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revenue forecasting

What An MBA Didn’t Teach You About Sales

The sales profession is challenging. You need to work hard at it to succeed. You need to learn from the best. You need to improve your skills continuously. If you think you can sell since you are a hit at parties and have a lot of friends, you may soon find that you are a failure as a salesperson. Blunt truth:

because the sales profession is so hard, you have to focus on doing everything in sales very well, or you will be considered a failure.

I call this blog, Skinned Knees because I try to relate all of the learning that I have done over the past 4+ decades (while skinning my knees in the learning process).

I hope that you learn from my mistakes so that your business will grow!

Revenue Forecasting Should Be Built on Evidence, Not Hope

Most sales forecasts are not really forecasts. They are seller opinions, manager adjustments, CRM fields, historical averages, and optimism packaged into a number that leadership is expected to trust.

That may have been acceptable when forecasting was mostly an internal sales exercise. It is not acceptable when the board, finance, hiring plans, customer success capacity, and investor expectations are all tied to the revenue number.

The core problem is not that sales leaders are careless. The problem is that many revenue teams are still using an architecture that cannot produce predictability. Spreadsheets, commit calls, and stage rollups organize information, but they do not necessarily reveal the buyer’s truth.

The better question is not, “How confident is the rep?”

The better question is, “What did the buyer actually do?”

That shift changes the entire operating model. Forecasting moves from hope-based to evidence-based. Deals are no longer judged by the confidence in a seller’s voice but by observable buyer behavior: recent engagement, executive involvement, mutual action plans, legal or procurement movement, real next steps, and date-driven urgency.

This is where artificial intelligence and revenue intelligence become useful, but only if the management system is ready for them. AI can identify patterns, detect risk, surface stalled deals, and compare buyer behavior against historical outcomes. But it cannot compensate for weak sales processes, vague stage definitions, poor CRM hygiene, or managers who refuse to inspect the evidence.

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The Dual Blueprint Requirement: Why Growth Demands Two Plans, Not One

Launching a company or steering one through a merger, turnaround, or major transition requires clarity about how value will be created and, just as importantly, how revenue will actually be generated.

Many leadership teams recognize the need for a Business Plan, but overlook that sustainable growth requires a second, complementary plan. The main breakdown is not the strategy itself, but the assumption that strategy automatically creates revenue. Bridging strategy and revenue requires a distinct plan for that conversion, targeting a different audience.

The Business Plan sets direction from the top down. The Sales Plan is validated by demonstrating how that direction can become actual revenue from the bottom up.

Both are essential. Neither works in isolation.

The Business Plan: Charting the Course (Top-Down)

The Business Plan exists to answer specific questions for a particular audience. Its primary readers are CEOs, CFOs, bankers, private equity partners, and venture investors. These stakeholders are evaluating risk, scale, and return. They want to know where the company is going and why the destination is worth the journey.

At its core, the Business Plan articulates strategic intent. It defines the mission, the long-term objectives, and the differentiated value proposition that the company believes the market will reward. It frames the opportunity in language that aligns leadership, capital, and governance.

Market analysis in this context is necessarily high-level. It focuses on the total addressable market, industry dynamics, competitive positioning, and macro trends. The goal is not to explain how every deal will be won, but to establish that a meaningful opportunity exists and that the company has a credible right to pursue it.

Financial projections follow the same logic. They are built on broad assumptions: projected market share, average selling price, renewal and retention rates, inflation, and multi-year revenue targets. These numbers are directional. They signal ambition and scale rather than operational certainty.

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