Computational Marketing

Computational Marketing Lab

We combine computing, data science, and economics to advance the theory and practice of marketing and to make it effective in technology-driven businesses.

Mission & Leadership

The Computational Marketing Lab brings together affiliated faculty, students, and industry practitioners to collaborate on issues related to computation and data-driven marketing. We develop research that combines data with economics, machine learning, and statistics tools and interprets it through the lens of social science frameworks to answer strategic questions of interest to the organization.

For industry, the collaboration helps to identify durable solutions to marketing issues that embed them within a well-posed societal, economic, competitive, human-centric framework. For academia, the collaboration helps ensure that scholarly research addresses problems that are practically relevant, and that leverages the data, business context, and field-experimentation possibilities provided by industry.

Faculty Director

The Jonathan B. Lovelace Professor of Marketing

Strategy & Research

The primary purpose of the lab is to support data-driven, cutting-edge research at the intersection of computational marketing, social science, and business. In addition to facilitating research, the lab invests in disseminating research findings broadly and in generating a productive dialogue between academia and industry by publishing and presenting research at industry and academic conferences and journals, and by hosting events.

A key industry collaborator will be, whose expertise and data assets can be accessed by members of the lab for research.

Featured Research

This paper presents an empirical framework to analyze salesforce compensation. It uses a model built on economic theory and solved using numerical dynamic programming. The model is implemented at a large contact lens manufacturer in the United States to improve salesforce contracts as part of a multimillion-dollar, multi-year collaboration. The improvements resulted in a 9% increase in overall revenues, indicating the success of the field-implementation. The results bear out the face validity of computational economic models for real-world compensation design and marketing decision-making.

In the News


The director of Stanford GSB’s Computational Marketing Lab talks about the “horrendously complex science” behind online advertising research.


When it comes to retail, Asia is where the action is. With a huge market, high mobile use, and integrated ecosystems for social, search, and e-commerce, the opportunities are enormous for companies that can break in. Harikesh S. Nair explains the opportunities and the risks of entering the Chinese retail market.


Many brands spend money on social media without knowing whether offering a coupon or connecting with customers will be more effective. Research by Harikesh S. Nair shows brands how to engage in a meaningful way.

Wall Street Journal

Consumers are willing to spend a lot more for a traditional product than for essentially the same thing in app form. A Stanford professor explains why.

CMO Australia

Data-driven marketing professor and former JD business strategist scientist shares how an ad experimentation platform he built is helping with the conundrum of measuring digital advertising.

Wall Street Journal

Human-resources departments are becoming corporate data centers, probing in-house stores of information to figure out who they should hire and promote, how much those employees should be paid, and even how managers should manage.

Progressive Grocer

When stores like Walmart, Sam’s Club and Costco began their rapid expansion in the 1990s, supermarkets were thrown for a loop. Their limited service, thinner assortments and “everyday low pricing” of grocery items created enormous cost savings and increased credibility with consumers. What was a Safeway or a Stop & Shop to do in the face of such brutal competition?