If I look at my feeds, too many conversations with clients and others, we have an obsession on forecasting. I’m constantly astounded by the amount of time sellers spend in forecast reviews with their managers, and management meetings around the forecast. I wonder if it is time well spent. I wonder if it’s time that will actually have a positive impact on the revenue actually booked.
Don’t get me wrong, forecasts are important. It’s not just something that helps us project whether we will hit our goals. But the organization depends on it. The CEO and CFO have to set expectations with the board, investors, and shareholders. Perhaps, more importantly, it’s critical for the rest of the organization. Whether manufacturing is scheduling the production of products, professional services orgs making sure they have the resources available, customer service making sure they can support the anticipated demand.
But it’s how we actually look at the forecasting process, who’s involved, and the accuracy we can actually attribute to the forecast that’s critical.
I’m going to approach this from several points of view, they will converge.
Forecast accuracy improves as the individual transaction matters less.
You might be thinking, “Well dugghh Dave….” But the behaviors I see don’t follow this.
Let me look at two extremes. Consumer product sales forecasts and large complex B2B sales.
In consumer product sales, the seller actually never touches a transaction. The consumer pulls a product off the shelf, clicks on “Buy Now” on Amazon or something similar. The seller never sees or cares whether you walk into the store.
When we look at consumer product sales, we are looking at 1000’s to millions of transactions during a period of time. No individual transaction has an impact, but collectively they enable the org to assess whether they will hit their revenue goals.
Over time, we collect millions of data points around the transactions. Store promotions, shelf or page placement, end cap, time of year, even time of day, seasonality, holidays, events, number of stores, location of stores, the lists go on. The amount of data organizations has to analyze, determining demand and purchase trends is stunning. This enables orgs to forecast sales with high though not completely perfect accuracy.
And the salesperson is never involved in the forecast, they are making sure retailers are implementing product promotions, ensuring endcaps, or any number of things that influence purchase.
Contrast that with complex B2B. The seller is involved and manages each deal. Relative to consumer products, the total number of deals worked is very small. They are assessing a stage, the boxes they have checked to get to that stage, and a probability. And too often, their response to a manager forecast request is, “What do you need,” because their evaluation depends on it.
Too often, the number isn’t an estimate, but a negotiation with the manager. And it has nothing to do with whether the customer is going to buy.
As we look at the data richness on which to base forecasting, it’s appallingly small. How many deals have we done with this customer, how many in this market, how many with this product line, how many against this competitor, or how many has this seller done?
We’re looking at a very small number of data points against which to do the analysis. And as any statistician would tell you, the accuracy of the projection is likely to be very low
We recognize this, so we ask the seller to make an assessment, “How likely is this to close by quarter end.” There are problems with this, but the seller responds with something, “We’ll do it, I think there’s an 80% chance, …,” whatever.
The discussions become exercises in prediction and justification. The seller starts defending the number, rather than examining the deal. Sellers learn how to give the answer that manager’s want, the one that ends the interrogation.
The B2B seller’s job isn’t to estimate the outcome, but to change it.
The contrast and its impact on forecast accuracy couldn’t be sharper. The CPG forecast is based on what buyers actually did; the B2B forecast is based on what the seller thinks, or says to make their manager happy. You can immediately see the gaps that are likely to drive very poor forecast accuracy in B2B.
The seller job isn’t to estimate outcomes, it’s to create outcomes.
With each deal, the B2B seller faces a binary outcome. It’s to win or lose. 0% or 100%.
So, what does an 80% probability estimate on the deal mean? Is it the probability they will win the deal? Is it the probability the deal will land this quarter, with a 20% chance of it slipping into next quarter? Will it be this much?
All three are conflated into 80%, with little understanding of what that really means.
There’s an interesting angle to the 80%, it removes the seller from being the person driving the deal, putting them at a distance as the observer.
Sellers should be spending their time on one thing, doing everything possible to win the deal. They aren’t focused on anything other than 100%.
If they are managing the deal properly, the customer has committed to the date they need the solution in place. They understand the consequences of not achieving this. This establishes the target close date, not “We need it this quarter.”
This is the sharp contrast between B2C and B2B. The B2C seller touches no transaction, and the B2B sellers’ handprints are imprinted on every transaction.
From a leadership point of view, the job is not to sit on the sidelines figuring out how you will bet on the salesperson and each deal on the board. Your job is to do everything possible to help them win, coaching, getting the support they need, removing barriers to their success.
These strategy sessions aren’t just about better selling. They are the way we produce a better number. The deal that’s been thoroughly thought through and aligned with the customer is more likely to produce a win at that customer committed target date than anything else we can do.
Again, the focus is on winning each deal, not figuring out which 80% deserve your focus.
So how do managers develop a forecast?
Like B2C, the B2B forecast looks at the aggregate of transactions, not each individual transaction.
First, is every deal real, qualified? Has the customer committed to its importance, they have identified the date they need a solution in place, and the understanding of the consequences and risks of not doing so?
Are we managing the deal effectively through the buying process. At each stage, in sync and in agreement with where we and the customer are and the next steps.
Are we doing everything our past experience and judgment indicate is critical to helping the customer make a decision, creating the value the decision is for us?
As we look at the aggregate value of these in our pipelines, do our pipelines reflect the reality of where the customer and we are in the process? Do we have a high-quality pipeline?
Then, like B2C, we can look at our history with these types of deals. For example, we can look at collective history of deals in the closing stage of the process. What percent of the deals did we win? What percent came in on the projected target date? What percent came in at the projected value?
While the data and trend analysis may not be as rich, it is possible to develop highly accurate forecasts, but only if the foundations are in place.
My challenge to you is if you can do this, why settle for a rep saying, “I think it’s 80% if that’s what you need?”
What do forecast discussions with our sellers turn into?
We now see the seller can’t contribute much to the forecast on this deal. Yet we take a lot of time talking to them about the forecast.
What if we stopped managing the forecast and helped our people better manage their deals? What if we turned each hour of forecast discussion into something different?
What if we turned each discussion into a discussion about where and how the seller can have their greatest impact in not getting 80% there, but in winning the deal?
We can have tremendous influence on each transaction. We can understand what the customer is trying to accomplish, help them make sense of the problem, help them navigate the buying process to achieve their goal. And help them confidently achieve their goal when they need to achieve this.
The conversations most impactful to our forecasts are not about the forecast, but about the deal, and what we must be doing with our customers to result in a decision.
Imagine the hours that are spent on actually doing the work to win the deals, rather than spent on guessing whether we will win them or not.
More accurate forecasts.
In B2B forecasting, we don’t have the volume of data points and analysis our B2C colleagues have.
What we have is a collection of individual deals. If we are working those deals helping the customer achieve what they need to achieve, we learn with each deal.
Each deal, whether we win or lose, develops our understanding. Our forecasts get better as our understanding gets better.
The deeper our understanding and our ability to more effectively work with our customers both drives higher win rates and more accurate forecasts.
And isn’t that what we are really trying to achieve?
Afterword: Another stunning AI based discussion. I’m constantly amazed with how well they translate these ideas into practical examples. The example they provide near the end is the “Aha” moment. Be sure to listen to this.
