Big Data and Marketing Analytics in Gaming: Combining Empirical Models and Field Experimentation

摘要

Efforts on developing, implementing, and evaluating a marketing analytics framework at a real-world company are described. The framework uses individual-level transaction data to fit empirical models of consumer response to marketing efforts and uses these estimates to optimize segmentation and targeting. The models feature themes emphasized in the academic marketing science literature, including incorporation of consumer heterogeneity and state dependence into choice, and controls for the endogeneity of the firm's historical targeting rule in estimation. To control for the endogeneity, we present an approach that involves conducting estimation separately across fixed partitions of the score variable that targeting is based on, which may be useful in other behavioral targeting settings. The models are customized to facilitate casino operations and are implemented at the MGM Resorts International's group of companies. The framework is evaluated using a randomized trial implemented at MGM involving about 1.5 million consumers. Using the new model produces about $1 million to $5 million in incremental profits per campaign, translating to about 20c in incremental profit per dollar spent relative to the status quo. At current levels of marketing spending, this implies between $10 million and $15 million in incremental annual profit for the firm. The case study underscores the value of using empirically relevant marketing analytics solutions for improving outcomes for firms in real-world settings.