Sales forecasting isn’t about guessing or fortune telling. It’s about using market research and historical data to project future sales for a given period.
Done well, the ability to forecast sales can help your business make better financial decisions, manage inventory, and plan ahead for future growth.
The tricky part is putting together a sales forecast that is realistic and accurate enough to actually be useful.
There are two main sales forecasting methodologies that sales teams use to predict sales and unlock even more revenue in the future: sales trends predictions and weighted pipeline forecasting.
But before we dive into the sales forecasting methodologies mentioned above, let’s take a look at the difference between long-race and short-range forecasting, as well as the various factors that will impact your results.
Short-Term vs. Long-Term Sales Forecasting
A short-term sales forecast can be calculated monthly, quarterly, bi-annually, or annually. A short-term forecast is helpful for setting challenging-yet-attainable sales quotas, ensuring production is on schedule to meet demand, and making smart hiring decisions. For businesses that face rapid changes in market or demand, short-term forecasting is ideal for projecting upcoming sales and revenue.
Long-term sales forecasting, on the other hand, looks at a business’ sales projections for periods of 5 or 10 years into the future, or even longer in some cases. However, this type of forecasting is typically only relevant in industries that require higher upfront costs and investments in equipment like construction or real estate sales.
Both short-term and long-term sales forecasting methodologies are impacted by internal and external factors.
What Factors Impact Your Sales Forecast?
Your sales forecast is influenced by a number of different factors, some of which are within your control (internal) and some of which are not (external). Depending on which sales forecasting methodology you use, these factors might be e pre-built into your calculation.
Sales Forecasting: Internal Factors
- Changes to your sales territory. If you shuffle territory assignments, redefine boundaries, or introduce a new sales territory management plan (whether your territories are geographical in nature or not), you can expect to see a temporary drop in sales. However, sales should bounce back to an even higher point once your reps adjust to their new, optimized territories.
- Changes to your compensation plan. If you make changes to your sales compensation plan or commission structure, you’ll likely see it reflected in your sales numbers. For instance, if you switch to a structure that rewards reps for increasing revenue more rather than closing more deals, your forecast should anticipate fewer new accounts with a focus on landing higher-value customers.
- Changes in the size of your team. When you hire more sales reps, you should expect an increase in sales (once they’re onboarded and trained), since you’ll have more people on your team working to close deals. Likewise, when you have to let someone go or when a rep quits or retires, your revenue might dip until you replace those team members.
- Changes to your products or services. Whenever your business introduces new revenue streams, releases highly-anticipated features, or restructures how offerings bundled or priced, the changes will impact your forecast. If a new offering enables sales reps to speed up the sales cycle or increase their win-rate, for instance, your forecast should reflect that positive gains.
Sales Forecasting: External Factors
- Changes made by competitors. The decisions and actions of your competitors can affect the outcome of your sales forecasts. For example, if a major competitor in your space suddenly discounts their prices, that will impact your ability to sell at your current prices. Or if new competitors emerge, you might struggle to maintain your market share unless you can adapt.
- Market changes in supply and demand. Paying attention to what’s happening within your industry plays a huge role in your ability to forecast sales accurately. If there’s a growing need for your product or service, that’s a sign you can be more optimistic in your sales forecast and projected growth (especially if there’s a gap in market supply).
- Seasonality. Depending on what you sell, your sales might naturally rise and fall during certain times of the year. This is different than market changes, as seasonal highs and lows occur on a cyclical basis. Since seasonality is reasonably easy to predict, you should factor it into any sales forecasts based off of MRR or historical data to avoid skewing your results.
- The rate of inflation. For long-term forecasting, in particular, you need to account for potential inflation and how it will affect your costs and pricing strategies.
Sales Forecasting Methodology #1: Revenue Projections Based on Historical Sales Data
The typical way that many companies forecast their future sales is by looking at historical sales data, analyzing trends over time, and projecting forward. This process involves a bit of math, but it’s fairly straightforward.
Many companies will take these raw forecasts and then adjust them based on the time horizon being measured and other factors like seasonality, changes in the sales process, or adjusted predictions from the sales team. There are three steps you can follow to use this sales forecasting methodology.
Step 1: Forecast Sales by Units and Revenue (Or Service Units)
Start by figuring out the average price per unit (or service unit, billable hours, etc.) and the number of units you’re likely to sell over a given period.
Your initial sales forecast can be calculated by multiplying the number of expected sales by the average price. If you’re planning to increase prices next year, for instance, you can use this method to estimate how that decision will impact revenue.
The tricky part, of course, is accurately predicting how many units your team can move in a given week, month, or year. That’s where historical data can be brought in to paint a more realistic picture.
Use your historical sales data to map out the trajectory of your sales over time. You should be able to take data points from various points in the past to approximate the rate of change in your sales over time, then apply that rate to the most recent sales data to forecast future changes in sales volume.
Then just multiply that by your price point to determine a basic estimate of future revenue.
Step 2: Adjust Your Sales Forecast Based on Seasonality and Market Fluctuations
Unless you’re first starting out, your business should have some existing sales data that you can refer to at this step. You should look at all sources of revenue gained and lost during a particular period, including new customers, monthly charges, and customer churn.
Historical forecasting can help you come up with a quick ballpark estimate for future sales. However, the main concerns with this forecasting model are that it assumes demand and growth are both constant, without accounting for seasonality or long-term fluctuations in market demand.
To improve accuracy, you should look at the same time period from a past year and compare it to the present year. For instance, rather than forecasting your 2019 sales based on the average monthly sales for all of 2018, you could break it down by month or quarter. So, you could use sales from April 2018 to forecast for April 2019 – but those numbers might look completely different from your sales in January or October.
Furthermore, you can fine-tune even further by factoring in your historical year-over-year or quarterly sales trends.
Step 3: Layer In an Intuitive Sales Forecast from the Sales Team
In many ways, sales forecasting is both an art and a science. Intuitive sales forecasting involves asking reps to estimate how likely they are to close a given deal, when they expect to close, and the anticipated value of that deal.
You can also apply this math to your existing forecast based on historical data to try to adjust for any irregularities in your current pipeline that may affect future revenue projections.
Since this step relies on making educated guesses, accuracy hinges on each rep’s experience level, knowledge of their prospects, and their ability to make an honest assessment of each situation (rather than an overly optimistic projection).
Though this can be effective if sales reps are realistic about their abilities, the state of their pipeline, and their average win-rate, intuitive forecasting is not always reliable. That said, sometimes you have no choice but to trust your gut – especially if your business is brand new and you don’t have much existing sales data to use as a benchmark.
Sales Forecasting Methodology #2: Weighted Pipeline Forecasting
The second way to predict future revenue is to forecast based on your current sales pipeline.
In many cases, this is the preferred method because it automatically incorporates many of the manual adjustments made in retrospective sales forecasting, but it’s based on the current sales pipeline that exists in real-time.
Forecasting future sales based on your existing pipeline does have some limitations, though. Namely, the forecast window is limited by your sales cycle. If your deals typically close within 1 month, then it’s difficult to predict revenue on a 6-month time horizon based on what’s in your sales pipeline today. That’s why many companies use a combination of forecasting methods to look both short-, medium-, and long-term.
This methodology uses a weighted sales pipeline to forecast your upcoming wins and revenue. Calculations are based on what stage of the pipeline each deal is at (and how likely it is to close based on that stage) as well as the potential value of each opportunity.
For example, your weighted pipeline might breakdown sales stages like this:
- Step 1: Prospecting – 10% close rate
- Step 2: Qualification – 25% close rate
- Step 3: Proposal – 50% close rate
- Step 4: Demo – 65% close rate
- Step 5: Negotiation – 80% close rate
- Step 6: Close the Deal – 100% close rate
- Lost or Dead Lead – 0% close rate
So, let’s say you have three prospects at different stages in your pipeline, with potential deal values of $10,000, $12,000, and $20,000, respectively.
Prospect A is someone you’ve emailed back and forth with a few times. This places them in the prospecting or outreach stage, with an estimated 10% likelihood of closing.
Prospect B has been qualified as a viable customer, but you haven’t set up a demo or shipped over a proposal yet. So, this opportunity is currently at the qualification stage, giving it a 25% likelihood of closing.
Prospect C is negotiating terms and finalizing the contract, but they’re pretty much ready to seal the deal. As it’s reached the negotiation stage, this deal has an 80% chance of closing.
Using opportunity sales forecasting methodology, you would multiply the likelihood of closing by the potential deal value. In this example, your calculation would look like this:
Prospect A → $10,000 * .10 = $1000
Prospect B → $12,000 * .25 = $3000
Prospect C → $20,000 * .80 = $16,000
Your sales forecast is the sum of these weighted values. So, in this example, your weighted pipeline holds a projected value of $20,000. As new deals enter and as these deals move further along your pipeline (or drop out altogether), you would adjust the calculation to reflect how those factors influence the total value and the likelihood of closing.
This sales forecasting methodology incorporates pretty much all of the relevant data that is incorporated in the retrospective or historical forecasting methodology:
- Average win/close rate at each stage of the sales cycle
- Sales cycle length
- Potential deal value
The difference here is that these numbers are baked into the forecast and then applied only to deals already in the sales pipeline.
Bonus Tips for Better Sales Forecasting
- Start with Historical Data: Always refer to your historical sales data (if available) to set a benchmark, even if you’re using a different forecasting method.
- Get Team-Wide Buy-In: Communicate with your entire sales team about the importance of collecting data and keeping it clean. If everyone makes an effort to ensure the accuracy and completeness of their data, your sales forecasts will be right on the money.
- Look at Multiple Factors: Include enough input to account for 2-3 relevant variables. Whether you’re looking at rep performance, pipeline stage, deal age, or some other metric, be consistent in what and how you track your sales data.
- Build an Effective Sales Stack: Ensure your sales team has the right technology to help them capture, manage, and analyze sales data. For example, a CRM platform that provides you with a visual pipeline and deal stage tracking can simplify sales forecasting immensely.
- Use a Powerful CRM: Simplify data tracking, collection, and analysis with a robust CRM. A platform like Propeller will make it easy to manage your customer information, generate sales reports, and plug your data into the forecast model of your choice.
Take the Guesswork Out of Sales Forecasting
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