We’ll go out on a limb and wager that their sales forecast is the one sales report that almost every sales leader out there – from VPs to managers, and even CEOs – thinks about every single day.
And how could they not?!
A sales forecast is a projection of what your sales team’s performance will be at the end of a given measurement period (typically monthly or quarterly), or how much business you can expect to close this period. For future planning, resource allocation, appeasing the suits on your Board and setting expectations, this is absolutely critical.
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When it is accurate and reliable, of course. Which is decidedly not always the case.
The fact is that many sales leaders and organizations continue to rely on sales forecasting methods that aren’t very accurate. Not only does inaccurate forecasting cause you to look bad in front of your CEO–it makes long-term planing and growth impossible.
Basic Sales Forecasting Methods
Opportunity Stage Forecasting
In your Salesforce.com CRM system, each opportunity your team works on should have a status, or an Opportunity Stage. These stages represent specific milestones that each specific opportunity should have crossed or touched, before being put into that stage. The names (and milestones) of these stages will differ, depending on your company and industry.
For example, in the B2B software world, typical stage names – and their accompanying milestone accomplishments – might be actions such as “Completing an assessment call,” “Delivering a demo,” “Enrolling in a free trial,” or “Submitting a quote.” Other companies might use less-blunt stage names, such as “Discovery,” “Buying Process,” or “Fulfillment.” Once the rep has taken the opportunity through a demo or successfully enrolled them in a free trial program, they are moved into that stage in Salesforce.com.
In terms of sales forecasting, each stage has a probability associated with it. An opportunity in the Discovery stage might have a probability of closing of 10%, while an opportunity in a later stage like “Submitting a quote” is probably very likely of closing, around 90%.
Sales leaders who use this basic sales forecasting method will then multiply the amount or value associated to each opportunity with a close date in this reporting period by the probability of opportunities in that stage closing. You then get a simple sales forecast for that period, like in this quick example here:
This Month's Forecast
|Opportunity Name||Stage||Probability||Amount||Forecasted Amount|
- Pretty basic math. If you have $100,000 worth of opportunities in an early stage, one that has a 25% probability of closing, you can reasonably expect $25,000 of those opportunities to be Closed-Won in this period.
- Extremely objective. Even if your sales rep feels supremely confident about eventually bringing home a presently early-stage opportunity, the probability of closing that opp remains static. The emotional element has been neutralized from the sales forecasting equation.
- Arbitrary probabilities. The relative likelihoods of closing at each opportunity stage isn’t rigorously derived, nor regularly tested and improved. Just because you’ve historically closed 25% of your early-stage opportunities, does that mean you actually have a 25% chance of closing all those early-stage opps? Not necessarily.
- Sales cycles aren’t considered. An old opportunity that is 2 years old, but with a close date this month, is counted with the same likelihood as a newer opportunity that is only a week old, but also has a close date in this month. In reality, older opportunities that have languished in stages are unlikely to close at the same rate.
- Accurate close dates are an absolute must. Any inaccurate close date – or any other erroneous data, such as hitting specific milestones – can throw the whole forecast off.
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To tackle some of the shortcomings of the aforementioned Opportunity Stages method, some sales teams use Forecast Stages to generate their sales forecasts. This ignores the milestones we talked about above, and simply asks sales reps to make a subjective assessment of the opportunities they’re working on. These are typically:
- Commit – “Based on the conversations I’ve had, and my assessment of this opportunity’s needs relative to our product, I am confident that they will buy. I am personally committing to bringing this opportunity in.”
- Best Case – “I wouldn’t bet my life on it but if a few things break right and go our way – if we are able to get the CFO on a demo this week, for example – this opportunity could definitely come in.”
With this type of sales forecasting method, the worst-case scenario would be bringing in all the opportunities filed under the Forecast Stage “Commit.” The best-case scenario would entail everything breaking right and closing not only your Commits, but also those optimistic stretch deals.
- Companies can calculate their forecast well in advance. They don’t have to wait until opportunities reach late stages of the buying process. A sales rep could have just started working an opportunity, but if they feel good about its buying profile, can slot it right into “Commit.” This is helpful for sales teams with lengthy sales cycles or external field reps.
- It’s incredibly qualitative and subjective. Are sales reps really the most trustworthy bunch? Just because your sales rep tells you that she is certain of bringing this opportunity home, can you really bet the bank on that? Remember, sales reps are notorious for having “happy ears,” especially after a promising conversation. This will invariably lead to overly optimistic forecasting, and no Sales VP wants to be the one to over promise and under deliver.
These two sales forecasting methods are both flawed in their own ways. There are a host of other factors and variables – sales cycle, engagement, and momentum, for example – that are not considered with the above two methods.
Additionally, there are more quantitative and statistically reliable sales forecasting methods that can ultimately produce more accurate forecasts. Unfortunately, the math behind these algorithmically based methods can get complex, too hairy and time-consuming for a sales manager to undertake. A powerful third-party reporting and analytics solution would be the best choice here, taking into consideration historical data and performances coupled with a rep’s subjective assessment to produce a more efficient, reliable and advanced sales forecasting method.
Advanced, Data-Backed Sales Forecasting Methods
These two sales forecasting methods take into account a whole host of data, including your current sales pipeline, your historical performance, and other variables. As such, they are a lot more quantitatively reliable, grounded in sales data instead of finger-in-the-wind intuition.
Historical-Based Sales Forecasting
This sales forecasting method takes a “top-down” view. It draws uses a regression model that draws a sloped line across your last 18 months of bookings, and then extrapolates that data to determine your next month’s forecast, with this steady growth reflected. In essence, it looks at how you’ve done to determine how you will do going forward.
This method ignores how many opportunities and how much value is actually in your sales pipeline. What it does is plot the next data point on the trendline of your historical performance over the past year. If you suffer a massive drop-off in your sales pipeline – prospecting just didn’t go well last month – but still use the historical-based sales forecasting method, the accuracy of your forecast will not be high.
For companies who are in a steady state and do a good job of keeping the machine running – with general steadiness, consistency and predictability month-over-month, this should be the sales forecasting method of choice.
- Data accuracy doesn’t matter (as much). If you’ve torn your hair out and you still can’t get your reps to reliably enter consistent and accurate data, your sales forecasts won’t suffer, if you use this method.
- Works well with limited variance month-to-month. A lot of companies and sales teams are in a good place, where they’re mostly maintaining the status quo. This method is much easier for them, without any loss in accuracy.
- Doesn’t work well if you have lots of variance in your monthly bookings. If your monthly bookings looks like this example below – with lots of peaks and valleys, and unpredictability – this sales forecasting method will not work for you.
- Doesn’t consider your sales pipeline. If you had an amazing prospecting month recently – with great work from your outbound reps and your marketing team – your sales pipeline will likely swell, giving you more opportunities to work on and, presumably, close. This sales forecasting method doesn’t consider your current pipeline opportunities.
Pipeline-Based Sales Forecasting
This sales forecasting method takes a “bottoms-up” approach, starting with all the opportunities you currently have in your open sales pipeline. This method meticulously – and arduously, if you’re not using a program with a reliable algorithm – goes through each opportunity in your pipeline and calculates its individual chance to close. Needless to say, this method is pretty thorough.
The chance of each opportunity closing is calculated through a series of variables unique to your company and selling process. Some variables considered include opportunity value, age in stage, probability to close in the period, and opportunity owner’s personal win rate at that stage. History might show that your company’s “sweet spot” of opportunities are opportunities worth between $2,000 and $5,000, with a sales cycle of 15 days. All your current open opportunities that fit those parameters would then be given a high probability of closing.
The individual forecasts on each opportunity is then “rolled up” to give you your monthly or quarterly forecast, based solely on all your pipeline opportunities in that time period.
- Variance in your sales pipeline is accounted for. In historical based forecasts, your month-over-month upward trajectory will project your next month’s performance, even if you suffer an unforeseen drop in pipeline. If you don’t have very much consistency month-to-month in terms of how much pipeline you’re able to generate, this sales forecasting method will account for that.
- It’s solidly data-backed. And by your own historical data and benchmarks too! This type of sales forecasting method considers data and probabilities that is unique to your company. Instead of relying on Salesforce.com’s standard opportunity stages and probabilities, you can apply your own variables to your own opportunities and forecast.
- It relies heavily on perfect data quality. Of course, as with any data-backed analysis, your results won’t mean squat if the data is unclean or incomplete. You need your reps to always enter accurate data into their CRM, on fields such as close date and opportunity value.
There is a wealth of sales forecasting methods out there, from basic ones that rely on intuition to advanced ones that require complex algorithms, from those that look at your historical performance to those that look at your sales pipeline. Whichever one you choose, having the right sales forecasting method – and accompanying accurate sales forecasts – is critical to helping you manage your sales team and running your business.
Choose your sales forecasting method wisely.