# Revenue Forecasting for Enterprise Products

arXiv: https://arxiv.org/abs/1701.06624 · HTML: https://mlsanity.com/paper/1701.06624/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2016.

## Built on by (most cited first, 1 of 1)

- [Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls](https://mlsanity.com/paper/2107.04851/) (2021)

## Abstract (arXiv)

For any business, planning is a continuous process, and typically business-owners focus on making both long-term planning aligned with a particular strategy as well as short-term planning that accommodates the dynamic market situations. An ability to perform an accurate financial forecast is crucial for effective planning. In this paper, we focus on providing an intelligent and efficient solution that will help in forecasting revenue using machine learning algorithms. We experiment with three different revenue forecasting models, and here we provide detailed insights into the methodology and their relative performance measured on real finance data. As a real-world application of our models, we partner with Microsoft's Finance organization (department that reports Microsoft's finances) to provide them a guidance on the projected revenue for upcoming quarters.