The Marketing Analytics Practitioner’s Guide
snippets from the guide (Introduction ⇩)
In the age of analytics, The Marketing Analytics Practitioner’s Guide serves as a comprehensive guide to marketing management, covering the underlying concepts and their application.
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 guide blends the art and the science of marketing to reflect how the discipline has matured in the age of analytics.
Application oriented, it imparts an understanding of how to interpret market intelligence and use analytics and marketing research for taking day-to-day marketing decisions, and for developing and executing marketing strategies.
Article — Redefining how we learn marketing.
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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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