Chapter 1

Why Adobe Analytics?

IN THIS CHAPTER

Understanding why you're analyzing data

Identifying where your data comes from

Configuring and analyzing data in Adobe

In this chapter, you begin your journey into analytics powered by Adobe. In the remainder of this book, we dive deeply into specific features of Adobe Analytics, enabling you to perform in minutes analyses that would take days with other tools. But here at the beginning, it's important to be able to identify why you're analyzing data as well as how the data is populated and configured.

Adobe Analytics has been a premier web, mobile, and customer-focused analysis tool for well over a decade. If you're new to Adobe Analytics or reading this book to beef up your ability to wield this powerful set of tools, experience with similar tools, such as Google Analytics, Webtrends, or Microsoft Excel, is valuable. But whether you're reading this with substantial background in data analytics or the concept is new to you -?or anywhere in between -?we first pull the lens back to understand the history of web data so you can better understand the role it plays today.

In this chapter, we give you a chance to expand your horizons in terms of how you think about why you're analyzing data using Adobe in the first place. Next, we answer that age-old question: "Where does my data come from?" That is, we dig into how data gets pushed onto the Adobe platform. Finally, we present an overview of what's involved in sifting and squeezing valuable insights out of all the data you have access to in Adobe Analytics. So, buckle up your seat belts and let's begin!

Understanding Why You're Using Adobe Analytics

People have been attempting to analyze data generated by interactions with the World Wide Web since Tim Berners-Lee invented it. Yes, that process has become exponentially more developed and complex, but we're pretty sure one of the first questions asked after the first website went live was: "So, is anyone going to it?"

If we fast-forward a few decades, you'll be hard-pressed to walk through an international airport today without seeing ads for cloud technology, data security, and digital transformation. The business of data analysis has exploded, and there is no sign of it slowing down. According to a 2018 Forbes study, "Over the last two years alone, 90 percent of the data in the world was generated" (www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/ - 7ceaab1460ba). That equates to 2.5 quintillion (18 zeros) bytes of data captured every day. And most of it is coming from the web, mobile phones, and the Internet of Things (IoT), meaning the universe of devices that connect to the Internet, each other, or both, ranging from wearable devices to refrigerators.

Now that you have a feel for this ever-expanding amount of data, it's time to think about what to do with it. You might remember a time when you couldn't go into a meeting without hearing the words "big data." The thinking was, "Let's collect all the data we can and figure out what to do with it later." However, as made clear by the consistent decline in searches of that term on Google Trends (https://trends.google.com/trends/explore?date=all&geo=US&q=big%20data), a new sheriff is in town. And that new sheriff is driven by analysis. Collecting data is a start, but analysis is required to derive meaningful insights, form hypotheses, and take action.

Avoiding HiPPO!

Data analysis is what helps people avoid HiPPO. Zoo animals? Have Eric and David gone crazy in the first chapter? No, we're talking about highest paid person's opinion. The HiPPO acronym has come to describe the phenomenon of people in any grouping in an organization deferring to the opinion of the person highest in that group (generally the highest paid), resulting in unscientific and often harmful analysis and conclusions not supported by data. Table 1-1 provides a scenario of how this plays out.

TABLE 1-1 Business Decisions Not Based on Data

Employee

Comment

HiPPO

We need to sell more widgets!

Marketer

A lot of our new customers tell us they like our new TV ad.

HiPPO

I don't like the jingle; it gets stuck in my head.

Marketer

I really think we should try expanding it to more markets.

HiPPO

Here's the budget for additional ads in the local newspaper.

Marketer

[Sheds a tear while heading back to desk to look for a new gig]

Don't let your organization's decision-making process be driven by HiPPO. Imagine how much better that conversation could have gone if it was based on actual data, as shown in Table 1-2.

TABLE 1-2 Business Decisions Based on Data

Employee

Comment

CEO

We need to sell more widgets!

Marketer

A lot of our new customers tell us they like our new TV ad.

CEO

That jingle really gets stuck in my head; how can we learn whether it's positively affecting sales?

Marketer

Let's run some online preroll video tests (short audio or video ads that run before a user's selected audio or video) and judge the ad's effectiveness.

CEO

Sounds great. Can you use segmentation to make sure the results are not skewed by the fact that I've viewed the video a thousand times? (Segmentation is a marketer's ability to filter analysis or action to a specific set of users based on behavior, demographics, or other factors. Chapter 2 dives into segments.)

Marketer

Of course. We'll test, learn, and even save some money!

See how much better that went? It's all due to the decision-making process based on analyzing data that has been segmented (filtered) to avoid distorting the results. HiPPO shouldn't drive decisions when data can provide context and insight.

Now that we've used a hyperbolic (but revealing) example to illustrate why data needs to be the basis for decision-making in marketing, it's time to think about how else we can use data. We've seen data used to help make decisions on brand logos, campaign headlines, button colors, navigational menu hierarchy, internal and external search optimization, article titles, product bundle options, checkout steps, page layout, and more! And we've seen data used to measure not just sales but the effectiveness of customer support tools, educational resources, and branding campaigns. In essence, data analysis can inform the quality of any part of a website, mobile app, digital screen, desktop application, or even voice skill.

Knowing when you need Adobe Analytics

We hope you agree that data analysis needs to be ingrained in your everyday work life, but you may be asking yourself, "How do I know when it's time to use Adobe Analytics?" The Adobe Analytics sales team has been trying to answer this question since the product was first sold as SuperStats by the Omniture team in 1996. Before that, most web masters (remember that term?) like us were using basic server-log analysis tools just to figure out if anyone was even visiting the site!

Analyzing the effectiveness of websites has become even more complicated as the analytics industry has matured. In 2005, Google purchased Urchin - an early pioneer in the business of analyzing web traffic - and quickly made it available for free. Today, that product is known as Google Analytics, and it paves the way for tens of millions of people to take their first steps into the world of web analytics. Adobe purchased Omniture in 2009 to kick-start a slew of acquisitions that became the Adobe vision for an integrated enterprise marketing cloud, now called Adobe Experience Cloud.

Adobe has succeeded with this vision of an enterprise marketing cloud vision in large part because of the success of Adobe Analytics. It is the foundation that sits as the data hub in Adobe Experience Cloud. Adobe Analytics has been successful for plenty more reasons than this. Forrester, a market research firm that tests and compares developments in technology, reported that Adobe was the clear leader in its current offering. Forrester writes that Adobe "has concentrated on making the UI more intuitive and building on capabilities that allow the exploration of data breakdowns, relationships, and comparisons."

Knowing the difference between reporting and analysis

When it comes to data, we believe it's important to distinguish reporting from analysis. These terms are often used interchangeably outside the...