Case Study: Modeling Vehicle Choice and Simulating Market Share

Abstract

We introduce a new method and workflow for predicting the market shares of future products using pre-launch data, such as syndicated studies conducted before the product is introduced. By employing Bayesian networks along with BayesiaLab and the Bayesia Market Simulator, this approach allows for reliable, practical, and cost-effective market share simulations. The method is illustrated through a case study on the U.S. launch of the Porsche Panamera for the 2010 model year, showcasing its effectiveness even in niche markets with limited data.

Objective

This tutorial is intended for marketing professionals interested in using Bayesian networks. It uses a real-world case study to demonstrate the features of BayesiaLab, offering valuable insights for analysts across different fields. The guide includes detailed steps on data preparation, network learning, and market share simulation, which can be applied to a wide variety of research tasks beyond just marketing.

Introduction

Market share is a crucial performance metric that is vital for setting sales volume expectations during product planning. To accurately forecast future market shares, it is necessary to understand consumer behavior, even though their decision-making processes are complex and uncertain. By employing choice modeling, especially Bayesian networks, it is possible to integrate both observable data and tacit knowledge to simulate consumer choices.

Bayesian Networks for Choice Modeling

Bayesian networks offer a localized and systematic method for structuring probabilistic information, making them ideal for representing consumer choice dynamics. Unlike traditional utility-based choice models:

1. Non-Restrictive Dimensions: Bayesian networks preserve trade-offs between attributes, e.g., fuel economy vs. price, without collapsing into single scalar values.
2. Nonparametric Flexibility: They do not require predefined functional forms, allowing for the discovery of nonlinear relationships.
3. Inherent Probabilistic Framework: Bayesian networks handle uncertainty natively without the need for additional error terms.

Case Study: Porsche Panamera

Background

The Porsche Panamera, introduced as a four-door luxury sports sedan, entered a competitive segment featuring established contenders like the Mercedes-Benz S-Class and BMW 7-Series. Beyond these competitors, it also faced potential cannibalization from Porsche's own Cayenne SUV.

Study Objective

The case study aims to predict the Panamera's market share using Revealed Preference (RP) data from the 2009 New Vehicle Experience Survey (NVES) without conducting new research.

Methodology

Data Preparation

1. Variable Selection: Selected ~50 variables, focusing on vehicle attributes, consumer demographics, and attitudes.
2. Discretization: Continuous variables, such as price, were discretized into meaningful intervals using algorithms like K-Means.

Network Learning

1. Forbidden Arcs: Restricted learning to relationships between product and market variables to avoid encoding existing product configurations.
2. Unsupervised Learning: Utilized EQ learning to discover associations between variables.

Simulation

1. Baseline Scenario: Defined existing product scenarios using NVES data.
2. Bayesia Market Simulator: Simulated market shares by importing Bayesian networks and testing new product scenarios, such as variations of the Panamera.

Results

The simulation accurately predicted the Panamera's market share, aligning closely with its observed performance post-launch. Additional insights included:

• Identification of substitution and cannibalization effects.
• Scenario testing for product and market changes.

Limitations

While effective for simulating variations of existing configurations, this approach is less suited for entirely new technologies, such as electric vehicles. Expert judgment is essential for defining adequate product and market scenarios.

Outlook

Future enhancements include:

• Expert Knowledge Integration: Leveraging elicited expertise to augment Bayesian networks.
• Satisfaction-Driven Models: Incorporating consumer ratings to evaluate the impact of product improvements.
• Broader Applications: Extending simulations to brand-level or segment-level market dynamics.

Summary

BayesiaLab and Bayesia Market Simulator offer a robust, scientifically grounded framework for choice modeling and market share simulation. By transforming existing consumer data into actionable insights, these tools empower practitioners to make data-driven decisions with precision and efficiency.