Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

Kilroy, David P.; Healy, Graham; Caton, Simon

doi:10.1109/access.2022.3165043

Cited by 14 publications

(18 citation statements)

References 106 publications

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“…dog food, cereal, soda etc.) [11,18,27]. New products added into GNPD are collected by "mystery shoppers" from around the world, who scan shops selling CPG goods [18,27] and add ≈33,000 products per month [11] to the database.…”

Section: Methodsmentioning

confidence: 99%

“…The evaluation approach is also not automated and therefore is not easily repeatable. The second study which stands out for its evaluation is [18] which assesses customer need keyphrases in a manner similar to this study. Specifically, it extracts needs at a future time period from the Mintel product database for comparison to needs from UGC.…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 summarizes key studies and highlights the key contributions of TCN: 1) comparing needs from UGC to real customer needs e.g. needs generated by professionals [17] or from a database of products [18]; 2) evaluating needs across a variety of categories e.g. toothpaste, soda etc.…”

Section: Related Workmentioning

confidence: 99%

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The Trending Customer Needs (TCN) Dataset: A Benchmarking and Automated Evaluation Approach for New Product Development

Kilroy,

Caton,

Healy

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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In recent years, there have been many studies which summarize User Generated Content as lists of ranked keyphrases representing customer needs for the purposes of New Product Development. However, methods for the evaluation of keyphrase lists do not robustly assess solutions for these purposes. Therefore, in this paper we present the "Trending Customer Needs" (TCN) dataset of over 9000 top trending customer need keyphrases organized by month from 2007-2021 which spans 37 product categories in the area of Consumer Packaged Goods (e.g. toothpaste, eyeliner, beer etc.). TCN is a curated dataset for the benchmarking of supervised machine learning approaches in the prediction of customer needs using User Generated Content. We describe the process of curating TCN while ensuring its quality. Finally, we demonstrate its utility via a case study of Reddit discourse as a potential predictor for future customer needs in Consumer Packaged Goods.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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The Trending Customer Needs (TCN) Dataset: A Benchmarking and Automated Evaluation Approach for New Product Development

Kilroy,

Caton,

Healy

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Extracting product‐related knowledge from chatter is more efficient than the traditional interview‐based “voice of customers” approach (Yang et al, 2019). Kilroy et al (2022) captured future customer needs based on a document filtering method (discovering potentially relevant social media content) and a key‐phrase ranking method (identifying terms with increasing frequency). Saura et al (2021) used topic modeling and text analysis to extract innovation insights from chatter for product development.…”

Section: Literature Reviewmentioning

confidence: 99%

Capturing product/service improvement ideas from social media based on lead user theory

Yin,

Jiang,

Jain

et al. 2023

J of Product Innov Manag

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Capturing valuable product/service improvement ideas is helpful for the development of new features. However, the existing methods for capturing such improvement ideas have the disadvantages of high cost, long time lag, information overload, and difficulty in getting a response. We propose an innovative framework based on lead user theory for capturing product/service improvement ideas from user‐generated content on social media (henceforth called “chatter”). To identify the chatter containing improvement ideas, we design a machine‐learning‐based imbalanced classification model. Additionally, we use text summarization technology to get a rough sense of improvement ideas from the selected chatter. We validate the proposed framework by a case study in the automotive industry. The results demonstrate that the ideas extracted by our framework are breakthrough innovative, useful, feasible, and adoptable.

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“…Benchmarking Machine Learning Models for Need Identification. Kilroy, Caton, and Healy (2023) examine needmining in their paper entitled The Trending Customer Needs (TCN) Dataset: A Benchmarking and Automated Evaluation Approach for New Product Development. This paper takes a step back to broadly assess highly used key phrases in the consumer packaged goods market segment.…”

Section: Papers At a Glancementioning

confidence: 99%

Introduction to the Minitrack on Data Analytics, Data Mining, and Machine Learning for Social Media

Babb,

Mentzer,

Yates

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Thank you for your interest in the Data Analytics, Data Mining, and Machine Learning for Social Media Minitrack. This year's paper contributions vary in scope and focus and thus provide a rich spectral view of developing insights and understanding from social media. According to Monique Thomas of Later, social media is experiencing a significant evolution: "We've said goodbye to simple status updates (we miss you!) and hello to social selling and growing digital communities." However, our digital communities continue to struggle with the challenge of being free and open, allowing anyone to express their thoughts and ideas, while at the same time working to combat misinformation and deep fakes. We are grateful to social media scholars who continue on various paths of sensemaking to better understand social media's impact on our society. This year's papers provide many new insights into this vital area of research.

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Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

Cited by 14 publications

References 106 publications

The Trending Customer Needs (TCN) Dataset: A Benchmarking and Automated Evaluation Approach for New Product Development

The Trending Customer Needs (TCN) Dataset: A Benchmarking and Automated Evaluation Approach for New Product Development

Capturing product/service improvement ideas from social media based on lead user theory

Introduction to the Minitrack on Data Analytics, Data Mining, and Machine Learning for Social Media

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