Research
Published and Accepted Papers
Christopher Amaral, Ceren Kolsarici, and Mikhail Nediak (2023), “The Impact of Discriminatory Pricing Based on Customer Risk: An Empirical Investigation Using Indirect Lending Through Retail Networks”, European Journal of Marketing.
(Journal Rating: ABDC-A* / AJG-3, Impact Factor: 4.687)
Purpose: To understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared to sales force price delegation in the purchase of an aftermarket good through an indirect retail channel with symmetric information.
Design/Methodology/Approach: Using individual-level loan application and approval data from a North American financial institution and segment-level customer risk as the price discrimination criterion for the firm, we develop a three-stage model that accounts for (i) the salesperson’s price decision within the limits of the latitude provided by the firm, (ii) the firm’s decision to approve or not approve a sales application, and (iii) the customer’s decision to accept or reject a sales offer conditional on the firm’s approval. Next, we compare the profitability of this sales force price delegation model to that of a segment-level centralized pricing model where agent incentives and consumer prices are simultaneously optimized using a quasi-Newton nonlinear optimization algorithm (i.e., BFGS).
Findings: The results suggest that implementation of analytics-driven centralized discriminatory pricing and optimal sales force incentives lead to double digit lifts in firm profits. Moreover, we find that the high-risk customer segment is less price sensitive and firms, upon leveraging this segment’s willingness to pay, not only improve their bottom-line but also allow these marginalized customers with traditionally low approval rates access to loans. This points out the important customer welfare implications of the findings.
Originality: Substantively, to the best of our knowledge, this paper is the first to 1) empirically investigate the profitability of analytics-drivensegment-level (i.e., discriminatory) centralized pricing compared to sales force price delegation in 2) indirect retail channels (i.e. where agents are external to the firm and have access to competitor products) 3) taking into account the decisions of thethree key stakeholdersof the process, namely, the consumer, the salesperson and the firm and 4)simultaneously optimizingsales commission and centralized consumer price.
Christopher Amaral and Ceren Kolsarici (2020), "The financial advice puzzle: The role of consumer heterogeneity in the advisor choice," Journal of Retailing and Consumer Services. (Journal Rating: ABDC-A / AJG-2, Impact Factor: 7.135)
The need for sound financial planning has increased recently due to significant changes in the investor landscape; yet, most individuals are incapable of making sound financial decisions. To alleviate these concerns, government and policymakers have considered financial advisors as a potential solution. In this paper, we investigate motivational drivers of financial advisor use, accounting for investor heterogeneity, with the goal of helping institutions and policy makers design targeted campaigns to increase the use of financial advisor services. The results from a latent class choice model reveal two distinct segments that differ in their approach to the financial advice decision. While higher levels of risk tolerance, trust, and self-efficacy increase financial advice use for both segments, albeit at much higher propensities for Segment 1, personality only matters for Segment 1. Moreover, the regulatory focus of the two segments differ with Segment 1 being promotion and Segment 2 being prevention focused. Using these results, we offer suggestions for marketing strategies regarding both segments.
Revising for Resubmission and Under Review
Christopher Amaral, Ceren Kolsarici, and Mikhail Nediak, “Optimizing Pricing Delegation to External Sales Forces via Commissions: An Empirical Investigation”, Under Review (3rd Round).
Sales force activities, including personal selling and pricing, are often outsourced by providers to external firms. Given the importance of these marketing tasks, organizations must ensure that sales compensation is optimized to maximize profitability. In this article, we develop a model of external salesperson behavior in the context of indirect lenders that sell auto loans through external sales representatives at auto dealerships. Using this model, which accounts for the key decision makers in an indirect lending context (i.e., lender, external sales representative, and customer), and a large data set from a North American financial institution, we examine the behavior of external sales representatives, focusing on the demand allocation and pricing decisions made by external salespeople and the impact that commissions have on these decisions. The results indicate that external sales representatives’ decision to allocate customer demand is influenced by commissions provided by competing providers; however, the effect is smaller than suggested in the literature. Also, external sales representatives use a sequential decision-making process, first selecting a lender to allocate customer demand to and then choosing an option from the selected lender’s rate sheet (i.e., menu of prices), rather than a simultaneous process, whereby pricing options from all providers are compared at the same time. Finally, optimal commissions increase exponentially with price to ensure that external sales representatives select higher prices for customers with higher willingness to pay, thus maximizing lender profitability.
Christopher Amaral, Ceren Kolsarici, Iina Ikonen, and Nicole Robitaille, “Motivating Sustainable Energy Consumption Within Organizations: The Role of Artificial Intelligence and Behavioural Nudging”, Under Review (1st Round).
(Finalist for the Gary L. Lilien ISMS-MSI-EMAC Practice Prize Competition 2024)
Organizations, as large energy consumers, are an important target for critical peak pricing programs. These aim to promote sustainability by reducing energy consumption during peak demand by charging high energy prices for energy consumed during critical hours. While research has shown such programs can effectively reduce individuals’ energy consumption, less is known about their effectiveness on organizations. In this research, we explore the effectiveness of critical peak pricing on organizations’ electricity consumption. We develop a two-stage marketing process utilizing both artificial intelligence and behavioural science to increase the effectiveness of these programs by improving the accuracy of energy demand forecasts and enhancing communication with behavioural nudges. Through this two-stage process, we demonstrate and improve the effectiveness of critical peak pricing in decreasing organizations’ energy consumption and quantify the impact on organizations’ electricity bills. We conclude by discussing the findings in relation to their implications for theory, practice, and government policy.