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<\/a><\/p>\nTable of Contents<\/strong><\/p>\n <\/a> <\/p>\n Revenue forecasting helps predict future revenue based on historical data, market demand, and current trends. Simply put, revenue<\/a> forecasting is the backbone of financial planning. It enables businesses to not only predict future sales revenue<\/a> but also make informed decisions about resource allocation, investments, and strategic initiatives. With revenue forecasting in place, businesses have a clear direction of what to do and what areas to invest in next.<\/p>\n As a primary function of financial planning, revenue forecasting helps companies set budgets, create P&L statements, and determine pricing<\/a>. Some additional ways it benefits a company is by helping them anticipate cash flow shortages, prepare for seasonal fluctuations, and identify growth opportunities.<\/p>\n Revenue forecasting also contributes to other key business functions, including:<\/p>\n Here are a few more examples of how revenue forecasting can be applied to your overall business planning and growth, according to a few experts I talked to.<\/p>\n I asked Parker Gilbert<\/a>, CEO and co-founder of Numeric, how his team benefits from revenue forecasting.<\/p>\n \u201cAt Numeric, revenue forecasting is how we make sure we’re investing in the right areas,\u201d he says. \u201cIt helps us decide whether to hire more engineers, expand into new markets, or even just how much coffee to order for the office (because, yes, even coffee consumption can be tied to revenue). Ultimately, it keeps us all aligned on our goals.\u201d<\/p>\n I also spoke to Jacob Barnes<\/a>, founder of the productivity app FlowSavvy<\/a>, about a specific use case for revenue forecasting and how it\u2019s helped his company.<\/p>\n \u201cIn Q1 of this year, we predicted an 18% rise in monthly subscriptions based on January and February growth,\u201d he says. \u201cThis allowed us to confidently expand our customer support team by 15% by March, assuring that we could successfully manage additional user concerns without jeopardizing our cash flow.\u201d<\/p>\n Barnes adds, \u201cBy projecting revenue early, we ensure that our spending is consistent with realistic expectations rather than overly ambitious ones that could deplete resources.\u201d<\/p>\n For Tracie Crites<\/a>, CMO of HEAVY Equipment Appraisal<\/a>, revenue forecasting played a crucial role in helping the company expand its services, she told me.<\/p>\n \u201cA few years ago, we started seeing an increase in demand for appraisals tied to insurance compliance and environmental regulations,\u201d she recalls.<\/p>\n \u201cUsing revenue forecasting, we could project potential earnings in these niche areas and determine that there was enough consistent demand to justify expanding. This meant we could confidently invest in training our team to specialize in these areas, develop new marketing initiatives, and allocate resources toward developing these offerings without jeopardizing our core business,\u201d Crites says.<\/p>\n Crites adds, \u201cRevenue forecasting acted as a safety net, allowing us to move forward with clear data showing where growth was likely. It wasn\u2019t just a guess \u2014 it was a calculated decision based on trends and data. This has kept us focused and adaptable, helping us expand in a way that stays true to client needs while giving us room to explore new revenue streams.\u201d<\/p>\n If you want to learn more about sales forecasting and analysis<\/a>, check out this free lesson from HubSpot. HubSpot Academy offers sales forecasting training<\/a> that walks through the best practices for creating a reliable sales forecast.<\/p>\n Like anything, revenue forecasting can be challenging if you don\u2019t have the right data or approach.<\/p>\n According to the people I talked to, a few common mistakes in revenue forecasting include over-relying on historical trends, ignoring market shifts, and failing to account for external factors.<\/p>\n Here are some of the challenges you may experience with revenue forecasting.<\/p>\n Kevin Shahnazari<\/a>, founder and CEO of FinlyWealth<\/a>, shares with me that overestimating revenue can come at a cost.<\/p>\n \u201cThe biggest pitfall during revenue forecasting, at least so far in my career experience, is being too positive,\u201d he says. \u201cEarly on, at the beginning of my tenure, I overestimated the amount of user growth by about 40%, resulting in hiring that was ahead of requirements. Since then, it has been a practice for me to always include a fudge factor and always have multiple scenarios.\u201d<\/p>\n Another mistake or challenge that companies experience is not having or using real-time data, says Spencer Romenco<\/a>, chief growth strategist at Growth Spurt<\/a>. Seeing changes in real time allows you to adjust your revenue projections and strategy accordingly.<\/p>\n \u201cOne of the biggest mistakes I see in revenue forecasting is not adapting the forecast to real-time changes in audience engagement and external market conditions,\u201d he says. \u201cMany companies rely solely on historical data without considering current audience behaviors, which can make projections unreliable.\u201d<\/p>\n Romenco shares his solution with me: \u201cAt Growth Spurt, we constantly update our forecasts by closely monitoring shifts in our video engagement metrics,\u201d he says. \u201cThis lets us predict revenue impacts if, for instance, engagement starts climbing or falling.\u201d<\/p>\n Gilbert pointed out another challenge that I hadn\u2019t considered: not collaborating across teams. If revenue forecasting doesn\u2019t include all of the right people \u2014 people who can contribute key data to the projections \u2014 then your forecast won\u2019t be what\u2019s best or most accurate for the overall business goals.<\/p>\n \u201cRevenue forecasting shouldn’t be siloed within the finance team,\u201d he says. \u201cWe involve stakeholders from across the company to get a holistic view.\u201d<\/p>\n <\/a> <\/p>\n Before you can dive into revenue forecasting, you have to choose a model that you want to follow. There are several revenue forecasting models, each with different scenarios for when it makes sense to use them.<\/p>\n A time series revenue forecasting model uses historical data to identify patterns, trends, and seasonality. These models analyze data over a period of time, such as monthly, quarterly, or yearly. This model works best for companies that have recurring revenue that can be measured within one of these timeframes.<\/p>\n I learned that some common methods of conducting time series forecasting include:<\/p>\n Best for: <\/strong>Businesses with cyclical revenue, such as subscription services.<\/p>\n Using linear regression to forecast sales<\/a> is another common method. Linear regression revenue forecasting involves analyzing two related variables on an X and Y axis. The goal is to see how well they align.<\/p>\n For example, you could use linear regression analysis to compare sales and profit. Both of these contribute to revenue, so they should align. If sales are increasing but profit is staying the same, this could indicate that you need to increase your prices.<\/p>\n Best for<\/strong>: Determining your pricing strategy.<\/p>\n The top-down revenue forecasting model uses macro-level analysis to estimate the company\u2019s total revenue. This type of forecasting takes a look at the total addressable market (TAM)<\/a>, market size, and competitor performance to predict potential market share and revenue.<\/p>\n Best for<\/strong>: Early-stage businesses entering competitive industries with access to market data.<\/p>\n As you can imagine, bottom-up revenue forecasting takes the opposite approach of the top-down model. Bottom-up forecasting starts at the granular level, taking a look at a company\u2019s detailed data around individual products, services, or customers.<\/p>\n For example, a retail company could look at its sales volume from the previous year and multiply that number by its anticipated price point for the upcoming year to come up with its projected revenue.<\/p>\n Best for:<\/strong> Businesses launching new products or services or with detailed sales data.<\/p>\n <\/a> <\/p>\n With all of the information I\u2019ve shared about revenue forecasting so far, now is a good time to break down exactly how to do revenue forecasting. Whether you\u2019re a startup founder, sales leader, or on your company\u2019s finance team, here are some easy steps to follow when conducting revenue forecasting.<\/p>\n And if you want to dive deeper into forecasting analytics, check out this free lesson<\/a> on HubSpot Academy.<\/p>\n I suggest starting by reviewing the revenue forecasting models above. Knowing which model makes the most sense for your business helps you kick off the process the right way. Using the wrong model is a quick way to create confusion or inaccurate projections.<\/p>\n The next step is to review your historical data. This helps you set the baseline revenue that you can work from. If you need help calculating things like average deal size, win-loss rate, churn rate, etc. check out our free sales metric calculator.<\/a><\/p>\n Many of the experts I spoke to, however, emphasized not relying too much on historical data without also considering external factors or real-time adjustments.<\/p>\n \u201cThe most prevalent mistake I find is merely relying on historical data,\u201d says Shahnazari. \u201cOn FinlyWealth, we combine historical trends with forward-looking indicators such as market sentiment and consumer credit trends, which has helped maintain over 90% accuracy in our forecasting.\u201d<\/p>\n In my experience, some markets can fluctuate significantly. That\u2019s why it\u2019s important to analyze market conditions on an ongoing basis and use that information to inform and adjust your revenue projection.<\/p>\n Analyzing markets helps you not only stay competitive but also identify opportunities to expand or add new products or services.<\/p>\n The key to getting revenue forecasting right? Stay flexible. Seasons change, markets change, customer behaviors may fluctuate, and trends can appear at a moment\u2019s notice. It\u2019s important to revisit your forecasted projections throughout the year to stay proactive.<\/p>\n \u201cRevenue forecasting is an ongoing process for us,\u201d says Gilbert. \u201cIt\u2018s not something we do once a year and then forget about. We\u2019re constantly revisiting and refining our forecasts as new information becomes available. It’s an iterative process that helps us stay agile.\u201d<\/p>\n\n
What is revenue forecasting?<\/h2>\n
Benefits of Revenue Forecasting<\/h3>\n
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Resource and Investment Planning<\/h4>\n
Team Expansion<\/h4>\n
Service Expansion<\/h4>\n
Challenges of Revenue Forecasting<\/h3>\n
Overestimating Revenue<\/h4>\n
Lack of Real-Time Data<\/h4>\n
Ignoring Cross-Collaboration<\/h4>\n
Revenue Forecasting Models<\/h2>\n
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<\/p>\n1. Time Series<\/h3>\n
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2. Linear Regression<\/h3>\n
3. Top-Down<\/h3>\n
4. Bottom-Up<\/h3>\n
How to Do Revenue Forecasting<\/h2>\n
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<\/p>\nDetermine which model to use.<\/h3>\n
Assess historical data and past revenue performance.<\/h3>\n
Analyze market conditions.<\/h3>\n
Adjust in real time.<\/h3>\n
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