Optimizing Financial Forecasting Using Cloud Based Machine Learning Models

Abstract

B2B companies compete fiercely to win sales opportunities, often of high value. Forecasting successfully whether a sales opportunity will be won or lost is vital for maximizing profitability. This task is further complicated by the fact that forecasting the outcome of a sales opportunity forms the early part of a cumbersome sales process. A typical sales process may take weeks to months with due diligence requiring huge human and operational resources. Thus, careful evaluations of sales opportunities during the early steps of this process become imperative. Quantifying the probability of winning prospective sales opportunities can clarify early evaluations and facilitate appropriate allocation of additional assessments to be performed later in the sales process. However, correctly forecasting the outcome of a sales opportunity is currently mostly subjective. Many sales software allow sales personnel to assign a subjective probability of winning for an opportunity which represents their confidence of winning that opportunity. Predictive modelling attempts at determining the objective probability of winning sales opportunities are scarce. As a result, the prediction of the sales outcome of an opportunity thus relies on subjective human prediction. Furthermore, understanding the past sales data on the performance of predictions can help improve future predictions but current understanding is limited. For a similar industry with similar sales processes, overall simple metrics such as win-rate or revenue difference can showcase historical performance but this approach is too crude to provide valuable insights of data flows such as winning their opportunities or regions on which prediction has improved over time.

ML and AI-based time-series forecasting has gained tremendous attention and is widely adopted in business areas such as sales, stock market, billing, pump failure maintenance, biotechnology, and global weather predicting. Optimizing financial forecasting using cloud-based machine learning models is an end-to-end built ML pipeline consisting of ingestion, prep, train, and evaluation for time series classification application. It automates data extraction and processing tasks such as: 1. uploading and formatting the training data, 2. feature engineering and selection, 3. model training and optimization, 4. saving and registering the trained model along with performance metrics and model parameters, 5. ML monitoring.