IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i16p1958-d615470.html
   My bibliography  Save this article

Machine Learning Approach for Targeting and Recommending a Product for Project Management

Author

Listed:
  • Hasmat Malik

    (BEARS, NUS Campus, University Town, Singapore 138602, Singapore
    Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak, Kuala Terengganu 21300, Terengganu, Malaysia)

  • Asyraf Afthanorhan

    (Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak, Kuala Terengganu 21300, Terengganu, Malaysia)

  • Noor Aina Amirah

    (Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak, Kuala Terengganu 21300, Terengganu, Malaysia)

  • Nuzhat Fatema

    (Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak, Kuala Terengganu 21300, Terengganu, Malaysia
    Intelligent Prognostic Private Limited, Delhi 110093, India)

Abstract

Conventionally, a market research and strategy for a product depends on the interviews and an explicit cluster/society to identify the customer’s needs. Customer-created information (CCI), such as call-center data, online reviews, and social media posts, provides an opportunity to recognize the customer’s needs more efficiently. Moreover, developed conventional approaches are not compatible with large CCI datasets because most of the CCI-contents are repetitive and uninformative. In this paper, a machine learning approach for identifying the customer needs from the CCI dataset is proposed and its performance is evaluated for targeting and recommending a new product for project management. After the identification of the needs of the customer, information can be used to develop a market strategy, new product launching, brand positioning and much more long/short term planning.

Suggested Citation

  • Hasmat Malik & Asyraf Afthanorhan & Noor Aina Amirah & Nuzhat Fatema, 2021. "Machine Learning Approach for Targeting and Recommending a Product for Project Management," Mathematics, MDPI, vol. 9(16), pages 1-29, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1958-:d:615470
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/16/1958/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/16/1958/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Abbie Griffin & John R. Hauser, 1993. "The Voice of the Customer," Marketing Science, INFORMS, vol. 12(1), pages 1-27.
    3. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    4. Peter S. Fader & Russell S. Winer, 2012. "Introduction to the Special Issue on the Emergence and Impact of User-Generated Content," Marketing Science, INFORMS, vol. 31(3), pages 369-371, May.
    5. Chan, Lai-Kow & Wu, Ming-Lu, 2002. "Quality function deployment: A literature review," European Journal of Operational Research, Elsevier, vol. 143(3), pages 463-497, December.
    6. Herrmann, Andreas & Huber, Frank & Braunstein, Christine, 2000. "Market-driven product and service design: Bridging the gap between customer needs, quality management, and customer satisfaction," International Journal of Production Economics, Elsevier, vol. 66(1), pages 77-96, June.
    7. V. Krishnan & Karl T. Ulrich, 2001. "Product Development Decisions: A Review of the Literature," Management Science, INFORMS, vol. 47(1), pages 1-21, January.
    8. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    9. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    10. Dong Soo Kim & Roger A. Bailey & Nino Hardt & Greg M. Allenby, 2017. "Benefit-Based Conjoint Analysis," Marketing Science, INFORMS, vol. 36(1), pages 54-69, January.
    11. Joachim Büschken & Greg M. Allenby, 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science, INFORMS, vol. 35(6), pages 953-975, November.
    12. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hanyang Luo & Wugang Song & Wanhua Zhou & Xudong Lin & Sumin Yu, 2023. "An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content," Sustainability, MDPI, vol. 15(18), pages 1-29, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Uttara Ananthakrishnan & Davide Proserpio & Siddhartha Sharma, 2023. "I Hear You: Does Quality Improve with Customer Voice?," Marketing Science, INFORMS, vol. 42(6), pages 1143-1161, November.
    3. Roelen-Blasberg, Tobias & Habel, Johannes & Klarmann, Martin, 2023. "Automated inference of product attributes and their importance from user-generated content: Can we replace traditional market research?," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 164-188.
    4. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
    5. Jiyeon Hong & Paul R. Hoban, 2022. "Writing More Compelling Creative Appeals: A Deep Learning-Based Approach," Marketing Science, INFORMS, vol. 41(5), pages 941-965, September.
    6. Shivaji Alaparthi & Manit Mishra, 2021. "BERT: a sentiment analysis odyssey," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 118-126, June.
    7. Christof Naumzik & Stefan Feuerriegel & Markus Weinmann, 2022. "I Will Survive: Predicting Business Failures from Customer Ratings," Marketing Science, INFORMS, vol. 41(1), pages 188-207, January.
    8. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.
    9. Dinesh Puranam & Vishal Narayan & Vrinda Kadiyali, 2017. "The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors," Marketing Science, INFORMS, vol. 36(5), pages 726-746, September.
    10. Jifeng Mu & Jonathan Z. Zhang, 2021. "Seller marketing capability, brand reputation, and consumer journeys on e-commerce platforms," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 994-1020, September.
    11. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    12. Xin (Shane) Wang & Feng Mai & Roger H. L. Chiang, 2014. "Database Submission ---Market Dynamics and User-Generated Content About Tablet Computers," Marketing Science, INFORMS, vol. 33(3), pages 449-458, May.
    13. Aron Culotta & Jennifer Cutler, 2016. "Mining Brand Perceptions from Twitter Social Networks," Marketing Science, INFORMS, vol. 35(3), pages 343-362, May.
    14. Kullak, Franziska S. & Baier, Daniel & Woratschek, Herbert, 2023. "How do customers meet their needs in in-store and online fashion shopping? A comparative study based on the jobs-to-be-done theory," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    15. Mengxia Zhang & Lan Luo, 2023. "Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp," Management Science, INFORMS, vol. 69(1), pages 25-50, January.
    16. Zhang, Min & Sun, Lin & Wang, G. Alan & Li, Yuzhuo & He, Shuguang, 2022. "Using neutral sentiment reviews to improve customer requirement identification and product design strategies," International Journal of Production Economics, Elsevier, vol. 254(C).
    17. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    18. Dinesh Puranam & Vrinda Kadiyali & Vishal Narayan, 2021. "The Impact of Increase in Minimum Wages on Consumer Perceptions of Service: A Transformer Model of Online Restaurant Reviews," Marketing Science, INFORMS, vol. 40(5), pages 985-1004, September.
    19. Meinel, Martin & Eismann, Tobias T. & Baccarella, Christian V. & Fixson, Sebastian K. & Voigt, Kai-Ingo, 2020. "Does applying design thinking result in better new product concepts than a traditional innovation approach? An experimental comparison study," European Management Journal, Elsevier, vol. 38(4), pages 661-671.
    20. Li, Yan-Lai & Tang, Jia-Fu & Chin, Kwai-Sang & Jiang, Yu-Shi & Han, Yi & Pu, Yun, 2011. "Estimating the final priority ratings of engineering characteristics in mature-period product improvement by MDBA and AHP," International Journal of Production Economics, Elsevier, vol. 131(2), pages 575-586, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1958-:d:615470. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.