Spreadsheet forecasts are based on what happened last quarter. FIRE forecasts are based on what is happening right now — structured velocity data, behavioural signals, and promotional benchmarks from every buyer interaction across every channel. After three cycles, prediction replaces guesswork.
A quarterly forecast based on last quarter's orders is already wrong on the day it is published. Markets move, buyer preferences shift, promotional windows open and close. Static data produces static predictions — and static predictions produce overstock or stockouts.
By the time an order lands in your ERP, the demand decision happened weeks ago. Order data tells you what already sold — not what is about to sell. You are planning production based on the rearview mirror while the road ahead curves.
Without structured behavioural data, you have no leading indicators for demand. Browsing interest, exploration patterns, reorder frequency shifts, promotional engagement — all the signals that predict future orders are invisible to your current systems.
Select a view to see how FIRE transforms raw signals into actionable forecasts.
Average days between orders per buyer, tracked across cycles. Accelerating velocity means growing demand.
Velocity differs by channel type. High-frequency buyers accelerate while quarterly buyers hold steady.
Each product has its own velocity curve. Comparing curves reveals which products are gaining and which are declining.
Reorder frequency per buyer, per SKU, per channel — tracked in real time. Velocity is the most reliable demand signal because it comes from actual buyer behaviour, not estimates. When velocity accelerates, demand is growing. When it decelerates, demand is shifting.
Browsing patterns predict purchases one to two cycles ahead. A buyer who explores a new category today will likely order from it next cycle. FIRE captures this exploration as a structured leading indicator for demand planning.
Every promotional window has a prior-cycle benchmark. FIRE measures uptake against that benchmark in real time — so you know within days whether a promotion is tracking above or below expectation, and can adjust allocation accordingly.
After three cycles of structured demand data, FIRE AI generates per-SKU, per-channel demand predictions. Not based on industry averages — based on your buyers, your velocity curves, your promotional patterns. The prediction improves with every cycle.
Book a personalised demo for your use case.
Tell us about your brand, your current B2B setup, and what you are looking to improve. We will show you exactly how FIRE works for your specific situation.
No generic demos. No slide decks. A real walkthrough with your products and your industry configuration.