1. Using Digital Analytics to Create Business Value Today’s business organizations must apply analytics to create new and incremental value. A significant and important source of analytical data in 2013 is digital experiences—from websites to social networks to mobile applications and more. Thus, it is critical in today’s economy for businesses to develop and enhance their understanding of how digital data is collected and analyzed to either or both generate new or incremental profitable revenue or reduce cost. Although digital analytics can significantly maximize profits in today’s competitive global markets regardless of sector or industry, creating and staffing a fully functional digital analytics organization is a complex and multifaceted initiative. Building a digital analytics organization requires rethinking and reengineering the people, processes, and technology used for creating analysis. After all, many companies believe digital analytics is about tools and technology (and data collection, like “tagging”). That belief is not accurate. While the technology and tools that support analysis are critical and necessary, they are insufficient by themselves in creating business value. Simply adding a standard basic JavaScript page tag for a free Web analytics tool to your digital experiences and providing access to reports does not create data-driven decision making or easily yield insights. Some companies believe that to be “data-driven,” they simply need to provide self-service access to business intelligence (BI) tools that provide department-specific reports and dashboards—or the basic, vanilla reporting in free or paid analytics tools. Both these approaches are helpful to some degree and certainly move the firm toward building a digital analytics organization that considers analyses as part of the decision-making process—both strategic and tactic. After all, providing the business with the tools that collect and report data is, as previously mentioned, definitely critical and absolutely necessary. But tools and reporting are only part of digital analytics operations. Technical work and tool activities, whether used by themselves or together, are entirely insufficient for creating sustained business value through the application of digital data in business context. In other words, all the technology, servers, tagging, and tools can help you count and measure all sorts of digital metrics and dimensions, but do not by themselves (or even with the default installation) provide for any inherent actionability or impact directly delivering business value. The value from analytics is created by humans—alongside machines, tools, and technologies— analyzing data to provide insights and answers to business questions and within established and sustained business processes. Digital analytics teams enable fact-based decision making and measure the performance and profitability of digital business channels. Data from the digital channel enhances offline data—and the combination of both (called data integration) can yield new insights and opportunities. If your company isn’t forming a team of analysts to address its digital data— whether you have big data or not—then it’s operating at a competitive disadvantage. A lack of data analysis leads to missing enormous business opportunities. A well-resourced, funded, processoriented digital analytics team backed up by cross-functional teams from IT to marketing to finance can help your business in many ways—from determining ways to reduce costs, improve efficiency, generate new and incremental revenue, improve customer satisfaction, and boost the profitability and impact of the digital business channel. To understand what is involved with digital analytics from the beginning to the end to the beginning of the next project, see Chapter 2, “Analytics Value Chain and the P’s of Digital Analytics.” Before discussing these concepts, let’s dig deeper into what composes digital analytics, the digital analytics organization, and how establishing and evolving deep competency in digital analysis now can bring immediate and future value to the corporation. Big Data and Data Science Requires Digital Analytics The need for a digital analytics organization is greater than ever before—for the amount of data available to apply toward solving a business challenge is more numerous and multivariate than at any time in human history. IBM estimates that humanity creates 2.4 quintillion bytes (quintillion is one billion billion) of data every day (see Figure 1.1)—so much that 90 percent of the data in the world today has been created in the last two years alone. Obviously, much of this new data is being created by digital systems or systems linked to the Internet. Because the multitude of digital data is growing exponentially every day, a digital analytics organization is absolutely necessary to generate insights, recommendations, optimizations, predictions, and profits from this data. Whether big data, data science, omnichannel data, media mix modeling, attribution, audience intelligence, customer profiling, or predictive analytics from the applied analysis of digital data, it is essential to create a team accountable and responsible for digital data analysis. This analysis can be used for decision making, business planning, performance measurement, Key Performance Indicator (KPI) reporting, merchandising, prediction, automation, targeting, and optimization. As you read this book, you can learn how to lay solid foundations for building a successful digital analytics organization to make sense of and value from digital data analysis. Figure 1.1 Humanity creates 2.4 quintillion bytes of data every day. That’s the number above: 24 billion billion bytes per day. The volume of the data being created right now and that will be created in the future is, of course, staggering even beyond IBM’s estimates. International Data Corporation (IDC) projects that the digital universe will double in size through 2020 and reach 40 ZB (zetabytes), which means 5,247 GB for every person on Earth in 2020. The behavioral data—call it the digital behavioral universe currently being and going to be created from the clickstream and the digital footprints of every person across Earth interacting, participating, and behaving with this data—means that exponentially more behavioral data will be created on top of the predicted 40 ZB digital universe in 2020 (see Figure 1.2). Data collected about the human behavior, transactions, and metadata may be many multiples of the size of the site content. In other words, if the average size of a web page in 2013 is approximately 1.4 MB, then the behavioral and transactional data and metadata collected about visitors during their visits could be many hundred megabytes or more— especially when considering data integration from both internal and external data sources, such as advertising, audience, and Customer Relationship Management (CRM) data. The future of analytics will be enabled by innovation on top of all this big data created digitally from websites, mobile sites, social media, advertising, and any other Internet-enabled experience—from interactive TV and billboards to set-top boxes to video game consoles to Internet-enabled appliances to the mobile ecosystem and world of apps. Figure 1.2 It is estimated that by 2020, there could be four times more digital data than all the grains of sand on Earth. Source: IDC and Wolfram Alpha According to the Pew Research Center’s Internet & American Life Project, during 2012 in the United States (US), more than: • 59 percent of people used a search engine to find information and send email. • 48 percent used a social network such as Facebook, LinkedIn, or Google Plus. • 45 percent got news online, whereas 45 percent went online just for fun and to pass the time. • 35 percent looked for information such as checking a hobby or interest. Actually, the United Nations claims that more people on Earth have access to mobile phones than restrooms. Six billion of the world’s 7 billion people have access to mobile phones. Only 4.5 billion people have access to working restrooms. Meanwhile, 2.5 billion people don’t have proper sanitation. Big data created from mobile devices is more common than the global infrastructure used for human sanitation. The volume of digital analytics data being collected about online behavior is already being tapped and mined in 2013 (see Figure 1.3); however, the promise of digital analytics remains still largely unrealized and not demystified. EMC estimates that the majority of new data is largely untagged, file-based, and unstructured data, which means little is known about it. Only 3 percent of the data being created today is useful for analyses, whereas only .05 percent of that data is actually being analyzed. Thus, 99.95 percent of useful data available today for analysis is not being analyzed (see Figure 1.4). By 2020, IDC estimates a 67 percent increase in data available for analysis. Figure 1.3 Growth in digital data per person. Source: IDC Figure 1.4 The opportunity to create value exists in the 99.95 percent of data available for analysis that is not being analyzed. Without a digital analytics organization firmly in place, a business will not be able to take advantage of the opportunity in digital data analysis that has resulted from all this data now and the huge surge of audience, media, and consumer data in the future. A business, of course, can only create competitive advantage with data if they can hire talented people who have digital analytics skills. Right now, a huge gap also exists in talented people to analyze and create insights from the data, which is an obstacle to staffing digital analytics teams. As a result of all the big data in the public and private sector, McKinsey estimates that 1,500,000 more “data-savvy” managers (who can understand and use analysis) and 140,000– 190,000 new roles for analytical talent are needed to support the growth in big data in the future. The digital analyst and the digital analytics team needed to make sense of all this new data rarely exists and certainly not in sufficient quantities to create value from current and future big data. Actually, the industry faces an acute shortage and huge gap of the talent and technology needed to tag and analyze digital data even though analytical jobs are top-paying, high wage jobs. It can take months to find a talented digital analyst and even longer to find managers and other analytical business leaders. This fact is precisely why this book can help you and your business determine how to manage and succeed with digital analytics while minding the gap in analytics talent. The need for building your own digital analytics organization is totally real, because you certainly can’t easily or quickly hire even a single analyst and rarely a talented manager and never an entire team of analysts in one shot. This book tells you what you need to know right now to get started building your own digital analytics organization and/or what you can do to take your existing digital analytics organization to the next level. This business book is as much about building a digital analytics team as it is about building a digital analytics organization. The team exists within the organization, and the organization exists within the business. Thus, this book is about much more than digital analytics. This business book is a truly one-of-a-kind text, derived from real-world, practitioner experience that is about understanding what is truly necessary to create, manage, win, and succeed with digital analytics, while focusing on analytical ideas, methods, and frameworks for generating sustainable business and shareholder value. Defining Digital Analytics But what is digital analytics? Digital analytics is the current phrase for describing a set of business and technical activities that define, create, collect, verify, or transform digital data into reporting, research, analysis, optimizations, predictions, automations, and insights that create business value. The activity of digital analysis, at the highest and best application, helps companies increase revenue or reduce cost. The activities performed in digital analytics require coordinating processes, people, and technology internally within a company and externally from partners and vendors to produce analysis that answers business questions, makes recommendations based on mathematically and statistically rigorous methods, and informs successful business activities across many functions from sales to marketing to management. Digital analytics can help a business in many ways. The two goals for the highest and best usage of analytics are to create value by 1) generating profitable revenue, and 2) reducing cost. The McKinsey Global Institute (MGI) claims that a 60 percent increase in retailers’ operating margins are possible with big data, whereas just location-based big data has the potential to create a $600 billion market annually. The opportunity to generate commerce in an ethical and productive way is possible with digital data, but how does a person, a business, and a global enterprise get there? The answers are in this book with comments on the activities critical and necessary to analyze data, from the technical and process work (requirements/questions, data collection, definition, extraction, transformation, verification, and tool configuration) to the analytical methods to apply to data in order to analyze, report, and dashboard it. By bringing together data from different systems to create cohesive and relevant analysis, you can understand how digital data and analytics can be used to answer business questions and provide a foundation for fact-based decisions. This book explains how to build and manage digital analytics teams to tell “data stories” based on answering “business questions” asked to the analytics team by stakeholders. The analytical insights in these answers can provide recommendations and dataoriented guidance to management that helps make their company money. Digital analysts, the people on the digital analytics team, are able to navigate effectively the upstream technical and downstream social and organization processes inherent in executing a data-driven communication function via processes that unify teams across technology and the business. If that last sentence is hard to deconstruct or if it makes perfect sense, read on because this book covers the following topics: • The fundamental building blocks to understanding and creating processes for digital analytics, called the Analytics Value Chain. The Analytics Value Chain is a new concept I created for describing the process and work necessary for tactical and strategic success with digital analytics. The Analytics Value Chain starts with understanding business requirements and questions, to defining and collecting data, to verifying, reporting, and communicating analytics to the next steps of optimizing, predicting, and automating from digital data using data sciences. The goal of the value chain is, of course, the creation of economic value from digital analytics. • The P’s of digital analytics: people, pre-engagement, planning, platform, process, production, pronouncement, prediction, and profit • Business considerations when justifying investment in the analytics team, and how to propose an investment consideration for funding the creation or enhancement of a digital analytics team and its operations • Creating tactical and strategic goals for the analytics team and the responsibilities of the team • Buying or building analytics tools and what it takes to succeed with tool deployment and maintenance, including discussions about social media and mobile analytics tools • The importance of storytelling with analytics and using Exploratory Data Analytics (EDA) to understand digital analytics data • Applied analytics techniques, as a go-to reference for the types and shapes of data, including a business-focused review of basic statistics such as the mean, median, standard deviation, and variance and other more advanced statistical concepts • A review of data visualization techniques, such as plotting data, histograms, and other charts and visualizations • Analysis of digital data for a businessperson: data correlation, and linear and logistic regression • Good ideas and best practices when experimenting with data, sampling data, and building data models • How digital analytics fits into other analytics, research fields, and qualitative disciplines such as competitive intelligence, market research, and Voice of Customer (VoC) data • Data governance and the role of defining, collecting, testing, verifying, and managing changes to data, analysis, and reporting and how the Data Governance team plays a critical role • How to set up a digital optimization program; a review of optimization using digital data with A/B (champion/challenger) and multivariate testing, while reviewing the statistical and mathematical models behind optimization and optimization engines, such as Taguchi and Choice modeling • An overview of common and popular KPIs used by consultants, brands, and practitioners—and a review of useful ways to get started creating and extending your KPIs • The importance of reporting and analysis and the difference between them, including RASTA dashboarding (Relevant, Accurately actionable answering, Simply structured and specific, Timely, Annotated, and commented) and LIVES reporting (Linked, Interactive, Visually-driven, Echeloned, and Strategic) • The use of digital data for the many types of targeting—from geographic to cookie to behavioral and more • A discussion of omnichannel data and the convergence and integration of data from multiple channels for understanding the customer, media, audiences, and for creating addressable advertising solutions using digital data • The future of analytics from interacting with data in customer experiences to using sense and respond technologies for customer interacting and alerting to perceptual analytics • The Analytical Economy and the importance of consumer and customer privacy and ethics within all facets of digital analytics now and into the future
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