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A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach

Author

Listed:
  • Alireza Rezazadeh

    (Electrical and Computer Engineering Department, University of Illinois at Chicago, Chicago, IL 60607, USA)

Abstract

Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, undertaking this prediction has relied mostly on subjective human evaluations in the process of sales decision-making. In this paper, we addressed the problem of forecasting the outcome of Business to Business (B2B) sales by proposing a thorough data-driven Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Azure ML). This workflow consists of two pipelines: (1) An ML pipeline to train probabilistic predictive models on the historical sales opportunities data. In this pipeline, data is enriched with an extensive feature enhancement step and then used to train an ensemble of ML classification models in parallel. (2) A prediction pipeline to use the trained ML model and infer the likelihood of winning new sales opportunities along with calculating optimal decision boundaries. The effectiveness of the proposed workflow was evaluated on a real sales dataset of a major global B2B consulting firm. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value.

Suggested Citation

  • Alireza Rezazadeh, 2020. "A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach," Forecasting, MDPI, vol. 2(3), pages 1-17, August.
    • Handle: RePEc:gam:jforec:v:2:y:2020:i:3:p:15-283:d:395634
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    References listed on IDEAS

    as
    1. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Davis, Donna F. & Mentzer, John T., 2007. "Organizational factors in sales forecasting management," International Journal of Forecasting, Elsevier, vol. 23(3), pages 475-495.
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    Cited by:

    1. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    2. Alireza Rezazadeh & Yasamin Jafarian & Ali Kord, 2022. "Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features," Forecasting, MDPI, vol. 4(1), pages 1-13, February.
    3. Sonia Leva, 2021. "Editorial for Special Issue: “Feature Papers of Forecasting”," Forecasting, MDPI, vol. 3(1), pages 1-3, February.

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