Examination of prior history, seasonality, market-moving events, etc., results in a realistic revenue prediction,
which is the cornerstone of a company’s planning.

Machine learning allows you to anticipate customer responses to external and internal factors that affect sales.

Neural Designer is a data science and machine learning platform that lets you quickly build sales forecasting models.


  1. Objectives.
  2. Benefits.
  3. Approach.
  4. Conclusions.


Sales forecasting aims to estimate the future demand for products or services in a company.

Companies can base their forecasts on internal and external data.

Some standard variables used in sales forecasting are the following:

  • Internal data:
    • Past sales data.
    • Marketing actions.
    • Website visits.
    • Etc.
  • External data:
    • Seasonal trends.
    • Market moving events.
    • Economic trends.
    • Etc.

Depending on the forecast window, we can talk about short-term forecasting (hours, days) or long-term forecasting (weeks, months). Of course, the factors affecting short-term sales are different from those involving long-term sales.


Sales forecasting can be helpful to companies in many different ways.


The core purpose of revenue forecasting is to plan your future expenses better.


Knowing the number of future clients or sales allows for better employee management.


Neural networks are the most essential techniques for machine learning and artificial intelligence. The following flow chart shows how to build and use a neural network for sales forecasting.

  The first step is to create a data set by collecting all the internal and external information related to the company’s sales.

 Then, we need to build a neural network to forecast future sales.

 A training strategy is applied to the neural network to discover the underlying relationships in the sales data.

 To improve the model’s predictive capabilities, we can also apply model selection by trying combinations of variables and choosing those with more impact on sales.

 Then, the resulting model undergoes an exhaustive testing analysis.

 Finally, the neural network is used to predict the company’s future sales after model deployment.

The data science and machine learning platform Neural Designer guides you through this process so you can focus on your business and not the details behind machine learning.


Predictive modeling helps you estimate the number of products the store will sell to prepare the inventory and manage the cash flow.

It detects the factors that most influence sales and provides information vital to making essential decisions in your company’s growth.

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