Dynamic Web System Upgrading with Strategic Customers Analysis Using FP growth Algorithm

Abstract: A stylized dynamic pricing model in which a monopolist price a product only at the beginning and change in price will be done with a set of constraints based on the product availability and customer strategic behavior analyzed by frequent pattern (FP) Algorithm. Using this algorithm the least accesses products are analyzed and the product price is adjusted to increase the sale. First, this paper prescribes a threshold policy for customer purchasing: the customer will buy the product if his valuation for this product is above a threshold, and will not otherwise. The threshold increases as TSP decreases, customer transaction cost increases, or customers become more risk averse. Second, we derive the optimal price of each period and identify the optimal policy for web system upgrading there exists a threshold for each period such that the online retailer should upgrade their web system to the state of art (i.e., achieve highest available TSP) if current TSP is below the threshold, and should not upgrade otherwise. The threshold (total discounted profit) increases as customer transaction cost decreases, customer valuations for the product become higher, or customers become more (less) risk averse. Third, the online retailer tends to price higher if it ignores customer strategic behavior. The cost of ignoring customer strategic behavior is substantial. The profit loss rate of ignoring customer strategic behavior increases as customer transaction cost increases, customer valuations for the product become lower, or customers become more risk averse.

Keywords: Online retailing, strategic customers, technology adoption, TCP.