How to make accurate sales and revenue forecasting

One of the crucial tasks for the company is sales/revenue forecasting. Nowadays still there are plenty of companies where forecasting is merely based on sales manager intuition. Errors and drawbacks occurred during this process and analysis oversimplification lead to poor planning and budgeting of resources. The impact can be significant for the company and can reflect on its future prospects.


Forecasting is really time consuming process for sales managers. It requires quite a lot of time to prepare the data usually taken from different systems and databases. Putting data together, analyzing some historical information to identify trends and find key regularities – all these steps require systems approach and careful analysis in order not to miss any significant pattern.

How does predictive analysis work?

Predictive algorithms use already known results represented by the data collected from both internal sources (internal databases of the company) and external sources (social media). This data have to be thoroughly preprocessed and only then the data can be used to train a forecasting model, which is in its turn, can be used to predict values for new data.

After time-consuming data preprocessing and preparation goes the modelling part. In general predictive algorithms strive to identify correlations between inputs (data we use to predict sales volume or revenue amount, for instance) and the output (sales or revenue) to make predictions about future sales/revenue volume.

Predictive models can quickly adapt to new patterns, trends and changes, but it is necessary to fill in the model with new data and reprocess the data and renew the model regularly.

How can data analysis help with forecasting?

There are some key directions concerning sales/revenue forecasting where data science can be really useful:

  • Defining key metrics for forecasting efficiency. There are many different features that can be used in forecasting purposes but not all of them are valuable and have a significant impact on predictive variable.
  • Data integration. Quite common problem for many companies is data located in different databases. It is important to have all the necessary and preprocessed data at one place to work with.
  • Predictive analysis. Using machine learning tools for accurate prediction.
  • Forecasting process maintenance and visualization. Graphical representation of the results can be very important because it can provide the company some valuable and reliable insights concerning the latest trends. It also can help to identify possible changes in sales or revenue.


Accurately forecasting sales and revenue are vital for every business. Regular straightforward way to predict these values based on intuition and insufficient analysis often turns out to be full of different drawbacks and poor quality. Data science with advanced predictive algorithms can benefit company opportunities and open high future prospects.