To harness the knowledge capital embedded in data, companies need to integrate the power and accuracy of data analysis into their marketing strategy.
Digital technologies and systems for tracking online behaviors and interactions generate an enormous amount of information that we can now analyze with a degree of depth and granularity never before achieved. These activities of measuring, collecting, analyzing, and reporting are digital analytics, processes that have now become crucial to how companies and institutions function, especially in information contexts in which we are all, businesses and consumers alike, literally awash in information.
Digital analytics are being used to solve different kinds of business problems and affect every aspect of business: from finance to operations, from human resources to marketing (cascading through all business departments). Today, all parties who interact in various capacities in a market—businesses, individuals, agencies, intermediaries—are also consumers (and producers) of digital analytics.
On the enterprise side, digital analytics now play a key role in developing Customer Experience Management strategies. In fact, if the Customer Experience is the set of interactions with customers, both offline and online, from first contact to retention, digital analytics make it possible to understand and optimize these behaviors individually and as a whole, providing the insights needed to design personalized customer experiences.
Before explaining how digital analytics impacts marketing initiatives, let’s provide a basic definition to clarify any doubts.
What is digital analytics?
The term “digital analytics” refers to all the processes of collecting, organizing, and interpreting data that are natively digital or translated into a digital form, and which are produced in the course of consumer-brand interactions throughout the customer journey.
Digital analytics can be used to measure and evaluate the performance of various marketing activities and to provide companies with the insights they need to design the most effective communication and sales actions. In this sense, digital analytics are both data analysis activities and the results of these analyses.
Digital analytics makes data understandable by returning it in the form of metrics, numbers by which companies (and marketers in particular) are able to measure, quantify, and give meaning, including operational meaning, to their actions. Is the content effective? Which channel offers the best performance? Is campaign performance satisfactory? Digital analytics allows these questions (and many more) to be answered and gives marketing and sales teams a comprehensive view of how leads and customers interact with the brand.
Digital analytics activities provide useful knowledge for companies, who use it to give strength and accuracy to their marketing strategies and make the relationship established with their customers more effective and long-lasting, taking advantage of a trend toward personalization that has been gaining strength in recent years.
Methods and content that brands use to build relationships with customers—online video, search, display ads, social media—provide analysts with a wealth of data on how customers themselves use digital channels as they pursue their specific buying and consumption agendas.
Knowing how to evaluate the success of a customer-business relationship and understand the customer journey requires a framework that is suited to analyzing data flows. This is perhaps the most important aspect of data analysis within digital marketing workflows: the ability to transform the information gathered into a comprehensive, consistent and meaningful report.
While the opportunities for data analytics—and expectations about its benefits—have grown by leaps and bounds with the evolution of technology, the ubiquity of data analysis that we enjoy today is the original product of technological changes over the past half century, but it did not develop out of nowhere and suddenly. To express its ideas, humanity has been analyzing and using data for millennia.
To understand what digital analytics is and how it can improve marketing activities, let us try to provide a broader view of our relationship and interaction with data, looking at it from a historical perspective.
Brief human history of data analysis: expressing ideas with data
If data has always existed, we can identify a long early phase that ended a few decades ago with the creation of digital data. A 7,000-year history that began in the humblest forms—simple maps used to document and describe the world—and has evolved into the modern practice we know today and extends to statistics, medicine, politics, and many other fields. A discipline that over the centuries has progressively added new capabilities, addressed ever-changing critical issues, and eventually emerged, in the words of Kevin Hartman (who was Director of Analytics at Google and is now Chief Analytics Evangelist also at Google), as “a balanced blend of art and science.”
- Even before 1600 geometric diagrams and maps aided navigation and exploration. The 17th century saw the development of analytic geometry, theories of probability measurement, and political arithmetic. In the 1700s, artists created new graphic forms to express notions and describe phenomena, even the very complex.
- Between 1800 and 1849, industrial innovations produced increasing streams of information that had to be returned in an orderly and understandable visual form. The latter part of the 19th century is considered by many to be a Golden Age in data analysis, with its graphic innovations of unparalleled beauty.
- The early decades of the 1900s were the “dark ages” of data analysis, during which the enthusiasm of the previous century was supplanted by an attitude of generic compliance with formality.
- A new impetus in research on tools and methodologies of data analysis was recorded from 1950 onward, particularly on visualization techniques that allowed a progressive democratization of data. The development of interactive computer systems and high-dimensional data continued unabated until 1994: computers and applications created effective and extraordinarily powerful images by processing ever-increasing amounts of information and exploiting the knowledge already gained about how to visualize data.
- After 1994, when the first digital banner ad was introduced, internet use grew impetuously. While in the United States less than 5% of users surfed the web in 1994, by 2014, this had risen to 75% and close to 90% in 2019 (Sources: Nielsen Online, ITU, PEW Research, and Internet World Stats). The 20 years between 1994 and 2014 (when the internet and major platforms had reached full maturity) did not simply witness the addition of another communication channel: technological transformation produced a change in the very structure of the relationship between brands and consumers, allowing consumers to interact online in ways that were rigidly precluded offline. It was during this period that the shift from broadcasting in traditional media to narrowcasting in digital channels took place. Companies were able to equip themselves with analytical tools that could gather information about consumer behavior like never before and could rely on innovative methods of measuring and evaluating marketing initiatives.
Knowing how data analysis has evolved is important because it gives insight into how far it has come to the computer applications that generate data-based content and images today. The early history of data analysis ends with the creation of digital analytics, which in turn ushers in the information age phase where we’re living today (Source: Digital Marketing Analytics: In Theory And In Practice, Kevin Hartman).
How to fully use digital analytics in marketing: from Google’s ZMOT to McKinsey’s CDJ
Today, we are living in a historical moment where one phase in the history of data analytics has come to fruition and another has been ushered in, where digital analytics and data analysis have now assumed absolute relevance in corporate communications, marketing, and advertising.
The proliferation of touchpoints throughout the funnel has multiplied the opportunities for interaction, causing the demand for increasingly accurate analytics to soar. Access to information has grown, as has the availability of mobile devices. Companies have pursued the digital transformation of their businesses by investing in digital analytics, with the goal of bringing order to extremely chaotic information environments and optimizing business processes.
Data has become the most valuable resource for anyone making or attempting to influence a decision, including for consumers who actively search online for information to support their choices.
From the use of personal computers in the 1980s, to the spread of the web in the 1990s, to the incredible success of smartphones in the 2000s, the trajectories that people follow during their acquisition journeys and the ways in which brands engage with customers have radically changed. To frame these new dynamics, companies have begun to take on particular frameworks as theoretical frameworks in which to place digital analytics, thanks to which they can give meaning to behaviors that are less and less easily categorized.
Zero Moment of Truth: How Google captures the moment of consumer choice
In 2011, Google introduced the concept of the “Zero Moment of Truth” to denote the time interval between the onset of a need and the stimulus to seek solutions to satisfy it, and the “First Moment Of Truth,” the situation that, according to P&G’s three-step model, occurs whenever consumers are faced with deciding between alternative propositions. With ZMOT, Google intended to capture the erratic and branching nature of the logic that drives contemporary shopping choices, with consumers coming to the shelf armed with a lot more information: from product reviews read on a specialized site to a Facebook account from a personal experience with that product, from a celebrity’s tweet about falling in love with that brand and its services, to the thousands of ads and endorsements we are exposed to every day.
The Zero Moment of Truth is a snapshot of this messy and largely unpredictable overlap of information flows that consumers navigate to govern to achieve their specific goals, switching quickly from one source to another and moving fluidly between the online and offline worlds. Digital analytics are essential in accounting for such nonlinear journeys.
McKinsey’s Customer Decision Journey: decision making under the microscope
McKinsey’s Customer Decision Journey (CDJ) seeks to return the cross-system of influences that are exerted on consumers during their buying process. Specifically, it identifies the critical moments that consumers experience before they buy. Digital analytics allows this situation of indecision and trial and error to be translated into a series of inputs that will provide marketers with insights useful in designing and implementing their strategies. The Customer Decision Journey consists of several steps, with each representing a distinct stage in the decision-making process. At each step, brands gain increasingly accurate information about their relationship with consumers, which analysts can use to broaden and articulate their knowledge of their target audience. The CDJ makes it possible to recognize the logic behind each customer’s journey, regardless of the product being evaluated.
Digital Analytics enable building profiled and meaningful customer experiences
While theoretical frameworks are now widely known among practitioners, data analysis technologies are also becoming more accessible, both because of advances in open-source tools and because of the presence of qualified partners who can help companies implement and profitably use complex and rigorous computing solutions.
In addition to the technological element, the economic, social, and cultural environment has also exerted a profound influence on the demand for data analysis. The search for solutions capable of reducing conditions of uncertainty and the need for greater accountability on the part of companies have contributed to the emergence of digital-based business models and given considerable impetus to the use of digital analytics.
A digital analytics-based marketer, able to juggle different media and channels, now operates using the enormous amount of information that organizations have access to, information that comes from a multitude of different sources, both proprietary and third-party. Behavioral, contextual, psychographic, demographic, and geographic data and the results of less immediate measurements such as customer satisfaction with a brand, are used to attribute operational meaning to each interaction with the brand and to construct, from this interpretation, more profiled and meaningful experiences.