Predicting Consumer Choice Set Using Product Association Network and Data Analytics

2013 ◽  
Author(s):  
Mingxian Wang ◽  
Wei Chen
Keyword(s):  
Engineering Design ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Choice Model ◽  
Choice Modeling ◽  
Large Set ◽  
Association Network ◽  
Choice Sets ◽  
Choice Set ◽  
Product Association

Although discrete choice analysis has been shown to be useful for modeling consumer preferences and choice behaviors in the field of engineering design, information of choice set composition is often not available in majority of the collected consumer purchase data. When a large set of choice alternatives exist for a product, such as automotive vehicles, randomly choosing a small set of product alternatives to form a choice set for each individual consumer will result in misleading choice modeling results. In this work, we propose a data-analytics approach to mine existing data of choice sets and predict the choice set for each individual customer in a new choice modeling scenario where the choice set information is lacking. The proposed data-analytics approach integrates product association analysis, network analysis, consumer segmentation, and predictive analytics. Using the J.D. Power vehicle survey as the existing choice set data, we demonstrate that the association network approach is capable of recognizing and expressively summarizing meaningful product relations in choice sets. Our method accounts for consumer heterogeneity using the stochastic generation algorithm where the probability of selecting an alternative into a choice set integrates the information of customer profile clusters and products chosen frequencies. By comparing multiple multinomial logit models using different choice set compositions, we show that the choice model estimates are sensitive to the choice set compositions and our proposed method leads to improved modeling results. Our method also provides insights into market segmentation that can guide engineering design decisions.

A Data-Driven Network Analysis Approach to Predicting Customer Choice Sets for Choice Modeling in Engineering Design

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Mingxian Wang ◽  
Wei Chen
Keyword(s):  
Network Analysis ◽  
Engineering Design ◽  
Choice Model ◽  
Choice Modeling ◽  
Data Driven ◽  
Association Network ◽  
Customer Choice ◽  
Choice Sets ◽  
Existing Data ◽  
Choice Set

In this paper, we propose a data-driven network analysis based approach to predict individual choice sets for customer choice modeling in engineering design. We apply data analytics to mine existing data of customer choice sets, which is then used to predict choice sets for individual customers in a new choice modeling scenario where choice set information is lacking. Product association network is constructed to identify product communities based on existing data of customer choice sets, where links between products reflect the proximity or similarity of two products in customers' perceptual space. To account for customer heterogeneity, customers are classified into clusters (segments) based on their profile attributes and for each cluster the product consideration frequency is computed. For predicting choice sets in a new choice modeling scenario, a probabilistic sampling approach is proposed to integrate product associations, customer segments, and the link strengths in the product association network. In case studies, we first implement the approach using an example with simulated choice set data. The quality of predicted choice sets is examined by assessing the estimation bias of the developed choice model. We then demonstrate the proposed approach using actual survey data of vehicle choice, illustrating the benefits of improving a choice model through choice set prediction and the potential of using such choice models to support engineering design decisions. This research also highlights the benefits and potentials of using network techniques for understanding customer preferences in product design.

The Normalization of Consumer Valuations: Context-Dependent Preferences From Neurobiological Constraints

2020 ◽  
Author(s):  
Ryan Webb ◽  
Paul W. Glimcher ◽  
Kenway Louie
Keyword(s):  
Decision Making ◽  
Discrete Choice ◽  
Choice Model ◽  
Judgment And Decision Making ◽  
Choice Behaviour ◽  
Divisive Normalization ◽  
Choice Sets ◽  
Observed Behaviour ◽  
The Brain ◽  
Choice Set

Consumer valuations are shaped by choice sets, exemplified by patterns of substitution between alternatives as choice sets are varied. Building on recent neuroeconomic evidence that valuations are transformed during the choice process, we incorporate the canonical divisive normalization computation into a discrete choice model and characterize how choice behaviour depends on both size and composition of the choice set. We then examine evidence for such behaviour from two choice experiments that vary the size and composition of the choice set. We find that divisive normalization more accurately captures observed behaviour than alternative models, including an example range normalization model. These results are robust across experimental paradigms. Finally, we demonstrate that Divisive Normalization implements an efficient means for the brain to represent valuations given neurobiological constraints, yielding the fewest choice errors possible given those constraints. This paper was accepted by Elke Weber, judgment and decision making.

scholarly journals Path complexity and bicyclist route choice set quality assessment

2019 ◽  
Author(s):  
Thomas Koch ◽  
Luk Knapen ◽  
Elenna Dugundji
Keyword(s):  
Shortest Paths ◽  
Route Choice ◽  
Large Set ◽  
Map Matching ◽  
Demand Modeling ◽  
Urban Road ◽  
Modeling Process ◽  
Choice Sets ◽  
Minimum Number ◽  
Choice Set

AbstractEveryday route choices made by bicyclists are known to be more difficult to explain than vehicle routes, yet prediction of these choices is essential for guiding infrastructural investment in safe cycling. Building route choice sets is a difficult task. Even including detailed attributes such as the number of left turns, the number of speed bumps, distance and other route choice properties we still see that choice set quality measures suggest poor replication of observed paths. In this paper we study how the concept of route complexity can help generate and analyze plausible choice sets in the demand modeling process. The complexity of a given path in a graph is the minimum number of shortest paths that is required to specify that path. Complexity is a path attribute which could potentially be considered to be important for route choice in a similar way. The complexity was determined for a large set of observed routes and for routes in the generated choice sets for the corresponding origin-destination pairs. The respective distributions are shown to be significantly different so that the choice sets do not reflect the traveler preferences, this is in line with classical choice set quality indicators. Secondly, we investigate often used choice set quality methods and formulate measures that are less sensitive to small differences between routes that can be argued to be insignificant or irrelevant. Such difference may be partially due to inaccuracy in map-matching observations to dense urban road networks.

scholarly journals Development of a Physicians’ Choice Model Using Mixed Logit With Random Prices for Drugs Case Study on Diabetes Type II

2020 ◽  
Author(s):  
Christine Claude Huttin ◽  
Jerry Hausman
Keyword(s):  
Choice Model ◽  
Mixed Logit ◽  
Diabetes Type ◽  
Base Line ◽  
Health Plans ◽  
Type Ii ◽  
Diabetes Type Ii ◽  
Oral Agents ◽  
Choice Sets ◽  
Choice Set

Abstract This paper presents a first experiment with random generator of drug prices and a first simulation on physicians’ treatment choices (case on pharmacotherapies) for diabetes type II care. It also aims to compare the effects of the price variables according to public versus private health plans on physicians’ choices (Medicare versus commercial Health Plans). The base line model used is a Mixed Logit model with Random Price variables. A series of experiments with random parameters generations is designed with various sequences and number of draws. The model is tested on a real analytical dataset, extracted from the CDC physician survey (National Ambulatory Care Survey, NAMCS), for patients with diabetes type II without complications, for previous predictive econometrics with ENDEPUSresearch, Inc. The model uses a first drug choice set with three alternatives: oral agents only, combined therapies, no drug. The choice models introduce qualitative dependent variables and complement the series of cumulative logistic models per disease. The matlab code for the new specification test on the Independence of Irrelevant Alternatives at individual level is modified to fit this type of medical applications; first runs compare main parameters of a full choice set versus reduced choice sets of alternatives. It is planned to design more experiments for extended choice sets and widespread applications, in order to lead to user friendly tools for medical systems. The collaboration with Professor Jerry Hausman on the US market will help with use of results and new ways to adjust the reliability on the selection of alternatives; it may provide additional guidance to the algorithms used by professionals and for health policies.

A Data-Driven Methodology to Construct Customer Choice Sets Using Online Data and Customer Reviews

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Dedy Suryadi ◽  
Harrison M. Kim
Keyword(s):  
Choice Model ◽  
Predictive Ability ◽  
Product Attributes ◽  
Customer Choice ◽  
Online Data ◽  
Data Set ◽  
Customer Reviews ◽  
Choice Sets ◽  
Quality Choice ◽  
Choice Set

Abstract The recent development in engineering design has incorporated customer preferences by involving a choice model. In generating a choice model to produce a good quality estimate of parameters related to product attributes, a high-quality choice set is essential. However, the choice set data are often not available. This research proposes a methodology that utilizes online data and customer reviews to construct customer choice sets in the absence of both the actual choice set and the customer sociodemographic data. The methodology consists of three main parts, i.e., clustering the products based on their attributes, clustering the customers based on their reviews, and constructing the choice sets based on a sampling probability scenario that relies on product and customer clusters. The proposed scenario is called Normalized, which multiplies the product cluster and customer cluster fractions to obtain the probability sampling distribution. There are two utility functions proposed, i.e., a linear combination of product attributes only and a function that includes the interactions of product attributes and customer reviews. The methodology is implemented to a data set of laptops. The Normalized scenario performs significantly better than the baseline, Random, in predicting the test set data. Moreover, the inclusion of customer reviews into the utility function also significantly increases the predictive ability of the model. The research shows that using the product attribute data and customer reviews to construct choice sets generates choice models with higher predictive ability than randomly constructed choice sets.

scholarly journals META-ANALYSIS OF CHOICE SET GENERATION EFFECTS ON ROUTE CHOICE MODEL ESTIMATES AND PREDICTIONS

Transport ◽  
2012 ◽  
Vol 27 (3) ◽  
pp. 286-298 ◽  
Author(s):  
Carlo Giacomo Prato
Keyword(s):  
Large Scale ◽  
Choice Model ◽  
Meta Analysis ◽  
Route Choice ◽  
Behavior Modeling ◽  
Path Generation ◽  
Choice Sets ◽  
True Model ◽  
Choice Set Generation ◽  
Choice Set

Large scale applications of behaviorally realistic transport models pose several challenges to transport modelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignment equilibrium problem help modelers in enhancing the route choice behavior modeling, but require them to generate choice sets by selecting a path generation technique and its parameters according to personal judgments. This paper proposes a methodology and an experimental setting to provide general indications about objective judgments for an effective route choice set generation. Initially, path generation techniques are implemented within a synthetic network to generate possible subjective choice sets considered by travelers. Next, ‘true model estimates’ and ‘postulated predicted routes’ are assumed from the simulation of a route choice model. Then, objective choice sets are applied for model estimation and results are compared to the ‘true model estimates’. Last, predictions from the simulation of models estimated with objective choice sets are compared to the ‘postulated predicted routes’. A meta-analytical approach allows synthesizing the effect of judgments for the implementation of path generation techniques, since a large number of models generate a large amount of results that are otherwise difficult to summarize and to process. Meta-analysis estimates suggest that transport modelers should implement stochastic path generation techniques with average variance of its distribution parameters and correction for unequal sampling probabilities of the alternative routes in order to obtain satisfactory results in terms of coverage of ‘postulated chosen routes’, reproduction of ‘true model estimates’ and prediction of ‘postulated predicted routes’.

Air Passengers’ Preferences for Aviation Voluntary Carbon Offsetting: A Co-benefits Perspective

2021 ◽  
pp. 004728752110303
Author(s):  
Beile Zhang ◽  
Brent W. Ritchie ◽  
Judith Mair ◽  
Sally Driml
Keyword(s):  
Discrete Choice ◽  
Latent Class ◽  
Choice Model ◽  
Choice Modeling ◽  
Positive Outcomes ◽  
Market Segments ◽  
Discrete Choice Modeling ◽  
Educational Benefits ◽  
Carbon Offsetting ◽  
Simple Reduction

Co-benefits are positive outcomes from voluntary carbon offsetting (VCO) programs beyond simple reduction in carbon emissions, which include biodiversity, air quality, economic, health, and educational benefits. Given the rates of aviation VCOs remain at less than 10%, this study investigated air passengers’ preferences for co-benefits as well as certification, location, and cost of VCO programs. Using discrete choice modeling, this study shows that aviation VCO programs with higher levels of co-benefits, particularly biodiversity and health benefits, are preferred by air passengers and confirms a preference for domestically based and certified VCO programs. The latent class choice model identified three classes with different preferences for VCO program attributes and demographic characteristics. The results of this study contribute to the knowledge of VCO co-benefits and imply that airlines should take note of this preference for biodiversity and health co-benefits when designing VCO programs and differentiate between market segments to increase the uptake of VCOs.

Data Analytics and STEM Student Success: The Impact of Predictive Analytics-Informed Academic Advising Among Undeclared First-Year Engineering Students

2018 ◽  
Vol 22 (3) ◽  
pp. 497-521 ◽  
Author(s):  
Yu (April) Chen ◽  
Sylvester Upah
Keyword(s):  
Higher Education ◽  
Student Success ◽  
Academic Advising ◽  
Data Analytics ◽  
Predictive Analytics ◽  
First Year ◽  
Program Of Study ◽  
Science Technology ◽  
Mathematics Student ◽  
And Mathematics

Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success. This study took the first step to investigate the influence of using predictive analytics on academic advising in engineering majors. Specifically, we examined the effects of predictive analytics-informed academic advising among undeclared first-year engineering student with regard to changing a major and selecting a program of study. We utilized the propensity score matching technique to compare students who received predictive analytics-informed advising with those who did not. Results indicated that students who received predictive analytics-informed advising were more likely to change a major than their counterparts. No significant effects was detected regarding selecting a program of study. Implications of the findings for policy, practice, and future research were discussed.

scholarly journals A link based network route choice model with unrestricted choice set

2013 ◽  
Vol 56 ◽  
pp. 70-80 ◽  
Author(s):  
Mogens Fosgerau ◽  
Emma Frejinger ◽  
Anders Karlstrom
Keyword(s):  
Choice Model ◽  
Route Choice ◽  
Choice Set
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