Subscription and casual customers’ differential sensitivity to meteorological characteristics

Although meteorological conditions (temperature, humidity, windspeed, seasons and the like) extend observable ubiquitous influence on our purchase and consumption behavior, however, due to ancillary nature of its influences, only recently it has attracted interest among marketing scholars. Mood account, psychological bias account, product-weather association account along with others are proposed in the literature to explain why meteorological conditions influence consumption behavior. Adding to that discourse, this research by showing that subscription customers and casual customers show different responsiveness levels toward similar meteorological conditions, builds a case for acquired (learning) account of meteorological influences. Analyzing public bikeshare (PBS) data, this research finds that daily aggregate subscription ridership variations are relatively more sensitive to weather situation changes (i.e., temperature, humidity, wind-speed, seasons) than casual ridership variations. The observed non-uniform sensitivity levels suggest that consumers tend to associate weather conditions with corresponding usage benefits, and that association is richer in case of higher usage levels. Because subscription customers usage levels tend to be higher, pertinent associations can be richer, and consequently responsiveness can be higher as well. The findings are obtained after accounting for the possible non-linear influences of the changes in the meteorological variables on daily aggregate ridership behavior. Based on the findings, this research presents a CAPM based risk management metric for mitigating temperature linked variation in demand.