Expected Revenue delivers a reliable forecast of future sales in many companies.
It’s a sales forecast you can defend in front of the board.
Unfortunately, however, some executives dismiss Expected Revenue in Salesforce as irrelevant.
That’s a pity because not having an accurate revenue forecast is the bane of many sales managers’ lives.
After all, your gut instinct won’t cut it.
Nor will a top-down percentage applied across all open opportunities due to close this month.
However, when used correctly, Expected Revenue delivers a sales forecast report that stands up to detailed analysis and scrutiny.
Nevertheless, here’s the rub with Expected Revenue.
If the Opportunity Probabilities are wrong, then so is your Expected Revenue report.
And unfortunately, Opportunity Probabilities ARE usually wrong.
They are inaccurate because, in most Salesforce implementations, the probability links directly – and only – to the Opportunity Stage.
As such, the probability reflects how far the Opportunity is through the sales process. However, that doesn’t say anything about the chances of winning the deal.
Fortunately, you can reduce the dependency upon the Opportunity Stage. It’s even possible to set opportunity probabilities automatically, based on proven historical evidence.
If you do this, then the Expected Revenue report is a realistic revenue forecast and a key sales performance indicator.
Let’s dive in.
What is Expected Revenue?
Expected Revenue is the Opportunity Amount multiplied by the probability. That gives a dollar value for each Opportunity.
Add up these dollars for all your deals, and you have the Expected Revenue report for each month, quarter, or year.
Consequently, decisions that drive sales team behavior are better informed.
For example, if the Expected Revenue is higher than the sales target, then focus heavily on closing the remaining pipeline deals.
Alternatively, if the Expected Revenue is too low, then the sales team must generate more pipeline to hit quota.
How is Expected Revenue different from Weighted Pipeline?
The Weighted Pipeline is the value of each open Opportunity multiplied by the probability of successfully winning the deal. Expected Revenue includes the Weighted Pipeline plus won deals at 100% probability.
The two concepts are, therefore, related. Expected Revenue includes won and pipeline opportunities, whereas the Weighted Pipeline refers to open deals only.
If you want to predict your sales revenue for the month, run an Expected Revenue report. Alternatively, if you’re focusing on the funnel only, run a pipeline report.
Why is Expected Revenue sometimes dismissed?
Here’s the view some sales executives take:
Deals are binary. The outcome of each Opportunity is a win or a loss. You win the full value of the Opportunity, or you win nothing.
Expected Revenue, these managers say, is irrelevant because it doesn’t reflect this binary result. Instead, the Expected Revenue report includes a figure for each pipeline opportunity, that will never materialize.
For example, let’s say you have a pipeline opportunity for $1,000. If you win the deal, it’s worth $1,000. Alternatively, if you lose the deal, it’s worth zero.
However, the Expected Revenue report will include a figure for this pipeline opportunity somewhere between $1 and $999, depending on the probability. Whatever the number is, it’s not an amount the customer is ever going to pay.
But wait a moment.
Why is Expected Revenue a powerful metric?
Let’s say you have 50 deals due to close next month or this quarter.
You know you will win some and lose some.
But here’s the problem:
Unfortunately, you don’t know which you will win and which you will lose. Crystal balls, after all, are in short supply.
However, suppose you knew this information in advance. Then you would do two things.
First, your sales forecast will take 100% of the value of those opportunities you will win. Likewise, you will apply a zero amount for the deals that will be lost.
Second, you wouldn’t bother chasing the opportunities that you will lose, would you?
However, life isn’t like that, unfortunately. No matter how good your qualification process, we all know it’s hard to predict the outcome of a sales deal.
Nevertheless, we are still under pressure to produce reliable sales forecasts, despite the uncertainty.
Fortunately, Expected Revenue is a powerful tool for creating robust revenue forecasts. However, here’s the catch: it relies on setting realistic probabilities for each pipeline opportunity.
What’s the problem with Opportunity Probability?
The Opportunity Probability is wrong on many deals because it relates directly and only to the Opportunity Stage.
In other words, if the Stage moves forward, the probability automatically increases. That happens irrespective of whether your chance of winning the deal has increased.
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. So far, so good.
Next, all four sales teams submit their proposals. They move the Stage onto Proposal Submitted – which for each company, has an Opportunity Probability of let’s say, 30%.
The chance of any sales team winning the deal has not changed. There are four of them left. So, all things being equal, each still has a 25% chance of winning.
However, in each company, the Expected Revenue of the deal has increased. And the combined Opportunity Probability has also increased – to 120%.
That doesn’t make sense, of course.
What does this mean?
It means that to produce a reliable Expected Revenue report, we need a better way to estimate opportunity probability.
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What is the probability of winning a sales deal?
For any company, the probability of successfully closing an Opportunity depends on many factors.
These might include geographic sector, product category, tender versus pitch deal, and others.
Nevertheless, one factor common to most businesses is this: whether you are selling to a new or existing customer.
Usually, the chance of winning a deal is significantly higher with an existing customer, compared to a new prospect.
However, in most Salesforce implementations, the Opportunity Stages are common to both new and existing customers. Consequently, the opportunity probabilities are identical for any given stage.
That contradicts what we know – that the probability is normally higher for deals with existing customers.
So here’s what you do.
Manually adjust the opportunity probability.
Not many people realize you can do this. That is, override the opportunity probability associated with each Stage.
Nevertheless, if you take this simple step, then opportunity probabilities will be more accurate. As a result, your Expected Revenue report will be more reliable.
Next time you do a pipeline review or conduct a salesperson one-to-one, check that where appropriate, the opportunity probabilities reflect your best judgment on the likelihood of winning the deal. In other words, use human judgment to update the opportunity probability.
That’s a simple step that has a high impact. You can, however, get more scientific.
Historic Opportunity Conversion Rates
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 ratios. We can use that to our advantage.
Specifically, we can gather information on those factors that help us set realistic opportunity probabilities.
In other words, by reviewing the opportunity probability from similar historical deals, it’s possible to forecast the future. That means we can predict the Expected Revenue with even more confidence.
New versus Existing Customer conversion rates
For example, 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 provide information about conversion rates for existing versus new customers. Specifically:
- 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 passed through the same Stage for new customers.
- 76% of Opportunities with existing customers that entered the “3. Proposal Made” Stage closed successfully. This compares with 65% of Opportunities that went into this Stage for new customers.
- 92% of Opportunities with existing customers that entered the “4. Negotiation” Stage were won. This figure compares with 79% of Opportunities that entered this Stage for new customers.
In other words, the report provides the information we need to more scientifically differentiate Opportunity Probability between new and existing customers.
This information is an excellent starting point for creating accurate Expected Revenue forecasts.
Salesperson conversion rates
Now, let’s consider the difference in opportunity conversion rates between salespeople.
The report shows that Dave Apthorp wins 60% of all his Opportunities compared to 27% for Peter Hemsworth and 36% for Shaun Yates. You can see this in the “1 Prospecting” row.
Look at other rows in the report. They tell us the Opportunity Conversion rate 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 Reports
Our customers use the information in these reports to calculate the 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 close successfully.
That figure automatically enters our custom Opportunity Probability field. Now the Expected Revenue becomes $7,050.
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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 historical conversion rates, automatically populates for each Opportunity. Consequently, this produces a more realistic (and in this case, higher) Expected Revenue.
Accurate Forecasts Using Expected Revenue
Expected Revenue calculates by multiplying the opportunity probability with 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 also 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.
Accurate Expected Revenue reports mean accurate sales forecasts.
To find out more about how to create accurate sales forecasts using Expected Revenue in your business, then get in touch.
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