scholarly journals On (Re-Scaled) Multi-Attempt Approximation of Customer Choice Model and its Application to Assortment Optimization

2016 ◽  
Author(s):  
Hakjin Chung ◽  
Hyun-Soo Ahn ◽  
Stefanus Jasin
Keyword(s):  
Choice Model ◽  
Customer Choice ◽  
Assortment Optimization ◽  
Customer Choice Model

Constrained Assortment Optimization Under the Paired Combinatorial Logit Model

2021 ◽  
Author(s):  
Rohan Ghuge ◽  
Joseph Kwon ◽  
Viswanath Nagarajan ◽  
Adetee Sharma
Keyword(s):  
Logit Model ◽  
Choice Model ◽  
Customer Behavior ◽  
Multidimensional Space ◽  
Customer Choice ◽  
Assortment Optimization ◽  
Selected Subset ◽  
Unconstrained Problem ◽  
Key Aspects ◽  
Customer Choice Model

Assortment optimization involves selecting a subset of products to offer to customers in order to maximize revenue. Often, the selected subset must also satisfy some constraints, such as capacity or space usage. Two key aspects in assortment optimization are (1) modeling customer behavior and (2) computing optimal or near-optimal assortments efficiently. The paired combinatorial logit (PCL) model is a generic customer choice model that allows for arbitrary correlations in the utilities of different products. The PCL model has greater modeling power than other choice models, such as multinomial-logit and nested-logit. In “Constrained Assortment Optimization Under the Paired Combinatorial Logit Model,” Ghuge, Kwon, Nagarajan, and and Sharma provide efficient algorithms that find provably near-optimal solutions for PCL assortment optimization under several types of constraints. These include the basic unconstrained problem (which is already intractable to solve exactly), multidimensional space constraints, and partition constraints. The authors also demonstrate via extensive experiments that their algorithms typically achieve over 95% of the optimal revenues.

Assortment Optimization under the Multi-Purchase Multinomial Logit Choice Model

2021 ◽  
Author(s):  
Jacob Feldman ◽  
Danny Segev ◽  
Huseyin Topaloglu ◽  
Laura Wagner ◽  
Yicheng Bai
Keyword(s):  
Choice Model ◽  
Multinomial Logit ◽  
Assortment Optimization

Constrained Assortment Optimization Under the Markov Chain–based Choice Model

2020 ◽  
Vol 66 (2) ◽  
pp. 698-721 ◽  
Author(s):  
Antoine Désir ◽  
Vineet Goyal ◽  
Danny Segev ◽  
Chun Ye
Keyword(s):  
Markov Chain ◽  
Choice Model ◽  
Assortment Optimization

The role of self-efficacy and customer satisfaction in driving loyalty to the mobile shopping application

2018 ◽  
Vol 46 (3) ◽  
pp. 283-303 ◽  
Author(s):  
Rakhi Thakur
Keyword(s):  
Self Efficacy ◽  
Structural Equation ◽  
Choice Model ◽  
Positive Impact ◽  
Partial Least Square ◽  
Least Square ◽  
Customer Choice ◽  
Content Type ◽  
Mobile Shopping

Purpose The purpose of this paper is to develop and empirically test a model that examines the relationship between post-adoption self-efficacy, satisfaction, and loyalty in the usage of mobile shopping applications. Design/methodology/approach A structured questionnaire was used to collect data from respondents who had used mobile shopping applications to make purchases. Data analysis was done using partial least square structural equation modelling. Findings The results show that self-efficacy and satisfaction have a positive impact on continuance intention; however, the same may not lead to advocacy. The results also show that some antecedents of self-efficacy and satisfaction at the post-adoption stage differ from the pre-adoption intention stage. Practical implications The findings of the study provide a better understanding of the factors likely to influence loyalty among customers using mobile shopping applications. The findings also provide valuable insights into the factors that e-retailers need to focus to build self-efficacy among their customers using mobile interface. Originality/value The contribution of the paper lies in eliciting the differences between customer choice model at the pre-adoption and post-adoption stage for mobile shopping. Furthermore, the study demonstrated the role of a cognitive factor of self-efficacy in loyalty at the post-adoption stage that is pre-dominantly researched with affective factor of satisfaction.

Assortment Optimization and Pricing Under the Multinomial Logit Model with Impatient Customers: Sequential Recommendation and Selection

2021 ◽  
Author(s):  
Pin Gao ◽  
Yuhang Ma ◽  
Ningyuan Chen ◽  
Guillermo Gallego ◽  
Anran Li ◽  
...  
Keyword(s):  
Logit Model ◽  
Choice Model ◽  
Multinomial Logit Model ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Multinomial Logit ◽  
Impatient Customers ◽  
Purchasing Decisions ◽  
Assortment Optimization ◽  
Decision Variables

Sequential Recommendation Under the Multinomial Logit Model with Impatient Customers In many applications, customers incrementally view a subset of offered products and make purchasing decisions before observing all the offered products. In this case, the decision faced by a firm is not only what assortment of products to offer, but also in what sequence to offer the products. In “Assortment Optimization and Pricing Under the Multinomial Logit Model with Impatient Customers: Sequential Recommendation and Selection”, Gao, Ma, Chen, Gallego, Li, Rusmevichientong, and Topaloglu propose a choice model where each customer incrementally view the assortment of products in multiple stages, and their patience level determines the maximum number of stages. Under this choice model, the authors develop a polynomial-time algorithm that finds a revenue-maximizing sequence of assortments. If the sequence of assortments is fixed, the problem of finding revenue-maximizing prices can be transformed to a convex program. They combine these results to develop an effective approximation algorithm when both the sequence of assortments and prices are decision variables.

scholarly journals A Demand Estimation Algorithm for Inventory Management Systems Using Censored Data

2017 ◽  
Vol 7 (6) ◽  
pp. 2215-2221
Author(s):  
A. Nikseresht ◽  
K. Ziarati
Keyword(s):  
Inventory Management ◽  
Censored Data ◽  
Choice Model ◽  
Product Category ◽  
Least Square Method ◽  
Estimation Algorithm ◽  
Least Square ◽  
Model Parameters ◽  
Demand Model ◽  
Customer Choice

During the selling time horizon of a product category, a number of products may become unavailable sooner than others and the customers may substitute their desired product with another or leave the system without purchase. So, the recorded sales do not show the actual demand of each product. In this paper, a nonparametric algorithm to estimate true demand using censored data is proposed. A customer choice model is employed to model the demand and then a nonlinear least square method is used to estimate the demand model parameters without assuming any distribution on customer’s arrival. A simple heuristic approach is applied to make the objective function convex, making the algorithm perform much faster and guaranteeing the convergence. Simulated dataset of different sizes are used to evaluate the proposed method. The results show a 23% improvement in root mean square error between estimated and simulated true demand, in contrast to alternate methods usually used in practice.

scholarly journals Breaking the Symmetry in Queues with Delayed Information

2021 ◽  
Vol 31 (09) ◽  
pp. 2130027
Author(s):  
Philip Doldo ◽  
Jamol Pender ◽  
Richard Rand
Keyword(s):  
Queue Length ◽  
Choice Behavior ◽  
Multiple Scales ◽  
Choice Model ◽  
Fluid Model ◽  
Queueing Systems ◽  
Model Parameters ◽  
Customer Choice ◽  
Service Rates ◽  
Customer Choice Behavior

Giving customers queue length information about a service system has the potential to influence the decision of a customer to join a queue. Thus, it is imperative for managers of queueing systems to understand how the information that they provide will affect the performance of the system. To this end, we construct and analyze a two-dimensional deterministic fluid model that incorporates customer choice behavior based on delayed queue length information. Reports in the existing literature always assume that all queues have identical parameters and the underlying dynamical system is symmetric. However, in this paper, we relax this symmetry assumption by allowing the arrival rates, service rates, and the choice model parameters to be different for each queue. Our methodology exploits the method of multiple scales and asymptotic analysis to understand how to break the symmetry. We find that the asymmetry can have a large impact on the underlying dynamics of the queueing system.

Sign in / Sign up

Export Citation Format

Share Document