Customers’ Multihoming Behavior in Ride-Hailing: Empirical Evidence from Uber and Lyft

Problem definition: Are customers loyal to a ride-hailing platform or they see this service as a commodity and multihome (i.e., check several platforms before booking a ride)? Using a large panel dataset on ride-hailing transactions, we investigate to what extent customers multihome. Our dataset offers a unique opportunity to study this question as we observe the repeated choices of riders for both Uber and Lyft. Our dataset comprises more than 1.4 million rides completed by 162 thousand riders in NYC in 2018. Methodology/results: We develop a comprehensive structural model that incorporates both operational (price and waiting time) and behavioral factors (e.g., platform stickiness) to explain riders’ choices. Our model also accounts for the dynamic interactions between customers and platforms by assuming that riders update their beliefs on price and waiting time in a Bayesian fashion. Finally, the riders’ propensity to multihome is modeled by incorporating the consideration set formation of customers into our framework. We find that riders’ choices are not fully explained by operational factors, hence indicating that customers view the platforms as differentiated service providers. While 83.4% of riders took rides with a single platform, our model shows that even the remaining 16.6%, who used both Uber and Lyft at least once, considered both platforms only 43.4% of the time. Managerial implications: It is crucial for ride-hailing platforms to capture this single (or multi)-homing behavior while designing price discounts. Specifically, personalized discounts may be ineffective if the platform is not part of the customer’s consideration set. Our results show that targeting customers earlier in their lifecycle can enhance the platform’s market share by 77.56% more than their current discounting strategy. We also find that targeting customers with low search friction results in a 24.78% increase in market share relative to targeting customers with high search friction.