Sharan Jagpal’s research while affiliated with Rutgers Business School and other places

The original article unfortunately contained a mistake

This paper proposes a theoretical framework for decision making when the firm needs to make marketing and production/order decisions simultaneously before demand uncertainty is resolved. We discuss the theoretical properties of the framework for single and multi-product firms; in addition, we show that the framework can be extended to allow for competitive reaction in a duopoly setting. We propose an empirical method to operationalize the model and compare the results to those from extant methods. The empirical results for both single and multi-product firms show that the proposed method outperforms decision making using standard econometric methods. In particular, depending on customer lifetime value (CLV) and other error costs and price elasticities, the loss in potential profits by using the standard regression-based methodology or quantile regression can be considerable.

We develop a flexible methodology to protect marketing data in the context of a business ecosystem in which data providers seek to meet the information needs of data users, but wish to deter invalid use of the data by potential intruders. In this context we propose a Bayesian probability model that produces protected synthetic data. A key feature of our proposed method is that the data provider can balance the trade-off between information loss resulting from data protection and risk of disclosure to intruders. We apply our methodology to the problem facing a vendor of retail point-of-sale data whose customers use the data to estimate price elasticities and promotion effects. At the same time, the data provider wishes to protect the identities of sample stores from possible intrusion. We define metrics to measure the average and maximum loss of protection implied by a data protection method. We show that, by enabling the data provider to choose the degree of protection to infuse into the synthetic data, our method performs well relative to seven benchmark data protection methods, including the extant approach of aggregating data across stores. Data are available at https://doi.org/10.1287/mksc.2017.1064 .

Data sharing is a strategically important marketing initiative in many industries. Increasingly, companies seek to enhance the value of their customer data by supplementing this information with customer-level information from another company. However, this arrangement requires one company to reveal its customer-level data to another and face privacy risks which may result in substantial losses in brand value, customer trust, and competitive advantage, or legal penalties from not conforming to regulations. To overcome this problem, we propose a decision-theoretic approach for use by companies to protect their customer segmentation data prior to entering into collaborative arrangements. Our approach extends the literature because it allows the data provider to protect all customer segmentation data at the individual customer level instead of only at the aggregate level. We show that the optimal data protection strategy depends on a risk-return tradeoff based on the probabilities of misclassification of customers into segments, the opportunity costs of erroneously assigning segment membership, and the anticipated cost of a data breach.