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Predictive Sales Forecasting: What You Need to Know

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Predictive sales forecasting is a more advanced way of forecasting sales compared to traditional sales forecasts. Keep reading to learn more about predictive analytics and sales forecasting, and how to use them to your advantage.

What is predictive sales forecasting?

Predictive sales forecasting involves using data and statistical models to predict future sales performance. It’s used in a variety of industries, including finance, retail, manufacturing, and more.

Traditional forecasting vs. predictive sales forecasting

Traditional sales forecasting is based on qualitative (e.g., a sales rep’s knowledge of specific opportunities) or quantitative data (e.g., historical sales, sales cycle length, etc.). However, it often doesn’t account for the many external factors that can impact a forecast.

While we strongly recommend setting up a bottom-up forecasting process, predictive forecasting capabilities are useful add-ons to increase accuracy.

Predictive sales forecasting uses machine learning algorithms to analyze a variety of inputs and data sets across different areas of the business (such as survey reports, social media sentiment, macroeconomic indicators, and more) and make predictions about sales performance.

Benefits of predictive sales forecasting

The two main benefits of predictive sales forecasting include improved accuracy and real-time forecasts.

Since predictive sales forecasting takes more factors into account compared to traditional forecasting, it tends to result in a more accurate sales forecast.

More accurate sales forecasts, in turn, enable better resource allocation, goal setting, inventory management, and planning.

Additionally, predictive sales forecasting allows businesses to have real-time sales forecasts, which adapt instantly when market changes are detected.

Streamline sales forecasting and improve forecasting accuracy.

Challenges of predictive sales forecasting

While highly beneficial for most types of businesses, predictive sales forecasting isn’t without its challenges.

Firstly, predictive sales forecasting can’t result in accurate forecasts if you’re missing data or are working with inaccurate data.

Secondly, driving adoption of predictive sales forecasting can be challenging since it often requires substantial training and support to ensure sales team members can integrate it into their existing sales forecasting methodology and process.

How to get started with predictive sales forecasting

Ready to get started with predictive sales forecasting? Follow these steps:

1. Collect data

The first step involves identifying the right data for your forecast and then collecting it.

This will likely include internal data (such as historical data on sales and customer demographics) and data from external sources (e.g., social media sentiment or news).

2. Use statistical modeling and analysis

Once the data is collected, it will need to be modeled and analyzed in order to uncover patterns, seasonal trends, or anomalies that will help inform your forecast.
A variety of techniques can be employed here, including:

  • Linear regression – Involves analyzing the relationship between a dependent variable (sales revenue) and one or more independent variables (e.g., advertising spend, economic indicators, etc.). This technique assumes a linear relationship between variables and isn’t capable of accounting for complex non-linear relationships between variables.
  • Time series analysis – A technique based on analyzing historical and current data to predict future values at a specific point in time.
  • Machine learning algorithms – There are plenty of machine learning algorithms that can be used for the purposes of sales forecasting, including gradient boosting and random forests.

3. Create the forecast

The final step is to create and present the sales forecast. The presentation will depend on the software you’re using to produce the forecast.

Ideally, it should offer an easy-to-understand overview of future sales performance that allows you to review key metrics at a glance.

Frequently asked questions about predictive sales forecasting

We’ve answered the most frequently asked questions about predictive sales forecasting below:

What data is needed for predictive sales forecasting?

What data you’re going to need for predictive sales forecasting will depend on your business, the type of sales forecast you’re looking to create, and the statistical model and forecasting method you’re using. 

In most cases, you’ll need historical sales data, customer and market data, and data on any other factors that can impact sales for your business.

What methods or algorithms are used in predictive sales forecasting?

Predictive sales forecasting uses a variety of methods and algorithms. Some popular ones include linear regression, time series analysis, and machine learning algorithms such as random forests and gradient boosting.

Can predictive sales forecasting be accurate?

Predictive sales forecasting tends to be more accurate compared to traditional sales forecasts. 

However, it’s important to keep in mind that any sales forecast, including one created with predictive sales forecasting techniques, is a prediction, not a guarantee.

What is the future of predictive sales forecasting?

Predictive sales forecasting is expected to become more accurate in the future, thanks to advancements in artificial intelligence and machine learning.

A better way to do sales forecasting

Over time, most businesses discover that their sales forecasts are usually highly inaccurate. Additionally, it’s very time-consuming and cumbersome for sales managers to create the forecasts in the first place.

This is where a tool like Weflow can help.

Weflow allows you to submit, review, and track changes to your forecasts in just a few clicks. It supports collaborative forecasting, waterfalls, and quarterly predictions.

Collaborative forecasting in Weflow

Weflow also helps businesses to create highly accurate sales forecasts by enabling improved data hygiene through automated sales activity tracking. This way, all relevant data ends up in your CRM instance to ensure your forecasts are based on complete and accurate data.

Weflow is available as a web app and a Chrome extension. Get started for free today.

Streamline sales forecasting and improve forecasting accuracy.

Wrap up

You’ve reached the end of this guide. Hopefully, you now have a good grasp of how predictive sales forecasting works.

If you’re looking to learn more about sales forecasting, check out these guides:

Also, make sure to bookmark the Weflow blog and come back for more great content on sales forecasting, pipeline management, and revenue operations.

Boris Mustapic

Boris Mustapic is the Head of Content at Weflow. He has over a decade of experience in the digital marketing industry, and has helped dozens of B2B SaaS brands drive growth through content marketing.

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Boris Mustapic