Contents of ebook 1

Foreword

Preface

Introduction

1 Preparing to Measure Success

2 Choosing the Right Tool

3 Setting Expectations and Building the Process

4 Assessing Your Data Quality

5 Jumpstart Guide to Key Features

Appendix: Terminology Explained

Index

About the Author

Contents of ebook 2

Foreword

Preface

Introduction

6 Jumpstart Guide to Key Tracking Methods

7 Data Responsibilities

8 Building Your Insights Team

9 Using Key Performance Indicators and Dashboards

10 Insights and Success Stories

Appendix: Terminology Explained

Index

Foreword

If you have $100 to invest in magnificent, glorious success from your analytics efforts, invest $10 in tools and implementation and invest $90 in big brains (people!).

I humbly postulated that ground truth as the 10/90 rule on May 19, 2006. With every passing year, I’ve come to believe in that rule more and more (and more and more). The reason is quite simple. Every facet of the business world is throwing off ever more data, and every facet of our personal existence (and insistence on sharing) is throwing off ever more data. Data, it turns out, is free; identifying specific actions business leaders can take based on rigorous analysis is not free.

This is why I’m so excited about Brian’s book. It dispenses with the normal omg, omg, look at how much data is there and is that not amazing, let us spend 18 months on implementation, and gets to what it really takes to shift from data puking to recommending business actions based on data.

Here’s one of my personal examples of the difference in emphasis, and what ultimately drives success. In every company, every leader wants a dashboard. “Get me a summary of the business performance. Decisions shall be made!” Analysts scurry around and an intense burst of data, manifested as tables and charts, is presented on a vanilla-scented piece of paper. Happiness? Job promotions?

Sadly, no.

It turns out that the higher you go up the chain of command, the more analytical skills go down, and the context required to make sense of the numbers on the dashboard is also dramatically reduced. Few decisions are made, and if there is a meeting to discuss this it devolves into a discussion of the data quality, missing data, colors in charts, and everything except making a business decision.

The answer? Words in English. More specifically: insights, actions, business impact.

Every dashboard in the world should include as few tables and charts as possible. It should include insights written in English (or your native language) by the analyst, followed by the recommended actions and—the most important critical must-have bit—the impact on the business if the actions are taken.

That vanilla-scented piece of paper will no longer drive one more awful discussion about the data itself; it will drive a discussion of which actions to take first. Hallelujah!!

It is incredible to realize that in the end, data by itself does nothing. It is just data. It is the $90 part—the big brains—that identifies insights, actions, and business impact that will push your company’s profitability and customer delight to new, incredible, heights.

Next time you receive a dashboard, look for the balance between tables, charts, and English text, and you’ll know if it will add value or waste time.

That’s my little appetizer for you as you dive into Brian’s wonderful book.

The entire book is awesome. It is beautifully structured, and you should go from Chapter 1 to Chapter 10 on your we will make the most of data voyage. But if you wanted to be a little naughty and jump around, my favorites are Chapter 8 (you can read it anytime, and you can’t work on the recommendations soon enough!) and Chapter 10 (every time you find a task daunting, find hope in the success of others in the case studies).

I wish you all the very best. Carpe diem!

Avinash Kaushik

Digital Marketing Evangelist: Google

Author: Web Analytics 2.0, Web Analytics: An Hour a Day

Preface

This is my fourth book on Google Analytics, but this one is different. Rather than making it a tool-specific practitioner’s bible (as my Advanced Web Metrics series endeavored to be), I approached this book as I do my work: helping ambitious organizations make a success of their business by using data intelligently.

As I have come to realize over the years, success does not depend on tool expertise alone. The bigger issue is the organization. It needs to trust the data and have confidence in the process, structure, and people behind it—things not directly related to the tools being used. So I approached this book very much from the business point of view first, then worked backward toward the nontechnical aspects of the tool—Google Analytics. My intention is that senior managers, stakeholders, and practitioners all speak a shared language and set a common path to building a credible data-driven environment. I hope the method has worked.

As for all authors, my writing of this book was not a solo exercise. It required love, support, help, guidance, advice, friendship—even random and unrelated conversations. (You would be surprised at what can spark an idea connected with data!) The people I list here are those who have directly contributed to the book or to my thinking about applying successful analytics.

Sara Clifton’s never-ending love, support, and guidance keep me on the right track and always help me to see the bigger picture of measurement, digital, and life in general.

Shelby Thayer has sanity-checked every word of my last three books, including this one. She is a great analyst, with a ton of experience at driving web measurement acceptance within a large organization, and her feedback and experiences have helped me significantly in writing a tightly focused book.

Brad Townsend is my valued technical editor. He is a smart (and modest) Googler who, as a software engineer, knows the technicalities and back end of Google Analytics like no other. David Vallejo, an expert Google Analytics implementer, developer, and all-round smart guy, helped me enormously with his technical problem-solving skills. Nothing can’t be done with this guy at hand! Dave Evans expertly reviewed Chapter 7 (“Data Responsibilities”) and provided insightful discussions about data privacy law. Dick Margulis is my trusted editor, who has now helped me write and structure three books and is my go-to man for navigating the tricky waters of the publishing world. His guidance and advice have been invaluable.

Avinash Kaushik has honored me (again) by writing the foreword to this book and setting the scene so enthusiastically and logically for the reader—in a way that only he can. I am lucky to count him as a friend and former colleague. He inspires me (and many others) with his advocacy and excitement for all things that can be measured.

John Wedderburn, Tobias Johansson, and the team at Search Integration (where I work) have engaged in many “quality time” meetings and open-ended discussions that have broadened and deepened my knowledge.

And last but not least, the vibrant and smart GACP community pushes back the boundaries of what can be done with Google Analytics, and importantly, what can be simplified with it.

I hope I have remembered everyone.

Brian Clifton

January 2015

Introduction

According to a recent survey of IT professionals,¹ “55% of big data analytics projects are abandoned.” Most of the respondents said that the top two reasons the projects fail are that managers lack the right expertise in house to connect the dots around data to form appropriate insights, and that projects lack business context around data. Similarly, the “Online Measurement and Strategy Report 2013” from Econsultancy² asked companies, “Do you have a company-wide strategy that ties data collection and analysis to business objectives?” Only 19% said yes, a figure that had hardly changed during the previous five years.

I wrote this book for those managers struggling to make headway—to empower you to make informed decisions and overcome the obstacles.

My goal with this book is to get you to think in terms of insights—not Google Analytics data. An insight is knowledge that you can relate to. It’s a story that puts you in the shoes of your visitors, so that you can understand their requirements when they come to and view your website, app, or other digital content.

A company’s ability to satisfy the needs of a website visitor depends on two important factors:

• Visitor expectations, discerned from how they got to your content—what search engine, campaign ads, or social conversation drove their decision to seek you out
• User experience—how easy it was to use your content, to navigate around, find information, engage with you (contact you, purchase, subscribe, give feedback)

It is your organization’s ability to manage, analyze, and improve these two factors that determines your digital success (or not). In this book I describe how insights are used to pull all of the relevant data points together to build a story of your visitors’ journey and their experiences. With that knowledge you can improve these: as I show in Chapter 10, improvement can be dramatic performance gains in terms of your online visibility, revenue, or efficiency savings.

Yet Google Analytics doesn’t provide insights by itself—no tool can. Producing insights requires an understanding of your business and its products, your value proposition, your website content, its engagement points and processes, and of course its marketing plan. Google Analytics provides the data (and lots of it) that enables you to assess these. However, people—not machines—build insights. This is the role of your analytics team. They sift through the noise to find the useful data, translate it into information to explain what is happening, then build stories of useful knowledge for the organization—the insights.

This book is about showing you how to do that. This book is about knowing what to focus on, what you can expect in return, the talent you need to hire, the processes you need to put in place, the pitfalls to avoid, and how much investment is required in order to make it all happen.

This is a detailed book by necessity. Building an environment where you can trust your data, understand it, and make important decisions based on it requires a deep level of immersion, not an executive summary. However, my approach throughout this book is to focus on the insight gained for the business, not the minutiae.

This book is for you if you are a manager who needs an overview of the key principles of website measurement, the capabilities of Google Analytics, and how to grow and give direction to your organization when it comes to its digital strategy. Your ultimate interest is in insights and knowledge, not more data!

In short, I aim to put you in control and provide a perspective on the entire process of building a data-driven environment using Google Analytics.

REFERENCES

1    http://visual.ly/cios-big-data

2    https://econsultancy.com/reports/online-measurement-and-strategy-report

You know measurement is important to your success—be it for you, your organization, or your career. In many respects, Google Analytics is just a tool, like the plethora of other data tools organizations use to help them make better decisions.

But web analytics—the area, technique, and industry that Google Analytics resides in—is different. Its reach and potential are far greater than any other tool you have. Why? Because not only can it measure the engagement, transactions, and revenue from your customers, it can also measure your potential customers—where they come from, what they are looking for, and how close they come to becoming a customer before they bail out (and where on your site or mobile app this happens).

This integration of customer and potential customer data is unique to web analytics—and therefore extremely powerful. For example, the vast majority of websites have very low conversion rates—typically 3% (Figure 1.1).¹ That is, only 3 visitors out of 100 go on to become customers. While many a business analyst is tasked with optimizing for that thin sliver of a segment, there is clearly a much greater potential in understanding why the other 97% of people that expressed an interest in you (they visited your website) did not convert—and using this information to improve matters.

Google Analytics can be used to analyze both customer and non customer behavior. All that is required is a single digital touch point during their engagement with your organization. Usually, the touch point is a visit to your website, though with Universal Analytics (the latest enhancements to Google Analytics, described in more detail in Chapter 6, in ebook 2), it need not be. For example, a potential customer receives an offer from you via snail mail. This contains a coupon that they take into your brick-and-mortar store to make their purchase. At the point of sale, your store sends the purchase details (product name, value, coupon code, and so forth) via the Internet to your Google Analytics account. The result is that Google Analytics can generate a report on the performance of your direct mailings and sales in your store.

The digital touch point in this example was the actual purchase. If

then

Suppose your direct mail encourages recipients to visit your website first in order to obtain their coupon code. The second digital touch point is your website, and visits to it reflect the interest in your offer. Now you have a simple yet powerful set of data that can be analyzed—even if the recipient does not go on to purchase. For example, if

then

The extra data point collected in this second list (c) tells you that evaluating the results of a direct mailing is not as black and white as just the number of purchases: 8,000 people are actually interested in your offer; without this piece of information, it looks like only 725 people are interested. Armed with this extra data, you can now improve your direct mailing to increase that number—that is, grow the interest level. Simultaneously, you can work to improve your website landing pages to go beyond 9.1% conversion, and you can also try to increase the order value for those purchases (perhaps you can upsell and cross-sell related products). The result is that you now have a great deal more options to improve sales and measure the impact of your various efforts—so you can focus on the most profitable. Powerful numbers indeed.

THE VALUE OF WEB ANALYTICS DATA

Consider the following exercise:

Many a business analyst is tasked with customer analytics (see sidebar, “Business Intelligence Defined”). Results can be used to compile a campaign for the upsell and cross-sell of products as well as to improve customer retention. Typical revenue increases are on the order of 1–9%.

Let’s invest the same amount of energy with a web analyst for the same organization. In this case, it is to understand the pain points of your website conversion process. The information gained is then used to reduce the friction of the process and improve the conversion rate—that is, generate more customers. In my experience, typical improvements are double digits, often triple digits (Figure 1.2).

In addition, the changes made to improve the conversion process on your website are compound. For example, if you make your website 10% more efficient at converting a visitor to a customer, that improvement is not just a one-off hit—it lasts perpetually (though not quite in practice). Therefore, in twelve months’ time when different visitors are coming to your site, 10% more of them will be converting than before. Additionally, a visitor who turns away in frustration because of a poor user experience is unlikely to return. But for every new visitor you acquire through an improved process, there is a better chance they will convert and become a long-term paying customer. The result of both of these is that your increased revenue will grow way beyond the initial uplift. Your marketing efforts just became a whole lot more efficient!

The huge potential of being able to convert more visitors into customers, or being smarter at acquiring higher-value visitors, is the great ability that web analytics brings to the table. And Google Analytics is a class-leading product in this field.

Suppose that by looking closely at how visitors interact with your website and using techniques such as sales funnel analysis, exit points, bounce rates (single-page visits), and engagement metrics, you were able to improve your online conversion rate from 3% to 4% (a 33% improvement in the base rate). What would that mean for your bottom line? Let’s look at a hypothetical example. If

then

The visitor acquisition cost is the same regardless of the conversion rate. The non-marketing profit margin, marketing costs, and revenue per conversion are also independent of the conversion rate:

Table 1.1 shows the results of improving the conversion rate from 3% to 4%.

The last two rows of Table 1.1 put the analysis into context: profit will rise by $37,500 and return on investment will quadruple to 50%. Note that this is achieved solely by improving the conversion rate of the site—visitor acquisition costs remain the same. This is the value that web analytics can bring to your business. As a practitioner of over 15 years, I see this potential time and time again. It’s real and attainable.

WHAT’S DIFFERENT ABOUT WEBSITE MEASUREMENT?

I often find that measuring the performance of a website is misunderstood by senior managers. That is not surprising considering that over the years the web analytics industry has struggled to define itself as it migrated from the IT department into marketing.

The principle of web analytics is straightforward:

However, managers who require data to guide their decisions are much more used to certainties—for example, the certainties that come from customer analytics about how much money you make, what the profit is, and how many customers you have, as well as operational analytics that tell you how many staff you have, what they cost, what your manufacturing costs are, and so forth.

By certainties, I am referring to hard numbers—solid numbers, where there is little or no error. If you wanted to know how much cash your company took in last month, you could simply print out your bank statement. That number is definite because it represents confirmed transactions. Your bank has done all the hard work to ensure that only valid payments are processed, transactions are legitimate, and the money sitting in your account is actually yours. Similarly, if you wanted to know the number of customers you have, you could make a query to your CRM system. That number is definite because it represents real people—the names and addresses of customers who have ordered and paid you. Your sales team has done all the hard work to ensure this is correct.

Things are very different when it comes to web analytics. This is because all your reported numbers are fuzzy, fluffy, hazy—in other words, inaccurate. Unlike the traditional business analyst, the web analyst needs to take responsibility for data quality.

Data Quality and Ownership

Data quality problems come from a variety of sources, the most common being an incomplete or poorly implemented setup: there is no bank or sales team verifying the data (your analytics team must do this, though I have found from experience this rarely happens). But even if you were able to attain a perfect setup, there are inherent inaccuracies. That’s because the vast majority of collected data from your website is from anonymous visitors (the other 97%)—you have no idea who they are. Therefore, there isn’t a one-to-one correlation of data to a specific person.

For example, if a visitor does not log in to your website or connect with you in some unique way, that visitor will be counted as a separate visitor to your website should they return using a different device—a tablet versus a smartphone versus a laptop. As far as your web analytics tool is concerned, this is counted as three different one-time visitors. The same effect happens if they use multiple browsers for subsequent visits, such as Internet Explorer, Chrome, or Firefox.

What about transactions?

You may feel that e-commerce data collected by your web analytics tool would fare better and be more accurate than anonymous visitor data. After all, a transaction is confirmed as completed, so you have the extra check taking place. That is true, but web analytics tools are poor at handling cancellations and returns because of what this actually correlates to. For example, a return of goods will cancel out the effectiveness of the campaigns that drove the visitor to your site in the first place. But if, say, I ordered the wrong size shoes from your website and returned them, the campaigns that drove my interest in your business are still valid, and should be credited for the purchase. So it makes more sense, from a marketing evaluation viewpoint, to keep all transactions within your web analytics data (except of course any fraudulent, test, or obvious error transactions).

As with all data, accuracy is important. Even a perfect setup of Google Analytics will degrade over time as your website changes, the web changes, and user behavior changes. In order to trust your web analytics data and therefore have confidence in it to make important strategic decisions, you must take ownership of data quality.

A final point on accuracy is the perception by many people, even smart people, that the vast volumes of data web analytics tools collect make the data accurate. My feeling is that most people are aware of the concept of small sample sizes yielding inaccurate results. Therefore, it is tempting to think that a huge pool of data drowns out any inaccuracies. However, web analytics data is so easy to collect that this common assumption is not true. The reverse can be the case: it is all too easy to collect inaccurate data and lots of noise, drowning out the important signals.

WHERE GOOGLE ANALYTICS FITS

Your website is in a unique position. As the first point of call for your digital presence, it is where your customers and noncustomers (potential customers, job seekers, investors, press, even competitors) go to find information on your business, products, and services. As such, web analytics is the only place where you get to see the data for all of these, side by side.

The side-by-side comparison of disparate data is invaluable for a senior manager. It lets you zoom out and see the bigger picture so you can use the same metrics and methodology to determine the relative success of each area—compare apples with apples, in other words. And that provides you with context—something often missing when analysts come to you with their deep-in-the-woods investigations. Context ensures you focus on the areas that have the biggest impact for your business.

That said, a single tool is not a panacea. Google Analytics (Figure 1.3) is in a unique position to connect your organization’s digital activities with traditional offline marketing and your existing customers. Nonetheless, those individual areas still require their own tools for day-to-day management. For analyzing and understanding the performance of these other areas, use your central analytics platform—Google Analytics—as your unified measurement tool. Otherwise you will waste a great deal of time and energy chasing numbers from different tools that in principle should match but in reality never do. When it comes to counting, there are in fact differing techniques, methodologies, and definitions for the same thing!

Google Analytics collects and reports data. It is great for telling you what happened and when, but it does not tell you why it happened. That is where your analysts come in. A good web analyst uses her knowledge and experience to build a hypothesis to explain the why. Then she hunts down other data points to either support or refute the hypothesis— potentially outside of Google Analytics. If the data is inconclusive, a test (experiment) is performed. More on this in Chapter 8 (in ebook 2).

Data Is Not a Silo

Web analytics is different from other forms of business analytics—in terms of its potential (customer and