Machine learning (ML) methods could have a larger footprint in strategy and management research if used to conduct exploratory data analysis and to identify robust patterns in the data that are relatively difficult to identify using traditional methods. Compared to traditional causal inference methods, ML algorithms make far fewer a priori assumptions about the functional form of the underlying model that best represents the data, thus researchers could use ML methods to explore novel and robust patterns in data. Once identified, these patterns can be tested using traditional econometric methods. Using a motivating example, we demonstrate how to implement three specific ML algorithms (decision trees, random forests, and neural networks) to reveal patterns in data and offer guidance on selecting ML algorithms. We also demonstrate how to use visual tools to evaluate the resulting model performance and interpret predictions of the model to illuminate patterns in the data. We also provide sample code to employ ML algorithms used in the paper.

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.