Bayesian nonparametric models in market segmentation: a systematic review and research agenda
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Published:
Jul 17, 2026
Keywords:
Bayesian nonparametrics, market segmentation, clustering, Dirichlet process, systematic review.
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Abstract
Introduction: Bayesian nonparametric (BNP) models have emerged as flexible approaches for clustering and segmentation by allowing data-driven identification of latent structures. Despite their methodological advances, their application in marketing remains limited. Objective: To systematically examine the literature on Bayesian nonparametric (BNP) models for clustering and segmentation, with a focus on their applicability in marketing contexts. Methodology: A PRISMA-based protocol was employed to identify and analyze 43 Scopus-indexed peer-reviewed articles. Bibliometric and thematic analyses were conducted to characterize research trends and methodological developments. Results: The results indicate a marked growth in scientific output, concentrated in statistical and methodological journals, with a strong emphasis on Dirichlet processes, mixture models, and clustering techniques. A taxonomy is proposed that structures the literature into six categories, ranging from foundational BNP models to advanced hierarchical and data-adaptive extensions. Conclusions: The findings reveal a persistent gap between methodological advances and their application in marketing, where traditional clustering approaches prevail. BNP models remain underutilized despite their capacity to capture latent heterogeneity and emergent segmentation structures. General study area: Marketing. Specific study area: Market segmentation, Bayesian nonparametric models, clustering. Article type: Systematic literature review.
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Calle Monroy, T. C., Pesántez Vicuña, S. F., Izquierdo Galarza, C. M., & Pazmiño Garzón, D. L. (2026). Bayesian nonparametric models in market segmentation: a systematic review and research agenda. ConcienciaDigital, 9(3), 28-59. https://doi.org/10.33262/concienciadigital.v9i3.3704
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References
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Canale, A., Corradin, R., & Nipoti, B. (2022). Importance conditional sampling for Pitman–Yor mixtures. Statistics and Computing, 32, 40. https://doi.org/10.1007/s11222-022-10096-0
Chen, T., Zhao, H., Tan, C., Constable, T., Yip, S., & Zhao, Y. (2025). Bayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition. Biostatistics, 26(1), kxae045. https://doi.org/10.1093/biostatistics/kxae045
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Franzolini, B., & Rebaudo, G. (2024). Entropy regularization in probabilistic clustering. Statistical Methods & Applications, 33, 37–60. https://doi.org/10.1007/s10260-023-00716-y
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Grigorova, I., Efremov, A., & Karamfilov, A. (2025). An automated machine learning framework for customer segmentation integrating RFM analysis and clustering techniques. International Journal of Financial Studies, 13(4), 243. https://doi.org/10.3390/ijfs13040243
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Janicki, R., Raim, A. M., Holan, S. H., & Maples, J. J. (2022). Bayesian nonparametric multivariate spatial mixture mixed effects models with application to American Community Survey special tabulations. The Annals of Applied Statistics, 16(1), 144–168. https://doi.org/10.1214/21-AOAS1494
Johan, M. E., (2025). Implementation of customer segmentation model using K-Means and DBSCAN for fashion industry product transaction. International Journal on Informatics Visualization, 9(6). http://dx.doi.org/10.62527/joiv.9.6.2978
Karulkar, Y., Srivastava, S., Nandwana, R., & Stanley, S. (2025). The growing complexity of consumer choices: Unravelling consumer patterns with K-means and fuzzy logic. Journal of Statistical Theory and Applications, 24, 1165–1195. https://doi.org/10.1007/s44199-025-00143-w
Khoi, P. D., Trong, T. M., & Gan, C. (2025). Dirichlet mixed process integrated Bayesian estimation for individual securities. Journal of Risk and Financial Management, 18(6), 304. https://doi.org/10.3390/jrfm18060304
Kim, M., Lindberg, D., Crane, M., & Bezbradica, M. (2023). Dirichlet process log skew-normal mixture with a missing-at-random-covariate in insurance claim analysis. Econometrics, 11(4), 24. https://doi.org/10.3390/econometrics11040024
Lavigne, A., & Liverani, S. (2024). Quantifying the uncertainty of partitions for infinite mixture models. Statistics and Probability Letters, 204 (109930), 109930. https://doi.org/10.1016/j.spl.2023.109930
Linero, A. R. (2023). Prior and posterior checking of implicit causal assumptions. Biometrics, 79(4), 3153–3164. https://doi.org/10.1111/biom.13886
Marín Díaz, G. (2025). A fuzzy-XAI framework for customer segmentation and risk detection: Integrating RFM, 2-tuple modeling, and strategic scoring. Mathematics, 13(13), 2141. https://doi.org/10.3390/math13132141
Maruotti, A., Bulla, J., & Mark, T. (2019). Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach. Metron. International Journal of Statistics, 77, 19–42. https://doi.org/10.1007/s40300-019-00150-9
Niu, S., Yin, Q., Maa, J., Song, Y., Xu, Y., Bai, L., Pan, W., Yang, X. (2024). Enhancing healthcare decision support through explainable AI models for risk prediction. Decision Support Systems, 181(114228), 114228. https://doi.org/10.1016/j.dss.2024.114228
Park, J., Sim, K., Yang, D., Kim, Y., Hashizume, M., & Chung, Y. (2025). Nonparametric Bayesian Poisson hurdle random effects model: An application to temperature–suicide association study. Environmental and Ecological Statistics, 32, 579–601. https://doi.org/10.1007/s10651-025-00655-9
Ren, S., Wang, X., Liu, P., & Zhang, J. (2023). Bayesian nonparametric mixtures of exponential random graph models for ensembles of networks. Social Networks, 74, 156–165. https://doi.org/10.1016/j.socnet.2023.03.005
Rodríguez-Álvarez, M. X., Inácio, V., & Klein, N. (2025). Density regression via Dirichlet process mixtures of normal structured additive regression models. Statistics and Computing, 35, 47. https://doi.org/10.1007/s11222-025-10567-0
Sadaghiani, A. K., & Nunez-Yanez, J. (2025). n-HDP-GNN: Community-aware Bayesian clustering for distributed graph neural networks. The Journal of Supercomputing, 81, 1560. https://doi.org/10.1007/s11227-025-08017-9
Schiavon, L. (2025). Addressing topic modelling via reduced latent space clustering. Statistical Methods & Application, 34, 1–20. https://doi.org/10.1007/s10260-025-00779-z
Selva, F., Fuentes-García, R., & Gil-Leyva, M. F. (2025). Pyrichlet: a Python package for density estimation and clustering using Gaussian mixture models. Journal of Statistical Software, 112(8), 1–39. https://doi.org/10.18637/jss.v112.i08
Stephenson, B. J. K., Wu, S. M., & Dominici, F. (2024). Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model. Journal of the Royal Statistical Society: Series A, 187(2), 496–512. https://doi.org/10.1093/jrsssa/qnad135
Stylianou, T., & Pantelidou, A. (2025). A machine learning approach to consumer behavior in supermarket analytics. Decision Analytics Journal, 16(100600). https://doi.org/10.1016/j.dajour.2025.100600
Suh, Y. (2025). Discovering customer segments through interaction behaviors. Journal of Big Data, 12, 57. https://doi.org/10.1186/s40537-025-01111-y
Sukthong, P., & Charuengramporn, P. (2026). Performance evaluation of imputation techniques for telecommunications customer clustering. ECTI Transactions on Computer and Information Technology, 20(1), 174–192. https://ph01.tci-thaijo.org/index.php/ecticit/article/view/264267
Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006). Hierarchical Dirichlet Processes. Journal of the American Statistical Association, 101(476), 1566–1581. https://doi.org/10.1198/016214506000000302
Verde, P. E., & Rosner, G. L. (2025). A bias-corrected Bayesian nonparametric model for combining studies with varying quality in meta-analysis. Biometrical Journal, 67, e70034. https://doi.org/10.1002/bimj.70034
Wang, Y., Degleris, A., Williams, A., & Linderman, S. W. (2024). Spatiotemporal clustering with Neyman–Scott processes via connections to Bayesian nonparametric mixture models. Journal of the American Statistical Association, 119(547), 2382–2395. https://doi.org/10.1080/01621459.2023.2257896
Waters, A., Fronczyk, K., Guindani, M., Baraniuk, R. G., & Vannucci, M. (2015). A Bayesian nonparametric approach for the analysis of multiple categorical item responses. Journal of Statistical Planning and Inference, 166, 52–66. https://doi.org/10.1016/j.jspi.2014.07.004
Wu, D., & Liu, X. (2025). HC-means clustering algorithm for precision marketing on e-commerce platforms. Systems and Soft Computing, 7(200236), 200236. https://doi.org/10.1016/j.sasc.2025.200236
Yu, Q., & Quinn, K. M. (2022). A multidimensional pairwise comparison model for heterogeneous perceptions with an application to modelling the perceived truthfulness of public statements on COVID-19. Journal of the Royal Statistical Society. Series A, (Statistics in Society), 185(3), 1049–1073. https://doi.org/10.1111/rssa.12810
Arima, S., Polettini, S., Pasculli, G., Gesualdo, L., Pesce, F., & Procaccini, D.-A. (2024). A Bayesian nonparametric approach to correct for underreporting in count data. Biostatistics, 25(3), 904–918. https://doi.org/10.1093/biostatistics/kxad027
Battiston, M., & Rimella, L. (2025). Disclosure risk assessment with Bayesian non-parametric hierarchical modelling. Statistics and Computing, 35, 158. https://doi.org/10.1007/s11222-025-10693-9
Benlakhdar, S., Nadarajah, S., Rziza, M., & Oulad Haj Thami, R. (2025). Density-adaptive clustering of multivariate angular data using Dirichlet process mixture models with circular normal distribution for artificial intelligence applications. Statistics Optimization and Information Computing, 13(4), 1404–1412. https://doi.org/10.19139/soic-2310-5070-2146
Canale, A., Corradin, R., & Nipoti, B. (2022). Importance conditional sampling for Pitman–Yor mixtures. Statistics and Computing, 32, 40. https://doi.org/10.1007/s11222-022-10096-0
Chen, T., Zhao, H., Tan, C., Constable, T., Yip, S., & Zhao, Y. (2025). Bayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition. Biostatistics, 26(1), kxae045. https://doi.org/10.1093/biostatistics/kxae045
Dew, R., Ascarza, E., Netzer, O., & Sicherman, N. (2024). Detecting routines: Applications to ridesharing customer relationship management. Journal of Marketing Research, 61(2), 368–392. https://doi.org/10.1177/00222437231189185
Diana, A., Matechou, E., Griffin, J., Arnold, T., Tenan, S., & Volponi, S. (2023). A general modeling framework for open wildlife populations based on the Polya tree prior. Biometrics, 79(3), 2171–2183. https://doi.org/10.1111/biom.13756
Dijkstra, S. H. E., Hinne, M., Segers, E., & Molenaar, I. (2023). Clustering children’s learning behaviour to identify self-regulated learning support needs. Computers in Human Behavior, 145 (107754), 107754. https://doi.org/10.1016/j.chb.2023.107754
Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2), 209–230. https://doi.org/10.1214/aos/1176342360
Ferrari, D. (2023). hdpGLM: An R package to estimate heterogeneous effects in generalized linear models using hierarchical Dirichlet process. Journal of Statistical Software, 107(10), 1-37. https://doi.org/10.18637/jss.v107.i10
Franzolini, B., & Rebaudo, G. (2024). Entropy regularization in probabilistic clustering. Statistical Methods & Applications, 33, 37–60. https://doi.org/10.1007/s10260-023-00716-y
Frühwirth-Schnatter, S., Pittner, S., Weber, A., & Winter-Ebmer, R. (2018). Analyzing plant closure effects using time-varying mixture-of-experts Markov chain clustering. The Annals of Applied Statistics, 12(3), 1796-1830. https://doi.org/10.1214/17-aoas1132
Giampino, A., Canale, A., & Nipoti, B. (2025). A Bayesian model for co-clustering ordinal data with informative missing entries. Statistics and Computing, 35, 164. https://doi.org/10.1007/s11222-025-10703-w
Goeken, N., Kurz, P., & Steiner, W. J. (2024). Multimodal preference heterogeneity in choice-based conjoint analysis: A simulation study. Journal of Business Economics, 94(1), 137–185. https://doi.org/10.1007/s11573-023-01156-6
González Rodríguez, A. J., & Kolyada, A. (2025). An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s business modeling and strategic planning methodology. BioProducts Business, 10(3). https://doi.org/10.22382/bpb-2025-003
Grigorova, I., Efremov, A., & Karamfilov, A. (2025). An automated machine learning framework for customer segmentation integrating RFM analysis and clustering techniques. International Journal of Financial Studies, 13(4), 243. https://doi.org/10.3390/ijfs13040243
Gwee, X. Y., Gormley, I. C., & Fop, M. (2025). Model-based clustering for network data via a latent shrinkage position cluster model. Network Science, 13(e19). https://doi.org/10.1017/nws.2025.10014
Janicki, R., Raim, A. M., Holan, S. H., & Maples, J. J. (2022). Bayesian nonparametric multivariate spatial mixture mixed effects models with application to American Community Survey special tabulations. The Annals of Applied Statistics, 16(1), 144–168. https://doi.org/10.1214/21-AOAS1494
Johan, M. E., (2025). Implementation of customer segmentation model using K-Means and DBSCAN for fashion industry product transaction. International Journal on Informatics Visualization, 9(6). http://dx.doi.org/10.62527/joiv.9.6.2978
Karulkar, Y., Srivastava, S., Nandwana, R., & Stanley, S. (2025). The growing complexity of consumer choices: Unravelling consumer patterns with K-means and fuzzy logic. Journal of Statistical Theory and Applications, 24, 1165–1195. https://doi.org/10.1007/s44199-025-00143-w
Khoi, P. D., Trong, T. M., & Gan, C. (2025). Dirichlet mixed process integrated Bayesian estimation for individual securities. Journal of Risk and Financial Management, 18(6), 304. https://doi.org/10.3390/jrfm18060304
Kim, M., Lindberg, D., Crane, M., & Bezbradica, M. (2023). Dirichlet process log skew-normal mixture with a missing-at-random-covariate in insurance claim analysis. Econometrics, 11(4), 24. https://doi.org/10.3390/econometrics11040024
Lavigne, A., & Liverani, S. (2024). Quantifying the uncertainty of partitions for infinite mixture models. Statistics and Probability Letters, 204 (109930), 109930. https://doi.org/10.1016/j.spl.2023.109930
Linero, A. R. (2023). Prior and posterior checking of implicit causal assumptions. Biometrics, 79(4), 3153–3164. https://doi.org/10.1111/biom.13886
Marín Díaz, G. (2025). A fuzzy-XAI framework for customer segmentation and risk detection: Integrating RFM, 2-tuple modeling, and strategic scoring. Mathematics, 13(13), 2141. https://doi.org/10.3390/math13132141
Maruotti, A., Bulla, J., & Mark, T. (2019). Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach. Metron. International Journal of Statistics, 77, 19–42. https://doi.org/10.1007/s40300-019-00150-9
Niu, S., Yin, Q., Maa, J., Song, Y., Xu, Y., Bai, L., Pan, W., Yang, X. (2024). Enhancing healthcare decision support through explainable AI models for risk prediction. Decision Support Systems, 181(114228), 114228. https://doi.org/10.1016/j.dss.2024.114228
Park, J., Sim, K., Yang, D., Kim, Y., Hashizume, M., & Chung, Y. (2025). Nonparametric Bayesian Poisson hurdle random effects model: An application to temperature–suicide association study. Environmental and Ecological Statistics, 32, 579–601. https://doi.org/10.1007/s10651-025-00655-9
Ren, S., Wang, X., Liu, P., & Zhang, J. (2023). Bayesian nonparametric mixtures of exponential random graph models for ensembles of networks. Social Networks, 74, 156–165. https://doi.org/10.1016/j.socnet.2023.03.005
Rodríguez-Álvarez, M. X., Inácio, V., & Klein, N. (2025). Density regression via Dirichlet process mixtures of normal structured additive regression models. Statistics and Computing, 35, 47. https://doi.org/10.1007/s11222-025-10567-0
Sadaghiani, A. K., & Nunez-Yanez, J. (2025). n-HDP-GNN: Community-aware Bayesian clustering for distributed graph neural networks. The Journal of Supercomputing, 81, 1560. https://doi.org/10.1007/s11227-025-08017-9
Schiavon, L. (2025). Addressing topic modelling via reduced latent space clustering. Statistical Methods & Application, 34, 1–20. https://doi.org/10.1007/s10260-025-00779-z
Selva, F., Fuentes-García, R., & Gil-Leyva, M. F. (2025). Pyrichlet: a Python package for density estimation and clustering using Gaussian mixture models. Journal of Statistical Software, 112(8), 1–39. https://doi.org/10.18637/jss.v112.i08
Stephenson, B. J. K., Wu, S. M., & Dominici, F. (2024). Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model. Journal of the Royal Statistical Society: Series A, 187(2), 496–512. https://doi.org/10.1093/jrsssa/qnad135
Stylianou, T., & Pantelidou, A. (2025). A machine learning approach to consumer behavior in supermarket analytics. Decision Analytics Journal, 16(100600). https://doi.org/10.1016/j.dajour.2025.100600
Suh, Y. (2025). Discovering customer segments through interaction behaviors. Journal of Big Data, 12, 57. https://doi.org/10.1186/s40537-025-01111-y
Sukthong, P., & Charuengramporn, P. (2026). Performance evaluation of imputation techniques for telecommunications customer clustering. ECTI Transactions on Computer and Information Technology, 20(1), 174–192. https://ph01.tci-thaijo.org/index.php/ecticit/article/view/264267
Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006). Hierarchical Dirichlet Processes. Journal of the American Statistical Association, 101(476), 1566–1581. https://doi.org/10.1198/016214506000000302
Verde, P. E., & Rosner, G. L. (2025). A bias-corrected Bayesian nonparametric model for combining studies with varying quality in meta-analysis. Biometrical Journal, 67, e70034. https://doi.org/10.1002/bimj.70034
Wang, Y., Degleris, A., Williams, A., & Linderman, S. W. (2024). Spatiotemporal clustering with Neyman–Scott processes via connections to Bayesian nonparametric mixture models. Journal of the American Statistical Association, 119(547), 2382–2395. https://doi.org/10.1080/01621459.2023.2257896
Waters, A., Fronczyk, K., Guindani, M., Baraniuk, R. G., & Vannucci, M. (2015). A Bayesian nonparametric approach for the analysis of multiple categorical item responses. Journal of Statistical Planning and Inference, 166, 52–66. https://doi.org/10.1016/j.jspi.2014.07.004
Wu, D., & Liu, X. (2025). HC-means clustering algorithm for precision marketing on e-commerce platforms. Systems and Soft Computing, 7(200236), 200236. https://doi.org/10.1016/j.sasc.2025.200236
Yu, Q., & Quinn, K. M. (2022). A multidimensional pairwise comparison model for heterogeneous perceptions with an application to modelling the perceived truthfulness of public statements on COVID-19. Journal of the Royal Statistical Society. Series A, (Statistics in Society), 185(3), 1049–1073. https://doi.org/10.1111/rssa.12810