Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash

This paper investigates an approach to both speed up business decision-making and lower the cost of learning through experimentation by factorizing business policies and employing fractional factorial experimental designs for their evaluation. We illustrate how this method integrates with advances in the estimation of heterogeneous treatment effects, elaborating on its advantages and foundational assumptions. We empirically demonstrate the implementation and benefits of our approach and assess its validity in evaluating consumer promotion policies at DoorDash, which is one of the largest delivery platforms in the US. Our approach discovers a policy with 5% incremental profit at 67% lower implementation cost.Comment: 14 page

Modeling the Dynamics of Consumer Behavior from Massive Interaction Data

Recent technological innovations (e.g. e-commerce platforms, automated retail stores) have enabled dramatic changes in people's shopping experiences, as well as the accessibility to incredible volumes of consumer-product interaction data. As a result, machine learning (ML) systems can be widely developed to help people navigate relevant information and make decisions. Traditional ML systems have achieved great success on various well-defined problems such as speech recognition and facial recognition. Unlike these tasks where datasets and objectives are clearly benchmarked, modeling consumer behavior can be rather complicated; for example, consumer activities can be affected by real-time shopping contexts, collected interaction data can be noisy and biased, interests from multiple parties (both consumers and producers) can be involved in the predictive objectives.The primary goal of this dissertation is to address the obstacles in modeling consumer activities through computational approaches, but with careful considerations from economic and societal perspectives. Intellectually, such models help us to understand the forces that guide consumer behavior. Methodologically, I build algorithms capable of processing massive interaction datasets by connecting well-developed ML techniques and well-established economic theories. Practically, my work has applications ranging from recommender systems, e-commerce and business intelligence

How to assess the impact of an electronic document? And what does impact mean anyway? : reliable usage statistics in heterogeneous repository communities

<b>Purpose</b> Usually impact of research and researchers is tried to be quantified by using citation data: Either by journal-centered citation data as in the case of the journal impact factor JIF or by author-centered citation data as in the case of the Hirsch- or h-index. The paper discusses a range of impact measures, especially usage based metrics. Furthermore the authors report the results of two surveys. The surveys focused on innovative features for open access repositories — with an emphasis on functionalities based on usage information. <b>Design/methodology/approach</b> The first part of the article analyzes both citation-based and usage-based metrics. The second part is based on the findings of the surveys: One in form of a brainstorming session with information professionals and scientists at the OAI6 conference in Geneva, the second in form of expert interviews mainly with scientists. <b>Findings</b> The results of the surveys indicate an interest in the social aspects of science like visualizations of social graphs both for persons and their publications. Furthermore usage data is considered an appropriate measure to describe quality and coverage of scientific documents, admittedly the consistence of usage information among repository has to be kept in mind. The scientist that took part in the survey also asked for community services, assuming these might help to identify relevant scientific information more easily. Some of the other topics of interest were personalization or easy submission procedures. <b>Originality/value</b> This paper delineates current discussions about citation-based and usage-based metrics. Based on the results of the surveys it depicts which functionalities could enhance repositories, what features are required by scientists and information professionals and whether usage-based services are considered valuable. These results also outline some elements of future repository research

Monetary and Fiscal Stimuli, Ownership Structure, and China's Housing Market

In the recent financial crisis, macroeconomic stimuli produced mixed results across developed economies. In contrast, China's stimulus boosted real GDP growth from an annualized 6.2% in the first quarter of 2009 trough to 11.9% in the first quarter of 2010. Amidst this phenomenal response, land auction and house prices in major cities soared. We argue that the speed and efficacy of China's stimulus derives from state control over its banking system and corporate sector. Beijing ordered state-owned banks to lend, and they lent. Beijing ordered centrally-controlled state-owned enterprises (SOEs) to invest, and they invested. However, our data show that much of this investment was highly leveraged purchases of real estate. Residential land auction prices in eight major cities rose about 100% in 2009, controlling for quality variation. Moreover, higher price rises occur these SOEs are more active buyers. We argue that these centrally-controlled SOEs overbid substantially, fueling a real estate bubble; and that China's seemingly highly effective macroeconomic stimulus package may well have induced costly resource misallocation.

Retail forecasting: research and practice

This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice