Math for Strategists

Tarun   Khanna; Jan W. Rivkin

Background Note | HBS Case Collection | November 2004 (Revised April 2005)

Math for Strategists

Abstract

Great strategists rely heavily on numbers as they go about their work. Offers an overview of the high- and low-brow quantitative tools that students encounter during the Strategy course. The class explores high-brow tools in detail; the focus here is on low-brow calculations. Such calculations come up often in class but because they seem so simple, they get little airtime or explanation. From past class experience, roughly 20% of the students in each section come into the course with the intuition and experience to do these simple calculations themselves. The other 80% understand the calculations after they see them and grasp their value, but don't spot the opportunities to do the math themselves, before class.

Keywords: Mathematical Methods; Strategy;

Format: Print

18 pages

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Citation:

Khanna, Tarun, and Jan W. Rivkin. "Math for Strategists." Harvard Business School Background Note 705-433, November 2004. (Revised April 2005.)

We demonstrate how a novel synthesis of three methods—(1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural network algorithm—can shed light on questions related to CEO oral communication. With videos and corresponding transcripts of interviews with emerging market CEOs, we employ this synthesis of methods to discover five distinct communication styles that incorporate both verbal and nonverbal aspects of communication. Our data are comprised of interviews that represent unedited expressions and content, making them especially suitable as data sources for the measurement of an individual’s communication style. We then perform a proof-of-concept analysis, correlating CEO communication styles to M&A outcomes, highlighting the value of combining text and videographic data to define styles. We also discuss the benefits of using our methods versus current research methods.

Case | HBS Case Collection | January 2019 (Revised October 2019)

Rural Taobao: Alibaba's Expansion into Rural E-Commerce

Tarun Khanna, Ryan Allen, Adam Frost and Wesley Koo

Alibaba's Rural Taobao initiative had been launched in 2014 as a public service initiative to increase e-commerce adoption in China’s remote rural areas. In the first two iterations of the initiative, dubbed “1.0” and “2.0,” Alibaba had partnered with local businesses and young returnees to operate village stations. Village stations served as logistical and educational hubs to help villagers make online purchases. Despite significant growth in sales through village stations, the business model was operating at a loss, and competitors JD and Pinduoduo were growing quickly, threatening to challenge Alibaba by claiming the rural e-commerce markets. Bill Wang, the head of the initiative, was tasked with designing and implementing the next model for Alibaba’s future expansion into rural areas. The decision centers around whether Alibaba should copy the successes of competitors JD and Pinduoduo, escalate commitment to its original value proposition (hands-on service to villagers), or embrace an entirely new model. The case highlights the competing pressures of financial performance, CSR and corporate mission, government relations, and long-term competitive positioning that are common to major strategic decisions.

Keywords: strategy; Business growth; ecommerce; corporate social responsibility; Business and Government; emerging market; Market Platforms; Online Technology; Emerging Markets; Rural Scope; Growth and Development Strategy; Competitive Strategy; Corporate Social Responsibility and Impact; Business and Government Relations; Decision Making; China;

Citation:

Khanna, Tarun, Ryan Allen, Adam Frost, and Wesley Koo. "Rural Taobao: Alibaba's Expansion into Rural E-Commerce." Harvard Business School Case 719-433, January 2019. (Revised October 2019.) View Details

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India's start-up ecosystem is amongst the largest globally, with a variety of funding options from angel investors, venture capital and corporate venture capital to debt. Classic consumer focused start-ups which look to leverage technology have been able to raise significant amounts of capital but the situation is more challenging for deep science and technology focused startups. This note focuses on these issues and the factors behind them.

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Math for Strategists

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