IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 8

Published on: August 2026

DESIGN AND DEVELOPMENT OF AN ADVANCED MACHINE LEARNING FRAMEWORK FOR PREDICTING B2B SALES SUCCESS

T. Narasimhulu Dr. B. Poorna Satyannarayana S. Durga Prasad

Department of CSE, BITS (A), Vizag, 530048

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

The aims of this project are two-fold, firstly to help a Fortune 500 paper & paper product company encode what makes for sales success and secondly to create a model that will predict with a reasonable accuracy of a sales success. The ultimate goal is to boost sales close rates, reduce sales cycles and lower the cost of sales, thus generating the desired long-term benefit of the company generating additional revenue on top and additional profit at the bottom. The research team developed a number of models to predict the win probability for individual sales opportunities, selecting the model that most optimally predicted the win and was able to provide insights to serve as the core for a client tool. To do this, the team used the company's structured and unstructured data from its customer relationship management (CRM) system, Salesforce.com. They tried a variety of methods such as binomial logit, decision tree methods such as boosting with gradient boost and random forest. Individual attributes of customers, opportunities, and internal documentation methods that have the greatest influence on sales success were identified. The best model achieved a accuracy of 80%, precision of 86% and recall of 77% for predicting win propensity, which was an improvement over the current accuracy of sales predictions.

 

Keywords: B2B Sales; Machine Learning; Predictive Analytics; Sales Success Prediction; Customer Conversion; XGBoost; Random Forest.

How to Cite this Paper

Narasimhulu, T., Satyannarayana, B. P. & Prasad, S. D. (2026). Design and Development of an Advanced Machine Learning Framework for Predicting B2b Sales Success. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(8), 1-9. https://doi.org/10.55041/ijcope.v2i8.194

Narasimhulu, T., et al.. "Design and Development of an Advanced Machine Learning Framework for Predicting B2b Sales Success." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i8.194.

Narasimhulu, T.,B. Satyannarayana, and S. Prasad. "Design and Development of an Advanced Machine Learning Framework for Predicting B2b Sales Success." International Journal of Creative and Open Research in Engineering and Management 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i8.194.

Search & Index

References


  • Yan, J., et al. “On Machine Learning towards Predictive Sales Pipeline Analytics.” Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015, pp. 1945–1951. https://doi.org/10.1609/aaai.v29i1.9455

  • Ali, M., and Y. Lee. “CRM Sales Prediction Using Continuous Time-Evolving Classification.” Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018. https://doi.org/10.1609/aaai.v32i1.11418

  • Mortensen, S. V., et al. “Predicting and Defining B2B Sales Success with Machine Learning.” 2019 IEEE Symposium on Industrial Electronics & Applications (SIEDS), IEEE, 2019. https://doi.org/10.1109/SIEDS.2019.8735638

  • Harrison, D. E., and H. Ajjan. “CRM Technology: Bridging the Gap between Marketing Education and Practice.” Journal of Marketing Analytics, vol. 7, 2019, pp. 112–123. https://doi.org/10.1057/s41270-019-00063-6

  • Yan Qi, Chenliang Li, Han Deng, Min Cai, Yunwei Qi, Yuming Deng. 2019.A Deep Neural Framework for Sales Forecasting in E-Commerce. In Pro-ceedings of 28th ACM International Conference on Information and Knowl-edge Management (CIKM’19). ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3357384.3357883

  • Rezazadeh, A. “A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach.” Forecasting, vol. 2, no. 3, 2020, pp. 267–283. https://doi.org/10.3390/forecast2030015

  • Calixto, N.; Ferreira, J. Salespeople Performance Evaluation with Predictive Analytics in B2B.  Sci.202010, 4036. https://doi.org/10.3390/app10114036

  • Rohaan, D., et al. “Using Supervised Machine Learning for B2B Sales Forecasting: A Case Study of Spare Parts Sales Forecasting at an After-Sales Service Provider.” Expert Systems with Applications, vol. 188, 2022, article 115925. https://doi.org/10.1016/j.eswa.2021.115925

  • Bi, X., et al. “Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness.” INFORMS Journal on Computing, 2022. https://doi.org/10.1287/ijoc.2021.1147

  • Fries, M., and T. Ludwig. “Why Are the Sales Forecasts So Low? Socio-Technical Challenges of Using Machine Learning for Forecasting Sales in a Bakery.” Computer Supported Cooperative Work, 2022.
    https://doi.org/10.1007/s10606-022-09458-z

Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
  • Authors retain copyright.
  • Peer Review Type: Double-Blind Peer Review
  • Published on: Aug 24 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Scroll to Top