I’ve come to learn about a seasonality effect on queries I get on Forecast Accuracy. We’re mid way through the second calendar quarter of the year. Executives are seeing they missed the number in the first quarter, we’re now midway through the second quarter and they are starting to worry.
For some with XaaS models or long sales cycles, if they are off on YTD attainment, it will be a real struggle to fill the gap and make their numbers.
The confluence of these factors create a number of queries around “How do I improve forecast accuracy! We’re really struggling!”
Some execs struggle to be patient as I start probing, “What do you mean by forecast accuracy?”
There’s always that momentary pause on the phone. I know the executive is thinking, “This guy’s supposed to be smart, why is he asking that question, doesn’t everyone understand what forecast accuracy is?”
Sometimes, the pause is too long, I ask the question again, “What do you mean by forecast accuracy?”
Again, often there’s a pause.
I ask, “Is it making your monthly or quarterly number?”
At this point, some of the tension dissipates. The exec immediately responds, “Absolutely!”
Then I ask, “Do you care about the composition of that number? For example, if you have a goal of $100M, do you care more about the $100M or the composition of that $100M?”
Too often, the response is, “I just need to make the $100M!”
At that point I usually respond, “Oh I understand, it seems that you don’t really care about forecast accuracy, you are just struggling to make your number—that’s a bit of a different problem to solve….. “
” You just need to see more deals closing in the quarter. I understand the urgency of the problem, but it’s a different problem than forecast accuracy.”
Sometimes people ask me to explain…
“Forecast accuracy is about a specific deal—when will that deal close, for what value, what is the composition of that deal?” Forecast accuracy recognizes that the rest of the corporation needs to understand what we are selling and when we are likely to get the order.
Let’s take an extreme example. Let’s say, I forecast, “The customer will buy $1M of product A, they have confirmed they will make the decision and issue a purchase order in 60 days. They say we are, by far the preferred supplier.”
That set wheels in motion within the company. If you are a lean/agile product company, supply chain management starts things going to make sure they will have the right inventory of parts available to make the product, starting in 60 days. They place orders on their suppliers. Manufacturing starts planning to build and ship the product, once the order comes in. If you are a services company, managers start planning to make sure the right people are available to start the project and block their time–not making them available to others competing for the resources.
Now fast forward 60 days. We get the order right on time, it’s for $1M, but it’s for product B!
From a sales point of view, the attitude may be, “We made our number!” We do a quick round of fist pumps, a sigh of relief, then look at what we have to do next month.
But within the company, it’s a disaster! All our planning, all our inventory, all our resource allocation has been turned upside down. We may now be stuck with the wrong inventory, supply chain management may have to pay a premium to get the right parts, manufacturing schedules have to be adjusted—all of this has a cost/productivity impact. If it’s services, there are similar impacts with the service delivery teams.
(Of course, we do know that supply chain management, manufacturing, operations, service delivery know how bad sales is about forecasting, so they hedge their investments and planning….)
It should be obvious, but too often isn’t. Forecast accuracy is not just about hitting the number. Forecast accuracy is really about individual deals and the roll up of those deals.
Forecast accuracy is: Is the deal coming in when we forecast it would? Is it for the value we committed it would be? Does it match what we actually forecast it would be–that is the product and services the customer wants to order.
Tomorrow, I’ll tackle the issue, “How do we improve forecast accuracy?” As you might guess, too many approach this completely backwards–which is why we struggle so badly with forecast accuracy.