Business Intelligence and Smart Pricing in Uncertain Competitive Environment
thesisposted on 29.10.2016 by Ferdi Eruysal
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Business intelligence provided relatively low-cost capabilities to collect and analyze vast amount of customer information. Accumulation of customer specific information allows firms to categorize customers into segments and offer customized prices. The impact of business intelligence and smart pricing on competition and consumer purchase behavior in a game-theoretic model with two asymmetric firms is studied. We first introduce a basic game-theoretical model where customers differ in brand loyalty. Then, the basic model was extended to study segment granularity, product cost, privacy-conscious customers and classification errors. First, we extend the basic model by introducing multiple segments. Our analysis shows that firms do not necessarily become worse off. The firm dominating the industry is likely to increase its profits at the expense of the rival firm, and consumer welfare will improve with business intelligence. Two fundamental parameters, market dominance and the technology cost to industry dominance ratio, are introduced. High technology cost coupled with high dominance indicator results in a situation in which only very dominant firm acquires business intelligence. Second, we extend the basic model by incorporating product cost. Previous literature implicitly assumes that product cost differential can be aggregated into the price tolerance, and thus product cost can be assumed away. We show that a change in product cost does not translate into a correlated adjustment of the price tolerance. We identify four distinct subcases whose equilibrium conditions are significantly different in terms of market share, prices and profits. The case where product cost is considered to be negligible or equal across firms is shown to be one special case. Third, we extend the basic model by introducing privacy-conscious customers and classification errors. Privacy-conscious customers could inhibit firms’ data collection initiatives, resulting therefore in incorrect segmentation. Our analysis shows that firms offer lower prices when they make classification errors. This result is counter-intuitive. Even though, smart pricing is not considered as fair, it drastically improves competition which leads to more affordable prices. Most important contribution of this study is that an increase in information quality lower firms’ profits as this makes them more competitive.