Google Analytics Breakthrough E-Book

Feras Alhlou

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 946

Veröffentlichungsjahr: 2016

Beschreibung

A complete, start-to-finish guide to Google Analytics instrumentation and reporting Google Analytics Breakthrough is a much-needed comprehensive resource for the world's most widely adopted analytics tool. Designed to provide a complete, best-practices foundation in measurement strategy, implementation, reporting, and optimization, this book systematically demystifies the broad range of Google Analytics features and configurations. Throughout the end-to-end learning experience, you'll sharpen your core competencies, discover hidden functionality, learn to avoid common pitfalls, and develop next-generation tracking and analysis strategies so you can understand what is helping or hindering your digital performance and begin driving more success. Google Analytics Breakthrough offers practical instruction and expert perspectives on the full range of implementation and reporting skills: * Learn how to campaign-tag inbound links to uncover the email, social, PPC, and banner/remarketing traffic hiding as other traffic sources and to confidently measure the ROI of each marketing channel * Add event tracking to capture the many important user interactions that Google Analytics does not record by default, such as video plays, PDF downloads, scrolling, and AJAX updates * Master Google Tag Manager for greater flexibility and process control in implementation * Set up goals and Enhanced Ecommerce tracking to measure performance against organizational KPIs and configure conversion funnels to isolate drop-off * Create audience segments that map to your audience constituencies, amplify trends, and help identify optimization opportunities * Populate custom dimensions that reflect your organization, your content, and your visitors so Google Analytics can speak your language * Gain a more complete view of customer behavior with mobile app and cross-device tracking * Incorporate related tools and techniques: third-party data visualization, CRM integration for long-term value and lead qualification, marketing automation, phone conversion tracking, usability, and A/B testing * Improve data storytelling and foster analytics adoption in the enterprise Millions of organizations have installed Google Analytics, including an estimated 67 percent of Fortune 500 companies, but deficiencies plague most implementations, and inadequate reporting practices continue to hinder meaningful analysis. By following the strategies and techniques in Google Analytics Breakthrough, you can address the gaps in your own still set, transcend the common limitations, and begin using Google Analytics for real competitive advantage. Critical contributions from industry luminaries such as Brian Clifton, Tim Ash, Bryan and Jeffrey Eisenberg, and Jim Sterne - and a foreword by Avinash Kaushik - enhance the learning experience and empower you to drive consistent, real-world improvement through analytics.

Leseprobe

Google Analytics Breakthrough

From Zero to Business Impact

Feras Alhlou, Shiraz Asif, and Eric Fettman

Cover image: Colorful pixels © simon2579/istockphoto.com Cover design: Wiley

Copyright © 2016 by Feras Alhlou, Shiraz Asif, and Eric Fettman. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

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Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.

ISBN 9781119144014 (Hardcover) ISBN 9781119231707 (ePDF) ISBN 9781119231691 (ePub)

In Memory of Shiraz Asif

When we embarked on Google Analytics Breakthrough, we could never have imagined that Shiraz would no longer be with us for the book's publication. He became ill with the flu and later pneumonia in February 2016, and after battling for several weeks in the ICU, he passed away on the morning of Friday, March 18th. Shiraz is survived by his parents, siblings, in-laws, loving wife and four young children.

Those who knew Shiraz personally and professionally understand that he was among the hardest-working colleagues, most generous mentors, and most thoughtful friends. He was always a catalyst for development and change, and his thirst for knowledge benefitted everyone around him.

Shiraz, we miss you and will always remember you. May your honorable character, kindheartedness, and open spirit inspire us all to embrace each day in gratitude for the gift of life.

Feras Alhlou, Eric Fettman, and the entire E-Nor family

CONTENTS

Foreword

Acknowledgments

About the Author(s)

About the Contributors

1 Introduction

Why This Book?

Who Should Read This Book?

Chapter Summary

Get Started

2 Google Analytics Reporting Overview: User Characteristics and Behavior

Google Analytics Reporting: User Characteristics and Behavior

Dimensions and Metrics

Real-Time Reports

Key Takeaways

Actions and Exercises

3 Measurement Strategy

Objective: Business Impact

Measurement Plan

Six Steps for Analytics Effectiveness

Key Takeaways

Actions and Exercises

4 Account Creation and Tracking Code Installation

Creating a Google Analytics Account

Google Analytics Account Structure

Installing the Tracking Code

Key Takeaways

Actions and Exercises

5 Google Tag Manager Concepts

Google Tag Manager Concepts

Benefits of Google Tag Manager

Creating a Google Tag Manager Account and Container

Deploying Google Analytics through Google Tag Manager

Access Rights

Migrating to Google Tag Manager from Native Tracking

GTM ENVIRONMENTS

Key Takeaways

Actions and Exercises

6 Events, Virtual Pageviews, Social Actions, and Errors

The Need for Event Tracking

Event Tracking in GTM

Virtual Pageviews

Tracking Google Analytics Events through the Google Tag Manager Data Layer and Custom Event Trigger

Tracking Social Interactions

Error Tracking

Key Takeaways

Actions and Exercises

7 Acquisition Reports

Acquisition Terminology and Concepts

Campaign Tracking: Google Analytics Needs Your Help for Attribution

Channel Customizations

Tracking Organic Traffic

Key Takeaways

Actions and Exercises

Note

8 Goal and Ecommerce Tracking

Goal Tracking

Ecommerce Tracking

Multi-Channel Funnel Reports

Integrating with Third-Party Shopping Carts

Key Takeaways

Actions and Exercises

9 View Settings, View Filters, and Access Rights

Why Do We Need Multiple Views?

Best Practice: Working, Test, and Unfiltered Views

View Settings

View Filters

Access Rights

Change History

Trash Can

Key Takeaways

Actions and Exercises

10 Segments

Segment to Focus and Amplify

Mapping Customer Constituencies as Custom Segments

Sampling

Segments versus Filtered Views

Key Takeaways

Actions and Exercises

Note

11 Dashboards, Custom Reports, and Intelligence Alerts

Dashboards

Custom Reports

Shortcuts

Intelligence Alerts

Annotations

Key Takeaways

Actions and Exercises

12 Implementation Customizations

Custom Dimensions

Content Grouping

Custom Metrics

Calculated Metrics

Demographics and Interests

Enhanced Link Attribution

Tracking Info Customizations

Cross-Domain and Roll-Up Reporting

Cross-Device Tracking with User ID

Key Takeaways

Actions and Exercises

13 Mobile App Measurement

Tracking Mobile Apps

Why is Mobile Important

Mobile Strategies

What to Measure

Mobile configuration in Google Analytics

Setting up Google Analytics in Your App

Account Structure Best Practices in Mobile Properties

Real-time app reporting

Integrations

Mobile Campaign Tracking

Mobile Privacy

Key Takeaways

Actions and Exercises

14 Google Analytics Integrations— The Power of Together

AdWords

AdSense

YouTube in GA

Analytics 360 Integrations

Additional Integrations

Key Takeaways

Actions and Exercises

15 Integrating Google Analytics with CRM Data

Long-Term Perspective

Calculating Cost per Qualified Lead

Joining Google Analytics and CRM Data with Visitor ID

Key Takeaways

Actions and Exercises

16 Advanced Reporting and Visualization with Third-Party Tools

Framing the Issue: How to Get Data Out of GA

ETLV–The Full Reporting Automation Cycle

Advanced Use Cases for BigQuery/Tableau

Key Takeaways

Actions and Exercises

17 Data Import and Measurement Protocol

Data Import

Measurement Protocol

Key Takeaways

Actions and Exercises

18 Analytics 360

Why Analytics 360?

Increased Capacity

Service-Level Agreements (SLAs)

Analytics 360 Resources

Where to Buy—Resellers or Google Direct?

Key Takeaways

Actions and Exercises

Appendix A: Broadening Your Optimization Program

Qualitative Inputs

Overlay Reporting

Testing

Marketing Automation and Personalization

Notes

Appendix B: Resources

Index

EULA

List of Tables

Chapter 2

Table 2.1

Chapter 3

Table 3.1

Chapter 6

Table 6.1

Table 6.2

Table 6.3

Chapter 7

Table 7.1

Table 7.2

Table 7.3

Chapter 9

Table 9.1

Table 9.2

Table 9.3

Chapter 10

Table 10.1

Table 10.2

Chapter 13

Table 13.1

Table 13.2

Chapter 15

Table 15.1

Table 15.2

Table 15.3

Table 15.4

Table 15.5

Table 15.6

Table 15.7

Table 15.8

Table 15.9

Table 15.10

Chapter 16

Table 16.1

Table 16.2

Chapter 17

Table 17.1

Table 17.2

Table 17.3

Appendix A

Table A.1

List of Illustrations

Chapter 2

Figure 2.1

The four main reporting sections appear at the bottom of the left navigation panel.

Figure 2.2

Metrics in the Audience Overview.

Figure 2.3

The _ga cookie allows Google Analytics to associate multiple hits with a single session and multiple sessions with a single user.

Figure 2.4

Pageviews and other hit types determine bounce rate and session count.

Figure 2.5

By displaying two metrics on the main over-time graph in one of the GA reports, we see Ecommerce Conversion Rate trending down, which may clarify the upward trend in Session Duration as a negative user experience.

Figure 2.6

With the date selection set to the past 12 months and rows plotted, we can see growth in sessions from both new and returning visitors.

Figure 2.7

Metrics in the All Pages report.

Figure 2.8

The Navigation Summary in the Pages report shows how users are navigating to and from the selected page.

Figure 2.9

The Behavior Flow report can help you identify drop-off points at several stages in user navigation through your website.

Figure 2.10

Within most table reports, you can display different primary dimensions and metric groups.

Figure 2.11

Landing Pages report, with Source/Medium set as secondary dimension.

Figure 2.12

Since Source/Medium is applied to the Landing Pages report as a secondary dimension, we need to access the advanced table filter panel to filter the table by a complete or partial Source/Medium value, such as

email

. You can also use an advanced table filter to filter by a metric threshold (e.g., greater than 50% bounce rate).

Figure 2.13

Weighted sort takes sessions into account to generate a more meaningful and actionable sort than straight ascending or descending order for metrics such as bounce rate.

Figure 2.14

The Previous Year time comparison produces a one- or two-day offset by day of the week that you can adjust for better analysis.

Figure 2.15

Most reports default to the Data display but allow you to present the same underlying data in four alternate formats.

Figure 2.16

The comparison display charts each dimension row against site average for a specific metric, such as bounce rate in this case.

Figure 2.17

By switching the Mobile Overview report to Percentage display and comparing the corresponding period from the previous year, we can see the year-over-year metric percentages (sessions, in this case) for mobile (blue), desktop (green), and tablet (orange).

Figure 2.18

Based on the session counts and the bounce rates in Pivot, it would be advisable to further investigate the high bounce rates for Chrome on Android and Safari on iOS.

Figure 2.19

The Search Terms report can provide input for website optimization, content development, and marketing.

Figure 2.20

Page Timings defaults to the Comparison display to illustrate page performance by load time.

Figure 2.21

The May 11–May 17 cohort is showing the best user retention in week 2 since their acquisition date.

Figure 2.22

The active user count in the Real-Time Overview is based on a five-minute window.

Chapter 3

Figure 3.1

The optimization pyramid builds toward insight, action, and impact.

Figure 3.2

The Network report quickly dispelled the prevailing notion that existing customers didn’t visit our website.

Chapter 4

Figure 4.1

Google Analytics account sign-up screen.

Figure 4.2

Creating a new account from the Admin screen.

Figure 4.3

Account and property settings.

Figure 4.4

GA tracking code.

Figure 4.5

Google Analytics account structure.

Figure 4.6

Most Web pages are constructed from shared templates.

Figure 4.7

Pageview hit includes a great deal of data in addition to the page data itself.

Figure 4.8

Google Tag Assistant.

Figure 4.9

The Google Analytics Report section of the Google Tag Assistant recording shows the GA pageview and event hits that were generated on the page and displays additional details, such as event dimensions and custom dimensions.

Figure 4.10

The GA Debug Extension displays GA hit data in Chrome’s JavaScript console.

Figure 4.11

It can be helpful to remove your own domain from the Referral Exclusion list to more easily identify pages that are missing GA tracking.

Figure 4.12

The Google Tag Assistant extension for Chrome indicates that the page still contains the GA Classic tracking code.

Figure 4.13

You can migrate to Universal and Google Tag Manager in the same process.

Figure 4.14

In documentation,

analytics.js

is often used to signify Google Universal analytics, while

ga.js

corresponds to Classic.

Figure 4.15

Using a table filter to check for PII within URLs.

Figure 4.16

Checking event tracking parameters for PII.

Figure 4.17

Checking Ecommerce affiliation field for PII.

Figure 4.18

Sample health check audit summary.

Chapter 5

Figure 5.1

While user-defined variables aren’t needed in all cases, they provide much of GTMs flexibility for tags and triggers.

Figure 5.2

GTM can help the marketing and analytics team deploy analytics, optimization, and advertising scripts (even if IT retains control of publishing).

Figure 5.3

A GTM account normally corresponds to your organization, and a GTM container normally corresponds to a website or mobile app.

Figure 5.4

Configuration of a Google Analytics pageview tag in Google Tag Manager.

Figure 5.5

Simple text-constant variable for our GA property ID—not obligatory, but best practice.

Figure 5.6

Starting preview/debug mode.

Figure 5.7

The Google Analytics Pageviews tag is firing in preview/debug mode.

Figure 5.8

You can keep your tags prefixed with PENDING until they’re ready for publishing, at which point you can prefix them with ACTIVE.

Figure 5.9

Two options for account permissions (apart from no account permissions).

Figure 5.10

Container permissions.

Figure 5.11

Google two-step login enforces additional security.

Figure 5.12

When you configure a new GTM environment, specify the hostname of your development or staging server (or a subdirectory under the hostname of your live environment) as the Destination URL.

Figure 5.13

The container’s Environments panel lists the default environments as well as any custom environments that you have created.

Figure 5.14

As one option for making the GTM changes in your Development environment visible to other users, you can ask your developers to copy the GTM environment snippet onto all development pages.

Figure 5.15

As long as the Live container code is present on the development page, you can share a link with other users that allows them to preview and debug changes in the GTM development environment that you have set up.

Figure 5.16

You have the option to publish a container version to the Live environment or any custom environments that you have configured.

Figure 5.17

Page Hostname is a built-in variable that you must enable for use in triggers and tags.

Figure 5.18

This trigger is defined to match the hostname in your live/production environment, but we use it as a trigger exception in Figure 5.16 to block tag firing.

Figure 5.19

You can apply the Live Environment trigger as an exception until you’re ready for the tag to begin firing in your production environment.

Figure 5.20

Trigger exceptions, such as Live Environment in this example, appear prominently on the main Tags panel.

Figure 5.21

Custom JavaScript variable for development or production Google Analytics property ID.

Figure 5.22

This lookup table returns the same outputs as the manual JavaScript variable shown in Figure 5.21

Chapter 6

Figure 6.1

This mockup represents a wide range of user interactions that are not tracked in GA by default.

Figure 6.2

Several types of mobile interactions are not tracked in Google Analytics by default.

Figure 6.3

Top Events report, with Event Category displayed as primary dimension and Event Action displayed as secondary dimension.

Figure 6.4

Pages applied as secondary dimension to the Top Events report.

Figure 6.5

An event call with Event Category, Event Action, and Event Label.

Figure 6.6

We could opt to record this event with Event Category, Event Action, and Event Label.

Figure 6.7

If Event Category and Event Action adequately describe the action, it's valid to omit Event Label, which is optional.

Figure 6.8

Metrics fragmentation due to inconsistent event naming.

Figure 6.9

The

Click URL

variable is built into GTM but not enabled by default.

Figure 6.10

Our PDF event tag uses two static values and one variable.

Figure 6.11

This trigger uses the

Click URL

variable to detect a click on a link that ends with

.pdf

.

Figure 6.12

Two-condition trigger for clicks on offsite links.

Figure 6.13

The GTM events from which you can define a trigger.

Figure 6.14

Based on a GTM click event, this trigger is configured to fire when a website user clicks on an outbound link.

Figure 6.15

This Timer trigger will fire after a user has spent 30 seconds on a page.

Figure 6.16

A Custom Event trigger listens for a variable named

event

to be pushed to the data layer with a specific value—

loggedIn

, in this case.

Figure 6.17

GTM Preview and Debug mode displays the initial pageview hit and then the event hit when you click the PDF link.

Figure 6.18

The GA Debug extension for Chrome that we first activated to verify pageviews in Chapter 4 can also display the event dimensions.

Figure 6.19

You can access the Real-Time ˃ Events report in GA to verify that the event is firing.

Figure 6.20

Set Non-Interaction Hit to True only for events that the visitor does not initiate.

Figure 6.21

Virtual pageviews appear integrated with physical pageviews in the Pages report.

Figure 6.22

Enabling the built-in Click Text variables.

Figure 6.23

This variable returns the href value of the clicked link.

Figure 6.24

The

page

setting that overrides the default page URL is what distinguishes a virtual pageview from a regular physical pageview. Here we’re also setting the page

title

to populate with the text of the clicked link.

Figure 6.25

The Real-Time ˃ Content report shows that our virtual pageview tag configuration has successfully overwritten the default

Page

and

Title

dimensions.

Figure 6.26

In this multiscreen process, the screens update, but the URL does not change, and the page does not reload.

Figure 6.27

This custom GTM variable reads the

value

attribute of the button, which appears as the button text.

Figure 6.28

Instead of using the button text directly as the page dimension for the virtual pageview, we’ll use the Button Text variable defined in Figure 6.27 as the input variable in a lookup table and retrieve better page dimension values to populate into our virtual pageviews.

Figure 6.29

The trigger for the multiscreen virtual pageviews specifies conditions for the page path and the class name of the button on the form.

Figure 6.30

By configuring a few tags, triggers, and variables, we’ll be able to track many types of clickthroughs as events.

Figure 6.31

This DOM Ready trigger will ensure that the browser has parsed all HTML in the page before the script in the autotracker tag begins searching for links to configure to write to the data layer.

Figure 6.32

This event tag will pull in values that the link clicks, as configured with the autotracker tag above, have populated into the data layer.

Figure 6.33

Four simple data layer variables will read in the GA event values from the data layer, as shown here for event category.

Figure 6.34

This trigger fires when the

event

data layer variable is populated as

eventTracker

.

Figure 6.35

By drilling down into the scroll event within the Top Events report and applying Page as a secondary dimension, we can gauge scroll depth on our blog pages.

Figure 6.36

Top Events report, with Page applied as secondary dimension, displaying the navigation data that we’re populating into the data layer. You could drill down to Event Action and Event Label to show more granular navigation details.

Figure 6.37

A Google Analytics tag in Google Tag Manager with Track Type set to

Social

populates the Social ˃ Plugins report.

Figure 6.38

You can create this Custom HTML tag to set up the listener and callback for Facebook content likes.

Figure 6.39

This Google Analytics social tag reads the values that we wrote to the GTM data layer from Facebook and Twitter callback functions as well as LinkedIn and Pinterest click actions.

Figure 6.40

We can configure a trigger to isolate the LinkedIn share button based on the

id

attribute of the corresponding ˂a˃ tag.

Figure 6.41

This trigger is using the built-in Click Text variable to isolate the Facebook Share button.

Figure 6.42

The Plugins report is populated with social hits.

Figure 6.43

Similarly to many websites, the page title indicates a request for a nonexisting page on the

London Times.

Figure 6.44

With Page Title selected as the secondary dimension and filtered, we can list the invalid page requests.

Figure 6.45

This Custom JavaScript variable prepends an error indicator to the document path.

Figure 6.46

Instead of generating a regular GA pageview on our error pages, we’ll trigger a pageview tag that overwrites that default page and title values with the error prepends.

Figure 6.47

Add the 404 and 500 triggers as exceptions to the main GA pageview tracker to avoid double pageview counting.

Figure 6.48

By sorting the Landing Pages report for your error identifier and applying Full Referral as a secondary dimension, you can see the external origins of bad requests.

Figure 6.49

Enabling the built-in JavaScript error trigger.

Figure 6.50

Enabling the built-in JavaScript error variables.

Figure 6.51

The GA event tag is configured with the JavaScript error variables enabled in Figure 6.50. Apply the trigger configured in Figure 6.49 to this tag.

Figure 6.52

JavaScript error listed in the Top Events report.

Chapter 7

Figure 7.1

All sessions have a source and medium value, as indicated in the Source/Medium report. Source is more specific, Medium more general, and both dimensions can be populated with either the default values or the campaign tag values that override the defaults.

Figure 7.2

The Referrals report displays sessions recorded with

referral

as the medium value.

Figure 7.3

By drilling down into a referring source in the Referrals report, you can see the specific originating page, or

Referral Path

.

Figure 7.4

By comparing Sessions (size) to Ecommerce Conversion Rate (color) for each Channel, you can readily identify underperforming channels and the share of sessions that they account for.

Figure 7.5

A URL builder tool such as Google’s helps you format campaign parameters in an inbound link.

Figure 7.6

The tooltip for Bing Ads Auto-tagging indicates the four GA campaign parameters that will be added to inbound links.

Figure 7.7

Source reporting fragmented due to inconsistent utm_source values.

Figure 7.8

With separate campaign names for each traffic source participating in the same campaign, you can immediately identify the source, even with Campaign as the default primary dimension in the All Campaigns report.

Figure 7.9

The same campaign name offers the benefit of aggregated metrics for that campaign across different channels, but you’d need to add Source as a secondary dimension to identify the exact origin of the campaign traffic.

Figure 7.10

You can use a Custom JavaScript variable in GTM to combine referrer into

utm_source

.

Figure 7.11

Applying Source/Medium as a secondary dimension in the Channels report reveals Source/Medium values that did not match any of the default or customized channel definitions and therefore forced GA to bucket the traffic as (Other).

Figure 7.12

On the Channel Grouping Settings screen, you can customize a default channel definition, create a new channel definition, and reorder the channel matching.

Figure 7.13

If you have Edit rights to the view, you can click Channel Settings in the top section of the View Admin to customize the Default Channel Grouping or create a new view-level Channel Grouping.

Figure 7.14

All users who have Read & Analyze rights to the view can click Custom Channel Groupings in the bottom section of the View Admin to create a private, user-level channel grouping.

Figure 7.15

With Source selected as the secondary dimension, the Organic Keywords report shows

(not provided)

as the top keyword recorded for Google, Yahoo, and Bing.

Figure 7.16

The Search Analytics report in Google Search Console displays impression and clickthrough data for branded and nonbranded keywords.

Figure 7.17

Based on data downloaded from Google Analytics Benchmarking reports. The data was downloaded to Excel from GA, where these two pie charts were created.

Figure 7.18

Drilldown options are at the top of each of the three benchmarking reports.

Figure 7.19

Based on data downloaded from GA Benchmarking reports after choosing a more granular Industry Vertical (i.e., a subset of the original). Data downloaded to Excel, where pie charts were created.

Figure 7.20

Sessions using time period comparisons with us on the left (July 2015 is 154,407) and peers on the right (July 2015 is 110,923).

Figure 7.21

By using the GA benchmarking data and also benchmarking against a third-party data source, we can see that our own organic search growth exceeds that of our industry peers and of the Internet overall.

Figure 7.22

Direct sessions appear with the more specific medium and source values of a previous session.

Figure 7.23

The Campaign Timeout—six months by default—determines the lookback for direct sessions to appear as a more specific previous traffic source. The six-month period is refreshed with each returning session.

Chapter 8

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.

Figure 8.2

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

Figure 8.3

For a Destination goal, you can choose one of three match types.

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.

Figure 8.5

Funnel configuration is possible for Destination goals only. Though a funnel is not required, it is recommended wherever designated steps precede the goal completion.

Figure 8.6

Metrics in the Goal Overview report.

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.

Figure 8.8

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

Figure 8.9

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

Figure 8.10

Goal Flow report offers some advantages over Funnel Visualization but may require more interpretation.

Figure 8.11

Reverse Goal Path report shows how visitors are actually getting to your goal pages.

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.

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.

Figure 8.15

A GA tag with Track Type set to Transaction automatically reads Ecommerce variables that you have populated into the data layer.

Figure 8.16

Metrics in the Ecommerce Overview report.

Figure 8.17

The Shopping Behavior report shows progress and drop-off from site entry through transaction.

Figure 8.18

You can configure the Checkout Behavior report to display the specific steps within checkout.

Figure 8.19

With Enhanced Ecommerce implemented, the Product Performance report lists the Cart-to-Detail and Buy-to-Detail.

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.

Figure 8.21

Internal Promotion Report shows click and purchase metrics for internal promotional banners and text links.

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.

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.

Figure 8.24

Product detail, product impression, and promotion impression data is recorded when this product description page loads.

Figure 8.25

The Checkout Behavior steps appear as you have named them in the Enhanced Ecommerce Settings.

Figure 8.26

You can capture selections in the checkout process such as credit card as Enhanced Ecommerce checkout options.

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.

Figure 8.29

Which are the last accessories that car purchasers sacrifice?

Figure 8.30

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

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.

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.

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.

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.

Figure 8.37

The Assisted Conversions report indicates how often a channel provided a conversion assist prior to a converting return session. A value greater than 1 for Assisted/Last Click or Direct Conversions indicates that the channel is stronger for assists than for the converting sessions.

Figure 8.38

You can configure several options at the top of the MCF reports.

Figure 8.39

These two rows from the Top Conversion Path report show, respectively, that 569 conversions occurred on an organic search clickthrough preceded by a paid search clickthrough and that 565 conversions occurred during a direct session preceded by two email clickthroughs.

Figure 8.40

You can set the primary dimension in the MCF reports to Source/Medium, as above, or to Campaign.

Figure 8.41

This portion of the Time Lag report indicates that 35.64% (the inverse of 64.36%) of conversions occurred more than one day after the initial session.

Figure 8.42

This portion of the Path Length report indicates a nearly even split between conversions that occur on the first visit and conversions that required two or more sessions.

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

Built-in attribution models.

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.

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.

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.

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.

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.

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.

Chapter 9

Figure 9.1

In Google Analytics, a view is a representation of property data that has been processed with different settings and filters.

Figure 9.2

As an essential practice for Google Analytics, you should maintain a test view, an unfiltered view, and one or more working views for each property.

Figure 9.3

Example view settings.

Figure 9.4

Because these three Page dimension values represent the same actual page, they fragment the metrics in the Pages report.

Figure 9.5

Without consolidation of Pages through Exclude URL Query Parameters, the unfiltered view for this website shows 21,679 Page (aka Request URI) variations.

Figure 9.6

By specifying Exclude URL Query Parameters, we have consolidated the 21,679 Request URI variations in Figure 9.5 down to just 51, with the same number of total pageviews in both cases.

Figure 9.7

The URL Parameters report in Google Search Console can help you identify the query parameters that you need to list in Exclude URL Query Parameters.

Figure 9.8

Single filter to exclude sessions originating from any IP address within a range.

Figure 9.9

This filter rewrites the medium of social clickthroughs to social.

Figure 9.10

This filter consolidates case variations in the Pages report and anywhere else that Request URI appears.

Figure 9.11

This filter will allow only traffic within the

/tutorials/

subdirectory to appear in the view.

Figure 9.12

The (.*) regex capturing group serves to extract the search term from the Request URI, so we can then output it into the Search Term dimension.

Figure 9.13

Because include filters are not cumulative, users from Australia will never make it into this view.

Figure 9.14

The regex pipe symbol allows us to include two or more dimension values within the same include filter.

Figure 9.15

Once you have created a filter view, the Add Filter to View panel allows you to apply it to any other view in the property.

Figure 9.16

At each level of the account hierarchy, you can assign four levels of privileges.

Figure 9.17

If you share a custom segment, other GA users with Read & Analyze rights to the view will be able to apply the segment to reports, and GA users who also have Collaborate rights will be able to edit or delete the segment.

Figure 9.18

You could create a view containing data only for sessions that originated as clickthroughs from another website and then assign rights to that view only.

Figure 9.19

The Change History documents configuration and permissions changes at the account, property, and view levels.

Figure 9.20

GA users with Edit access to the account have 35 days to restore a trashed item.

Figure 9.21

View deletion notification.

Chapter 10

Figure 10.1

Segments focus your analysis on visitor subsets.

Figure 10.2

System (or “built-in”) segments listed in the segment panel.

Figure 10.3

The conversion rate trend is more pronounced within the Non-Bounce Sessions segment than within All Sessions.

Figure 10.4

By making a copy of the built-in Non-Converters segment, we can see that it encompasses users who completed neither a goal nor an Ecommerce transaction.

Figure 10.5

Applying the Non-Converters segment to the Exit Pages report highlights opportunities to optimize the payment and search results pages.

Figure 10.6

You can start your segment definition in one of the top sections, such as Traffic Sources, or in Advanced ˃ Conditions, where additional options are available.

Figure 10.7

A segment based on the Page dimension includes all data from the sessions in which the page was viewed at least once.

Figure 10.8

In this example, sessions that included the promo page generated nearly twice the conversion rate of sessions that did not include the promo page.

Figure 10.9

This sequence segment will tell us the number of sessions in which the Top 10 Reasons pageview preceded the email signup.

Figure 10.10

This segment identifies engagement in terms of goal completion, started or completed Ecommerce transactions, page depth (unique pageviews of different pages), or session duration. Note that the

Product Adds to Basket

metric may appear as

Product Adds to Cart

depending on localization.

Figure 10.11

Engagers and All Sessions segments applied to the Source/Medium report, with the report display set to Performance.

Figure 10.12

By changing the segment scope from Session to User, we can trace activity on the main part of our website back to landing pages on the blog, even from a previous session.

Figure 10.13

This Path Length report demonstrates that many mortgage seekers are converting after the first session.

Figure 10.14

This behavioral segment matches any user who submitted a mortgage calculation in any session.

Figure 10.15

Users who submitted a mortgage calculation generated a considerably higher conversion rate.

Figure 10.16

You can identify the constituency of potential hosts on airbnb.com by a clickthrough on Become a Host, which accesses the /rooms/new page.

Figure 10.17

Potential Host segment based on a

/rooms/new

pageview.

Figure 10.18

Though the number of goal conversions for the List Room goal is the same for all sessions and for potential hosts, the conversion rate for the List Room goal has a much more focused context within the Potential Host segment.

1

Figure 10.19

Custom segment options.

Figure 10.20

In the Multi-Channel Funnel reports, you can apply specialized segments based on the traffic sources that generated conversions.

Figure 10.21

This diagram illustrates how sampling can skew your reporting.

Figure 10.22

When we apply the built-in Tablet Traffic segment, the sample size drops from 100% to 5.05%.

Figure 10.23

With Analytics 360 you can export unsampled data as comma- or tab-separated values.

Chapter 11

Figure 11.1

Options for creating a dashboard: Blank Canvas, Starter Dashboard, and Import from Gallery

Figure 11.2

If you have Collaborate permission in the view, you can Share Object to move the dashboard from private to shared.

Figure 11.3

Custom report configured with three metrics, a single dimension, and a filter.

Figure 11.4

Streamlined report, customized for the recipient.

Figure 11.5

In custom reports, you can use built-in dimensions such as Day of Week Name that do not appear as primary dimensions in the built-in reports.

Figure 11.6

A data point in a report, such as the 96.10% bounce rate for Android phones on the signup page, may not be clear or impactful for executives, coworkers, or clients.

Figure 11.7

Configuring an Intelligence Alert to monitor a 10% weekly increase in sessions.

Figure 11.8

10% Session Increase Alert received by email.

Figure 11.9

10% Intelligence Alert configuration for Page Not Found.

Figure 11.10

This time comparison on the Goal ˃ Overview report likely indicates that the weekly alert we received this year was due to a seasonal trend.

Figure 11.11

This time comparison on the Goal ˃ Overview report, with Plot Rows applied, shows that the one-week spike in goal completions was due to increased performance from a specific traffic source rather than a seasonal trend.

Figure 11.12

From the main over-time graph in any Google Analytics report, you can display the annotations panel to create a new annotation.

Chapter 12

Figure 12.1

Custom dimensions allow you to record elements such as article author and category, which GA would not specifically capture by default.

Figure 12.2

We’re reading the author value from the page text—specifically, the second span tag within the div tag that has

footer

as class.

Figure 12.3

If author and category don’t appear in the page, and you instead work with your developers to populate them into the data layer, you can read them in with simple GTM data layer variables.

Figure 12.4

You populate custom dimensions within a GA tag (usually pageview or event) in GTM. The index corresponds to the order in which you created the custom dimensions in the property admin. The dimension values are normally set to GTM variables that read from the data layer or the page text/markup.

Figure 12.5

In this custom report, we’ve defined Author as the primary dimension.

Figure 12.6

In this custom report, we’re reporting Ecommerce metrics by Customer Status, which we read in from the back end and stored in GA as a user-scope custom dimension.

Figure 12.7

This custom report displays Effective Conversion based on Room Search completions rather than all sessions, thereby amplifying the conversion rate and highlighting the May 29 dip.

Figure 12.8

U.S. Search Trend shows a high concentration of search dates soon before check-in dates.

Figure 12.9

Europe Search Trend shows searches occurring longer before check-in dates.

Figure 12.10

In the Pages report, we have selected Sport as the content grouping to display metrics for four content groups that we have populated.

Figure 12.11

With Sport set up as the first-slot content grouping in the View Admin, we can read the sport value into the Pageview tag as a DOM Element or data layer variable as shown in Figures 12.2 and 12.3

Figure 12.12

Kilometers Pledged appearing as a custom metric in a custom report.

Figure 12.13

This Custom JavaScript variable reads the value of the kilometers field of the walk pledge signup form.

Figure 12.14

In the GA Event tag, we’re populating the Kilometers variable as a custom metric.

Figure 12.15

This trigger will fire the Walk Signup event when the signup form is submitted.

Figure 12.16

Calculated metric for Goal 1 Conversion Rate based on users rather than sessions.

Figure 12.17

You can easily enable Enhanced Link Attribution in the GA pageview tag in GTM.

Figure 12.18

For country-specific search engines to appear as sources in GA, you need to add them as organic search sources.

Figure 12.19

This first filter matches the original source and medium values for Google image clickthroughs and rewrites the medium as

organic

.

Figure 12.20

With medium rewritten to organic, this second filter still matches the original source, and the output constructor adds

com

or

co.uk

extracted as the wildcard in Field A.

Figure 12.21

For cross-domain tracking, you must make configuration changes to maintain the session when the user crosses domains.

Figure 12.22

For cross-domain tracking, specify a value of true for allowLinker in the GA tag.

Figure 12.23

In Auto Link Domains, list the domains that you need to track across.

Figure 12.24

You can implement roll-up reporting by including the same tracking code, without modifications, in two or more websites.

Figure 12.25

To keep the same cookie across subdomains, set cookieDomain to your root domain.

Figure 12.26

By default, you can’t distinguish between different domains or subdomains unless you add Hostname as a secondary dimension or an individual hostname as a custom segment.

Figure 12.27

Custom view filter that prepends hostname to Request URI.

Figure 12.28

With the view filter in Figure 12.25 applied, GA includes hostname in the Page dimension.

Figure 12.29

With this view filter applied, we’ll display data for

windturbine.com

only (with or without

www.

).

Figure 12.30

For a hit to be included in the User-ID-enabled view (and therefore the Cross-Device reports), you need to populate the userId field, with a unique User ID that your developers have normally pushed into the data layer from your authentication system.

Figure 12.31

The Device Overlap report can display overlap not only for sessions but also for revenue that was accrued in different combinations of cross-device sessions.

Figure 12.32

The Device Paths report breaks down Users, Sessions, and Ecommerce metrics by pass-offs between device categories.

Figure 12.33

The Acquisition Device report shows revenue generated on the originating device or other devices per device category.

Figure 12.34

To anonymize IP, you must set the

anonymizeIp

field to

true

in your GA tags within Google Tag Manager.

Chapter 13

Figure 13.1

The striking growth in time spent by users in mobile apps.

Figure 13.2

Adding a mobile property.

Figure 13.3

The GTM Value Collection variable provides a major benefit to app developers by giving them the ability to bypass the app marketplace review processes and update elements of their app easily.

Figure 13.4

Real-Time data showing up roughly two minutes after actually taking place.

Figure 13.5

Data appearing in the Per Second window immediately, instead of after a dispatch-related delay.

Figure 13.6

The AdMob user interface shows performance of your mobile ads.

Figure 13.7

Searching for an app to connect to your AdMob account.

Figure 13.8

The starting step for linking Google Play to Google Analytics.

Figure 13.9

Configuring the Google Play/GA link.

Figure 13.10

The Google Play Sources report clearly shows which referral sources are driving traffic.

Figure 13.11

The Google Play URL builder inputs the required campaign information and builds a URL that can then be used in marketing materials.

Figure 13.12

The iOS version of the URL builder requires a couple of more iOS specific fields to ensure data is mapped to GA correctly.

Figure 13.13

The Audience Overview report indicates more sessions per user on iPad.

Figure 13.14

The app versions report shows fast adoption of the new version release.

Figure 13.15

With event tracking implemented, we can see which screens are generating the most video plays.

Figure 13.16

iPad remote screens, prior to redesign. Google Analytics showed very low usage of the Gesture Remote on the right relative to the Button Remote on the left.

Figure 13.17

Android redesign based on data from iOS.

Figure 13.18

TiVo’s Measurement Trifecta.

Figure 13.19

Eric Ries’s Learning Loop can apply to analytics and optimization for your digital properties.

Chapter 14

Figure 14.1

AdWords reports available in GA under the Acquisition reports.

Figure 14.2

In Google AdWords Settings ˃ Preferences, ensure that auto-tagging is set to “Yes.”

Figure 14.3

Display Targeting dimensions are available in the GA AdWords reports.

Figure 14.4

In the MCF reports, you can choose which interaction you want to include in your reports and analysis.

Figure 14.5

Display Network campaigns in the MCF reports.

Figure 14.6

In the Audience Builder, we’re targeting visitors who viewed a lead form but did not submit the lead in any session.

Figure 14.7

Once you’ve created a remarketing audience in GA, you’re prompted to create an associated AdWords campaign. You can also choose the remarketing audience for an AdWords campaign that you configure at a later time.

Figure 14.8

The Dynamic Attribute Linking that you configure in the property ties a custom dimension value to a record in your product or service feed; AdWords can then read that record to dynamically display specific content in the ad.

Figure 14.9

For Bid-Only RLSA, you only increase bidding for your remarketing audience.

Figure 14.10

AdSense reports are available in GA under the Behavior ˃ Publisher reports.

Figure 14.11

The AdSense Overview report shows key metrics such as Revenue and eCPM (revenue per 1,000 pageviews).

Figure 14.12

AdSense metrics are segmented by Content Category. Politics and Business (first and second row) bring in over 77% of the revenue.

Chapter 15

Figure 15.1

To download the report, visitors must submit a lead form.

Figure 15.2

The Campaigns report displaying goal completions for your four campaign channels.

Figure 15.3

The Top Conversion Path report indicates which campaign channels might also deserve credit for conversion assists.

Figure 15.4

Creating fields in Salesforce to store campaign fields and visitor ID.

Figure 15.5

The lead form contains the hidden fields that we populate with campaign values to pass to Salesforce custom fields. For storing a common ID in the two data sets, there are two options: generate on the client side and pass into Salesforce, or read from Salesforce.

Figure 15.6

Recording visitor ID as a custom dimension in the main Google Analytics pageview tag.

Figure 15.7

Campaign data and visitor ID stored in custom Salesforce fields. The Sales team can update the Lead Status field to

qualified

as warranted.

Figure 15.8

This Google Analytics Event tag in GTM will set the visitor ID custom dimension.

Figure 15.9

You can configure this GTM variable to read the visitorID value that we retrieved from the CRM so we can populate the visitor ID custom dimension into our catch-all event tag in Figure 15.8.

Figure 15.10

We’ll export this GA custom report and join with CRM data based on transaction ID. In this model, the customer long-term value will be associated with the Source/Medium of the customer’s original online transaction.

Figure 15.11

Spreadsheet joining GA custom report export with CRM acquired date and margin dollars.

Figure 15.12

Six-month close-up of 18-month value model. (Download the spreadsheet template at www.e-nor.com/gabook.)

Figure 15.13

Payback period and lifetime value by marketing channel.

Chapter 16

Figure 16.1

Output of a typical query to the Core Reporting API.

Figure 16.2

The visual blocks within the canvas make it extremely easy to follow the extraction and transformations occurring within your data set.

Figure 16.3

The Google Cloud Platform

Figure 16.4

Tableau and integrate GA and other data sources with flexible, stylized, and interactive formatting options.

Figure 16.5

Data flow through ETLV.

Figure 16.6

Results of a simple query to extract total hits by visit ID.

Figure 16.7

Looking at the hits generated during an individual user’s session.

Figure 16.8

Results from a query showing products purchased from a sporting goods store on a single day.

Figure 16.9

Results of the query showing which other products were purchased by customers who purchased Skates.

Figure 16.10

Queries for also-purchased products could serve as the basis for a recommendation engine.

Figure 16.11

Funnel configuration in Analytics Canvas.

Figure 16.12

Funnel analysis.

Figure 16.13

Analytics Canvas can generate BigQuery SQL.

Figure 16.14

Pageviews by device type.

Figure 16.15

The Shufflepoint Web based user interface allows easy drag and drop query building capability.

Chapter 17

Figure 17.1

Creating a custom dimension to receive the lead status value during data import.

Figure 17.2

For the data set schema, you’ll designate a key, which normally consists of a single dimension, such as the visitor ID that you created as a custom dimension, as well as one or more target fields to import against the key.

Figure 17.3

This schema is similar to Figure 17.2, but as the key, we’re using the User ID dimension that you can populate for cross-device tracking but that can also serve to join your CRM and GA data.

Figure 17.4

You import the CRM data into the data set defined in step 2.

Figure 17.5

Data import confirmation for the lead status data set.

Figure 17.6

Lead status applied as a secondary dimension in the Campaigns report.

Figure 17.7

When you create the Author and Category custom dimensions, specify the scope as Hit.

Figure 17.8

In the schema for the CMS data import, a query refinement is applied to match only the

articleId

value in the Page dimension.

Figure 17.9

With utm_id populated as the ga:campaignCode key dimension, you can import campaign medium, source, and name, and also custom dimensions such as campaign group.

Figure 17.10

Schema for cost data import. Campaign is among the nonrequired import values, but for many cost data imports, it will serve as the

de facto

key.

Figure 17.11

Campaign cost data export from Bing Ads.

Figure 17.12

Cost Analysis report for imported paid campaign cost data.

Figure 17.13

This custom report configuration integrates performance and cost data for AdWords and non-AdWords campaigns.

Figure 17.14

This custom report breaks down performance by the built-in Product dimension and the imported Color dimension.

Figure 17.15

Custom report showing goal conversion by imported US Regional custom dimension.

Figure 17.16

User journey, including online and offline interactions.

Figure 17.17

The online and offline process.

Figure 17.18

GA dashboard showing offline activity, including completed storage facility bookings and conversion paths.

Figure 17.19

Multi-Channel Funnel reports indicate which channels drove traffic that eventually converted offline.

Figure 17.20

Running the PHP scripts from the command prompt.

Figure 17.21

Real-time tweet reporting in Google Analytics.

Figure 17.22

Twitter integration.

Chapter 18

Figure 18.1

Analytics 360 allows you to export an Unsampled version of the report.

Figure 18.2

Scheduled and once-only exports appear under Customization ˃ Unsampled Exports.

Figure 18.3

With this Custom Table in place, you’ll be able to apply Source/Medium as a secondary dimension or add either segment, or access a Custom Table that contains a subset of the Custom Table configuration, all without sampling.

Figure 18.4

The Custom Funnel report displays continuation and drop-off between the stages that you define.

Figure 18.5

You can define a remarketing audience based on any continuation or drop-off point.

Figure 18.6

You can access all the integrated DCM reports in Analytics 360 under the Acquisition reports.

Figure 18.7

In this example, 89.26% of the total sessions came from people who viewed and clicked (click-through) the DCM ads and 47.08% from people who visited the site after being exposed (but didn’t click) to the DCM ad (view-through).

Figure 18.8

According to the DFA Model, for all DCM Campaigns for the Google Store, there were 2,932 view-through conversions and 119,571 click-through conversions.

Figure 18.9

As can be seen here, the

dfa/cpm

Source/Medium is present in a number of conversion paths.

Figure 18.10

You can access all the integrated DBM reports in Analytics 360 under the Acquisition reports.

Figure 18.11

You can access all the integrated DS reports in Analytics 360 under the Acquisition reports.

Figure 18.12

All conversions/transactions in the report are coming from the AdWords campaigns (the first two rows), while the Bing campaigns (the last two rows) could use some optimization.

Figure 18.13

DFP reports for Publisher Pages and Referrers can be found under the Publish reports in the Behavior section.

Figure 18.14

DFP Publisher Pages provide detailed reporting on a number of DFP metrics including DFP Impressions, Clicks, CTR, and Revenue.

Figure 18.15

Attribution Model Explorer shows the weighted average credit for the path positions prior to conversion for each marketing channel.

Appendix A

Figure A.1

Voice of the Customer data.

Figure A.2

If your qualitative data integrates into Google Analytics as events, you can create a Google Analytics segment based on event data for visitors who responded to a qualitative survey with very low overall satisfaction (0 – Very Bad OR 1 – Fair).

Figure A.3

As an example of segmentation based on survey responses that appear in Google Analytics as events. The Pages report above has three segments applied based on job role: Senior Management, Marketing, and Research.

Figure A.4

Following user research, a change to the button text and styling on StubHub’s ticket search results page drove a significant conversion increase.

Figure A.5

This custom report is showing performance by industry, one of the several types of firmographic data that Marketo can provide to Google Analytics as custom dimensions.

Figure A.6

You can apply segments based on lead status and firmographics data to your flow reports to identify where those audience segments are dropping off.

Figure A.7

This Google Analytics dashboard displays lead and firmographic data.

Figure A.8

An MA platform such as Marketo allow you to personalize a landing page by industry, as in this example, or other firmographics data, and report the performance of personalized variation in Google Analytics.

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