Resources
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Tutorials
- Introduction to Bayesian networks
- Bayesialab's introductory presentation
- Analysis toolbox
- Bayesian network learning
- Decision support
- Dynamic bayesian networks and action policy learning
- Filtered/Censored States
- Probabilistic structural equations
- Driver analysis and product optimization with BayesiaLab
- Modeling Vehicle Choice and Simulating Market Share
- Probabilistic Latent Factor Induction
- Paradoxes and Fallacies
- Unsupervised and Supervised Learning with BayesiaLab
- Causal Inference and Direct Effects
- BEKEE, Bayesia Expert Knowledge Elicitation Environment
- Causal Inference and Direct Effects
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User Guide
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BayesiaLab Technical Specifications
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Case studies
- Production optimisation through feedback analysis
- Satisfaction questionnaire analysis
- Football World Cup 2010
- Breast Cancer Diagnostics
- Microarray analysis
- Health trajectory analysis
- Biocomputing transcriptome analysis
- Risk analysis and security policy
- Difficult intubations analysis
- Cyber-crimes detection
- Customer characterization
- Organization name identification
- Implementation of a Bayesian score
- Modeling and simulation of dynamic systems
- Analysis of a perfume market
- Consumer segmentation
- Identifying drivers of liking
- Driver Analysis: The Missing Links
- Salmonella isolation
- Political Analysis
- Studying consumer drivers
- Employees satisfaction analysis
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Modeling Vehicle Choice and Simulating Market Share with Bayesian Networks
We present a new method and the associated workflow for estimating market shares of future products based exclusively on pre-introduction data, such as syndicated studies conducted prior to product launch. Our approach provides a highly practical, fast and economical alternative to conducting new primary research.
With Bayesian networks as the framework, and by employing the BayesiaLab and Bayesia Market Simulator software packages, this approach helps market researchers and product planners to reliably perform market share simulations on their desktop computers, which would have been entirely inconceivable in the past.
This innovative approach is explained step-by-step in a study about the introduction of the new Porsche Panamera in the U.S. market. The results confirm that market share simulation with Bayesian networks is feasible even in niche markets that provide relatively few observations.
We believe that making this method and the tools accessible to practitioners is an important contribution to real-world marketing. We are confident that for many companies this approach can yield a step-change in their forecasting ability.
We used this case study during a webinar, on January 4th, 2011.
You can download the webinar slides here.
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