Sales Forecasting: How to Apply Machine Learning

Hrvoje Smolic

Sales Forecasting

This article will look closely at how machine learning has revolutionized sales forecasting.

Many business decisions are based on instinct. For instance, if you're expecting peak demand during a seasonal holiday, you'll appropriate staff or purchase stock accordingly. However, business decisions based purely on instinct can often go sideways, leaving you struggling. 

The good news is that you can take the guesswork out of business decisions with the help of machine learning. 

And yes, machine learning has surpassed traditional approaches to sales forecasting in terms of efficiency and accuracy.

sales forecasting
Image by the Author - predictions based on historical data

What is Sales Forecasting?

Machine learning focuses on teaching machines how to make decisions by analyzing data and then learning from the outcome. The best part? You don't need extensive programming!

In sales forecasting specifically, machine learning algorithms can help businesses predict how consumers will behave by using data from past transactions and demographic information.

Companies leveraging machine learning algorithms for sales forecasts include Amazon, Airbnb, Facebook, Netflix, and Uber. 

Advantages Of Machine Learning In Sales Forecasting

Machine learning allows businesses to create more advanced forecasting models that utilize a larger data set with minimal human effort. 

Companies can improve their products and services based on consumer needs by applying machine learning algorithms to their data. They can also better predict consumer behavior, which means they will plan more accurately.

The commercial application of machine learning is more evident in business processes like marketing, planning, and sales forecasting than in others. For example, a salesperson can accurately use predictive analytics to forecast a potential customer's behavior. This means they can determine which email campaigns will be most effective.

The commercialization of machine learning is also seen in the retail industry. Machine learning algorithms are used to predict what stock each customer will buy and how many items they may want before coming in for their next purchase. That, in turn, allows for more efficient cash flow, better inventory management, and faster sales cycles, which help the business generate more revenue and higher profits.

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How Is Machine Learning Used In Sales Forecasting?

Machine learning uses various methods, including regression and clustering, to analyze millions of data points before making predictions. Data points include demographic information, behavioral trends, and past transactions. By analyzing this data, machine learning can predict what percentage of consumers will complete a transaction in the future and how they will behave while completing it.

How To Use Machine Learning For Sales Forecasting?

Companies use machine learning algorithms to forecast sales and revenue. That is done by predicting consumer behavior with data from past transactions. By doing this, companies can create accurate forecasts and prepare for future events.

Here's how you can do it:

Find A Model

The most common model used for forecasting is the Auto-Regressive Integrated Moving Average (ARIMA) model. This algorithm determines the causes behind data and then creates predictions using them. It uses Exponential Smoothing based on previous data to make these predictions.

Collect Data

Using machine learning to generate sales forecasts requires data—the more, the better. So, the first step is to collect as much data as you can during a period when there are no important events or changes in your finances (no outliers). The next step is to categorize this data into sets that include the following:

Complete – These data sets include demographic information, past transaction history, and other relevant details. 

Incomplete – These include only part of the relevant details with missing information like last names or background information. These incomplete data sets will help you determine the best way to collect data from consumers in the future.

Create And Test Your Model

The model created in step 1 will be used to make forecasts. You'll usually need to create a new dataset that includes past transaction data, demographic information, and other relevant details. Using this new dataset, the algorithm will analyze it based on the model created above.

Analyze Results

The last step is to analyze the results of the forecast. By analyzing these results, companies can determine how well their machine learning algorithms work.

For most techniques, the final forecast is a superposition of several contributions together (the algorithm adds up these several contributions on top of each other).

Lat's take a look at the example.

Global Trend

The graph shows the global trend detected from the historical data. The points in time where the slope of the trend changes are change points - that is where the trend changes compared to the trend in the previous period.
We detected trend minimum of 2589.16 on 2016-11-17, and maximum of 3920.54 on 2021-05-20

Sales Forecasting trend
Image by the Author - Global Trend

Weekly seasonality

The Weekly Seasonality graph shows a detected pattern in historical data that repeats every week (7 days).
We detected that the biggest positive impact happens on Tuesdays, and that the biggest negative impact happens on Fridays.

Sales Forecasting weekly seasonality
Image by the Author - weekly seasonality

Yearly seasonality

The Yearly Seasonality graph shows a detected pattern in historical data that repeats every year.
We detected that the biggest positive impact happens in August, and the biggest negative impact happens in December.

Sales Forecasting yearly seasonality
Image by the Author - yearly seasonality

Holiday effects

The graph shows the percentage effects of the special dates and holidays in historical and future data. Special dates could have either positive or negative impacts. If repeated through the data, then the same effect (increase or decrease) will be applied to each of its instances in the future.

Here we can see, for example, how the 1st day of each year had a strong negative impact on sales.

Sales Forecasting holiday effects and promotions
Image by the Author - holidays and promotions

All components together

When we add up all these components together, we get our prediction graph:

Sales Forecasting supeposition
Image by the Author - final prediction

Machine Learning Models Used In Sales Forecasts

Machine Learning is an evolving field which means several models are being developed and tested on different datasets daily. Below, we've broken down two of the most common machine learning models used in sales forecasting.

Regression Algorithms

Regression algorithms are commonly used in machine learning to forecast sales volume. They do this by analyzing large amounts of data based on the past to determine patterns to predict future events. These algorithms are typically used with incomplete data to produce the best results.

For example, a business selling apparel can use regression algorithms to predict sales volume by analyzing historical data. Machine learning can create models that analyze the data and develop forecasts that can be used internally to make cash flow predictions or externally to start a new advertising campaign.

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Multivariate Models

Multivariate models are an expansion of regression algorithms. By using multiple variables, they allow more accurate forecasts to be made by using a large data set. The most common use for multivariate models is in forecasting the overall sales volume, but they can also be used to determine the profitability of a given product or service.

Here's a good example: a clothing business can use these algorithms to determine the profit margin of specific products. They can analyze historical data from past transactions and forecast future profitability and sales volume for each product. That will help them decide which products to promote based on the highest return on investment.


Sales forecasting is a complex yet essential component of business intelligence—like purchasing and budgeting. It helps companies manage their cash flow and identify ways to improve their returns.

Sales forecasting requires extensive machine learning and statistics knowledge. Luckily, if you don't have the in-house talent to do the job, there are no-code machine learning solutions like Graphite with ready-to-go prebuilt models. You can run your sales forecast without writing a single line of code. 

Predictive analytics and machine learning algorithms have become an essential part of how companies forecast sales. They can be used in conjunction with traditional methods or alone. Either way, the goal is to accurately predict how much sales a company can expect during a given period based on past sales, demographic trends, and behavioral indicators.

Further Reading

- Complete Introduction to 4 Great Time Series Forecasting methods

- Popular Applications of Machine Learning in Business

- Regression in Machine Learning- What Is It And When To Use

- Tesla Stock Price Prediction With Machine Learning Using Graphite Note

- Top 14 No-Code Machine Learning Platforms To Use

Now that you are here...

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