Tag: Olin Award

Dear Olin community,

It is my pleasure to announce that the recipient of the 2019 Olin Award is Taking Assortment Optimization from Theory to Practice: Evidence from Large Field Experiments on Alibaba by Jake Feldman, assistant professor of operations and manufacturing management, and Dennis Zhang, assistant professor of operations and manufacturing management.

In its 12th year, the Olin Award was established to recognize scholarly research that has timely, practical applications. This year’s winning entry uses data from the Chinese online retail giant Alibaba to test the benefits—and recommend a solution—for presenting buyers the optimum variety of products available for purchase with individual online retail stores. Of the 16 papers submitted this year, six went on to the second round, rated by our corporate judges as research with potential impact to business.

Some of those papers will be presented in the coming months in the Olin Business Research Series. The winning paper will be presented at a luncheon on May 22, 2019, from noon till 1:30 p.m. We will send out a formal invite in the near future.

Special thanks to Olin Distinguished Executive in Residence Richard Mahoney and all of the judges for their ongoing support. We look forward to all of next year’s faculty submissions. Please join me in congratulating Jake Feldman and Dennis Zhang.

Pictured above: Richard Mahoney, Jake Feldman, Dennis Zhang and Dean Mark Taylor.

Ling Dong and Durai Sundaramoorthi have won the 2018 Olin Award for research that creates a framework that can help farmers select the proper seed varieties to maximize their crop yields from one season to the next.

The Olin Award, which includes business school recognition and a $10,000 prize, is intended to promote scholarly research that has timely practical applications for complex management problems.

“Soybean farmers are subjected to dozens and dozens of seed varieties,” said Richard J. Mahoney, former CEO of Monsanto and a Distinguished Executive-in-Residence at Olin, who initiated the $10,000 prize. “If you knew you’d have perfect weather, certain varieties would work better than others. A bad guess can be quite punishing.”

Dong, professor of operations and manufacturing management, and Sundaramoorthi, senior lecturer in management, received notice that they had received this year’s award, competing against a score of other papers and finalists, during a brief ceremony in Dean Mark Taylor’s office on Monday.

“Improving crop yield is a critical and necessary component of achieving food security and protecting natural resources and environmental quality for future generations,” Dong and Sundaramoorthi wrote in their award-winning paper, entitled, “Machine Learning Based Simulation and Optimization of Soybean Variety Selection.”

“We formulate a simulation-based optimization problem to determine the optimal soybean-mix to minimize the risk associated with the yield…to make soybean-mix recommendations to the farmers.”

A panel of judges evaluates each paper submitted for consideration for the Olin Award. After reviewing the entries, one judge wrote, “It is directly applicable to business results and is something that every farmer that raises soybeans can benefit from regardless of their size—scale independent.”

Wrote another: “Within the narrow application this would seem to have great potential to produce meaningful benefits.”

The research pair will be formally recognized at a luncheon yet to be scheduled, where they will have the opportunity to present their research.

Brand-new customers pose an interesting challenge to marketers in this era of big data. Marketing strategy and tactics are often driven by valuable insights gleaned from past customer data collected over time from repeat purchases and transactions. But newly acquired customers arrive without a data trail. Is it possible to predict the future behavior of new customers?

As a doctoral student in marketing, Arun Gopalakrishnan seized on this challenge to create a new computer model to provide guidance to managers who want, and need, to forecast newly acquired customers’ behaviors. Gopalakrishnan began this research as part of his doctoral studies at the Wharton School of the University of Pennsylvania and completed the research at Olin Business School where he is an assistant professor of marketing. His paper won the 2017 Olin Award for faculty research that impacts business.

Working with two professors at Wharton, Gopalakrishnan developed two cross‐cohort models (called vector changepoint models) that introduce a new framework for analyzing data that reveals insights into patterns of customer behavior over time.

Specifically, the new models reject the notion that pooling data from all previous customers to make an educated guess about the behavioral patterns of the newest customers provides an accurate forecast. In other words, the researchers found that new customers are not simply going to behave like the “average” existing customer. That assumption, according to the researchers, “ignores the potentially changing behavioral patterns” from one set of customers acquired during a certain time period to another.

The new mathematical model takes into account what it calls “regime changes” or past customer behavior changes that were influenced by new firm policy, government regulations, economic factors, competitors’ actions, or unknown drivers of change.

“Our findings suggest that simply using older cohorts [sets of customers acquired in the past] as a proxy for predicting new cohorts without understanding any potential regime changes may lead to inaccurate predictions because certain aspects of customer behavior may have changed, going from the oldest customer cohort to the newest one.”

When tested against other models, the Olin Award–winning research model/forecast tool outperforms other benchmarks. It can be applied to any industry that acquires customers who engage in repeat transactions over time. The new model also simplifies the process of mining the data.

Link to research summary and paper.

In a concentrated, continuing effort to link Washington University in St. Louis academic research to everyday business practice, the 10^th annual Olin Award recognizes an Olin Business School faculty member who joined two University of Pennsylvania professors in crafting a computer model to guide managers who need to forecast behaviors of newly acquired customers.

As is customary, a panel of senior executives review the papers submitted, and these executives ultimately apply some of the findings to their own businesses.

“This rare award rewards relevancy and focuses on the exceptional intellectual capital that applies to real business issues,” said Richard J. Mahoney, former CEO of Monsanto and a Distinguished Executive-in-Residence at Olin where he initiated the $10,000 prize. “I can tell you from decades of corporate experience that academic research all too often is overlooked in the business realm. So here at Olin we strive to connect the innovation and evidence of our faculty with a business community where this science can directly benefit both enterprise and consumers.”

St. Louis-based Edward Jones joined with Jackson Nickerson, the Frahm Family Professor of Organization & Strategy, to help to incorporate Collaborative Structured Inquiry from his 2009 Olin Award-winning paper (see chart, below). Emily Pitts, principal for Inclusion and Diversity, called it an “invaluable experience.”

“Working with Professor Nickerson using this process enabled our firm to make significant progress,” Pitts said. “We were able to identify division by division in our headquarters where we had areas of opportunity to improve on topics such as sourcing diverse talent, recruiting, hiring, developing or just creating a more inclusive environment. Ninety percent of all of our Home Office divisions have completed the process and identified tangible solutions. Some divisions have already begun to see measurable progress, and best practices are beginning to be duplicated across the firm to eventually become standard.”

Mahoney said the 2017 winner exemplifies how quality research can bring solutions to tangible issues affecting business today.

Arun Gopalakrishnan, assistant professor of marketing, is the co-author of “A Cross‐Cohort Changepoint Model for Customer‐Base Analysis,” forthcoming in Marketing Science. Along with two Wharton School professors with whom he conducted doctoral research*, Gopalakrishnan developed two cross‐cohort models — called vector changepoint models — that introduce a new framework for analyzing data which reveals insights into patterns of customer behavior over time. The researchers found that new customers are not simply going to behave like the “average” existing customer. That “average” assumption, according to the researchers, “ignores the potentially changing behavioral patterns” from one set of customers acquired during a certain time period to another.

The new mathematical model takes into account what it calls “regime changes” or past customer behavior changes that were influenced by new firm policy, government regulations, economic factors, competitors’ actions, or unknown drivers of change.

The model was applied to a data set from a public television broadcaster in the U.S. that relies on voluntary donations for part of its operating budget. Comprehensive, 10-year data from more than 50,000 donors were used to estimate the model parameters. That time period included the passing of the Telecommunications Act of 1996, which the model suggests is a type of regime change that may be driving changes in the behavior of donors acquired in or after 1996.

“Our insight is, you have to use the right data for forecasting what the new customers are going to do,” Gopalakrishnan said. “That doesn’t mean there is bad data, but we have found that ‘what’ a customer does is more important than demographic information. And in our data setting, the evidence suggests that new customers are going to behave differently from ‘old’ customers’ behavioral patterns.”

When tested against other models, the Olin Award-winning research model/tool outperformed other benchmarks. It can be applied to any industry that acquires customers who engage in repeat transactions over time. The new model also simplifies the process of mining the data, said Gopalakrishnan: “There is no need to run multiple models to figure out what fits best — our model allows for a large number of possible regime changes, including none, and can determine which model best fits the data in one unified framework.”

* Co-authors Eric T. Bradlow and Peter S. Fader are the co-directors of the Wharton Customer Analytics Initiative and professors at the Wharton School of the University of Pennsylvania.

Previous Winners of the Olin Award

 The Olin Business School, celebrating its centennial this year, has benefited from engaged partnerships with business executives and firms that support the school’s research centers and faculty pursuits to improve best practices, innovate and challenge management since 1917. During the decade of the Olin Award competition, nearly 200 papers repres
enting 11 disciplines, from accounting to supply chain management, have been submitted to a panel of leading business executives for review. The Olin faculty includes some of the most prolific and most frequently cited professors in their fields. Mark Taylor, recently appointed dean of Olin Business School, is ranked in the top 1 percent of economists in the world, according to IDEAS/REPEC global rankings of economists, Google Scholar citations, and ISI, Thomson Reuters Citation Analysis.

2016    Radha Gopalan & Todd Milbourn — “Compensation Goals and Firm Performance”

2015    Anne Marie Knott — “Explaining the Broken Link Between R&D and GDP Growth”

2015    Andrew Knight — “Who Defers to Whom and Why? Dual Pathways Linking Demographic Differences and Dyadic Deference to Team Effectiveness”

2014    Lamar Pierce — “Cleaning House: The Impact of Information Technology Monitoring on    Employee Theft and Productivity”

2013    Baojun Jiang — “Pricing and Persuasive Advertising in a Differentiated Market”

2012    Tat Chan (Chunhua Wu & Ying Xie) — “Measuring the Lifetime Value of Customers           Acquired from Google Search Advertising”

2011    Radha Goplalan, Todd Milbourn & Anjan Thakor — “The Optimal Duration of Executive     Compensation: Theory and Evidence”

2010    Judi McClean Parks — “Give and Take: Incentive Framing in Compensation Contracts”

2009    Markus Baer, Kurt Dirks & Jackson Nickerson — “A Theory of Strategic Problem     Formulation”

2008    Jackson Nickerson & Todd Zenger — “Envy, Comparison Costs and the Economic Theory of the Firm”

Link to the most recent research from Washington University’s Olin Business School.

Pictured above: Richard J. Mahoney, Arun Gopalakrishnan, and Dean Mark Taylor

News release from WashU’s The Source.

Professor Anne Marie Knott’s book based on her research into the connection between R&D investment and growth isn’t available until the middle of March, but it’s already attracting media buzz. How Innovation Really Works is featured in a column by Lee Schafer  in the Minneapolis Star Tribune:

In a book coming out next month called “How Innovation Really Works,” Knott lumps R & D tax credits in with a long list of other misconceptions, questioning the conventional wisdom of strategies like only chasing radical ideas or looking outside a company for new ideas, known as “open innovation.”

Yet she’s also hopeful. The conventional approach of having your own team of engineers and marketers solve problems still works. What has stalled innovation is mostly having executives routinely misunderstand the value of what they are getting from R & D spending. In other words, the innovation problem seems fixable.

Knott, who teaches strategy at Washington University in St. Louis, knows she’s a rare optimist. It’s now common to hear how we have run out of big ideas, as the Wall Street Journal recently reported. “My answer that is no, there is plenty of opportunity,” she said. “Firms have just gotten worse at the R & D they do.”

Armed with insights from her experience as an R&D project manager, 20 years of academic research, and two National Science Foundation grants, Knott devised RQ (Research Quotient), a revolutionary new tool that measures a company’s R&D capability―its ability to convert investment in R&D into products and services people want to buy or to reduce the cost of producing these.

RQ not only tells companies how “smart” they are, it provides a guide for how much they should invest in R&D to ensure that investment will increase revenues, profits, and market value.

Knott’s RQ research was the recipient of the Olin Award in 2015.