Google Analytics is Digital Marketing, but Digital Marketing is Not Analytics

Using data to make informed and timely decisions has become a success factor for most modern businesses and organizations. At the same time, with the increase in storage and computing capacity, and the development of new technologies and applications, such as the expansion of social networks, the widespread use of smartphones, IoT-based devices, etc., the volume and format of the data has changed dramatically, and the possibilities for analysing and processing it are impressive.

Probably due to this abrupt development, there is a lot of confusion and misconceptions about the activities and roles of the different professionals that coexist in the vast ecosystem of data.

One of the most common misconceptions that exists is to confuse Digital Marketing and even Market Research with Business Intelligence and Analytics. These are disciplines that apply different methods and tools to carry out their analyses, but above all with very different objectives.

The terms “Google Analytics” and “Analytics” are often used interchangeably by marketers. In fact, they represent two completely different concepts. Perhaps the reason for this confusion is that both Google Analytics and Analytics base their analysis on measurable metrics, but this is where the similarities end and the significant distinctions begin.

Let’s start first by explaining what Digital Marketing is and especially the mechanisms used by this discipline to generate information.

Digital marketing is related to ads that are displayed through digital channels, such as search engines, social media, websites, emails, mobile applications. This is a huge system of channels and every marketer should integrate it into their business so that they can provide their products and services all over the world, regardless of country and continent. Due to the wide spectrum that this form of advertising covers, it exponentially increases the possibilities of sale compared to conventional methods.

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To better understand the complex world of the Internet and its advertising channels and thus be able to apply the best formula for the success of your product, marketers will need specialized support, such as that provided by Google Analytics, in order to track website activity: total clicks, session duration, pages per session, bounce rate, etc. of the people who use the site, along with information about the source of the traffic.

These metrics are usually available while ads are still running and are therefore often used to improve campaign performance. If some ads outperform other ads in a particular metric, it may make sense to shift the budget from low-performing to high-performing ads.

Advertisers can choose to use multiple metrics to optimize their ads. For example, they may look at video views to see which video channel is best for a broader exposure of their message, while looking at video completion rates to judge which version of the ad is most appealing to the audience. In general, the more measurements you use, the deeper the information you get, of course, as long as the metrics are relevant.

However, it is also important to note that in the marketing world not all decisions are based on Google Analytics metrics and that many market research and digital marketing professionals still use subjective measures such as those obtained from qualitative methods.

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For example, a qualitative measurement might attempt to measure the quality of customer interaction. These can be reviews written before and after a marketing campaign, where each review receives a rating. These quality reviews are based on surveys that can be as simple as a Yes or No questionnaire or marking pre-set answers.

There are several techniques used to obtain quality measures, some of the most common are: Surveys, Focus Groups, Interviews and Benchmarking. In general, and despite the high subjectivity of these methods, some marketers continue to use them to try to gain a deeper understanding of the thoughts and points of view of consumers on a wide variety of topics.

Qualitative methods, if they are well designed and applied correctly, can be useful to investigate aspects that are not very evident in consumer behaviour.

The problem occurs when these techniques are combined with quantitative methods without knowing how to do it, and even worse is the fact of believing that a qualitative measure is quantitative just because you are using numerical scales, this is an error that often degenerates into a series of meaningless “statistical analyses”.

For this reason, much emphasis has been placed in recent years on the importance of equipping all business professions with the basic fundamentals of data analysis. This new skill has been called “Data Literature”. This discipline seeks to reinforce the necessary knowledge that every professional would need to be able to carry out the main data analyses related to their career.

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On the other hand, Business Analytics, Data Analytics, or simply Analytics is a process of analysing any type of business data to transform it into useful information that helps business leaders, managers and other end users to make informed decisions.

Analytics encompasses a wide variety of concepts, techniques, applications, and tools that enable analysts to gather, clean, classify, adapt, and analyse data.

The result of this process should allow to develop and execute queries on the data, create reports, data visualizations (Data Viz) and dashboards to make available, in an accessible and clear way, the information that the company’s decision makers need.

Through the use of Analytics, it is also possible to configure workflows that allow greater and better use of data in an organization.

Data scientists or data analysts can also report directly to their colleagues in other departments on what to do based on the results of their analysis. For example, solutions can be created so that even untrained people can use applications, based on specialized programs such as R or Python, to get their jobs done faster and easier. One of the most effective ways to optimize workflows is based on the implementation of machine learning models, as long as the objectives and data allow it.

Market Research and Digital Marketing are not Business Intelligence (BI) either. BI is a very well-defined discipline belonging to business analytics, which uses specific data analysis and visualization tools (dashboards) to describe the current state of a business and offer the most relevant information that an organization needs to support decision making.

Business intelligence can be involved in the different stages of a company’s value chain, from the analysis of performance indicators (KPIs), and metrics related to the supply chain, production, sales and even the assessment of future business scenarios based on the combination of different historical data.

In a BI analysis, like any other data analysis, raw data from different sources must be integrated, consolidated and cleaned using specific tools for this purpose, in order to ensure that end users handle accurate and consistent information.

Although some analyses are so simple that you could even use an Excel spreadsheet to report insights, BI platforms are increasingly popular as front-end interfaces for integrated data systems. This is because BI tools not only allow handling a greater amount of data in a much more practical and simple way, but also allow a large number of users to be involved to work on the same project, instead of the typical single analyst approach.

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Because Analytics is applicable to all data in a company, it is also applicable to marketing data, which we could call “Marketing Analytics”, but this also has nothing to do with Digital Marketing or Market Research. It is simply the application of Data Analytics using marketing metrics as input.

In the same way, this approach can be applied to other types of data, such as data from the human resources department, in which case it has been dubbed “People Analytics”

Not only are the methodologies and tools used by Marketing very different from those of Analytics, but they also have very different objectives.

Let’s remember that Marketing’s main objective is to sell a product or service, and in this sense, marketing professionals will do everything in their power so that the purchase decision benefits their proposal, no matter how good it is with respect to the other options.

On the contrary, Analytics tries to explore all possible options, analysing risks and identifying opportunities, in order to provide the necessary support to make the best decision. 

For example, in analytical jargon, the action of supporting or opposing a particular option without a convincing technical justification, is called bias. 

As we can see, these are disciplines with names that can sometimes be confused, but clearly located at opposite ends of the same rope.

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Regarding this point, in a recent publication on LinkedIn by Cassie Kozyrkov (Chief Decision Scientist at Google, Inc.) in the section “Misconception 2: Analytics versus journalism / marketing”, she wrote the following:

“Analytics is not marketing. The difference is that analytics is about expanding the decision-maker’s perspective while marketing is about narrowing it.”

In the Data Universe there are other misconceptions besides the ones shown here, the main purpose of articles like this is to shed some light on concepts that can be confusing even for seasoned professionals. I think that all roles are equally important, but it is essential to know where we are and not confuse pears with apples, because the more clarity we have on our path, the more and better things we can develop and share with those around us.