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

David Kilroy1, Graham Healy2, Simon Caton1

  • 1School of Computer Science, University College Dublin, Dublin, Ireland.

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Summary
This summary is machine-generated.

This study introduces a model to predict future popular product needs from online content. It accurately forecasts emerging customer demands, offering businesses early market access.

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Area of Science:

  • Computational Linguistics
  • Market Research
  • Data Science

Background:

  • Existing methods for extracting customer needs from user-generated content often overlook future product trends.
  • Identifying unmet needs for upcoming popular products remains a significant challenge in market analysis.

Purpose of the Study:

  • To develop a supervised keyphrase classification model for predicting future popular product needs.
  • To leverage the Trending Customer Needs (TCN) dataset for training a predictive algorithm.

Main Methods:

  • Utilized the Trending Customer Needs (TCN) dataset (2011-2021) covering various consumer packaged goods.
  • Employed a time series algorithm trained on Reddit keyphrase features to predict future needs (1-3 years ahead).
  • Implemented Multi-Task Learning to enable cross-category prediction.

Main Results:

  • The proposed model outperforms existing literature baselines.
  • Demonstrated accurate prediction of emerging needs even for product categories not included in the training data (e.g., predicting shampoo needs after training on toothpaste, cereal, and beer).

Conclusions:

  • The developed model effectively predicts future popular customer needs from online data.
  • This approach offers significant business advantages, including early market entry and competitive insights.