Forecasting during uncertainty

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In 2008, Brian Breslin joined Hewlett Packard Enterprise as Director of Sales Planning and Analysis. His job was to calculate sales quotes, set KPIs, generate forecasts, and generally predict the financial future. Ordinarily, that’s hard enough. But in the autumn of 2008, America’s financial system almost collapsed, throwing the economy into meltdown.

Breslin recalls sitting on a conference call with his CFO, who kept calling the imminent recession a “watershed moment” for the country. Even then, Breslin didn’t believe that the Great Recession was entirely unique.

“A few years later, there was some other watershed moment,” Breslin says. “You never know what’s coming — but something is. There’s going to be some sort of external factor and people will point to it and call it a watershed moment.”

Most crises have similarities. If you can learn to forecast accurately through one, you can learn to forecast through them all.

That’s not to say it’s an easy thing to do. Good forecasting is based on predictability, and crises are, by their very nature, unpredictable. During the pandemic, unemployment spiked, services activity plummeted, manufacturing dipped, and retail crashed. For most of the early months — April, May, and June 2020 — no one knew what was happening.

Even segments performing strongly now — technology, healthcare, consumer services — suffered a woeful couple of quarters. Indeed, around half of all B2B buyers report they’re holding off on purchases because of the pandemic.

When the market goes haywire, it’s easy to say that our forecasts simply aren’t fit for purpose. But Breslin isn’t so quick to judge. He says there is ambiguity in all forecasts. In times of economic prosperity, you don’t see the errors. But when crises hit, the disruption amplifies your errors.

“When you have a forecasting process that builds up layer by layer, you can imagine the opportunities for uncertainty or ambiguity to be double and triple-counted,” Breslin explains. “For example, if each manager layer is hedging the same risk, that same risk or uncertainty has been double or triple-counted, and the view you get at the top is dramatically distorted.”

Accurate forecasting during crises doesn’t require a radically new approach. Organizations can reset their forecasting by systematically eliminating the uncertainty, ambiguity, and errors from their original systems.

Define your forecast categories

All companies, no matter how well aligned, have competing power centers. If left unchecked, their conflict can hamstring your forecasting. Consider a finance team and a sales team within the same organization. Both deal with revenue, but each thinks about revenue very differently.

“Finance leaders will take the revenue forecast as a starting point and work back from there to create requirements,” Breslin explains. “Meanwhile, sales is using their CRM platform to forecast future performance.”

Because they see the world differently, the finance department will often use different forecast categories than sales. Those differences create uncertainty.

Other times, there are internal power centers. Different regions and sales teams have different sales leaders. Those leaders have different ideas on process and procedure.

Whatever the root cause, inconsistency in forecasting categories creates unreliable conclusions.

To build a robust company-wide forecast, everyone must use the same language. The specifics of your terminology aren’t important — but consistency is.

Breslin says there’s no quick fix here. 

You must address strategic disconnects at the top. Finance and sales leaders need to come together to discuss why they think about forecasting the way they do. For example, finance leaders often balk at using personal judgment to inform forecasts. But it’s a core part of sales, especially in field sales where personal relationships are king. Sales reps will always have a gut feel on whether a deal will close or not — and that’s invaluable data for forecasting.

Once the two sides understand each other, they can agree on how to normalize their language. Ultimately, you don’t need a shared world view among departments — but they do need a shared language.

Internal disagreements are less about compromise and more about training consistency. Locate where the uncertainties come from — individual sales leaders or regions — and iron them out with ongoing training.

When you onboard new sales leaders, make sure your training doesn’t leave leeway for people to make the same mistakes again.

“The reason you hire a sales leader is not for them to recreate a taxonomy or methodology, it's for coaching, setting sales strategy, coaching, hiring, firing, and so on,” says Breslin.

Use your training to set clear expectations. Explain not just how you do things but also why you do them that way. 

Agree on objective category criteria

Creating consistent terminology is a significant first step, but people can still disagree on what each category means. For example, does a deal with a long close date fall under commit or best case? It could fall under both.

Again, there’s no right or wrong way to design criteria for your deals. What matters is consistency.

When you’re trying to drive widespread change, there’s no substitute for front-line managers, the people with their boots on the ground.

“The number one way to get consistency across your sales teams is to first get consistency in how the frontline sales managers are coaching, talking, and managing,” says Breslin. 

No matter how many emails your sales enablement team sends out or training classes they run, frontline managers have the final say on whether or not something becomes a day-to-day behavior. What a manager asks of their reps, demonstrates to their reps, and reinforces in their reps is what drives long-term beliefs and behavior.

Separate sales stage from forecast category

It’s tempting to attach a forecast category (in your CRM or forecasting software) to each stage of the sales cycle. When a lead is in demand-gen, you count them as pipeline. When a prospect moves into proposal, you call them best case. When they reach negotiation, you categorize them as committed.

It feels intuitive. You might even want to link the two so all deals in the demo stage automatically fall into best case. But this is a mistake, says Breslin.

“There can be things that happen that come out of left field,” he explains. “Say we're in the late stages but we find out that there’s a new stakeholder who just left our major competitor. I would think that the probability of winning just went down significantly.”

In Breslin’s example, the sales stage didn’t change, but the forecast category did. If you forcibly link the two, you create uncertainty. Right now, with economies in turmoil, there are lots of “left field” developments to throw deals off the tracks.

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