The Web Analytics Kick Start Guide: A Primer on the Fundamentals of Web Analytics

154 Pages·2014·5.87 MB·English

by  Brent Dykes

About the Author
Brent is a respected expert in the field of web analytics, having spent the last 10
years as an analytics consultant and evangelist at Omniture and Adobe. He has
worked with both large and small organizations in developing measurement
strategies, performing analyses, and building digital analytics practices. Brent
is the author of Web Analytics Action Hero, which has helped analysts and
marketers to drive more action and value from their digital data. Brent has been
involved in digital marketing for the past 15 years with agency experience at
WPP’s Blast Radius as well as client-side experience at Lands’ End and Microsoft.
Brent earned his MBA at Brigham Young University, where he was a Hawes Scholar. He earned his
BBA (Marketing) degree from Simon Fraser University.
Special Thanks
Many smart and experienced digital analytics practitioners reviewed parts of this eBook as it was
being written and provided several valuable suggestions that improved its contents. I’d like to sincerely
thank Robert Blakeley, Bret Gundersen, Joe Christopher, Jan Exner, Michael Boyle, Brian Au, Nate
Smith, Dylan Lewis, Andrew Anderson, Katrine Kieldsen, Katie Burdett, Andy Powers, James Tall,
Chris Luka, Michael Harke, Michael Halbrook, Dasha Fitzpatrick, Mary Vardaman, Ben Gaines,
Brent Chiu-Watson, Brian Mermelshtein, Robert Feltey, and Steve Hammond. I’d also like to thank
my wife and children for allowing me to spend many hours writing yet another book when it was
supposed to be only another chapter. Finally, I thank my parents for their ongoing love and support.
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Contents
About the Author ...............................................................................................................................2
Special Thanks ....................................................................................................................................2
Introduction .......................................................................................................................................5
CHAPTER 1: THE DEFINITION AND EVOLUTION OF WEB ANALYTICS ...................7
Evolution of Web Analytics ..............................................................................................................8
Stage I: The Birth of Web Analytics (1993–1998) ................................................................................................................8
Stage II: The Emergence of Page Tagging (1999–2004) .....................................................................................................10
Stage III: The Rapid Growth of Web Analytics (2005–2009) ............................................................................................12
Stage IV: The Transition to Digital Analytics (2010 to present) ........................................................................................14
CHAPTER 2: THE BUSINESS ESSENTIALS OF WEB ANALYTICS .........................20
Why Web Analytics Matters ...........................................................................................................20
Strategy Always Comes First ..........................................................................................................22
Business Goals versus User Goals .................................................................................................24
Demystifying Metrics ......................................................................................................................25
Must-Know Web Metrics ................................................................................................................27
Page Views ............................................................................................................................................................................28
Visits .....................................................................................................................................................................................29
Unique Visitors ....................................................................................................................................................................30
Average Visit Duration (Average Time Spent)....................................................................................................................31
Page Views per Visit .............................................................................................................................................................32
Bounce Rate ..........................................................................................................................................................................33
Exit Rate ...............................................................................................................................................................................35
Conversion Rate ..................................................................................................................................................................36
How Web Metrics Work Together ......................................................................................................................................38
Measuring Events and Outcomes .................................................................................................40
Five Key Attributes of Effective Metrics .......................................................................................43
The Metrics Hierarchy ...................................................................................................................45
Key Performance Indicators (KPIs).....................................................................................................................................46
Secondary Metrics ................................................................................................................................................................47
Peripheral Metrics ................................................................................................................................................................49
The Importance of Dimensions ...........................................................................................................................................50
Data Tree Analogy ...............................................................................................................................................................52
Five Common Digital Business Models .......................................................................................54
1. E-commerce .....................................................................................................................................................................55
2. Lead Generation ..............................................................................................................................................................56
3. Advertising .......................................................................................................................................................................57
4. Self-Service .......................................................................................................................................................................58
5. Informational ...................................................................................................................................................................59
Tailor to Your Needs ............................................................................................................................................................60
Putting Metrics to Work .................................................................................................................60
1. To Be Meaningful, Metrics Need Comparisons ..............................................................................................................61
2. Rely More on Internal Benchmarks than Industry Indices ............................................................................................61
3. Absolute Values Go Hand-in-Hand with Percent Change.............................................................................................62
4. Trend Key Metrics Over Time .........................................................................................................................................63
5. Choose Appropriate Visualizations .................................................................................................................................63
6. Never Settle for Just Aggregate Numbers ........................................................................................................................64
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7. Don’t Fixate on a Single Metric .......................................................................................................................................64
8. Use Metrics to Inform, Not Justify Decisions..................................................................................................................65
CHAPTER 3: THE TECHNICAL ESSENTIALS OF WEB ANALYTICS .........................67
Data Collection Overview for Page Tagging ................................................................................68
Data Collection Deep Dive ..................................................................................................................................................69
A Closer Look at the Image Request ...................................................................................................................................72
Cookie Overview .............................................................................................................................75
Reporting Architecture ...................................................................................................................80
Variables and Reports ..........................................................................................................................................................83
Customization ......................................................................................................................................................................84
Attribution ............................................................................................................................................................................85
Persistence ............................................................................................................................................................................87
Expiration .............................................................................................................................................................................89
Key Focus Areas of Digital Measurement ....................................................................................90
Interaction or Event Tracking ..............................................................................................................................................91
Campaign Tracking ..............................................................................................................................................................93
Mobile Tracking ...................................................................................................................................................................97
Cross-Device Measurement ...............................................................................................................................................102
Data Enrichment and Integration .....................................................................................................................................105
Tag Management................................................................................................................................................................109
Not Technical, Just Tech-Savvy ....................................................................................................113
1. Garbage In, Garbage Out ..............................................................................................................................................114
2. The More You Put In, the More You Get Out ..............................................................................................................114
3. Separate Need-to-Know from Nice-to-Know ................................................................................................................115
4. Partner with Business-Savvy Technical Resources ......................................................................................................116
5. Maintain to Gain ...........................................................................................................................................................116
6. Let Technology Do the Heavy Lifting ...........................................................................................................................117
CHAPTER 4: THE PROCESS OF DIGITAL MEASUREMENT ..................................119
Digital Measurement Cycle ..........................................................................................................122
Data Collection ..............................................................................................................................122
Usefulness: Right or Wrong Data? .....................................................................................................................................124
Trust: Good or Bad Data? .................................................................................................................................................125
Ten Tips for Improving Data Quality ...............................................................................................................................128
Data Usage ......................................................................................................................................130
Transitioning from Data Collection to Data Usage ..................................................................131
The Art and Science of Digital Analysis .....................................................................................135
Pairing Insights with Executional Agility ...................................................................................141
Digital Measurement Maturity Path ...........................................................................................142
Stage 1: Basic Measurement ..............................................................................................................................................143
Stage 2: Custom Measurement ..........................................................................................................................................144
Stage 3: Integrated Measurement ......................................................................................................................................144
Stage 4: Analytics-Powered Solutions ...............................................................................................................................145
Stage 5: Customer Analytics .............................................................................................................................................145
Where Do You Stand? ........................................................................................................................................................146
Final Words ............................................................................................................................................147
Glossary .................................................................................................................................................150
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Introduction
Since the advent of web browsers such as Mosaic and Netscape Navigator, which
opened up the Internet to the masses, a variety of analytics tools have been
used to track online traffic to web pages over the past 20 years. As this area of
measurement grew, companies came to know it as web analytics. Just as the Internet
has transformed dramatically over this short period of time, web analytics has
also evolved in terms of the data that is collected and what can be done with it.
With companies shifting more of their marketing spend from traditional media to
digital media, more people are being exposed to the numbers and metrics captured
by web analytics tools. However, despite its increased prevalence in marketing and
other functions, many people still suffer from a knowledge gap when it comes to
web analytics. This gap can have serious consequences: Marketers and managers
may overlook valuable insights that would improve business performance or
misinterpret the numbers and make poor business decisions.
After I published my first book, Web Analytics Action Hero, I was surprised
by the number of people who were still looking for help with understanding
the fundamentals of web analytics. Although my book could help analysts or
data-driven marketers who were already familiar with web analytics, it wasn’t
designed to introduce inexperienced individuals to the field of web analytics. After
contemplating what insider knowledge I could share with people who were not
as well-versed in this area, I decided to write a primer that could help get more
people up to speed on this valuable topic.
The goal of this guide is to close the lingering knowledge gap and give anyone who NOTE: If you come across
is interested in learning more about web analytics a better understanding of what an unfamiliar acronym or
it is, what the essentials are, and how it can be applied successfully. This primer term as you read this primer,
I encourage you to check
won’t transform you into a data scientist, but it will give you the insights you
out the glossary at the end
need to take better advantage of your digital data and analytics resources, whether of this eBook for a potential
you’re using Adobe Analytics, Google Analytics, or another web analytics tool. definition.
Although I will cover both the technical and business aspects of web analytics, I
wrote this eBook for a non-technical audience who at least is familiar with how
the Internet works at a basic level (which means even my mother should be able
to read and learn something from this primer). To give you a solid understanding
of web analytics, this guide will cover the:
• Definition of web analytics
• Evolution of web analytics
• Business essentials
INTRODUCTION | 5
NOTE: If you’re looking • Technical essentials
to dive into the field of
analytics, I believe there’s no • Process of digital measurement
better place to start than web
My aim is to equip you with all the fundamental concepts you need to better
analytics for three reasons.
First, almost all small-to-large harness the power of this technology in your role and in your business. Before
companies with any kind of we begin your web analytics boot camp, however, I’d first like to step back and
web presence collect data on
define what web analytics is.
their various web properties
and make this data generally
accessible to their employees.
Second, web analytics is
a fairly mature, widely
available, and time-tested
technology when compared
to other emerging analytics
technologies. Third, despite
its relative maturity, it still
represents a healthy source
of untapped insights and
potential opportunities for
marketers and other business
professionals.
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CHAPTER 1
THE DEFINITION
AND EVOLUTION OF
WEB ANALYTICS
“What gets measured gets managed.”
– Peter F. Drucker
So, what is web analytics? I think it’s helpful to first focus on defining what NOTE: Sometimes analysis is
analytics is. According to Wikipedia it’s “the discovery and communication of mistakenly interchanged with
analytics. Analysis is defined
meaningful patterns in data.”1 I like the simplicity of this definition because it
as the process of examining
emphasizes two important aspects: the discovery of insights through analysis of
something in detail in order
collected data and the communication of those insights to influence decisions. to understand or explain
It’s missing the overall purpose of analytics, however, which is to improve the it. Analytics is a broader
category representing both
performance of what is being measured.
the discipline and technology
With this general definition of analytics in mind, one of the key challenges in that enable individuals to
perform analysis.
finding a suitable definition for web analytics today is that it is no longer just
about measuring websites and online behaviors. It now extends to analyzing social,
mobile, campaign, survey, competitive, and even web-influenced offline data
(such as closed sales from online leads). As a result, practitioners now commonly
refer to web analytics as digital analytics to better represent the broader focus
beyond just the Web.
To reflect this expanded focus I define web, or digital, analytics as the process of
measuring digital initiatives and using the data to improve business performance.
Digital initiatives represent an organization’s diverse investments in digital properties
(websites, mobile sites, mobile apps, and so on) and channels (banner ads, paid
search ads, e-mail, social media, and more). Management guru Peter Drucker
THE DEFINITION AND EVOLUTION OF WEB ANALYTICS  | 7
made a critical observation when he stated, “what gets measured gets managed.”
The driving force behind measuring your digital initiatives is to gain meaningful
insights that can help you maximize the return or success of these investments.
If you want to manage your digital investments, you need metrics to quantify
their performance. Imagine flying an airliner without a functioning instrument
panel. Doing so would make flying not only more difficult and inefficient but also
infinitely more hazardous. Without relevant data and insights, you’re essentially
flying blind—something no organization can afford to do. Let your competition
crash and burn, not your digital business.
“The only time Evolution of Web Analytics
you should ever
Today, web analytics is a mainstream technology that should be familiar to most
look back is
digital marketers—although perhaps not always well understood. Over the past
to see how far 20 years, web analytics has evolved significantly from its simple beginnings as
primarily an IT-focused tool to its current position as a core marketing technology
you’ve come.”
(Figure 1). Take the time to understand the origins, major challenges, and key
– Unknown advancements of web analytics, and you’ll have a better grasp of how the technology
arrived at its current capabilities, as well as the direction it is heading.
Emergence of Growth of Transi(cid:15)on to
Birth of Web Analy(cid:15)cs
Page Tagging Web Analy(cid:15)cs Digital Analy(cid:15)cs
(1993-1998)
(1999-2004) (2005-2009) (2010-present)
Figure 1: Web analytics appeared shortly after the birth of the World Wide Web and has
experienced four main stages while maturing as a digital marketing technology and discipline.
Stage I: The Birth of Web Analytics (1993–1998)
As web traffic grew steadily in the mid-1990s, IT departments found they needed
to monitor the performance of their corporate websites. This need fell outside of
their traditional business intelligence systems that were more focused on financial
and operational reporting. The first web analytics tools were based on log file
analysis. When someone visits a web page, a request is made to the web server for
the page’s content and the web server records each request or hit in a log file. IT
teams would use web analytics software to parse, or extract, useful information
from these otherwise unintelligible server files. From the log files, webmasters
(remember them?) could obtain basic reports on the visitor’s IP addresses, referrers,
pages, status codes (404 page not found errors), web browsers, user operating
8 | C HAPTER 1: THE DEFINITION AND EVOLUTION OF WEB ANALYTICS 
systems, and so on. All of this data formed the basis of early clickstream analysis: NOTE: Packet sniffing was
another method that was
the study of page requests to monitor web traffic patterns.
first used for web analytics
During this same time, hit counters were added to web pages, which acted by Accrue Software in
1996. Packet sniffing
like odometers showing the number of times a page was viewed. These simple
solutions (software or
counters were typically created with server-side scripts such as Perl or C (see
hardware) passively collected
note on page 10 on scripting). The hit counters provided only basic information clickstream data that was
on the popularity of different web pages, but they exposed people outside of the sent to and from the web
server. These solutions were
IT department to a basic web metric: page views (although they were called hit
very IT-dependent and costly.
counters, they weren’t really showing hits). Sharing page metrics with the general In addition, they raised
public might be superfluous, however, it was helpful information for internal privacy concerns as they
could capture everything
stakeholders. Eventually, companies such as Webtrends and Urchin realized their
(including passwords and
log file analysis tools should focus on reports for business users and marketers,
credit card numbers). Despite
not just IT professionals. not becoming a mainstream
web analytics solution,
In the beginning, web pages were primarily just text and links. A single hit
packet sniffing has found a
represented a page being served and viewed. When images, sound, video, style niche in customer experience
sheets, and so on were added to enrich online content, however, multiple requests replay with IBM Tealeaf and
Cloudmeter (formerly Atomic
or hits (up to 10 or more) to the web server were required to assemble a single
Labs).
page. With this change hits became a worthless metric because it was based on
the arbitrary nature of the page’s design elements and not its actual popularity.
Even though people still commonly refer to how many hits their websites receive
(more likely page views or visits), you won’t find the hits metric in any modern
web analytics tool.
KEY TAKEAWAYS
• IT teams and webmasters were the first users of early web analytics tools,
which were based on log file analysis of web server requests.
• Other internal teams were exposed to page view counts through hit counters,
which were visible odometers embedded on individual pages.
• Hits were no longer a relevant web metric once web pages included more
than just plain text and links.
• As businesses experimented with their first websites they became interested
in traffic volume but weren’t yet using data to improve performance.
Q
EVOLUTION OF WEB ANALYTICS  | 9
Stage II: The Emergence of Page Tagging (1999–2004)
By the late 1990s, online marketers were accustomed to relying on reports from
their IT teams to learn the traffic volumes for their companies’ websites. As
the Web continued to evolve, however, the log file analysis approach ran into a
couple of key challenges that began to undermine its ability to provide accurate
and insightful analysis of visitor behaviors.
First, different robots and search engine spiders emerged that were designed to
explore and index various websites on a recurring basis. By default, web server
log files captured both human and non-human interactions. Web analysts and
marketers discovered that the robot and spider traffic would interfere with
understanding actual human behaviors on a website, which meant they needed
to constantly remove the increasing volume of non-human traffic.
Second, to streamline the online experience, ISPs (Internet service providers) and
web browsers introduced the practice of caching web pages. ISPs would use proxy
servers as an intermediary to cache popular pages for their user base to speed up
download times. Web browsers stored a local copy of previously viewed pages on
a user’s computer so that they would load quickly when the individual navigated
back and forth between pages. Both forms of caching meant web content was
being viewed without requests being made to the web server, which lowered the
volume of traffic reported by log files.
After JavaScript became the standard for client-side scripting on web pages in the
late 1990s, the familiar hit counter evolved into a page tagging approach for web
analytics. Rather than outwardly sharing how many page views an individual web
NOTE: Scripts are a series of page received, JavaScript code was used to send online data about web pages and
commands that execute tasks visitors imperceptibly to a third-party web analytics vendor, which hosted all of
within a program. Scripting
the collected data. Rather than having to install software, analysts would access
can occur on the web server
the reports through an online interface, representing one of the earliest forms of
(server-side) or within the
web browser (client-side). software-as-a-service (SaaS) or cloud computing. Instead of paying a one-time
Server-side scripting (with fee for software, companies would pay a recurring monthly fee that depended
PHP, ASP, Ruby, and the
on the amount of data being collected. Marketers and other business owners still
like) is used to connect the
web server with databases needed web developers to insert the page tags into their websites, but they were
and other systems to build freed from depending on the IT team for batch-processed log file analysis reports.
dynamic HTML pages. Client-
side scripting (JavaScript) The page tagging approach quickly grew in popularity due to several distinct
adds richer functionality to advantages it offered over the log file analysis approach:
web pages such as mouse
rollover effects, dynamic • Data collection wasn’t impacted by page caching because the tags were
menus, slideshows, modal activated by the page being viewed, not requested from the web server.
windows, and responsive
design for mobile devices.
10 | CHAPTER 1: THE DEFINITION AND EVOLUTION OF WEB ANALYTICS