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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2511.11646 |
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| _version_ | 1866915689569189888 |
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| author | Li, Yinxing Ishigaki, Tsukasa |
| author_facet | Li, Yinxing Ishigaki, Tsukasa |
| contents | Product line extension is a strategically important managerial decision that requires anticipating how consumer segments and purchasing contexts may respond to hypothetical product designs that do not yet exist in the market. Such decisions are inherently uncertain because managers must infer future outcomes from historical purchase data without direct market observations. This study addresses this challenge by proposing a data-driven decision support framework that enables forward-looking what-if analysis based on historical transaction data. We introduce a Conditional Tabular Variational Autoencoder (CTVAE) that learns the conditional joint distribution of product attributes and consumer characteristics from large-scale tabular data. By conditioning the generative process on controllable design variables such as container type, volume, flavor, and calorie content, the proposed model generates synthetic consumer attribute distributions for hypothetical line-extended products. This enables systematic exploration of alternative design scenarios without costly market pretests. The framework is evaluated using home-scan panel data covering more than 20,000 consumers and 700 soft drink products. Empirical results show that the CTVAE outperforms existing tabular generative models in capturing conditional consumer attribute distributions. Simulation-based analyses further demonstrate that the generated synthetic data support knowledge-driven reasoning for assessing cannibalization risks and identifying potential target segments. These findings highlight the value of conditional deep generative models as core components of decision support systems for product line extension planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11646 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What-If Decision Support for Product Line Extension Using Conditional Deep Generative Models Li, Yinxing Ishigaki, Tsukasa Machine Learning Artificial Intelligence Information Retrieval Product line extension is a strategically important managerial decision that requires anticipating how consumer segments and purchasing contexts may respond to hypothetical product designs that do not yet exist in the market. Such decisions are inherently uncertain because managers must infer future outcomes from historical purchase data without direct market observations. This study addresses this challenge by proposing a data-driven decision support framework that enables forward-looking what-if analysis based on historical transaction data. We introduce a Conditional Tabular Variational Autoencoder (CTVAE) that learns the conditional joint distribution of product attributes and consumer characteristics from large-scale tabular data. By conditioning the generative process on controllable design variables such as container type, volume, flavor, and calorie content, the proposed model generates synthetic consumer attribute distributions for hypothetical line-extended products. This enables systematic exploration of alternative design scenarios without costly market pretests. The framework is evaluated using home-scan panel data covering more than 20,000 consumers and 700 soft drink products. Empirical results show that the CTVAE outperforms existing tabular generative models in capturing conditional consumer attribute distributions. Simulation-based analyses further demonstrate that the generated synthetic data support knowledge-driven reasoning for assessing cannibalization risks and identifying potential target segments. These findings highlight the value of conditional deep generative models as core components of decision support systems for product line extension planning. |
| title | What-If Decision Support for Product Line Extension Using Conditional Deep Generative Models |
| topic | Machine Learning Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2511.11646 |