  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.
