The paper and packaging company that provided the data for this research has a long history of sales expertise. This expertise is captured predominantly in the intuition of sales representatives, many of whom have worked in the industry for 20 years or more
Intuition is not easy to record and disseminate across an entire sales force, however, and thus one of the company’s most valuable resources is inaccessible to the broader organization. As a result, the company tasked this team with extracting the most important factors in driving sales success and modeling win propensities using data from their customer relationship management (CRM) system.
Most prior work in this space has been performed by private companies, both those that have developed proprietary technologies for internal use and those that sell B2B services related to predictive sales modeling. As a result, research in the field is typically unavailable to the public. Some examples include Implisit —a company recently acquired by Salesforce.com that focuses on data automation and predictive modeling—and Insight
Squared, which sells software that includes a capability to forecast sales outcomes.
Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, making 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 utilize 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.
Keywords: Costumer Relation Management; Business to Business Sales Prediction; Machine Learning; Predictive Modeling;
Chandan Nune, S. (2021). A Generalised Flow for B2B Sales Predictive Modelling. Afribary. Retrieved from https://afribary.com/works/a-generalized-flow-for-b2b-sales-predictive-modelling
Chandan Nune, Saathwik "A Generalised Flow for B2B Sales Predictive Modelling" Afribary. Afribary, 04 Sep. 2021, https://afribary.com/works/a-generalized-flow-for-b2b-sales-predictive-modelling. Accessed 18 Jan. 2022.
Chandan Nune, Saathwik . "A Generalised Flow for B2B Sales Predictive Modelling". Afribary, Afribary, 04 Sep. 2021. Web. 18 Jan. 2022. < https://afribary.com/works/a-generalized-flow-for-b2b-sales-predictive-modelling >.
Chandan Nune, Saathwik . "A Generalised Flow for B2B Sales Predictive Modelling" Afribary (2021). Accessed January 18, 2022. https://afribary.com/works/a-generalized-flow-for-b2b-sales-predictive-modelling