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We All Suck At Revenue Forecasting and Here’s Why

Posted March 5, 2019
inherent bias

You own the revenue number for the company. So — when I say there lurks a revenue sucker in your company do your ears perk up? It’s the kind that shows itself clearly, but manages to elude the questioning glare from the board and the executive team year after year.  It breaks our backs right as our revenue team thinks we’re about to cross the finish line.



Inherent bias is so deeply entrenched in the traditional revenue forecasting process, and to such a negative outcome, that it is the number one thing a CRO and revenue teams need to tackle in 2019.


Don’t believe me? Read on to be convinced.


According to the CSO Insights 2018 Sales Operations Optimization Study, the top two issues with accurate forecasting are:

  1. Salespeople are too subjective about close possibilities.
  2. Sales managers do not investigate their salespeople’s commits well enough.



Think about that. While the majority of the barriers listed here are about data, technology, and methodology, there are two primarily human-factored items. And those two? They are at the top. This means that our biggest revenue forecasting problems are people problems.


So let’s set aside the numbers for just a moment. No one has really been able to quantify this inherent bias into measurable data, yet it very clearly exists. Your revenue forecast cannot be relied upon if this bias remains embedded in your forecasting process.


There is, however, an effective way to protect against it: Stack it up next to your inherently unbiased customer activity information.


The Bias Nullifying Your Revenue Forecasting Process

Let’s look at how forecasting goes at a typical organization to see if we can determine the root cause of the gaps we repeatedly see between the forecasted sales numbers and the real deal.


Sometime (probably late) in 2018, the revenue team sat down and created a plan for 2019. They started by looking at bookings and customers to pull an annual run rate. It’s clean and cut: Their customers will pay them X amount next year.




And then they immediately injected bias into it. They said “Because we sold X amount last year, we should hit Y amount next year”. That’s a product of what we believe we can accomplish.


Based on their open pipeline and growth rate, they set a revenue goal for 2019. But that’s a dependent of how well their reps can forecast, and an assumption that their reps will improve (a growth assumption).


They may not have even used that amount of data. Perhaps they decided they just needed to hit X number and then designed a plan they could envision getting them there.


Biased. Sound familiar?


How you’re going to make that number is where the revenue team leaders can inject improvements to make it happen. My suggestion is to look back directly to your customer activity data because it is inherently unbiased.


Combat Sales Subjectivity With Customer Activity Data

As we move further up the funnel into marketing territory, two things begin to happen.

  1. Our attribution analytics become fuzzier.
  2. The data our systems capture becomes less and less biased. I.e. We have less and less of an opportunity to influence or inflate because we’re only tracking what our customers are doing.


Now, this should support the notion that our attribution data becomes more clear as we move up the funnel.


You know what I’m about to say. It doesn’t. Not yet.


Consider the opposite side of the funnel: As we push closer to Closed-Won, our attribution is perceived to become clearer. Yet our data is inherently more biased. As an account progresses from stage to stage, more hands touch it, and this muddies it to some extent.


Have you ever seen a rep miss their forecast (not their quota) by more than 20%? Of course you have. CSO Insights’ 2016 Sales Enablement Optimization Study found that only about 57% of salespeople make quota, while just 30% of firms have 75% or more of their salespeople hit quota.


Moreover, less than one third of companies achieve all or a majority of their sales effectiveness objectives.


Guess how many still said “We’re on track to sell way more next year!”




That’s how many firms raised their revenue targets for the following year. Does something with this seem … off? Yes, there is a level of unpredictability in our forecasts. But to what extent?


In the scenario above, what did the rep not know about their buyers? What did the buyers fail to tell that rep? Was the rep too optimistic about where their buyers were in their customer journey?


Want to know who had the answers to these questions? The buyer.


The problem is that they didn’t call them up to answer them directly. But they did answer them. They just did so through action (or lack there of). Your attribution must be set up to properly capture that as data.


To summarize what I mean by “inherently unbiased”:


The further we move away from our customers and away from the data, the greater the likelihood that we are moving further away from the truth.


Our solution to this is to allow the customer to decide.

By collecting and focusing on customer data and account engagement, you can combat subjectivity. These are forward-looking indicators of opportunity success.


When you do this, you gain better answers to common questions that have an impact to velocity, like:

  • When should Marketing hand leads to Sales?
  • Who in an account must Sales influence?
  • Which deals will close?


The benefits of this are measurably huge:

Customer journey alignment between Marketing and Sales improves win rates by as much as 15%, and can increase the lifetime value of each customer by 26%. It requires training of the Sales team on the customer journey, and collection of customer activity data that answers questions a Sales team may not have looked at previously.


To Revenue Team Leaders:

Have you thought about the inherent bias in your revenue forecasting process, or taken steps to remedy it?


Does your revenue cycle align with your customer journey, or are you leaving that valuable customer data un-mined?


The result? A more predictable and repeatable path to revenue.

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