B2B forecasting doesn’t need to be this hard

B2B forecasting is a breeze. Multiply pipeline by conversion, add quota, back into revenue, head to lunch.

That's the version FP&A teams daydream about, at least.

It's not the math that makes B2B forecasting hard—this is Excel, not calculus. It’s the mechanics of how the thing is put together that leads to the most headaches.

Axel Amar is here to help. He's the rare finance leader who’s worked in both B2B and B2C—before becoming VP of Finance at Airbyte, he led FP&A at car sharing marketplace Turo.

His take: B2B looks simpler than B2C, but timing variables make forecasts tricky to keep current.

Below are three takeaways from Axel’s conversation with Aleph Co-Founder and CEO Albert Gozzi on the 10X Finance Podcast.

1. B2B brings timing risk

B2C forecasting usually boils down to a straightforward equation:

Traffic * Conversion * Price = Revenue

Miss one input? You’ve got options. Paid can backfill organic, and pricing can cover conversion drops. There are levers you can pull.

But in B2B, those levers don’t behave the same way. Especially in sales-led motions, the hardest part isn’t forecasting how many deals you’ll have—it’s when they’ll close.

Deals can stall for all manner of reasons: renewal back-and-forth, upsells, role changes. One AE might fly through legal while another gets stuck in the mud. All of this timing risk adds up, making a monthly (not to mention weekly) forecast hard to pinpoint.

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What this means tactically:

• Model deal movement, not just total pipeline
• When a month misses significantly, ask: “What came in ahead of schedule? What slipped?”

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2. The most common finance mistake: Stopping at the financials

Getting the numbers right matters. Obviously. But if it consumes all your focus, you’re missing the point.

The best finance teams are good storytellers. They have a birds-eye view of the business that other teams don’t, and it’s their job to translate the financials into stories other teams can act on.

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DO:

• Help functions pinpoint a few financial drivers that matter most
• Use the same language and storyline every month

DON’T:

• Assume people will connect the dots from a P&L slide
• Treat variance commentary like a checkbox

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3. Dial in your systems before adding headcount

AI can’t replace your finance team, but it can help them do more with less. From now on, you should think about ways you can improve/redesign your workflows to make them more efficient before adding headcount.

Most scaling teams need to add headcount at some point. But you can be a lot more intentional about when you hire and what you hire for in an AI-powered finance org.

In other words: know what the system should do before you build the team around it.

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A practical sequencing framework:

1. Identify the deliverables you need to ship on a regular basis
2. Define what data and process will get you those answers consistently
3. Then—and only then—hire people to run and refine what’s working

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Math isn’t what makes B2B forecasting hard

It’s hard because the business is messy. That part’s unavoidable.

But your forecasting system can be built to better handle it.

Start by:

• Modeling when deals land (not just how many)
• Translating metrics into actions people can take
• Building a system that scales before you scale the team

These are just a few snippets of a wide-ranging conversion between Axel and Albert. Check out the full episode.

And if you want to see how Aleph helps finance teams build their own forecasting operating systems, try a free demo with your data.