction data, it does not provide direct insight into the steps leading to the transaction. The additional coding steps required for Enhanced Ecommerce provide richer reports, including product impressions and adds-to-cart, promotion clicks, and checkout abandonment.

Use a test view first. Configure goals, Ecommerce, and Enhanced Ecommerce in a test view before your working view(s).

Multi-Channel Funnel reports show channels that assist conversions, not just Last-Click Attribution. The MCF reports provide critical reporting on the channels responsible for non-converting sessions that preceded converting return sessions from other channels.

Attribution models represent assumptions. With the exception of the algorithmic Data-Driven attribution model available in Analytics 360, each attribution model represents shows how conversion credit would be allocated to channels if a certain assumption (e.g., each session deserving equal credit) were true. Data-Driven attribution, available in Analytics 360, algorithmically assigns different percentages of credit to assisting channels.

Page Value indicates goal and Ecommerce support. You can interpret Page Value as an indicator of the support that a page—especially if it’s not designed as part of the conversion process—is providing to goal completions and Ecommerce transactions.

Actions and Exercises

Articulate the KPIs for your website or mobile app. Revisiting our discussion in Chapter 3, “Measurement Strategy,” make sure you have a very clear idea of a successful session.
Set up goals, Ecommerce, or Enhanced Ecommerce. For your KPIs and any user action that indicates an increased level of interest (such as a contact-us submission), set up a goal or Ecommerce to correspond. If you can’t currently set up Enhanced Ecommerce tracking where it would be ideal, set up Ecommerce tracking; if you can’t set up Ecommerce tracking, set up a goal.
Create goal funnels. Create funnels for your destination goals. You can review the Reverse Goal Path report for existing goals and map the most common paths to conversion as actual funnel steps and thereby measure step-to-step drop-off.
Determine goal values. Using the guidance offered in the chapter, set suitable goal values for your goals. This is particularly important if you do not have Ecommerce tracking in place.
Confer with your third-party shopping provider. If you’re choosing or currently using a third-party provider for Ecommerce processing, determine their support for GA pageview, Ecommerce/Enhanced Ecommerce, and cross-domain tracking as applicable.
Review the Multi-Channel Funnel Reports and the Attribution Model Comparison Tool. Once goals and/or Ecommerce tracking are in place, review the MCF reports and the Model Comparison Tool. Which channels are performing best for the converting visit? Which channels are assisting conversions?

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Table of Contents for 8 Goal and Ecommerce Tracking

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Goal Tracking

Configuring a Goal

Twenty Goals per View

Goal Sets

Goal Type

Engagement Goals

Match Type

Match Type Begins With

Match Type Regular Expression

Goal Value

Goal Funnel

Funnel Is Recommended but Not Required

Goal and Funnel Reports

Goal Overview

Conversion Rate: Perhaps Not What You Think

Abandonment Rate

Funnel Visualization

Funnel Conversion Rate Is Based on Funnel Completions—Goal Conversion Rate Is Not

Funnel Step Completions Correspond to Unique Pageviews

Funnel Steps Don’t Need to Be Completed Sequentially

You Can’t Apply Segments to Funnel Visualizations

Goal Flow

Reverse Goal Path

Ecommerce Tracking

Configuring Basic Ecommerce Tracking

Populate the Data Layer with Ecommerce Variables

Create a Transaction Tag in GTM

Populate the Data Layer Before the Transaction Tag Fires

Reusing Existing Ecommerce Variables

Enable Ecommerce Setting

Ecommerce Reporting

Enhanced Ecommerce

Implementing Enhanced Ecommerce Tracking

What Data Do You Track for Enhanced Ecommerce?

Pageview or Event Tag for Enhanced Ecommerce

Occurrences to Track with a Pageview Tag

Occurrences to Track with an Event Tag

Product and Promotion Impressions

Product Click

Promotion Click

Product Detail

Add and Remove (To and From Cart)

Checkout Steps

Checkout Options

Purchase

Coupon

Enhanced Ecommerce in Testing and Live Environments

Ecommerce and Enhanced Ecommerce Tracking in Mobile Apps

Segmenting and Remarketing Based on Shopping and Checkout Behavior Funnels

Multi-Channel Funnel Reports

Last-Click Attribution

Multi-Channel Funnel Reports

Assisted Conversions

Top Conversion Paths

Time Lag

Path Length

Attribution Model Comparison Tool

Custom Channel Groupings

Page Value

Distinguishing between Goal Value and Ecommerce Revenue within Page Value

Integrating with Third-Party Shopping Carts

Key Takeaways

Actions and Exercises

Table of Contents for
8 Goal and Ecommerce Tracking