Let\u2019s break it down. \ud83d\udc47<\/p>\n
\n
A definition: What is sales forecasting?<\/h2>\n
Before I get started, let\u2019s get on the same page about what I mean when I say \u201csales forecasting\u201d.<\/p>\n
Here\u2019s a possible definition of what sales forecasting is:<\/p>\n
Sales forecasting is the process of estimating a company\u2019s sales revenue for a specific future time period, most commonly a month, quarter or year. It\u2019s a prediction of how much a company will sell in the future. Accurate sales forecasting enables companies to make informed business decisions about resource allocation and budgeting.<\/p><\/blockquote>\n
If you don\u2019t have an accurate sales forecast for your business, you\u2019re essentially flying blind. You don\u2019t know whether you\u2019re going to hit your targets, or whether you need to cut costs and\/or go attract some extra cash.<\/p>\n
And if you\u2019re an individual salesperson, it\u2019s obviously a great idea to track your progress towards achieving your sales targets. \ud83d\udcca<\/p>\n
\nHow to do it: Sales forecasting methods & formulas, incl. examples<\/h2>\n
There are many methods out there and I don\u2019t want to confuse you with all of those. Instead, I\u2019ll present you with one recommended method and then explain some alternative methods.<\/p>\n
I\u2019ll also present you with concrete examples and\/or formulas so you don\u2019t need to do any further Googling to understand how it all works. \ud83e\uddd0<\/p>\n
Pipeline Forecasting Method (Recommended)<\/h3>\n
If you\u2019re getting started with sales forecasting and looking to calculate a forecast both quickly and accurately, then you probably best use the \u201cPipeline Forecasting Method\u201d.<\/p>\n
This sales forecasting method uses what you know about the opportunities in your current sales pipeline<\/a><\/u> to estimate what the amount of sales is that you\u2019ll probably close within a specific time period. \ud83d\udcc8<\/p>\n
It uses the following forecasting formula:<\/p>\n
Forecasted Sales = Sum of ((Probability of Winning an Opportunity) x (Value of an Opportunity)) for all opportunities with an expected close date in the relevant period<\/p><\/blockquote>\n
For example, if you have the following sales opportunities in the pipeline:<\/p>\n
\n
- $40,000 opportunity with a 50% probability of closing it around 31st of January<\/li>\n
- $100,000 opportunity with a 20% probability of closing it around 10th of February<\/li>\n
- $200,000 opportunity with a 10% probability of closing it around 15th of March<\/li>\n
- $100,000 opportunity with a 20% probability of closing it around 30th of April<\/li>\n<\/ul>\n
Then your your sales forecast for the first quarter of the year will be:<\/p>\n
$40,000 * 50% + $100,000 * 20% + $200,000 * 10% = $60,000<\/p>\n
This is of course a much simplified calculation example to allow an easy verification of how the formula works.<\/p>\n
If you want to calculate this kind of sales forecast live and on an ongoing basis, this Pipeline Forecasting Method is entirely automated in a sales CRM solution<\/a><\/u> like Salesflare<\/a><\/u>. \u2728<\/p>\n
The \u201cExpected revenue vs. revenue goal\u201d report in the \u201cRevenue\u201d dashboard will not only forecast your revenue for the selected period, but also compare it to your revenue goal<\/a><\/u>. That way you know whether you\u2019re on track to reach your target. \ud83c\udfaf<\/p>\n
A good sales forecast will help you understand whether you\u2019re on track to reach your target \ud83c\udfaf<\/figcaption><\/figure>\n To get an accurate sales forecast, the following things are important:<\/p>\n
\n
- Have at least 10-20 non-closed \/ open opportunities in the pipeline with an expected close date in the relevant period.<\/li>\n
- Estimate the close date as accurately as possible. One way to do this is to explicitly ask your prospect or customer about their estimated timelines. Then correct this estimate based on your experience.<\/li>\n
- Estimate the value of the opportunities as accurately as possible. This usually becomes easier as the opportunity progresses through the sales pipeline, as along the way the scope of the opportunity becomes clearer and quotes are made. Luckily, opportunities that are earlier in the sales pipeline get multiplied by a lower probability percentage, so a single opportunity in an early stage will have less effect on the outcome.<\/li>\n
- Estimate the win probability of each opportunity as accurately as possible. The easiest and most accurate way is to use a win probability per stage. This \u201cstage probability\u201d can be adapted based on your historical performance. (Note that, as an exception, for pipelines with less opportunities of higher value, setting a probability per opportunity can yield more accurate results.)<\/li>\n<\/ol>\n
To accurately calculate this win probability per stage, a sales CRM solution like Salesflare can crunch the historical data about your opportunities. You can find the result of this data crunching in the built-in \u201cFunnel analysis<\/a>\u201d report on the \u201cRevenue\u201d dashboard. \ud83d\ude01<\/p>\n
Have Salesflare<\/a> calculate your real win probability per stage \ud83d\udd2e<\/figcaption><\/figure>\n This report additionally also gives you an idea of the conversion rates from stage to stage, as well as how long each won opportunity stayed on average in each stage of the sales pipeline.<\/p>\n
Other sales forecasting methods<\/h3>\n
There are several other possible sales forecasting methods, but most of those are either overcomplicated or require an overly large dataset to work. They are in most cases also unlikely to yield a more accurate result than the Pipeline Forecasting Method.<\/p>\n
For the sake of completeness and in case you\u2019re interested, or if you just want to have a simple sanity check for the results of your Pipeline Forecasting Method, here is a quick overview of some of these other methods including a concrete example or formula for each:<\/p>\n
Time series analysis<\/h4>\n
This method involves analyzing historical sales data to identify patterns and trends. It assumes that past patterns in sales will continue into the future.<\/p>\n
There are several time series based formulas you can use for this such as the \u201cnaive method\u201d, the simple moving average, the weighted moving average, and exponential smoothing:<\/p>\n
\n
- Naive method<\/strong>: Just assume that sales will be the same next month as this or last month.<\/li>\n
- Simple moving average<\/strong>: For example, to calculate the sales forecast for the next month, take the average of the sales results in the x preceding months.<\/li>\n
- Weighted moving average<\/strong>: For example, to calculate the sales forecast for the next month, take the sum of (sales results in month * weighting factor) for the x preceding months. Usually this weighting factor is higher for recent months and gets progressively lower for less recent months.<\/li>\n
- Exponential smoothing<\/strong>: This is usually like a weighted moving average with a weighting factor that gets exponentially smaller. There are however more complicated versions that can also support trends or seasonality (just Google \u201cdouble exponential smoothing\u201d and \u201ctriple exponential smoothing\u201d \u2013 it can get a little more complex).<\/li>\n<\/ul>\n
In short: this method of sales forecasting uses an extrapolation based on historical results that sounds or looks fancier than it really is. \ud83d\ude0f<\/p>\n
Regression analysis<\/h4>\n
This sales forecasting method does not purely use a time extrapolation.<\/p>\n
It uses a mathematical equation to model the relationship between a dependent variable (sales) and one or more independent variables (such as advertising spend or economic indicators). The basic formula for a linear regression is:<\/p>\n
y = a + bx<\/p><\/blockquote>\n
where y is the dependent variable (sales), a is the y-intercept, x is the independent variable (advertising spend), and b is the coefficient representing the change in y for a one-unit change in x.<\/p>\n
This kind of forecasting method is useful if you have identified factors that accurately predict your sales results and that you can themselves accurately forecast in the future. Plus you need someone who is versed in statistics to build the forecasting model. Otherwise, it\u2019s unlikely that you\u2019ll get dependable results. Or with one emoji: \ud83d\udeae<\/p>\n
\nSales forecasting in Excel or Google Sheets with our free template<\/h2>\n
If you want to get started quickly with forecasting your sales using the Pipeline Forecasting Method, we have built a free template for Excel and Google Sheets to do just that.<\/p>\n
You can download the free template here<\/a><\/u>. No email address asked. \ud83c\udd93<\/p>\n
You can use this free Excel & Google Sheets template<\/a> to forecast your sales \ud83d\ude4c<\/figcaption><\/figure>\n Some of its main functionalities:<\/p>\n
\n
- It presents a graph of your sales forecast for the year + per quarter.<\/li>\n
- You can adapt the stage and its probabilities to dynamically adjust your forecast.<\/li>\n
- You can even track your sales pipeline<\/a> in the template, tracking company names, contact details, last interactions, next steps, etc.<\/li>\n<\/ul>\n
Here is a video tutorial on what it does and how you can use it:<\/p>\n
\n
![](\"https:\/\/images.storychief.com\/account_481\/expected-revenue_11eb5341bef0b9d1ccdba21bdc8bb588_800.JPG\")
![](\"https:\/\/images.storychief.com\/account_481\/funnelanalysis-stageprobability_6dbc2e93e0e3b050c57d33de462b3542_800.jpg\")
![](\"https:\/\/images.storychief.com\/account_481\/sales-forecast-template_88dab5918b152e8af387060b67054b7f_800.jpg\")