IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/v25y2024i1d10.1007_s10799-023-00388-w.html
   My bibliography  Save this article

The state of lead scoring models and their impact on sales performance

Author

Listed:
  • Migao Wu

    (University of Ottawa)

  • Pavel Andreev

    (University of Ottawa)

  • Morad Benyoucef

    (University of Ottawa)

Abstract

Although lead scoring is an essential component of lead management, there is a lack of a comprehensive literature review and a classification framework dedicated to it. Lead scoring is an effective and efficient way of measuring the quality of leads. In addition, as a critical Information Technology tool, a proper lead scoring model acts as an alleviator to weaken the conflicts between sales and marketing functions. Yet, little is known regarding lead scoring models and their impact on sales performance. Lead scoring models are commonly categorized into two classes: traditional and predictive. While the former primarily relies on the experience and knowledge of salespeople and marketers, the latter utilizes data mining models and machine learning algorithms to support the scoring process. This study aims to review and analyze the existing literature on lead scoring models and their impact on sales performance. A systematic literature review was conducted to examine lead scoring models. A total of 44 studies have met the criteria and were included for analysis. Fourteen metrics were identified to measure the impact of lead scoring models on sales performance. With the increased use of data mining and machine learning techniques in the fourth industrial revolution, predictive lead scoring models are expected to replace traditional lead scoring models as they positively impact sales performance. Despite the relative cost of implementing and maintaining predictive lead scoring models, it is still beneficial to supersede traditional lead scoring models, given the higher effectiveness and efficiency of predictive lead scoring models. This study reveals that classification is the most popular data mining model, while decision tree and logistic regression are the most applied algorithms among all the predictive lead scoring models. This study contributes by systematizing and recommending which machine learning method (i.e., supervised and/or unsupervised) shall be used to build predictive lead scoring models based on the integrity of different types of data sources. Additionally, this study offers both theoretical and practical research directions in the lead scoring field.

Suggested Citation

  • Migao Wu & Pavel Andreev & Morad Benyoucef, 2024. "The state of lead scoring models and their impact on sales performance," Information Technology and Management, Springer, vol. 25(1), pages 69-98, March.
    • Handle: RePEc:spr:infotm:v:25:y:2024:i:1:d:10.1007_s10799-023-00388-w
    DOI: 10.1007/s10799-023-00388-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-023-00388-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10799-023-00388-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. YongSeog Kim & W. Nick Street & Gary J. Russell & Filippo Menczer, 2005. "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, INFORMS, vol. 51(2), pages 264-276, February.
    2. Abolfazl Kazemi & Mohammad Esmaeil Babaei & Mahsa Oroojeni Mohammad Javad, 2015. "A data mining approach for turning potential customers into real ones in basket purchase analysis," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 19(2), pages 139-158.
    3. J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.
    4. Ohiomah, Alhassan & Andreev, Pavel & Benyoucef, Morad & Hood, David, 2019. "The role of lead management systems in inside sales performance," Journal of Business Research, Elsevier, vol. 102(C), pages 163-177.
    5. Ossi Ylijoki, 2018. "Guidelines for assessing the value of a predictive algorithm: a case study," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(1), pages 19-26, March.
    6. Rutherford, Brian N. & Marshall, Greg W. & Park, JungKun, 2014. "The moderating effects of gender and inside versus outside sales role in multifaceted job satisfaction," Journal of Business Research, Elsevier, vol. 67(9), pages 1850-1856.
    7. P. Baecke & D. Van Den Poel, 2012. "Including Spatial Interdependence in Customer Acquisition Models: a Cross-Category Comparison," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/788, Ghent University, Faculty of Economics and Business Administration.
    8. Rakesh Verma & Saroj Koul & Sushanth S. Pai, 2016. "Identifying profitable clientele using the analytical hierarchy process," International Journal of Business and Systems Research, Inderscience Enterprises Ltd, vol. 10(2/3/4), pages 220-237.
    9. Alessandro Liberati & Douglas G Altman & Jennifer Tetzlaff & Cynthia Mulrow & Peter C Gøtzsche & John P A Ioannidis & Mike Clarke & P J Devereaux & Jos Kleijnen & David Moher, 2009. "The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-28, July.
    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. Hilke, Lukas, 2024. "When does marketing & sales collaboration affect the perceived lead quality? The moderating effects of IT systems," Junior Management Science (JUMS), Junior Management Science e. V., vol. 9(3), pages 1681-1699.
    2. Ramos, Carla & Claro, Danny P. & Germiniano, Renato, 2023. "The effect of inside sales and hybrid sales structures on customer value creation," Journal of Business Research, Elsevier, vol. 154(C).
    3. Tripathy, Prajukta & Jena, Pabitra Kumar & Mishra, Bikash Ranjan, 2024. "Systematic literature review and bibliometric analysis of energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    4. Elizabeth T Cafiero-Fonseca & Andrew Stawasz & Sydney T Johnson & Reiko Sato & David E Bloom, 2017. "The full benefits of adult pneumococcal vaccination: A systematic review," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-23, October.
    5. Ludoviko Zirimenya & Fatima Mahmud-Ajeigbe & Ruth McQuillan & You Li, 2020. "A systematic review and meta-analysis to assess the association between urogenital schistosomiasis and HIV/AIDS infection," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(6), pages 1-13, June.
    6. Desalegne Amare & Fentie Ambaw Getahun & Endalkachew Worku Mengesha & Getenet Dessie & Melashu Balew Shiferaw & Tegenaw Asemamaw Dires & Kefyalew Addis Alene, 2023. "Effectiveness of healthcare workers and volunteers training on improving tuberculosis case detection: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-13, March.
    7. Trang Nguyen & Sara Holton & Thach Tran & Jane Fisher, 2019. "Informal mental health interventions for people with severe mental illness in low and lower middle-income countries: A systematic review of effectiveness," International Journal of Social Psychiatry, , vol. 65(3), pages 194-206, May.
    8. Natalya Ivanova & Ekaterina Zolotova, 2023. "Landolt Indicator Values in Modern Research: A Review," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    9. Su Keng Tan & Wai Keung Leung & Alexander Tin Hong Tang & Roger A Zwahlen, 2017. "Effects of mandibular setback with or without maxillary advancement osteotomies on pharyngeal airways: An overview of systematic reviews," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-20, October.
    10. Vecchio, Riccardo & Caso, Gerarda & Cembalo, Luigi & Borrello, Massimiliano, 2020. "Is respondents’ inattention in online surveys a major issue for research?," Economia agro-alimentare / Food Economy, Italian Society of Agri-food Economics/Società Italiana di Economia Agro-Alimentare (SIEA), vol. 22(01), March.
    11. Alessandro Concari & Gerjo Kok & Pim Martens, 2020. "A Systematic Literature Review of Concepts and Factors Related to Pro-Environmental Consumer Behaviour in Relation to Waste Management Through an Interdisciplinary Approach," Sustainability, MDPI, vol. 12(11), pages 1-50, May.
    12. Damiano Pizzol & Mike Trott & Igor Grabovac & Mario Antunes & Anna Claudia Colangelo & Simona Ippoliti & Cristian Petre Ilie & Anne Carrie & Nicola Veronese & Lee Smith, 2021. "Laparoscopy in Low-Income Countries: 10-Year Experience and Systematic Literature Review," IJERPH, MDPI, vol. 18(11), pages 1-11, May.
    13. Yehuda Weizman & Oren Tirosh & Jeanie Beh & Franz Konstantin Fuss & Sonja Pedell, 2021. "Gait Assessment Using Wearable Sensor-Based Devices in People Living with Dementia: A Systematic Review," IJERPH, MDPI, vol. 18(23), pages 1-14, December.
    14. Jorge Arias-de la Torre & Elisa Puigdomenech & Jose M Valderas & Jonathan P Evans & Vicente Martín & Antonio J Molina & Nuria Rodríguez & Mireia Espallargues, 2019. "Availability of specific tools to assess patient reported outcomes in hip arthroplasty in Spain. Identifying the best candidates to incorporate in an arthroplasty register. A systematic review and sta," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.
    15. Alessandro Margherita & Emanuele Banchi & Alfredo Biffi & Gianluca di Castri & Rocco Morelli, 2022. "Beyond Total Cost Management (TCM) to Systemic Value Management (SVM): Transformational Trends and a Research Manifesto for an Evolving Discipline," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
    16. Stefano D’Angelo & Angelo Cavallo & Antonio Ghezzi & Francesco Di Lorenzo, 2024. "Understanding corporate entrepreneurship in the digital age: a review and research agenda," Review of Managerial Science, Springer, vol. 18(12), pages 3719-3774, December.
    17. Fabio Magnacca & Riccardo Giannetti, 2024. "Management accounting and new product development: a systematic literature review and future research directions," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 28(2), pages 651-685, June.
    18. Jacob Elnaggar & Fern Tsien & Lucio Miele & Chindo Hicks & Clayton Yates & Melisa Davis, 2019. "An Integrative Genomics Approach for Associating Genetic Susceptibility with the Tumor Immune Microenvironment in Triple Negative Breast Cancer," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 15(1), pages 1-12, February.
    19. Michael Masaracchio & William J Hanney & Xinliang Liu & Morey Kolber & Kaitlin Kirker, 2017. "Timing of rehabilitation on length of stay and cost in patients with hip or knee joint arthroplasty: A systematic review with meta-analysis," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-22, June.
    20. Evans, Rhiannon & White, James & Turley, Ruth & Slater, Thomas & Morgan, Helen & Strange, Heather & Scourfield, Jonathan, 2017. "Comparison of suicidal ideation, suicide attempt and suicide in children and young people in care and non-care populations: Systematic review and meta-analysis of prevalence," Children and Youth Services Review, Elsevier, vol. 82(C), pages 122-129.

    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:spr:infotm:v:25:y:2024:i:1:d:10.1007_s10799-023-00388-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.