Deep learning can provide accurate predictions of expected sales, using a model which factors in all relevant information about the sales environment. This can then be used when setting goals, hiring, budgeting, prospecting and much more.

A lack of insight into future sales can lead to bad resource allocation and cash flow problems as a result of poor budgeting and goal-setting.

Sales data is often very complicated and highly variable which is difficult to predict using traditional forecasting techniques. Deep learning models are able to detect dynamic and complicated relationships in the data in ways that are not possible with traditional modeling techniques. 

The model uses historical data about the external factors and how sales were impacted by them in the past. This is then used by business management teams to predict future sales.

The type of data that should be factored into sales forecasting models depends largely on the particular industry. Typical examples might include, economic developments, regulatory changes, details regarding the particular products or services, marketing efforts and much more.

We built a simple sales forecasting model using the Peltarion Platform. To see how to do this yourself with the use of our Google Sheets add-on check out the Sales forecasting in Google Sheets tutorial linked below. This model uses a very basic dataset but the method and theory is valid for much more powerful models if you have access to the right data.

Once you have built this, you can improve the performance of the model by following the advice in the improving models for tabulated data tutorial.