snippets from the eGuide   (Introduction ⇩)

(Introduction ⇧)

In the age of analytics, this multimedia platform serves as a comprehensive guide to marketing management, covering the underlying concepts and their application. As can be seen from the snippets, the focus is not on the statistical theory, but more on the application of new analytics techniques and established research methods to enhance the marketing mix.

As advances in technology transform the very nature of marketing, there has never been greater need for marketers to learn marketing.

Essentially a practitioner’s guide to marketing management in the 21st century, the Marketing Analytics web learning platform blends the art and the science of marketing to reflect how the discipline has matured in the age of analytics.

Application oriented, it fuses marketing concepts with the analytical tools that practitioners use, to impart an understanding of how to interpret and apply research information and big data.

The focus is primarily on the practical application of well-established tools, techniques and processes, as the platform sifts through all elements of the marketing mix.

eLearning Platform

Over 100 Registered Corporations: If your organization is listed, register with your corporate email to use the online guide.

It is only apt that a book on Marketing Analytics should exemplify the use of digital technology. Unlike passive eBooks that replicate print versions in their original linear state, the online guide is a full-blown, multi-media platform that greatly enhances the reader’s experience.

As a website, it is dynamic, fluid, and connected with relevant and useful content, both within and beyond the platform. That it is continually updated and enhanced, keeps the guide evergreen, abreast of the latest developments in a the rapidly evolving fields of analytics and digital marketing. (In addition to numerous updates, over 100 new sections and four new chapter have been added, in the two years since the platform was set-up).

It is interactive with the facilities such as (shareable) notes/comments at any of the approximately 500 sections in the guide. The question papers/exercises allow subscribers to view answers and explanations. The site also supports business analytic platforms so that students can practise as they learn.

The online guide is made available on an annual subscription basis. Subscribers login with their email ID and password.

Article — Redefining how we learn marketing.
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Next Chapter

Consumer Analytics and Big Data

Consumer Analytics and Big Data

The focus in this chapter, lies mainly on data management tools and technologies, machine learning techniques, data mining, crowd sourcing and co-creation, optimization techniques and visualization techniques. Big data and cognitive systems are also covered, and so too some of the application areas.

Consumer analytics is not as recent a phenomenon as it is popularly thought to be. Some companies at the forefront of consumer analytics were founded in the 1980s and 1990s. The biggest change over the years is not the science, but rather the technology, and the advent of big data.


Big Data


Big Data

Big data is the new frontier in consumer analytics. As organizations transact and interact with customers, they are generating a tremendous amount of digital exhaust data — a by-product of business activities. Over the years, this has resulted, in the explosion of business data and its management, along three dimensions — volume, variety and velocity.

According to the folks at IBM, some enterprises are generating “terabytes of data every hour of every day of the year”; the volume of information stored in the world would grow from hundreds of exabytes of data today to an estimated 35 zetabytes by 2020.

As more and more data is generated every hour, data velocity has grown tremendously. Much of the data is kept for short duration, and must be analysed as it flows.

Furthermore, with proliferation of smart devices, cameras, microphones, sensors, RFIDs, data has grown in terms of variety and complexity.

Today much of the world’s information comprises of huge, fast moving, unstructured data sets that cannot be processed or analysed by means of the conventional methods that apply to structured data. These data sets are collectively referred to today as big data. Dealing effectively with them requires new ways of data handling and analysis.

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