IDEAS home Printed from https://ideas.repec.org/a/aes/jetimm/v1y2023i2p40-45.html
   My bibliography  Save this article

Discovering Efficient Keywords – An Exploratory Study on Comparing the Use of ChatGPT and Other Third-party Tools

Author

Listed:
  • Pingjun Jiang

    (La Salle University)

Abstract

ChatGPT has become a popular keyword discovery tool since it was introduced to the market in late 2022. This study is to compare the effectiveness of this new keyword discovery tool and Spyfu, a widely used third-party tool. The comparison aims to examine which tool is more effective in discovering efficient keywords. The keyword efficiency scores are defined and calculated by using the Data Envelopment Analysis (DEA) based on historical data from a specific period of time. Both ChatGPT and a third-party tool (Spyfu) provide two lists of top keywords with Broad Match and Modified Broad Match. It is found that about forty percent of Spyfu recommended keywords turn out to be efficient, while less than twenty percent of the keywords generated by ChatGPT are efficient. The results of the study indicate that ChatGPT is not as effective in discovering efficient keywords compared to Spyfu. However, ChatGPT is found to be highly effective in predicting search trends and identifying long-tail keywords and query questions that are specific and targeted to the users' needs. A combination of both tools may provide the best results for keyword discovery and strategy fine-tuning.

Suggested Citation

  • Pingjun Jiang, 2023. "Discovering Efficient Keywords – An Exploratory Study on Comparing the Use of ChatGPT and Other Third-party Tools," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, vol. 1(2), pages 40-45, July.
    • Handle: RePEc:aes:jetimm:v:1:y:2023:i:2:p:40-45
    as

    Download full text from publisher

    File URL: http://www.etimm.ase.ro/RePEc/aes/jetimm/2023/JETIMM_V02_2023_62.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kamakura, Wagner A & Ratchford, Brian T & Agrawal, Jagdish, 1988. "Measuring Market Efficiency and Welfare Loss," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(3), pages 289-302, December.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Nagpal, Mayank & Petersen, J. Andrew, 2021. "Keyword Selection Strategies in Search Engine Optimization: How Relevant is Relevance?," Journal of Retailing, Elsevier, vol. 97(4), pages 746-763.
    4. Erdmann, Anett & Arilla, Ramón & Ponzoa, José M., 2022. "Search engine optimization: The long-term strategy of keyword choice," Journal of Business Research, Elsevier, vol. 144(C), pages 650-662.
    5. Cutler, Kelly, 2023. "ChatGPT and search engine optimisation: The future is here," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 9(1), pages 8-22, June.
    Full references (including those not matched with items on IDEAS)

    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. Kerstens, Kristiaan & Mounir, Amine & de Woestyne, Ignace Van, 2011. "Non-parametric frontier estimates of mutual fund performance using C- and L-moments: Some specification tests," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1190-1201, May.
    2. Donthu, Naveen & Hershberger, Edmund K. & Osmonbekov, Talai, 2005. "Benchmarking marketing productivity using data envelopment analysis," Journal of Business Research, Elsevier, vol. 58(11), pages 1474-1482, November.
    3. Ologunebi, John & Taiwo, Ebenezer, 2023. "The Importance of SEO and SEM in improving brand visibility in E-commerce industry; A study of Decathlon, Amazon and ASOS," MPRA Paper 119205, University Library of Munich, Germany.
    4. González, Eduardo & Cárcaba, Ana & Ventura, Juan, 2015. "How car dealers adjust prices to reach the product efficiency frontier in the Spanish automobile market," Omega, Elsevier, vol. 51(C), pages 38-48.
    5. Luo, Xueming & Donthu, Naveen, 2005. "Assessing advertising media spending inefficiencies in generating sales," Journal of Business Research, Elsevier, vol. 58(1), pages 28-36, January.
    6. Kerstin Pull & Birgit Pferdmenges & Uschi Backes-Gellner, 2017. "Do Research Training Groups Operate at Optimal Size?," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 18(2), pages 129-145, May.
    7. Khezrimotlagh, Dariush & Kaffash, Sepideh & Zhu, Joe, 2022. "U.S. airline mergers’ performance and productivity change," Journal of Air Transport Management, Elsevier, vol. 102(C).
    8. Nurhan Davutyan & Canan Yildirim, 2013. "Competitiveness in Turkish Banking: 2002-2011," Working Papers 774, Economic Research Forum, revised Sep 2013.
    9. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    10. Seufert, Juergen Heinz & Arjomandi, Amir & Dakpo, K. Hervé, 2017. "Evaluating airline operational performance: A Luenberger-Hicks-Moorsteen productivity indicator," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 52-68.
    11. Agrell, Per J. & Teusch, Jonas, 2020. "Predictability and strategic behavior under frontier regulation," Energy Policy, Elsevier, vol. 137(C).
    12. Christian Growitsch & Tooraj Jamasb & Christine Müller & Matthias Wissner, 2016. "Social Cost Efficient Service Quality: Integrating Customer Valuation in Incentive Regulation—Evidence from the Case of Norway," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 71-91, Springer.
    13. Tengfei Wang & Jong Woo Kang & Vincent F. Valentine, 2020. "A holistic analysis of national e-commerce and logistics development," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 22(3), pages 500-513, September.
    14. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, April.
    15. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    16. Afsharian, Mohsen & Ahn, Heinz & Lopes, Ana & Vilela, Bruno, 2019. "Pitfalls in estimating the X-factor: The case of energy transmission regulation in Brazil," Socio-Economic Planning Sciences, Elsevier, vol. 65(C), pages 1-9.
    17. Huaimo You & Hong Fang & Xu Wang & Siran Fang, 2018. "Environmental Efficiency of Photovoltaic Power Plants in China—A Comparative Study of Different Economic Zones and Plant Types," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    18. Coelli, Tim J., 1995. "Recent Developments In Frontier Modelling And Efficiency Measurement," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 39(3), pages 1-27, December.
    19. Azad, Md A.S. & Ancev, Tihomir, 2010. "Using ecological indices to measure economic and environmental performance of irrigated agriculture," Ecological Economics, Elsevier, vol. 69(8), pages 1731-1739, June.
    20. Wang, Zhao-Hua & Zeng, Hua-Lin & Wei, Yi-Ming & Zhang, Yi-Xiang, 2012. "Regional total factor energy efficiency: An empirical analysis of industrial sector in China," Applied Energy, Elsevier, vol. 97(C), pages 115-123.

    More about this item

    Keywords

    Data Envelopment Analysis (DEA); Keyword Efficiency; ChatGPT; Search Engine Marketing.;
    All these keywords.

    JEL classification:

    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

    Statistics

    Access and download statistics

    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:aes:jetimm:v:1:y:2023:i:2:p:40-45. 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: Lucian Onisor (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    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.