Have you ever wished you could get a window into the future to reliably optimise your business for success? Revenue forecasting is a way of predicting how much money your business will make over a certain period by analysing past financial data and current market trends.
Revenue and forecasting models use what has happened in the past to predict future revenue. This helps businesses to better manage their finances and make smarter decisions in the present, which makes it easier to plan for consistent growth.
In this article, we’ll examine the revenue forecasting model, exploring its benefits, challenges, and methodologies. We’ll also provide practical tips to enhance its accuracy and ensure sustainable business growth.
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Revenue forecasting vs. sales forecasting
Let’s start by looking at how revenue forecasting differs from its close cousin, sales forecasting. The main difference between sales and revenue forecasting is that sales forecasting is exclusively focused on sales revenue. It centres on the volume of goods or services that will be sold over a given period, along with the revenue those sales will bring.
By contrast, revenue forecasting predicts how much money a business will make across every revenue stream. On top of expected sales revenue, this might include money-making activities such as licensing and royalties, as well as any leasing agreements.
Because sales forecasts are solely focused on predicting how many products or services a business should expect to sell, they are most commonly used to set short-term sales objectives so that reps have a clear quota to work towards.
Revenue forecasting provides a broader outlook. Rather than being focused specifically on sales outcomes, it considers every revenue operation. This wider focus provides the basis for top-level organisational decision-making and long-term cash flow management.
Why is revenue forecasting important?
Revenue forecasting is the foundation for proper business planning. It gives organisations a window into the future to anticipate their financial trajectory, which helps them to optimise their current operations.
The beauty of forecasting revenue is that it isn’t a stab in the dark. It’s grounded in a business’s historical data and explores current trends in depth to provide a comprehensive overview of upcoming revenue streams. Some of the benefits include:
- Future planning: Revenue forecasting lets companies make impressively accurate predictions, which, in turn, acts like a compass for day-to-day decision-making and resource allocation.
- Anticipating challenges: Companies are more easily able to prepare for difficult periods when they have a full understanding of their finances. They can then use this information to proactively implement measures to limit risk and manage cash flow more efficiently.
- Strategic decision-making: Forecasting revenue gives business leaders insights into financial health that they can use to make smarter decisions. It helps with everything from brainstorming new product development to budget setting and marketing strategies.
- Setting targets: Revenue forecasting also helps organisations set realistic sales targets and track progress toward achieving them. They can align their sales cycle with overall growth objectives and ensure everyone is on track to meet revenue goals.
- Securing investment: Reliable forecasts show investors the long-term return potential they can expect. This increases your investment potential.
Revenue forecasting is both an art and a science. It relies on a foundation of quality data. However, it still requires a human touch to put quantitative insights to proper use. When everything comes together, revenue forecasting is one of the most powerful ways for a business to get a window into its future finances and tackle new opportunities and challenges with confidence.
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Key forecasting methods
There are several forecasting methods that can be used to predict future revenue. Most methods are quantitative — they use existing business data to make accurate predictions. However, there’s also plenty of room to introduce complementary qualitative methods that rely on opinions rather than hard facts and statistics.
We’ve separated the different approaches available into these two buckets. Let’s start by looking at the quantitative methods.
5 quantitative forecasting methods
These methods are the science behind revenue forecasting. They usually form the basis of any effective prediction, which means they’re the best place to start before introducing any qualitative approaches. There are five common quantitative forecasting methods.
1. The straight-line method
This method assumes that past growth rates will continue as they are, which means a business can simply multiply current rates by a number of months or years to estimate future revenue. Of course, this is rarely accurate. While the method is the simplest approach for getting a rough estimate, it shouldn’t be relied on as a rule.
2. Moving average
The moving average takes the average from a predefined set of forecasting data to estimate future values. For instance, looking at the average revenue in November, December, and January to predict revenue for February. This method works well for shorter-term forecasting, although it can lead to inaccurate results for businesses with volatile revenue.
3. Exponential smoothing
This method assigns more weight to recent revenue growth rate data. It assumes that more recent data is more indicative of future revenue trends. This is often a more nuanced and therefore more accurate model, but is mainly useful when revenue is growing or declining at a steady rate.
4. Regression analysis
Regression analysis explores the relationship between revenue and other variables like economic indicators and competitor activity. This provides the business with a more detailed understanding of the why behind their revenue forecasts, enabling smarter strategic decisions. The downside to this approach is that it requires a lot of data to be effective.
5. Monte Carlo simulation
Monte Carlo uses random sampling to produce various possible revenue outcomes, which provide a range of potential scenarios. This is an excellent choice if your business has multiple complex revenue streams.
3 qualitative forecasting methods
Qualitative methods are the art of revenue forecasting. It’s usually a good idea to use these strategies in tandem with a quantitative approach to get a more robust overview. Here are three common qualitative forecasting methods.
1. Expert opinions
Consulting industry experts and internal stakeholders is a good way to gather insights into revenue growth and upcoming market trends. This approach gives you other eyes, identifying risks and opportunities that aren’t apparent by exploring historical data.
2. Market research
Market research involves conducting interviews and surveys with potential customers to better understand their habits and preferences. This can be a great indicator of how willing consumers are to buy your product, which can be invaluable when trying to predict revenue trends.
3. The Delphi method
The Delphi method takes a more structured approach to expert opinions. It involves enlisting a panel of experts to anonymously answer a series of questions about your business and revenue potential. The answers are summarised and sent back to the panel of experts for review until a final resolution is reached.
How to forecast revenue
There is no single right way to go about reliable revenue forecasting. The best approach is to blend qualitative and quantitative approaches to gain a holistic view. Combining past business data as well as market research and expert opinions will provide a more confident estimate by approaching the topic from different angles.
How do you go about getting started? We recommend these seven steps.
Step 1: Know your data
Accurate revenue forecasting depends on accurate data. You need to have a complete and reliable understanding of your company’s historical financial performance before you can begin to predict the future.
A good foundation is to gather information from cash flow statements and income statements. Keep in mind that insightful data can come from anywhere, including CRM systems and marketing automation tools.
As such, it’s a good idea to start by unifying your siloed data sources. This will give you a 360-degree view of your entire data landscape to inform your financial modelling.
Step 2: Define your time periods
Decide which time period you’d like to forecast revenue for. Most often, you’ll see businesses do a one-year prediction for a general overview, followed by some near-term monthly/quarterly forecasts to gain more focused insights.
You can decide to make longer predictions ranging up to and beyond five years if you choose. However, be aware that the longer the forecast, the harder it is to ensure accuracy.
Step 3: Choose forecasting methods
Begin with a solid quantitative approach to make sure your forecasting is grounded in data before you branch out. Choose a quantitative model based on your business size and the degree of accuracy you require, as well as how predictable your revenue streams are.
Some models (like straight-line analysis) are a good foundation for companies with predictable revenue, but they should be supplemented with more advanced approaches like exponential smoothing and regression analysis to get more reliable insights. Other models like the Monte Carlo simulation are a better choice for demand forecasting if you have more complex needs.
Step 4: Consider internal factors
Look inward before you examine the wider market. Are you planning a geographic expansion? Do you have a new product that will soon be released? Perhaps you’re having difficulty with scaling due to issues with staffing or legacy infrastructure.
All of these internal factors will impact your revenue. Consider how they might impact consumer demand over your predefined time period and also take note of any other revenue streams that may impact your overall prediction. Regression analyses can help with this.
It’s a good idea to involve all key stakeholders and teams here to ensure you don’t miss anything.
Step 5: Take external factors into consideration
Next, look outward. Which external factors are likely to have an impact on your growth?
Consumer trends shift all the time, especially throughout the seasons. On a broader scale, economic conditions and global events often determine customer readiness to buy and spending power. New regulations and laws can also help or hinder your progress.
It can be valuable to use a combination of quantitative and qualitative approaches at this point. Getting opinions from experts on the trajectory of the broader market can uncover information that might not be available in the data alone.
Step 6: Select software to support your forecasting
Dedicated software will keep all of your data and forecasting efforts under one roof. Sales Cloud, for example, will give you an accurate view of all of your data in real-time. You can see your financials at a glance and also track sales performance to get current insight into your financial opportunities.
Step 7: Track your results and adjust accordingly
Once you’ve built your forecast, it’s important to continually track how your prediction aligns with reality over time. Be prepared to adapt your forecast if a new market trend or internal opportunity emerges that you hadn’t previously considered. A forecast is always an estimate. Be ready to rework and refine as new factors come into play.
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The challenges of revenue forecasting
Predicting revenue is incredibly beneficial, but it does have its challenges. Here are some to look out for.
Accurate data points
One of the most common challenges businesses face is gathering enough data for forecasts to be reliable. Many things can contribute to poor-quality data, including human error and other external factors beyond the business’s immediate control. Even if the information is high-quality, it can be challenging to aggregate it in one place.
External factors
Quantitative data may be the basis of proper revenue forecasting, but what happens when unseen external factors have their impact? Businesses often find that their sales forecasts become inaccurate when volatile markets shift unpredictably or an unforeseen event disrupts a supply line.
Finding the right forecasting method
It can be difficult to narrow down the right forecasting approach because it depends on several variables. There’s also often a conflict between accuracy and time: businesses want to get the most reliable information possible, but not every company has the resources to invest in advanced methods.
The positive news is that most of these challenges can be avoided with a comprehensive approach.
5 tips to improve revenue forecasting accuracy
There are best practices you can implement to avoid risk and keep your forecasts as accurate as possible. Here are five tips to improve your revenue forecasting accuracy.
1. Gather data
Getting a handle on your data is the most important first step toward an effective revenue forecast. Implement clear policies to ensure your data is standardised and high-quality. Update your information regularly.
2. Use artificial intelligence (AI) and machine learning (ML)
Machine learning and artificial intelligence can handle a lot of the busywork, including analysing huge quantities of data. They can also identify complex patterns and make accurate predictions to provide insights you may not have considered. This saves time while making your forecasts more reliable.
3. Blend quantitative and qualitative methods
Involving key stakeholders and getting insights from experts makes it less likely you’ll be caught off guard by external factors that weren’t apparent from your data alone.
4. Review and refine
Make and remake your revenue forecasts regularly, tracking how reality matches up to your predictions. Be willing to adapt your model as time progresses.
5. Test different approaches
Experiment with different forecasting approaches and backtest them to see how they perform. As long as you have enough data to feed into your models, you’ll find the best option organically rises to the top.
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Summing up
Revenue forecasting is a good way to predict how much money your business will make over a certain period by analysing data and trends. It does come with challenges — predicting external factors and the possibility of inaccurate data impacting your results can make it tricky to be confident in your forecasts without the right approach.
But by using the right mix of quantitative and qualitative models and relying on software to better keep track of your data, you’ll find you can improve and refine your forecasting model over time, gaining reliable outcomes that will spur business growth.
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FAQs
What factors come into play when forecasting revenue?
Historical sales data is almost always the starting point. Aside from that, it’s important to look at the big picture and explore customer behaviour, as this can directly impact demand. You should also examine external economic factors and stay aware of local and global events that could impact how willing consumers are to buy.
How do I choose the right methods of revenue forecasting?
You’ll want to look at both qualitative and quantitative methods. A good place to begin is by looking at the complexity of your revenue operations. Basic models like the straight-line forecasting method and moving averages work well if you have a fairly linear sales growth rate. If your revenue is more diverse and volatile, advanced models like regression analysis and the Monte Carlo simulation can help you test different scenarios for a more complete picture.
Why is forecasting revenue important?
Revenue forecasts help you strategise and make smarter decisions. For instance, they can show you when to scale your operation and by how much. They can also help finance teams allocate resources more effectively and set realistic financial goals while identifying and avoiding risks. All of this better prepares your company to maximise its profit margins.
