Analytics at Wharton Research Principal Stratification for Advertising Experiments Advertising experiments often suffer from noisy responses making precise estimation of the average treatment effect (ATE) and evaluating ROI difficult. We develop a principal stratification model that improves the precision of the ATE by dividing the customers into three strata --…Read More
AI & Machine Learning
Market Positioning Using Cross-Reward Effects in a Coalition Loyalty Program
Analytics at Wharton Research Market Positioning Using Cross-Reward Effects in a Coalition Loyalty Program While single-brand reward programs encourage customers to remain loyal to that one brand, coalition programs encourage customers to be “promiscuous” by offering points redeemable across partner stores. Despite the benefits of this “open relationship” with customers,…Read More
Identity in Charitable Giving
Analytics at Wharton Research Identity in Charitable Giving How does priming identity affect charitable giving? We show that individuals are more likely to donate when a facet of their identity associated with a norm of generosity is primed in an appeal. In large charitable giving field experiments run by the…Read More
An Integrated Model for Dynamic Brand Equity
Analytics at Wharton Research An Integrated Model for Dynamic Brand Equity This paper presents a unified statistical model designed to measure brand equity as it changes over time; and gauge the impact of increased brand equity on consumer's product choices. Our model extends traditional models of brand equity which posit that strong brands are simply "more…Read More
Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments
Analytics at Wharton Research Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments Firms using online advertising regularly run experiments with multiple versions of their ads since they are uncertain about which ones are most effective. Within a campaign, firms try to adapt to intermediate results of their tests, optimizing…Read More
Measuring Multi-Channel Advertising Response
Analytics at Wharton Research Measuring Multi-Channel Advertising Response Advances in data collection have made it increasingly easy to collect information on advertising exposures. However, translating this seemingly rich data into measures of advertising response has proven difficult, largely due to concerns that advertisers target customers with a higher propensity to buy or increase advertising during periods of peak demand. We…Read More
Bayesian Imputation for Anonymous Visits in CRM Data
Analytics at Wharton Research Bayesian Imputation for Anonymous Visits in CRM Data Targeting individual consumers has become a hallmark of direct and digital marketing, particularly as it has become easier to identify customers as they interact repeatedly with a company. However, across a wide variety of contexts and tracking technologies,…Read More
Randomized Markdowns and Online Monitoring
Analytics at Wharton Research Randomized Markdowns and Online Monitoring Online retail reduces the costs of obtaining information about a product's price and availability and of flexibly timing a purchase. Consequently, consumers can strategically time their purchases, weighing the costs of monitoring and the risk of inventory depletion against prospectively lower…Read More
Who are My Competitors? Let the Customer Decide
Analytics at Wharton Research Who are My Competitors? - Let the Customer Decide Many competitive industries find it challenging to identify their competition set, and despite the widely accepted importance of competition set to strategy development and daily operations, few data-driven approaches have been developed to address this challenge. In…Read More
An Information Stock Model of Customer Behavior in Multichannel Customer Support Services
Analytics at Wharton Research An Information Stock Model of Customer Behavior in Multichannel Customer Support Services We develop a model to understand and predict customers’ observed multichannel behavior in a customer support setting. Using individual-level data from a US-based health insurance firm, we model a customer's query frequency and choice…Read More