Research
Published and Accepted Papers
Christopher Amaral, Ceren Kolsarici, and Mikhail Nediak (2024), “Optimizing Pricing Delegation to External Sales Forces via Commissions: An Empirical Investigation”, Production and Operations Management.
(Journal Rating: UTD24 / FT50 / ABDC-A* / AJG-4, Impact Factor: 6.500)
In this paper, using data from indirect auto lending and a structural model of external sales representative (ESR) behavior, we investigate i) the role of commissions as a potential tool to influence ESRs’ pricing decisions under limited authority, ii) the impact of optimized commissions on firm profitability and iii) the implications for customer welfare. The results provide strong evidence for ESRs being strategic (vs. myopic) in their pricing and effort decisions; and in both cases, strategic behavior is inversely proportional to customer risk. Moreover, once optimized, commissions are an effective tool for firms to bridge the profitability gap between centralized pricing and pricing delegation. Our analyses on social justice and fairness reveal that customer groups along the dimensions of customer risk, income class, and gender, which have been traditionally marginalized in society, suffer from inequities in the indirect-lending ecosystem. While, the optimization of commissions does not intensify these biases, we found females to be the exception, and that the inequities due to gender bias not only persist in the optimized regime, but also deepen. Through counterfactual simulations, we propose two policies for firms to minimize social inequity, which helps them balance immediate profit-maximizing goals with responsible AI initiatives.
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, Iina Ikonen, and Nicole Robitaille, “Motivating Sustainable Energy Consumption Within Organizations: The Role of Artificial Intelligence and Behavioural Nudging”, Preparing for Submission.
(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.