Why it's necessary to take a new look at an old problem.
Gerry Skews
Most businesses treat demand forecasting like a weather report from a decade ago: rough, unreliable, and largely ignored until it’s too late. But in today’s data-rich, AI-powered world, we can model demand more accurately, dynamically, and insightfully than ever before — if we’re willing to change how we think about it.
This article introduces a new approach — drawn from the Business by Numbers playbook — that combines internal intelligence, external signals, and analytical clarity. It doesn’t just help you estimate demand. It helps you understand why demand behaves the way it does, and how to respond.
Why drive your car by staring in the rear view mirror?
Forecasting often falls into two camps: wishful extrapolation of last year’s numbers, or an over-engineered black box no one really understands. Both approaches fail when markets shift — and they always do.
Classic demand models tend to focus on lagging indicators: past sales, static assumptions, or limited market research. That’s like driving a car using only the rearview mirror.
The result? Misjudged launches. Overstocked warehouses. Missed trends. Panicked markdowns. Strategic paralysis. Businesses either chase demand after the fact, or guess at it with fingers crossed.
The Breakthrough: A Modular, Data-Blended, AI Inspired Model
The solution lies in a smarter, modular framework — one that blends economic theory, modern data, and adaptive logic. In our model, Market Demand is not a single number but a construct built from interrelated variables:
I: Customer Income Levels
N: Number of Buyers
S: Substitute Product Pressure
E: Customer Expectations
C: Complementary Product Effects
T: Consumer Preferences
Each variable in the model can be weighted based on context — giving you a tailored view for different markets, product types, or buyer segments. Want to launch a new luxury product in a recession? Income elasticity becomes critical. Launching a tech gadget with viral potential? Focus on expectations and trend signals.
The Engine: Rich Internal and External Data
What makes this model powerful is how it brings together both internal performance data and external market intelligence.
From within the business:
- Sales pipelines (CRM)
- Web behavior and conversion metrics
- Product performance and price elasticity
- Retention, churn, and customer lifetime value
From outside:
- Macro data from IMF, World Bank, and national stats
- Industry benchmarks (Statista, IBISWorld)
- Google Trends and e-commerce search behavior
- Social sentiment via Reddit, Twitter, and Brandwatch
These are no longer out-of-reach tools. Thanks to APIs, integrators, and AI platforms, even small and mid-sized businesses can plug into powerful demand indicators — often in real time.
The AI Layer : Market Signals @ Speed
The real game-changer? Intelligent agents.
AI can now:
- Crawl competitor sites, forums, and media
- Detect shifts in customer sentiment or unmet needs
- Extract trends from earnings calls, regulatory filings, and analyst reports
- Model scenarios with predictive analytics engines
These aren’t future possibilities — they’re today’s tools.
Companies using AI for demand modelling can spot surges, stalls, or substitutions before they appear in the sales data.
Imagine knowing — from Reddit chatter and Google searches — that interest in your product category is about to spike. That’s not guesswork. That’s insight.
Strategy, Not Speculation
This approach isn’t about being “more technical” — it’s about being more strategic.
Market demand modelling is no longer a reporting function. It’s a growth engine. A risk radar. A decision lens.
Done right, it informs:
- Which markets to enter
- How to position pricing
- What features customers truly value
- When to ramp up production or pull back
As one investor told us:
“We back businesses that know their market better than their market knows itself.”
And that’s the power of a demand model built for the real world — not just the spreadsheet.
Final Word
If you want to lead with data — not be led astray by it — start with demand.
But don’t settle for static estimates or stale trends.
Build a living model. Let the data breathe.
As we put it in the book:
“It’s not just a model. It’s a lens — where you can adjust the focus and the aperture.”
See the future every day.