Big Data in Marketing

Big Data in Marketing

Course Number 1955

Baker Foundation Professor John A. Deighton
Spring; Q3; 1.5 credits
14 Sessions
Enrollment: Limited to 45 students

It will be graded 50% on class participation and 50% on a short essay containing reflections and extensions of the principles in the course.

Educational Objective

We stand at the inception of a fundamentally new way to create and run markets. Marketing can gather, analyze, and deploy data on people and transactions at speeds and comprehensiveness not possible a decade before.

Some see a personal data economy as exhilarating. Former Harvard President Larry Summers said, "Data may be to the 21st century economy what oil was to the 20th, a hugely valuable asset essential to economic life . . ." Others call it surveillance capitalism. Shoshana Zubhoff, recently of HBS, warns of a "largely uncontested new expression of power," in which we will face, "unexpected and often illegible mechanisms of extraction, commodification, and control that effectively exile persons from their own behavior, while producing new markets of behavioral prediction and modification."

Either way, this is the world you will live in. Like every other new technology, data science will be deployed. The question is not whether it is exhilarating or appalling, because it is both and both must be managed. It is vital to know how it works and how to operate within its ecosystem. This course seeks to answer the questions how does it work and how do we act constructively to use its possibilities?

Career Focus

The course adopts the perspective of people who plan to be involved in the making of markets in data-rich contexts, whether as investors, entrepreneurs, or managers.

Course Content and Organization

The course has two main themes. One is organizational, mapping the ecosystem that is taking shape to serve marketing, sometimes known as Martech, of which Adtech is a subfield. The other is analysis. In a data-rich work environment it is absolutely crucial to have at least a rudimentary knowledge of analytical terms like regression, and machine learning, and data technologies like SQL, relational databases, Hadoop, MapReduce, and data visualization.

Early in the course we map the personal data supply chain - where data comes from, how it is processed into useful information products, how those products are integrated into or disrupt market-making services, and what role regulation plays in shaping information markets.

The organizational part of the course takes the Chief Marketing Officer perspective, looking at how they do their jobs, such as Allstate Insurance and the data science team at Legendary Pictures LLC, but also at the vendors they do business with. We will study vendors at each stage of the supply chain, with guests where possible. We have cases on Acxiom and Epsilon at the sourcing stage of the supply chain, on WPP's efforts to transform itself from an ad agency to a big data firm in the productizing phase, Oracle Data Cloud in the marketing of data products, programmatic ad buying, and the efforts of Facebook and Google to integrate back into the supply chain.

The analytics component of the course comprises two of the 14 sessions and will not be taught with cases. There will be discussion sessions on principles of data storage and processing, and some training in tools to perform standard analyses on datasets. No prerequisites are required for this training. Its primary goal is to ensure that all class members have some level of relevant vocabulary and can think with data.