We document the evolution of academic research through a bibliometric analysis of 123 retail analytics articles published in top operations management journals from 2000 to 2020. We isolate nine decision areas via manual coding that we verify using automated text analysis (topic modeling). We track variation across decision areas and method-usage evolution per analytics type, featuring the degree to which big data (e.g., clickstream, social media, product reviews) and analytics suited for these new data sources (e.g., machine learning) are used. Our analysis reveals a rapidly growing field that is evolving in terms of content (decisions, retail sector), data, and methodology. To determine the state of practice, we interviewed global practitioners on the current use of retail analytics. These interviews shed light on the barriers and enablers of adopting advanced analytics in retail. They also highlight what sets companies on the frontier (e.g., Amazon, Alibaba, Walmart) apart from the rest. Combining the insights from our survey of academic research and interviews with practitioners, we provide directions for future academic research that take advantage of the availability of big data.