A Sales Manager’s Guide to Forecasting
I once worked with a sales manager who had just finished a quarter in which his team had closed within $1,000 of the bookings number he’d been projecting for several weeks (approximately $900k). When asked what his secret was, he responded, “It’s better to be lucky than good.” While the spot-on level of precision that quarter certainly involved some luck, the truth was that this sales manager was excellent, and his forecasting acumen was a result of him having a firm grasp on his team’s work and their deals.
Skilled forecasting can build internal credibility as a demonstration of competence, and it can also give you the ability to act on issues that you see coming in the future rather than being forced to react to a bad quarter. If your forecasting shows that you’re likely to miss your number, you can come up with a plan to mitigate the amount by which you’ll miss in the near-term, and create a plan of action to “catch up” the next quarter. With this in hand, you can alert leadership proactively as to what’s going on and what you’re doing about it. This avoids having to deliver an unpleasant surprise in the 11th hour of the quarter and shows an ability to respond to issues as they arise.
Honing this skill will also help you as a sales manager to gain a deeper understanding of your business and the levers you can pull to drive changes. The best approaches to take to forecasting depend a great deal on what types of reps you manage and customers you sell to - an Account Executive team selling large Enterprise deals over long sales cycles will need to take a much different approach to a team of Account Managers renewing small, transactional business. The biggest difference driven by deal size and transaction volume will be around the degree to which you take a “top-down” approach, looking at the team as a whole, versus a “bottoms-up” view of individual deals.
Accordingly, this document is split into four sections:
- New Business Forecasting for Transactional Sales
- New Business Forecasting for Enterprise Sales
- Existing Business Forecasting for Transactional Sales
- Existing Business Forecasting for Enterprise Sales
We've also put together a Google Docs template that you can use for forecasting here.
New Business Forecasting for Transactional Sales
Top-Down Forecasting, or How to Math Your Way to a More Accurate Forecast
For an SMB or lower mid-market sales team doing a high volume of transactions over relatively short deal cycles, the chance that any single deal will impact the overall forecast is incredibly small, and the frequency with which individual deals enter and exit the pipeline is high. Therefore, a bottoms-up, deal-level approach - while informative to you as the manager - is probably not going to create the most effective forecast.
This is where top-down forecasting is going to be the most effective approach. Exactly what that top-down approach looks like will depend on your specific sales cycle time and forecasting cadence. If you’re expected to deliver a forecast monthly and have a sales cycle time of longer than 30 days, then at the start of each month, your team’s current pipeline already reflects the set of opportunities that you’re likely to close during that month. Your top-down forecast can focus in on the likelihood of closing those deals.
The process here is this: First, remove any opportunities that have been untouched for more than 30 days (those that have gone stale) or any opportunities that are significantly older than your average sales cycle time. You can use double your average sales cycle time as a rough rule of thumb unless or until you have a sales operations partner who has run more detailed analysis to let you know where the drop off in win probability happens as deals age for your specific team. (While you’re at it, you should probably have your reps close out those opps and send them back to SDR). What remains is your “active” opportunities - those deals that your AEs are engaged with and pushing towards close.
Then, you want to calculate your weighted active pipeline. Salesforce Lightning calculates this for you as an “Expected Revenue” amount for each opportunity. To the extent that your reps are good about putting in an “Amount”, and the “Probability” numbers for each stage in your sales process are accurate in Salesforce, you can use this value. If the Amount is blank, then you can substitute in your team’s average selling price. If the Probability numbers aren’t accurate, then you’ll want to work with your sales ops partner to calculate the actual win rate from each stage and use that to weight your team’s open pipeline instead. Either way, the amount of each opportunity times the probability that an opportunity in that stage will close, gives you your weighted pipeline.
This alone will give you a good starting point for where your team might be expected to end the month. There’s a coaching action for you to ensure that Close Dates are accurate, but that’s really the only ask that you need to make of your reps to get this far. This work may look something like this:
You likely still want to do a check on this number, though, and the easiest one to do is to check your pacing. High-volume, transactional sales teams typically have a pattern of when they book business. It may look something like 10% in week one, 15% in week two, 30% in week three, and 45% in week four of the month. Whatever that booking pattern has been historically, you can see if your team is currently meeting that pattern. Continuing the example in the screenshot above, if you’re at the end of Week 2, then using historical pacing to back-check your weighted pipeline number might look like this:
If that matches what you calculated as your weighted pipeline (or expected revenue, in Salesforce) above, then you have a data point that reinforces that as an accurate forecast number. If the pacing suggests a higher number, then maybe there’s an opportunity for your team to pull in some deals that currently have a close date of next month. If the pacing suggests a lower number, then you would want to understand what might be driving that and see if there are deals where the close date is too optimistic or ambitious.
All of this is straightforward enough if you’re forecasting inside your average sales cycle time, but what if you have a 30 day sales cycle and you’re forecasting for the quarter instead? That’s where you want to look at your leading indicator metrics to understand at what rate your team is having initial meetings and creating new opportunities.
You should know the basic metrics that go into your bookings formula - what your team’s average selling price for their deals is, on average, and what their win rate is across all opportunities. If you look across the past month or quarter, how many new opportunities are you generating per AE? If you assume that rate of new opportunity generation will continue, and each new opportunity looks like your existing opportunities, then you should have a good sense of what those new opportunities will translate into in terms of bookings for the portion of the quarter that’s outside of your average sales cycle time. Really, if new opportunity generation has been consistent, then a good starting point for your quarterly forecast - regardless of what’s currently in pipeline - would be:
# of new opps per week * average selling price * win rate * # weeks in quarter
However, if you want to get a little more sophisticated and think about what’s already in your pipeline for the quarter, then you need to add your current weighted pipeline for the next 30 days, or whatever your average sales cycle time is, to what has already been Closed Won and to the amount of bookings that you think will close that haven’t been created yet (to avoid double counting).
A shortcut to think about sales cycle time for this is to assume, as you are doing with the other metrics, that every new opportunity is “average”. That is, if every new opportunity being created today closes in exactly your average sales cycle time, then no opportunities created in the last 30 days of the quarter will close within the quarter. As a result, the formula to think about bookings that will be both created and closed before the end of the quarter is:
# of new opps per week * average selling price * win rate *
(# weeks remaining in the quarter - average sales cycle time in weeks)
Forecasting using this method might look something like this:
Of course, you can also use the pacing method to check this number, using pacing for the quarter in the same way that you used pacing for the month, above.
The idea in all of this is that your sales cycles are likely too short for forecasting based only on what’s in your pipeline to make sense over a time period that’s longer than a month, and your team should be closing enough deals that you can rely on something like reversion to the mean fairly heavily. It also means you should keep an extra-close eye on whether those averages are changing over time, so that you can understand how future bookings are likely to change as well.
New Business Forecasting for Enterprise Sales
Bottoms-Up Forecasting, or Tips for Telling Which Deals are Real and Which are Hooey
If each AE on your team only closes a few deals each quarter, and those deals have longer deal cycles, then you would take the opposite approach of the one outlined above. For you, the best way to forecast will be to sit down with each AE and go through a deal-by-deal outlook to create a bottoms-up forecast. In many ways, this is more difficult than the approach outlined for transactional sales above. It requires less math, but a great deal more judgment from you as the manager.
In this kind of a sales motion, a significant portion of your 1:1 with each AE each week is focused on pipeline review. This is your time to uncover what’s really going on with the deals in each AE’s pipeline. You’ll want to look for red flags and green flags with deals in the data and confirm those in discussion with your AEs. Green flags include things like a low number of Days Between Touches and a high Email Engagement Rate with the account. Red flags are the opposite - a high number of Days Between Touches or a low Email Engagement rate - as well as things like letting an opp go Untouched or having Pipeline that is Static, meaning it has been stuck in the same stage.
Using these, or similar, metrics, you might end up with a scorecard that looks something like this:
In this example, George (our AE) has two untouched opportunities, and four that have been stuck in the same stage for more than 30 days. Each of those stuck opportunities also has a low engagement rate, so our manager has zeroed out those opps in his own forecast. Conversely, some of the later stage opportunities with high engagement this manager feels ready to commit, so he has increased his own probability on those opportunities coming in to 100%, so their amounts will be fully included in his forecast.
The manager has also reduced the probability of a Stage 5 deal (Peterson) to 50%. With a lower engagement rate, it may be that this manager thinks that there’s only a 50% chance of that deal coming in. He has also zeroed out one of the Stage 1 deals with a lower engagement rate, perhaps because he believes that deal is unlikely to come in during the current quarter, if it comes in at all.
The manager’s changes have reduced George’s forecast from his weighted pipeline amount of $280k down to $261k for the quarter. If the manager goes through this exercise with each rep, the end result should be a pretty tight bottoms-up forecast.
Of course, across a team, there will always be “swing deals” - those deals large enough to “swing” the entire forecast if they do or don’t come in. Any deals like this should be called out separately in forecast meetings with executives - what deals these are, how much they’re worth if they do come in, what amount (if any) is being included against them in your overall forecast, your assessment of the deal, and what amount you could potentially cover with lower probability pipeline in the event that deal doesn’t come in.
When you have a final forecast, with any swing deals called out, you can always use the methods above in the transactional sales section, such as measuring your deals closed quarter-to-date against your average historical pacing, as a check on the net total of your judgment calls.
Existing Business Forecasting for Transactional Sales
Renewals and Upsells Behave Differently, But It’s Still Mostly Just Math
Upsells are really new business deals. The probability of them closing at any stage is likely different, and they’re more likely to have a short deal cycle like a “transactional” AE deal, but if your Account Management or Customer Success Team (I’m going to use AM as a catch-all for the remainder of this document) does a significant amount of expansion business - upsells that happen outside of the time of renewal - then forecasting those deals works in largely the same way as the AE deals discussed above.
If, instead, your team only does a very small amount of upsells, then the best way to forecast them is as a percentage of the total dollars in bookings that are up for renewal that quarter. If your increases tend to happen at the time of renewal, then adding 6% (or whatever the correct figure is) to your forecast to account for the historical amount of expansion bookings as a percentage of dollars up for renewal will likely be sufficient for forecasting purposes.
Accordingly, this section will focus on how to forecast renewals.
There are a number of data points you’ll want to go in knowing:
- How many deals are up for renewal?
- What is the total value of deals up for renewal?
- What is your historical dollar renewal rate and rate of churn?
- What is your historical rate of renewals falling out of the quarter and pushing to the following quarter?
As with the discussion of AE forecasting, above, the higher the number of renewals you need to forecast, the more likely it is that forecasting based on a bit of math using historical averages is going to deliver the most accurate forecast.
This will look something like this:
Walking through these columns, we start with the total value up for renewal in the quarter (the “Renewal Baseline”). I’m including here any baseline that moved into this quarter from last quarter as well as any baseline that was originally up for renewal this quarter. We then split out how much of that baseline is still open and how much has already been closed, so that we know what’s locked in for the quarter and what kind of a dollar retention rate we got off of what’s already been closed.
That will then inform the dollar retention rate that we want to assume for what’s still open. Here, you can assume a reversion to the historical average dollar retention rate, after subtracting for any baseline that you expect to push into the next quarter. Remember, we added into the baseline what pushed into this quarter because it stayed open beyond the day on which the old contract ended, so we need to account for the same thing happening here. What I’ve done in the example above is assuming that the 93% renewal rate that the team is currently averaging on their baseline is the historical average and subtracting out 5% that I expect to push to next quarter, to get an 88% expected renewal rate for the baseline that’s still open.
Finally, I multiple the expected renewal rate on open baseline times the amount of baseline still open, add that number to what’s already been closed won, and I have my implied renewal forecast for the quarter.
Adding that to my expansion forecast, I have a good sense of the total number where I expect my team to end the quarter.
Existing Business Forecasting for Enterprise Sales
Mission: No Surprises
For an AM covering large renewals, the goal will always be to interact with customers often enough that any issues are identified and resolved well before renewal conversations take place so that churn can be avoided where possible and flagged as being at-risk far in advance where churn is unavoidable. Because these AMs are closing relatively few deals each quarter, the best approach will be a bottoms-up one, but forecasting will incorporate elements of the new business forecasting outlined in the second section and an understanding of the renewal metrics from the section above. The forecasting process will involve sitting down with each AM and going through a deal-by-deal outlook to create a bottoms-up forecast, but it will still be useful to ensure that you have a good understanding of your overall renewal and retention metrics, which a more transactional AM Manager will use as their primary levers to create a forecast.
The result might look something like this:
The manager is taking a deal-by-deal approach with this rep and understanding for each deal that he knows the amount up for renewal, the amount that the rep has currently entered in SFDC, what the implied retention rate is, and (as with new business sales), whether there are any red flags within the sales process.
However, he’s also added some metrics at the bottom of this sheet to understand the overall renewal rate both QTD and implied by the forecast. If these deviate significantly from historical norms, the manager will know that he needs to dig in further. He will also summarize these values across his team to understand what these figures look like for the team as a whole.
I hope that this forecast overview is helpful to you, both to crush your own forecasting and to understand better the underlying metrics that drive your forecasted outcomes!
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