8 Sales Forecasting Models You Should Try to Find Your Fit

Regular sales forecasting allows you to see what’s ahead financially for your company and make smart, informed decisions for the future. It looks holistically at your entire revenue situation and empowers you to prepare for the internal and external factors that could impact your business success.

In market conditions as fast-paced and changing as they are today, this capability is becoming a competitive necessity for companies of all sizes and in every industry.

Read on to explore what’s involved in revenue forecasting, why it’s important, and the specific benefits it creates — and browse eight sales forecasting models you can implement for your business.

Quick Takeaways

• Sales forecasts look holistically at a company’s ability to generate revenue across the organization.
• Benefits of regular revenue forecasting include more accurate budgets, better resource allocation, and higher financial resilience.
• Eight common and effective sales forecasting models are straight line, moving average, linear regression, time series, ARIMA, Exponential Smoothing, Econometric Models, and Cohort Analysis.
• The best way to manage revenue forecasting is with an automated, AI-driven software tool.

What is Sales Forecasting?

Sales forecasting is the process of projecting the overall amount of revenue your company will earn during a specific time period — typically monthly, quarterly, or annually.

It’s performed by analyzing a number of data points to create estimated but informed projections. These include historical performance data, competitor data, supply chain metrics, current pipeline data, and larger market demand (among others).

Like sales forecasting, revenue forecasting looks holistically at your company’s ability to generate revenue. It considers all revenue streams and aims to understand the entire financial picture for your organization in the future.

Accurate revenue intelligence positions leaders across your organization to develop informed strategies and make smart decisions. It helps you foresee upcoming opportunities and challenges and prepare effectively for both. By doing so, you can prevent unfortunate surprises that could impact your company’s profitability and growth potential.

8 Models for Revenue Forecasting

Straight-line

True to its name, the straight-line revenue forecasting model is the most straightforward. It looks at historical revenue data and assumes that your company will continue on a similar trajectory in the future.

For example: If your company experiences a consistent 7% growth rate over the past 3 years, your straight-line revenue forecast will assume the same will continue in the year to follow and will calculate revenue estimates accordingly.

Moving average

The moving average revenue forecasting method is a trend analysis that looks at shorter chunks of time to understand revenue trajectory over a particular period. Moving average forecasts are helpful when you’re trying to understand how revenue ebbs and flows over certain periods (for example, depending on product demand).

A real-world situation where this may be applied is to understand the seasonality of particular product offerings to pinpoint the right inventory levels to order throughout the year.

Linear regression

Linear regression revenue forecasts analyze two related variables on an X and Y axis to measure how they impact revenue growth. Sales and profit are two common factors businesses use for this method.

For example, if sales are growing but revenue is not, it can indicate a problem with your pricing strategy or another growth challenge that needs to be addressed. In short, linear regression forecasts help companies understand if the moving parts contributing to revenue are aligned as intended.

Time series

Time series revenue forecasts take a cyclical view of revenue, aiming to use historical trend data to predict future earnings. It looks at historical data over a particular set of periods (i.e., months, quarters, or years) and looks for recurring trends based on factors such as seasonality or common fluctuations in the market.

A time series forecast is especially helpful for companies that rely on cyclical revenue models, like subscription-based SaaS companies.

ARIMA

ARIMA (AutoRegressive Integrated Moving Average) models focus on analyzing historical revenue data over specific periods, like months, quarters, or years. By studying these time series patterns, ARIMA identifies trends, seasonality, and fluctuations that impact revenue.

The ARIMA model can address non-stationarity in revenue data. This means it can ensure statistical properties remain consistent over time, which enhances the accuracy of forecasts.

ARIMA considers both lagged revenue values (AutoRegressive component) and past prediction errors (Moving Average component). This approach captures how past revenue values and forecasting errors influence future revenue, improving forecast reliability.

ARIMA is particularly beneficial for companies with cyclical revenue models, such as subscription-based SaaS companies. It excels at identifying recurring revenue patterns and predicting future earnings based on historical data.

Exponential smoothing

Exponential smoothing is a popular method used in revenue forecasting to analyze and predict future revenue trends.

Exponential smoothing focuses on smoothing out fluctuations and noise in historical revenue data. It assigns exponentially decreasing weights to past observations, giving more importance to recent data points while reducing the impact of older data.

As new revenue data becomes available, the model updates its forecasts by incorporating the latest information. This helps capture changing revenue trends and adjust predictions accordingly.

Exponential smoothing models can handle seasonal variations and long-term trends in revenue data. By adjusting the smoothing parameters, such as the smoothing factor or the level of seasonality, the model can accurately reflect seasonal patterns and trend changes.

Econometric models

Econometric models are used to analyze and predict revenue trends based on economic variables and external factors.

Econometric models analyze the relationship between revenue and economic variables such as GDP growth, inflation rates, interest rates, consumer spending, and other industry-specific indicators. By incorporating these variables, the model can assess how changes in the economy impact revenue.

Econometric models quantify the relationships between revenue and economic factors through regression analysis, time series modeling, and other statistical techniques.

It also allows for scenario analysis by simulating different economic scenarios and their effects on revenue. For example, the model can forecast revenue under optimistic, pessimistic, and baseline economic conditions, helping businesses assess risks and plan accordingly.

Cohort analysis

Cohort analysis is a method used to analyze and predict revenue trends based on customer segments or cohorts.

Cohort analysis segments customers into groups or cohorts based on shared characteristics or behaviors. These cohorts could be defined by acquisition date, customer type, geographic location, or product preferences.

Once cohorts are defined, cohort analysis can help you measure customer profitability by tracking how revenue evolves over time for each cohort. This sales forecasting analysis allows businesses to understand revenue trends, retention rates, customer lifetime value, and revenue contribution from different customer segments.

By analyzing historical revenue data and cohort behavior, businesses can forecast future revenue for each cohort. This approach accounts for cohort-specific trends, seasonality, and customer retention rates, providing more accurate predictions than aggregate revenue forecasts.

Benefits of Revenue Forecasting

Accurate budgeting

With accurate sales forecasts, you have an informed idea of how your company can spend money. This means a better ability to plan things like marketing campaigns, hiring, and equipment purchases. You can also maintain a more accurate view of cash flow and manage it with more intention.

Better resource allocation

Accurate budgeting means you can allocate resources across your organization with greater confidence. When you have a clear understanding of revenue from both a business-wide and department-specific perspective, you make better decisions about the smartest way to divide, purchase, and leverage resources to drive business results.

Ability to garner investor interest

For some companies — especially high-growth startups — attracting the interest of financial investors and building their confidence in the business is a high priority. Detailed and accurate sales projections are a key contributor to success in this regard.

To be willing to take a stake in your company, investors will expect a clear vision of your financial future and data-driven forecasts to back it up.

Objectivity

Data-driven revenue forecasts remove the guesswork from financial planning. Today’s tools and software allow for sophisticated data analysis and insights, meaning your company leaders no longer have to rely on assumptions or intuition to decipher what’s likely to happen in the future.

Higher financial resilience

Business today is becoming increasingly fast-paced and interconnected. Likewise, markets have proven especially unpredictable (and even volatile) over the past few years. Companies need to have accurate revenue forecasts to navigate this constant change and stay profitable in adverse economic conditions.

Beyond that, sales forecasting gives your company a better ability to recognize and pursue opportunities amidst uncertain conditions to maximize revenue and growth potential.
 

Customer relationship management

Sales forecasts contribute to strong customer relationship management (CRM). Businesses can tailor sales and service offerings, personalize customer interactions, nurture customer loyalty, or enhance overall customer satisfaction through data insights.
 

What Are the Right Tools to Use?

In the past, companies often used spreadsheets to track revenue and conduct forecasting. You can still find plenty of free revenue forecasting templates online. However, the truth is that spreadsheets have become outdated with the rise of platform-driven solutions equipped with automation and AI capabilities.

These tools can analyze much larger data sets in a fraction of the time it takes manually and uncover more sophisticated insights to inform your strategy.

If you’re looking for a tool to power your future revenue forecasting strategy, consider a platform solution that can meet modern business needs and keep pace with the competitive landscape.

How You Can Improve Your Revenue Forecasting Accuracy with Xactly

Xactly’s Intelligent Revenue Platform gives you a 360-degree view of your revenue. It enables you to make more informed decisions and act quickly to grow revenue, reduce risk, and contain costs.

Key pillars of the Xactly Intelligent Revenue Platform include:

• Automate - Integrate seamlessly with your existing tech stack and automate processes to eliminate time-consuming and error-prone manual tasks.
• Collaborative - Create iterative snapshots of your plan, model “what-if” scenarios, monthly plan updates, and analyze side-by-side comparisons.
• Data-informed - Enhance decision-making by leveraging AI to predict key metrics, including optimized territories, ideal ramp times, productivity targets, and seasonality in sales.
• Continuous - Proactively monitor and continuously improve sales plans with changes in business conditions to course-correct in real-time.

Putting these key pillars into action allows for a revenue performance transformation — schedule your demo today to learn more!