What Is Sales Forecasting: Steps, Benefits, Methods & Best Practices

Predicting sales aims to maintain a good cash flow plan, which estimates future cash inflows and outflows. Optimizing sales forecasts using a customer relationship management (CRM) system powered by artificial intelligence (AI) ensures the accuracy and reliability of your cash flow plan. For example, some CRMs come with AI sales assistants that recommend high-win probability deals that sales reps can focus their efforts on.

One of the core foundations of predicting business budget allocation is having accurate sales forecast data. With data-driven insights, startups and businesses can allocate resources efficiently. For instance, a startup can identify its most profitable product lines and focus marketing efforts and inventory on those to maximize its return on investment (ROI). This ensures your sales team receives excellent support to meet their quotas while safeguarding the business’s financial health.

Sales leaders visualize forecast data to spot adjacent issues like a drop in pipeline velocity, signaling potential bottlenecks, or sales-marketing misalignment even before problems arise. Let’s say one of your sales reps has been below their quota for the past two weeks. You can promptly coach them to prevent their performance from affecting the whole month. The same principle applies to monthly and quarterly sales performance monitoring.

The sales forecasting process can help estimate expenses and revenue based on short-term and long-term sales performance. For example, it can predict higher costs for additional staffing during a seasonal sales surge or estimate revenue from a new product launch over the next quarter. Hence, sales leaders can use sales forecasts to discover high-revenue sales opportunities by targeting repeat customers and broadening geographic reach.

Sales leaders use forecast data to track salespeople’s performance, such as win rates, and identify potential challenges early. Moreover, revenue forecasting can promote healthy competition by keeping sales reps informed about their performance compared with their peers. This also motivates them to apply the best practices of top performers to improve their performance.

Categorize your products and services into clear groups, such as core offerings, seasonal items, and upsell opportunities, to capture all revenue streams. As much as possible, be specific and include variations, like sizes or colors, to make a more detailed and accurate forecast.

Review historical data, trends, and market conditions to predict sales demand. It’s also crucial to consider seasonal fluctuations, upcoming promotions, and external events. These factors can significantly affect your sales during the forecast period.

Set the unit price of your product or service based on your production costs, desired profit margin, and market research to ensure competitiveness while maintaining profitability. Factor in bulk discounts, customization fees, seasonal pricing, and other variations to reflect the full range of your offerings accurately.

To calculate sales, multiply the estimated number of units sold by the price for each product or service. This will give you a clear picture of expected gross sales for each category, helping you gauge the overall income from your forecast.

Include all direct costs in your computation, such as materials, labor, and overhead specific to each product or service. Consider variable costs, like production and shipping, and fixed costs, such as rent or equipment depreciation, to get an accurate cost per unit.

Multiply the cost to produce and sell each product by the estimated sales volume to determine the total COGS for each item. This step helps you assess the total expense required to fulfill the forecasted sales and understand the impact on your bottom line.

Subtract the total cost from the total sales to compute your expected profit. This is a simplified example, but it captures the core principles of forecasting sales accurately. Learn more about the different types of sales forecasting methods and techniques below.

This projected sales forecast method takes data from previous time periods, accounts for new factors, such as growth or increased demand, and calculates estimated sales revenue. While this method is ideal for businesses at least a few years old, new businesses can also use historical trends by evaluating information from similar businesses.

Let’s look at a sales forecast example created using historical trends.

Use our calculator for a quick sales projection based on historical trends.

Let’s switch it up and say the business wants to predict how well it will do in terms of revenue during the busy season. In past years, the Q4 holiday season (October to December) accounted for 40% of revenue. Through September, it had made $80,000. Based on the consistent proportion of revenue generated in Q4, the business can expect total revenue for the year to be $133,333, with $53,333 done in Q4 alone.

Now let’s switch the factors of the known historical information. Imagine, after five years in business, ABC Online Store consistently makes 15% more than the previous year. There’s also a demand increase in the total market of 10%.

If ABC generated $150,000 last year, they could expect an increase of $22,500 (150,000 x 15%) due to standard growth and an additional increase of $17,250 due to new demand, for a total of $189,750.

Unlike simpler methods like historical trends, which focus on a single data point (like past sales or traffic), multivariable analysis allows businesses to account for several interdependent variables, like market conditions, seasonality, customer behavior, advertising spend, economic factors, and social media engagement.

This method uses advanced statistical models, often in the form of regression analysis or machine learning algorithms, to understand how different variables impact sales performance.

In general, the formula for a multivariable analysis-based sales forecast might look like this:

Where:

• Base Revenue is last year’s sales or current sales.
• Variable Impacts are the expected growth rates (calculated as percentages) for each factor, such as web traffic, conversion rate, advertising, and market growth.

Multivariable analysis forecasting is ideal for businesses that experience dynamic market conditions or have multiple factors influencing their sales performance. Evaluating relationships between different variables can help business owners make more precise and dynamic forecasts. However, it requires access to detailed data and analytical expertise to implement effectively.

A sales process that follows conversion rates of a sales pipeline or funnel can use conversion-based forecasting to estimate anticipated revenue. This is the best way to forecast sales in business-to-business (B2B) companies, usually finalized as deals.

Business-to-consumer (B2C) organizations can also use this method if they follow a typical sales cycle for closing deals. Ecommerce businesses can use it if they track website traffic, browsing, and purchasing rates. To calculate a sales forecast estimate based on the sales pipeline conversion rates, you can use the following formula:

Here’s how it works step-by-step:

1. Start with the total number of leads, including both the leads already in the funnel and any new ones expected to be added.
2. Apply the conversion rates starting from the first stage (e.g., “Aware of the brand to interested in more information”) through to the final stage (e.g., “Considered to offer accepted/deal closed”).
3. Estimate the expected revenue by multiplying the remaining leads at each stage by the deal value to forecast revenue.

Check the example scenario below to determine how conversion-based forecasting works:

Based on the data above, ABC estimates that of the 250 total leads, 40 of them (16%) will be interested in more information. Of those interested, 20 (50%) will want to receive an offer through a proposal, and 12 of them (60%) will strongly consider the offer by negotiating some of the terms.

In the end, nine of the leads are expected to become ABC clients, which translates into a total deal value and sales prediction of $216,000. Here’s the step-by-step process, along with the formula:

Next, let’s look at conversion-based forecasting for a B2C ecommerce company. Using industry averages and their internal marketing knowledge, ABC Online Store believes its digital campaigns will reach 2 million views (awareness) over the next 12 months.

It also anticipates 10% clicking and viewing the online store (interested). From the website, 30% will likely browse for a solid amount of time (consideration), and 5% of those will actually make a purchase (decision).

Let’s assume each purchase is for one unit of what’s being sold for $100 each. Based on this, 3,000 purchases will be made for a total projected revenue of $300,000.

This is the least accurate of the sales predicting methods because it doesn’t rely on actual historical data or conversion rates. Instead, you depend on your sales team’s performance expectations, market intelligence, and confidence to project the number of deals they expect to close or leads they anticipate generating.

When companies must rely primarily on sales rep knowledge for revenue predictions, mitigating the associated risks involves establishing structured frameworks and enhancing data-informed decision-making. For instance, sales managers can standardize how sales reps report their projections. This ensures consistency in key sales metrics such as deal size, probability of closing, and expected timeline.

That said, a sales forecast estimate based on rep knowledge is instrumental in interpreting or reviewing sales projections generated from historical data or forecasted conversions. This is to see if it’s aligned with your typical business performance.

The formulas to calculate the Expected Deal Value for each opportunity and the Total Sales Forecast are as follows:

For each deal, multiply the Forecasted Conversion (as a decimal, e.g., 30% = 0.30) by the Deal Value to get the Expected Deal Value.

Add up all the Expected Deal Values from each deal to find the Total Sales Forecast. Below is an example of sales prediction based on sales rep knowledge.

Moreover, this sales prediction method could be less probability-based and go exclusively on a “close” or “will not close” basis. For example, rather than asking for confidence levels, a manager could just ask for the estimated value and whether or not the rep thinks it will be closed within the time frame. Nevertheless, this method can motivate reps to prioritize closing deals, especially those who expressed high confidence that it would close.

Sales forecasts based on gut feelings can be unreliable, as they often overlook objective data that provide a clearer picture of deal probabilities. Relying on predictive analytics—tools that use historical data, customer behavior, and market trends—helps sales teams make more accurate, data-driven forecasts.

The global market for predictive analytics is expected to reach $61.9 billion by 2032, showing the increasing importance of data in decision-making. With predictive analytics, sales teams can improve forecasting accuracy and make smarter decisions that drive better results.

Lack of access to historical CRM or performance data is a major roadblock to accurate forecasting, says 66% of revenue teams surveyed in an Xactly benchmarking report. Newly established businesses often lack sufficient historical data to base their forecasts. In this situation, you can rely on market and competitive research when creating estimates. Moreover, you can generate two revenue projections based on optimistic and pessimistic numbers and then update them with actual data as time progresses.

Creating an accurate sales forecast is time-consuming, especially when done manually. So mobile apps and user-friendly websites are essential tools for sales teams to update forecasts on the go.

If you have several tools for sales management, the main challenge here lies in the seamless integration between the apps your team is using. Ensuring that your tech stack is integrated and user-friendly is key to turning these systems into valuable assets.

Any industry can be affected by general changes in the economy, such as those involving inflation, supply chains, and labor supply. Sometimes, forecasts could be affected by seasonality and the buyer behavior trends during specific time periods. It’s important to consider these factors and understand how they impact your profitability when you make your revenue estimates.

Many standalone forecasting tools provide real-time sales updates. As data grows rapidly, integrated tools like CRMs with forecasting features can help you predict trends more accurately for effective sales planning.

Predictive analytics and AI-driven insights are important in sales predicting software because they enable more accurate, data-driven predictions. This helps businesses optimize decision-making, resources, and growth strategies. Standalone tools with predictive models provide valuable insights for accurate sales predicting, while CRM tools enhance these predictions by leveraging customer and pipeline data already stored in your CRM.

Businesses have different sales cycles and processes. If you want a sales forecasting system that best works for your business, choose one with customization options. Standalone revenue forecasting software should allow you to adjust time frames, set custom metrics, or tailor methodologies according to your business needs. CRM-integrated solutions offer customization that mirrors your pipeline stages, ensuring forecasts match your unique sales process.

Scenario planning and “what-if” analysis help businesses see how changes in pricing or market conditions can affect sales. While standalone forecasting tools offer this, CRM-integrated tools use data like lead quality and rep performance to create even more accurate forecasts, reducing errors.

Many standalone sales predicting tools allow you to examine historical sales data, identifying trends, seasonality, and patterns that can inform future predictions. CRM-integrated forecasting tools enhance this feature, giving you access to detailed insights based on past customer interactions, deal progress, and conversion rates.

An effective sales forecasting tool should provide clear visibility into your sales pipeline and track opportunities at various stages. While standalone tools allow easy deal tracking by importing data from your CRM or other systems, CRM-integrated forecasting software offers deeper insights. A revenue forecasting CRM reveals the exact stage of each deal and its likelihood of closing, thereby enhancing forecast accuracy.

Visualization tools such as dashboards and reports are key to interpreting sales data quickly and effectively. Sales forecasting software should provide customizable, user-friendly dashboards to view and analyze key metrics quickly. CRM-integrated forecasting software is equipped with detailed and comprehensive reports to help sales teams make data-driven decisions at a glance.

Sales forecasting often involves multiple stakeholders, from sales reps to management, making collaboration features essential. With shared views, notes, and comments, sales team members stay aligned on the forecasting process. Many CRM systems enhance collaboration by offering shared pipelines, task assignments, and communication features. It ensures your entire sales process is transparent and collaborative.

Mobile accessibility is a must-have feature in any modern forecasting tool. Using a CRM system with revenue forecasting on mobile ensures timely, accurate tracking of sales predictions wherever team members are. Sales reps and managers can access real-time data, update forecasts, and make informed decisions on the go, enhancing flexibility and responsiveness.

Automation plays a key role in reducing manual work and ensuring timely updates. Sales forecasting software offers automated reporting capabilities that generate reports on a regular schedule or based on specific triggers, such as changes in sales data. If you want to take automation a step further, you can automatically update forecasts based on sales pipeline data using CRM software.

Sales forecasting involves predicting future sales based on historical data and market trends. For example, a company might analyze sales data from the past three years and use it to predict the number of units it will sell in the next quarter. This helps sales teams plan inventory, set realistic goals, and allocate resources effectively.

The best method to forecast sales depends on the business and data available, but a common approach is the time series analysis. It uses historical sales data to identify patterns, trends, and seasonality, allowing for more accurate predictions. If you have complex variables, regression, or multivariate analyses, refine forecasts.

Calculate a sales forecast by analyzing historical sales data to identify trends and seasonal patterns. Combine this data with market research, considering factors like industry growth, competitor performance, and economic conditions. Finally, adjust the forecast based on internal factors such as upcoming product launches, marketing plans, or changes in pricing.

The four steps to preparing a sales forecast are as follows:

1. Review past sales figures to identify trends, patterns, and seasonality.
2. Research current industry trends, competitor performance, and economic factors.
3. Consider company-specific factors like product launches, promotions, and capacity changes.
4. Apply an appropriate method, such as trend analysis, market potential, or sales team input, to predict future sales.

First, review historical sales data to identify trends and seasonality. Next, gather insights on market conditions, including industry growth, competitor performance, and economic factors. Incorporate internal elements like planned promotions or new product launches. Finally, choose a forecasting method to project future sales based on the data and factors you’ve gathered.