Apply Bbn Models to Identify Consumer Persona in High Dimensional Parameter Space

Apply BBN Models to Identify Consumer Persona in High-Dimensional Parameter Space

Presented at the 9th Annual BayesiaLab Conference on October 13, 2021.


It is a challenge to cluster and segment data in high-dimensional space. Traditional clustering methods relying on distance (e.g., k-means) or density (DBScan) generally fail to identify meaningful clusters in high dimensional space. We investigated clustering methods in high-dimensional space using Bayesian Belief Network (BBN) models, k-means, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), Polytomous variable Latent Class Analysis (PoLCA), and Profile Regression. These methods were used to cluster a set of users and prospective users of Setlist Beauty, which is a digital iPhone App owned by P&G. There are around 500 variables to describe these users. We found that the BBN model performs very well in high-dimensional clustering. Most importantly, it provides metrics to inform us what variables/questions can differentiate consumers and what answers to these questions characterize a consumer segment. These segments and metrics helped deliver actionable insights for targeted advertisement, acquisition, and App feature improvement, etc.

Presentation Video

Presentation Slides

About the Presenter

Dr. Yong Zhang leverages Bayesian data and modeling science to develop strategies for product design, manufacturing, storage, and transportation across P&G to improve consumers’ life quality and drive positive influence on the environment and society. He develops first principle and data science/machine learning methods and tools through Front End Innovation projects to enable and promote the capability across P&G for breakthrough consumer understanding and product innovation. The methods and tools can be used to extract and integrate information from a variety of data sources to find a “Body of Evidence” for consumer and product research based on Nonparametric Bayesian statistics and deep learning algorithms.

For North America

Bayesia USA

4235 Hillsboro Pike
Suite 300-688
Nashville, TN 37215, USA

+1 888-386-8383

Head Office

Bayesia S.A.S.

Parc Ceres, Batiment N 21
rue Ferdinand Buisson
53810 Change, France

For Asia/Pacific

Bayesia Singapore

1 Fusionopolis Place
#03-20 Galaxis
Singapore 138522

Copyright © 2024 Bayesia S.A.S., Bayesia USA, LLC, and Bayesia Singapore Pte. Ltd. All Rights Reserved.