Sales forecasting using machine learning

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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 response to external and internal factors that affect sales.

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

Contents:

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

Objectives

The objective of sales forecasting is 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:

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 affecting long-term sales.

Benefits

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

PLAN BUDGETING

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

ALLOCATE RESOURCES

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

Approach

Neural networks are the most important technique 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 that will 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, after model deployment, the neural network is used to predict the company's future sales.

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

Conclusions

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

It detects the factors that most influence sales and provides you with information that is of great importance to you to make essential decisions in your company's growth.

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