Prediction of future customer needs using machine learning across multiple product categories

David Kilroy, Graham Healy and Simon Caton

PLOS ONE, 2024, vol. 19, issue 8, 1-49

Abstract: In recent years, computational approaches for extracting customer needs from user generated content have been proposed. However, there is a lack of studies that focus on extracting unmet needs for future popular products. Therefore, this study presents a supervised keyphrase classification model which predicts needs that will become popular in real products in the marketplace. To do this, we utilize Trending Customer Needs (TCN)—a monthly dataset of trending keyphrase customer needs occurring in new products during 2011-2021 across multiple categories of Consumer Packaged Goods e.g. toothpaste, eyeliner, beer, etc. We are the first study to use this specific dataset and employ it by training a time series algorithm to learn the relationship between features we generate for each candidate keyphrase on Reddit to the ones in the dataset 1-3 years in the future. We show that our approach outperforms a baseline in the literature and through Multi-Task Learning can accurately predict needs for a category it wasn’t trained on e.g. train on toothpaste, cereal, and beer products yet still predict for shampoo products. The findings from this research could provide many advantages to businesses such as gaining early access into markets.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0307180

DOI: 10.1371/journal.pone.0307180

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