Application, Predictive Test, and Strategy Implications for a Dynamic Model of Consumer Response

This paper describes and evaluates the application of a dynamic stochastic model of consumer response. The model describes, then forecasts, how consumers respond to a new transportation service and to the marketing strategies used during its introduction. The model is estimated on survey data during the first 11 weeks of service. Forecasts over the next 19 weeks are then compared to actual ridership as measured by dispatch records. The model is simple. At any point in time, consumers are described by a set of 'behavioral states', indicating (1) whether they are aware of the new service (DART) and (2) what mode of transportation was used for their last trip. Behavior is described by movement among behavioral states. E.G., If a car user tries DART, he makes a transition from 'car used for last trip' to 'DART used for last trip'. The transition probabilities and the rate of transition are dependent on marketing strategies (direct mail, publicity), word of mouth, consumer perceptions, availability of a mode, and budget allocation to transportation. The advantages and disadvantages of the model and the measurements are discussed with respect to predictive ability and managerial utility.