Sales Forecasting
Sales forecasting procedure evaluates historical business data and produces a timeline of expected sales based on trends and seasonality in data.
Purpose
Sales forecasting algorithms aim to answer questions:
1. What quantity of SKU will we sell, or what amount of revenue can we expect? This can be broken down by some other business dimensions, like SKU, salesperson, country…
2. What is the comparison between actual sales and forecasted sales? If there is a discrepancy, why?
3. What method and which set of parameters will produce the most accurate forecast?
We generate and collect enormous volumes of all kinds of data. With current developments in both software and hardware we are able to give machines the data – so they can learn and predict outcomes by using algorithms to interpret raw data.
Models
Sales forecasting with perfect accuracy is impossible. But our models will give you the best guess. There is big inertia in 10 years’ worth of data. If nothing in particular changes in the future, models can give predictions within a 5% error margin.
Time series forecasting is one of the major building blocks of Machine Learning. There are many ways to accomplish this, like
- Autoregressive Integrated Moving Average (ARIMA),
- Seasonal Autoregressive Integrated Moving-Average (SARIMA)
- Bayesian structural time series model
- Holt-Winters methodology
Why Sales Forecasting Is Important
Sales planning
Sales forecasting helps sales managers planning their future activities. It will provide them with a business plan and help them achieve their targets.
Inventory optimization
Every company needs to manage their inventory. With accurate sales forecast, they will avoid both overstock and stock-out situations, which are very harmful for the business.
Financial Planning
Sales Forecast gives companies the information they need to predict revenue and, eventually, profit. With that information companies can organize and plan better. Also, forecasts can help to gain insights into areas where improvements can be made.