Not having an accurate revenue forecast is the bane of many sales managers’ lives.
Gut feel just won’t cut it.
Nor will a top-down percentage applied across all open opportunities.
Moreover, executives often dismiss the Expected Revenue report in salesforce as irrelevant or inaccurate.
That’s a pity.
Used correctly, the Expected Revenue report is a realistic forecast of future sales. It’s a sales forecast that stands up to detailed analysis and scrutiny.
But here’s the rub with Expected Revenue.
If the Opportunity Probability is wrong then so is your Expected Revenue forecast.
Unfortunately, the Opportunity Probability IS usually wrong.
It’s wrong because in most salesforce implementations, the probability links directly to the Opportunity Stage. It reflects how far the Opportunity is through the sales process. However, it doesn’t say anything about the chances of winning the deal.
But this relationship can be uncoupled. It’s even possible to set Opportunity Probabilities automatically, based on proven historical evidence.
That way, the Expected Revenue report becomes a realistic revenue forecast and a key sales performance indicator.
That’s the holy grail of sales management.
Expected Revenue Defined
Let’s be clear what we’re talking about here.
Expected Revenue (or Weighted Revenue if you prefer) is the Opportunity Amount multiplied by the Probability. That gives a dollar value for each Opportunity.
Add up these dollars for all your open deals and you have the Expected Revenue for each month or quarter.
If you calculate Expected Revenue on a realistic basis, sales manages know where they stand in relation to future sales targets.
That means decisions that drive sales team behavior are better informed.
For example, if the Expected Revenue is higher than the sales target, focus heavily on closing the deals you already have.
Alternatively, if the Expected Revenue is too low, then the sales team must generate more pipeline to meet target.
The Power of Expected Revenue
Many sales managers dismiss Expected Revenue as irrelevant.
That’s because it relies on calculating the weighted value of each Opportunity. Yet the outcome of each deal is a win or a loss. The full value of the Opportunity is won – or nothing is won.
It’s a binary outcome.
But wait a moment.
Let’s say you have a number of deals due to close next month or next quarter. You will win some and lose some.
The problem is you do not know which will be which. Crystal balls are hard to find.
Suppose you knew this information in advance. You would take 100% of the value of those opportunities that you will win. Likewise, you’d take zero value of the deals that will be lost.
But life isn’t like that.
Other than gut feel, you don’t know which will be won.
However, creating a forecast based on Expected Revenue is the way round that. The catch is it relies on setting a realistic probability for each opportunity.
The Problem with Opportunity Probability
The Opportunity Probability is wrong on many deals because it links only to the Opportunity Stage.
If the Stage moves forward, the Probability automatically increases. That happens irrespective of whether your chance of winning the deal has increased.
For example, let’s say four similar companies are pitching for a deal. They all have an Opportunity Stage called Needs Analysis. And let’s say they all have the Opportunity at 25% Probability.
All four sales teams submit their proposals. They all move the Stage onto Proposal Submitted – which for each company, has an Opportunity Probability of 30%.
All other things being equal, the individual chance of any one sales team winning the deal has not changed. There are four of them left. So each one has a 25% chance of winning.
In fact, it’s probably less than 25% because the prospect may decide not to proceed with any purchase.
However, the total Expected Revenue for each individual Opportunity has increased. Indeed, across the four combined companies, the total probability is 120%.
That clearly doesn’t make sense.
It means that a reliable Expected Revenue forecast needs a better way to estimate opportunity probability.
The Probability of Winning a Deal
For any one company, the Probability of successfully closing an Opportunity is dependent on many factors.
These might include geographic sector, product category, tender versus pitch deal and so on.
For our purposes, let’s consider two factors that apply to many businesses:
- New or existing customer. Usually the chance of winning a deal is significantly higher with an existing customer compared to a new prospect.
- The effectiveness of the sales person. Some sales people consistently close more deals compared to the rest of the team.
This where we need to consider history.
In financial services, there’s usually a warning that past performance is not an indicator of future returns.
With sales teams, it’s different. Past performance is an excellent indicator of future returns. We can use that to our advantage.
By extrapolating the Opportunity Probability from similar historic deals, it’s possible to forecast the future. It’s possible to confidently predict Expected Revenue.
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Historic Opportunity Conversion Rates
We have implemented functionality for our customers to gather data on historic opportunity probabilities and conversion rates.
New versus Existing Customer conversion rates
Look at the report and dashboard table below.
It shows the difference in opportunity conversion rates between new and existing customers.
The report and chart tells us about conversion rates for existing versus new customers. For example:
- 41% of all Opportunities with existing customers were successfully won, compared to 34% for new customers. See the “1. Prospecting” row in the report.
- 58% of Opportunities with existing customers that entered the “2. Investigation” Stage were won. This compares with 53% of Opportunities that entered the same Stage for new customers.
- 76% of Opportunities with existing customers that entered the “3. Proposal Made” Stage were successfully won. This compares with 65% of Opportunities that entered this Stage for new customers.
- 92% of Opportunities with existing customers that entered the “4. Negotiation” Stage were won. This compares with 79% of Opportunities that entered this Stage for new customers.
In other words, the report provides the information we need to differentiate Opportunity Probability between new and existing customers.
This is the starting point for more accurate Expected Revenue forecasts.
Sales person conversion rates
Now, let’s consider the difference in opportunity conversion rates between sales people.
The report shows that Dave Apthorp wins 60% of all his Opportunities compared to 27% for Peter Hemsworth and 36% for Shaun Yates. This is shown in the “1 Prospecting” row.
Look at other rows in the report. They tell us the Opportunity Conversion rate that for Opportunities that move into each Opportunity Stage.
For example, of all the deals that enter the “4 Negotiation” Stage, Dave successfully closes 90% compared to 78% for Peter and 86% for Shaun.
Accurate Expected Revenue
Our customers use the information in these reports to calculate Expected Revenue accurately.
To do this we need a custom Opportunity Probability field.
The field populates by a formula, based on the information we garnered from the conversion reports.
Let’s take an example.
Here’s an Opportunity for £15,000 with a New Customer. It’s in the Investigation Stage.
Based on the standard method, the Opportunity Probability is 25% and the Expected Revenue £3,750.
However, we know from our reports that 47% of Opportunities with new customers that enter the Investigation Stage are successfully closed.
That figure automatically enters our custom Opportunity Probability field. Now the Expected Revenue becomes £7050.
Alternatively, let’s consider what happens if this Opportunity is for an existing customer.
We know that 58% of all Opportunities with existing customers that enter the Investigation Stage close successfully.
Therefore, that figure automatically enters our custom Opportunity Probability field. This time the Expected Revenue is £8,700.
In other words, a realistic Opportunity Probability, based on historic conversion rates, automatically populates for each opportunity.
This, in turn, provides a more realistic (and in this case higher) Expected Revenue.
Accurate Expected Revenue Forecasts
Expected Revenue calculates by multiplying the opportunity probability by the value of the deal.
The problem is that our probabilities link directly to the Opportunity Stage.
However, if we use historical facts it’s different.
We know that 58% of Opportunities with existing customers that enter the Investigation Stage close successfully. We know that Dave Apthorp successfully closes 60% of all his Opportunities, compared to 36% for Shaun Yates.
Now we can use these facts to set realistic Opportunity Probabilities and drive accurate Expected Revenue reports.
And accurate Expected Revenue reports mean accurate sales forecasts.
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