Sales forecasting using Machine Learning
SpringML inviting business and sales leaders to its Man vs Machine Forecasting Duel - give them a day with your data and they will provide an algorithm based, unbiased forecast.
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By Girish Reddy, SpringML.
Forecasting sales is a common task performed by organizations. This usually involves manually intensive processes using spreadsheets that require input from various levels of an organization. This approach introduces bias and is generally not accurate especially during the initial few weeks of a quarter. In fact that's the time when an accurate forecast has the most benefit after all there's little value in providing an accurate forecast in the last week of a quarter.
Though the process of forecasting tends to be complex it is straightforward to determine its accuracy. One simply has to wait until the end of a forecasting period (e.g. end of quarter) and then compare forecasts with actuals. We are confident about the accuracy of our models and are inviting sales leaders to our Man vs Machine Forecasting Duel - give us a day with your data and we'll provide an algorithm based, unbiased forecast. At the end of the quarter you can evaluate our number by comparing with your internal forecast. Get started by visiting www.springml.com/sales-forecasting-challenge and submitting the form. The process is simple and allows you to quickly see what machine learning can do for your organization.
SpringML's app simplifies forecasting by executing machine learning models that run automatically and present a monthly or quarterly forecast of a customer's sales metric (e.g. Revenue, ACV, quantity). Sales leaders can These models consume both historical data to gauge trend and seasonality, as well as current pipeline of opportunities to then predict for the next 6 or 12 months. Accurate forecasts allow organizations to make informed business decisions. It gives insight into how a company should manage its resources - people, time and cash.
Here are the various techniques that make up our forecasting ensemble.
Learn more by emailing us at info@springml.com
By Girish Reddy, SpringML.
Forecasting sales is a common task performed by organizations. This usually involves manually intensive processes using spreadsheets that require input from various levels of an organization. This approach introduces bias and is generally not accurate especially during the initial few weeks of a quarter. In fact that's the time when an accurate forecast has the most benefit after all there's little value in providing an accurate forecast in the last week of a quarter.
Though the process of forecasting tends to be complex it is straightforward to determine its accuracy. One simply has to wait until the end of a forecasting period (e.g. end of quarter) and then compare forecasts with actuals. We are confident about the accuracy of our models and are inviting sales leaders to our Man vs Machine Forecasting Duel - give us a day with your data and we'll provide an algorithm based, unbiased forecast. At the end of the quarter you can evaluate our number by comparing with your internal forecast. Get started by visiting www.springml.com/sales-forecasting-challenge and submitting the form. The process is simple and allows you to quickly see what machine learning can do for your organization.
SpringML's app simplifies forecasting by executing machine learning models that run automatically and present a monthly or quarterly forecast of a customer's sales metric (e.g. Revenue, ACV, quantity). Sales leaders can These models consume both historical data to gauge trend and seasonality, as well as current pipeline of opportunities to then predict for the next 6 or 12 months. Accurate forecasts allow organizations to make informed business decisions. It gives insight into how a company should manage its resources - people, time and cash.
Here are the various techniques that make up our forecasting ensemble.
- Time series forecasting using Bayesian models (BSTS package in R), Tree based techniques and other traditional methods like ARIMA.
- Include predictors for time series - these could be any variables that add value to the model e.g. product usage, number of users, marketing spend, etc. Include external data where applicable such as industry trends, demographic info, etc.
- Evaluate current pipeline data by running classification algorithms on open opportunities - this forms a part of the ensemble.
- Evaluate ensemble on previous few months before finalizing the best set of models to use.
Learn more by emailing us at info@springml.com