Sales Forecasting Examples for Business

Most sales teams, and unquestionably all successful sales teams, have a system for predicting future sales. Successful companies invest heavily in technology to build sales forecasting models that help them increase profitability and reduce inefficiencies in their sales operations. What is sales forecasting exactly, and what are sales forecasting examples used in everyday business?

The process of estimating sales volume and accompanying revenue for a future period by applying experience, historical data, data on sales activity, and technologies related to predictive analytics is known as sales forecasting. Various organizations use forecasting (e.g., manufacturing, finance, healthcare) for short-term and long-term planning purposes. According to the Aberdeen Group, sales executives estimate that accurate sales forecasts directly impact their companies’ revenue and profits. Companies that excel at forecasting consistently perform 25% better than competitors that are less accurate at predicting future sales.

A solid sales prediction does not rely on estimations but instead makes use of data from the real world to determine what kinds of results are likely. Forecasting sales is vital for various reasons, including helping with resource planning and hiring, risk management, and budget estimates, among other things. One can make predictions with high accuracy using data about past trends and events.

There are a variety of techniques for making forecasts, and these techniques are ranked in order of increasing levels of accuracy, starting with the least accurate:

1/ Forecasting based on a gut feeling (lowest level of accuracy)

Starting at the bottom, sales professionals make educated guesses about how future performance “feels.” These projections are based on experience and anecdotal evidence, not data. Unfortunately, these guesses often fail to reflect the actual market conditions or target demographics because they are based on subjective factors rather than objective facts.

2/ Predictions based on almanacs

Using data on past sales to make projections about future expansion is probably the most commonly used sales forecasting method. This technique is relatively easy to implement but tends to produce inaccurate results because it fails to account for any recent changes in the market or changes in buyer behavior.

3/ The practice of funnel forecasting

Investigate your win rate, cycle, and pipeline coverage concerning project numbers. The goal is to identify patterns in your historical data and make projections about future sales accordingly.

4/ Prospective analysis of portfolios

Combines input from the bottom up with the knowledge and experience of leadership to reach a consensus on expected results and action steps to improve performance. This is often an iterative process that involves revisiting assumptions over time to ensure that it remains accurate and relevant.

5/ Multivariate regression (highest level of accuracy for traditional method)

Forecast future trends with greater accuracy with this statistical approach to make projections that combine historical data with a series of multivariate equations to produce a detailed model. It requires extensive data collection and analysis and produces highly accurate results, which can take months to complete.

Machine learning-powered sales forecasting examples

This leads us to the most advanced and potent technique for sales forecasting. Modern machine learning algorithms use previous data to train weights and biases to understand the predicted result. Even the developers of ML and AI are sometimes unaware of or don’t comprehend how these systems learn from the data.

But they do learn from your data.

The accuracy with which the system predicts future behavior depends on the quality of the training data used to develop the system. If the data is flawed or contains mistakes, the accuracy of the resulting predictions will also be compromised. However, if the training data is of high quality, the machine learning algorithm will produce accurate forecasts with minimal human intervention. This makes the machine learning approach superior to traditional methods like regression or funnels analysis in terms of accuracy and ease of use.

AI and machine learning applications in sales and marketing include account-based marketing, lead scoring, product recommendations, predictive lead scoring, demand generation, revenue generation, and more. As these technologies grow, more applications will be explored and developed. Sales teams can leverage powerful technologies to improve their performance and maximize their revenues while minimizing their time and efforts.

Data can be evaluated in real-time to provide rolling forecasts and exact predictions once the machine learning system has been suitably trained and tested. Many ML models claim to have 99th percentile accuracy rates and a reasonable, allowable margin of error. These systems can even perform some tasks, such as manual data entry, allowing sales teams to focus on activities with the highest impact on the business.

Consider training a model on data from your sales staff that includes a few features (data points) like the date, the duration needed to close an opportunity, the contract value, and the result. When trained on this data, the model can be used to take in comparable data (without the outcome) and correctly estimate by extracting information about seasonality, the impact of the contract value on the sales cycle, and more. It can then produce accurate forecasts for similar opportunities in the future by extrapolating this information to other data points in the dataset.

Modern machine learning systems can analyze vast amounts of data and sift through it to find patterns and insights humans simply can’t see on their own. They can even identify patterns in the data that are not apparent to humans but play a huge role in predicting an outcome.

How can machine learning-powered sales forecasting drive your growth?

You may lessen forecast variance among your reps, track their progress, and encourage them to improve with the recommendations from your data with automatic, real-time insights into sales activity data. Machine learning and AI allow you to make smarter decisions about when to launch new campaigns or pursue leads. It can also help unearth trends that could otherwise go unnoticed and help you quickly respond to changes in market conditions.

Finally, it can make data-driven decisions and improve communication across your entire organization by tracking essential metrics and delivering real-time actionable insights. With machine learning solutions, businesses can increase sales productivity and empower their employees to become more successful.

Forecasting sales offers many advantages. Specifically, you can:

Allocate resources effectively for future expansion

You’ll be able to predict your sales for the future year once you’ve created a detailed sales forecast. By doing so, you may plan your supply and inventory and hire staff according to business needs. Knowing what it would require to expand in advance is quite advantageous. For instance, if you plan to grow your business in Singapore, you could draft a budget based on historical trends and develop a realistic forecast for costs and profitability.

Make precise income projections and investment plans.

With an accurate sales forecast, you can determine when your company will have enough money for the investment you are planning to make. Funds are required to deploy resources. Using data obtained from historical data, you could predict the financial resources needed for your business to grow. This information benefits companies looking to invest significantly in expanding their business or launching a new product.

Identify problems in the work process

Thanks to sales forecasting, you can recognize the issue as soon as it arises. You will become aware of strange events and take the necessary action.

For instance, if your ROI is 20% below what was anticipated, there is a problem in the work process that needs to be addressed. Instead of waiting for the pain to become complicated, you may identify it sooner and solve it better. Analytics solutions are excellent at detecting patterns in past customer data and making forecasts based on those patterns.

Set overall company objectives

You can allocate resources, hire staff, identify issues, and manage risk with continuous sales forecasting. You can work toward strategically planning your business objectives in this manner. Furthermore, you will be able to provide more accurate estimates to customers and meet their expectations accordingly.

Enhance decision-making and promote the development

With forecasting, you can spot potential problems before they grow into monsters that destroy your company’s foundation. Accurate sales forecasting increases the likelihood that your company will expand. Therefore, you will have the chance to capitalize on business opportunities and reach new heights of success.

Eliminate guesswork

Sales forecasting enables you to make informed decisions about the direction of your company. This approach avoids uncertainty and allows you to take calculated risks with confidence. It also enables you to analyze the potential impact of certain decisions and measure the potential returns of your investment. This data is instrumental in making critical decisions that affect the success of your business.

Machine learning and artificial intelligence can predict future demand for your products and services. This technology can help you anticipate market trends and adapt to changing customer demands. It can even help you make intelligent business decisions that boost productivity and maximize profitability. However, to reap the benefits of this technology, you need to select the right tools and implement them correctly.