Prelaunch Demand Estimation
نویسندگان
چکیده
Demand estimation is important for new-product strategies, but is challenging in the absence of actual sales data. We develop a cost-effective method to estimate the demand of new products based on choice experiments. Our premise is that there exists a structural relationship between manifested demand and the probability of consumer choice being realized. We illustrate the mechanism using a theory model, in which consumers learn their product valuation through effort and their effort incentive depends on the realization probability. We run a large-scale choice experiment on a mobile game platform, where we randomize the price and realization probability of a new product. We find reduced-form support of the theoretical prediction and the decision effort mechanism. We then estimate a structural model of consumer choice. The structural estimates allow us to infer actual demand from choices of moderate to small realization probabilities.
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