This page explains what the simulator is doing under the hood and how each input changes the result. You don't need to read this to use the tool — but if a number looks weird and you want to know why, this is where to look.
On this page
The 30-second overview
You give the simulator your product economics, your launch plan, and a competitive scan. It then simulates 10,000 different versions of your launch — each one rolls slightly different ramp speeds, Advertising Cost of Sale, and daily velocities within the ranges you provided. The output is a probability distribution: what fraction of those 10,000 launches turned profitable, when break-even tends to happen, and how much cash you'll need at the lowest point.
Market Dominator — niche difficulty
The Market Dominator scans your top-10 competitors on Amazon (from a Helium10 Cerebro export) and tells you how saturated the niche is. It produces three outputs:
- A per-competitor color rating (5 tiers).
- A niche score from 0–100 (50 = neutral, 100 = wide open, 0 = fully entrenched).
- An auto-tune of the simulator's ramp/Advertising Cost of Sale/velocity assumptions to match the difficulty.
Per-competitor color rating
Each competitor's "Average %" is the average of (% of keywords they rank on page 1) and (% of total search volume their page-1 keywords cover). The higher that number, the more they own the niche.
| Average % | Label | Color | Weight |
|---|---|---|---|
| 0–20% | Super Weak | Green | +5 |
| 21–40% | Weak | Lime green | +3 |
| 41–60% | Normal | Yellow | 0 |
| 61–80% | Strong | Orange | −3 |
| 81–100% | Super Strong | Red | −5 |
Niche score
Sum the 10 competitor weights and add 50:
So 10 Super Weak competitors → score 100 (wide open). 10 Super Strong → score 0 (fully entrenched). Mixed → somewhere in between.
How niche score affects the simulator
The simulator multiplies your ramp days, Advertising Cost of Sale, and velocity ceiling by deltas that depend on the niche score. The curve is asymmetric — the penalty for hard niches grows exponentially, while the boost for easy niches is linear and milder (because easy niches still need execution; hard niches genuinely punish you):
| Niche score | Ramp days × | Advertising Cost of Sale × | Velocity × | What it means |
|---|---|---|---|---|
| 100 | 0.75 | 0.85 | 1.10 | Wide open — boost |
| 80 | 0.85 | 0.91 | 1.06 | Mostly weak |
| 50 | 1.00 | 1.00 | 1.00 | Neutral baseline |
| 30 | 1.44 | 1.32 | 0.76 | Tough niche |
| 10 | 2.08 | 1.74 | 0.57 | Very tough |
| 0 | 2.50 | 2.00 | 0.50 | Brutal — fully entrenched |
Max-penalty override (the "anchored rival" rule)
The reason: a single anchored rival ranking on page 1 for nearly every keyword will outspend, outrank, and outreview you on launch day. Two coordinated Strong rivals do the same. Even if the rest of the field is weak, that one wall is enough to bury a launch. The override is the simulator saying "I don't care that 9 of these competitors are easy — that one (or two) is the launch."
Niche traffic light vs niche score
The traffic light RED / ORANGE / GREEN at the niche level is a strict rule for the headline chip — any Strong/Super Strong competitor forces RED, any Normal forces ORANGE. The 0–100 niche score is the more nuanced metric and is what actually drives the math. They can disagree (e.g. score 90 but RED label because of one entrenched rival). When that happens, the max-penalty override above is what reconciles them.
Product economics inputs
These set your unit margins. They feed directly into break-even and final-profit math.
- Average Selling Price — what you actually collect after promo discounts. A higher selling price → fatter margin per unit → lower break-even.
- Cost of Goods / unit — your supplier cost. Most controllable lever; every $1 cut is pure margin.
- Amazon fulfillment fee / unit — Amazon's pick/pack/ship charge. Set by size tier.
- Referral fee (%) — Amazon's commission, usually 15%.
- Monthly storage fee / unit — punishes slow-moving inventory and over-ordering.
Launch costs & timing
- Launch month — biggest seasonality lever. Holiday-quarter (Oct–Dec) demand can be 2× baseline but Advertising Cost of Sale is also ~30% higher due to bid competition. January is the lull. The simulator applies monthly seasonality multipliers automatically.
- Inventory strategy — Conservative (75 days), Balanced (100 days, default), or Front-load for the holiday quarter (150 days, Jul–Oct only). More inventory ties up more cash and risks aged-inventory surcharges if you misjudge demand.
- Inbound shipping cost — sea/air freight per shipment.
- One-time launch costs — photos, branding, trademark, samples, tooling.
- Reorder lead time — typically 45 days for sea freight from China. Affects how aggressive your reorder triggers must be.
- Inbound placement fee ($/unit) — Amazon's 2024 charge for splitting inventory across warehouses.
- Aged inventory surcharge ($/unit/month) — applied past 180 days of expected demand. Punishes over-ordering for slow niches.
- Amazon payout lag (days) — Amazon holds your revenue ~14 days before disbursing. Affects peak cash drawdown.
- Daily paid ads budget cap — ceiling on daily ad spend.
Ranges — Low / Likely / High
The simulator doesn't ask for one number; it asks for three on the variables that matter most:
- Ramp days — how long until you hit steady-state velocity. Low = fast organic + ad ramp, High = slow ramp.
- Advertising Cost of Sale — the share of sales revenue spent on ads during ramp. Low = ads are efficient, High = ads bleed.
- Velocity — daily units sold at steady state. Low = quiet niche, High = hot niche.
Every one of the 10,000 simulations samples a value from each range using a triangular distribution centered on "Likely". So your ramp days might be 35 in one run, 60 in another, 95 in a third. That's why outcomes vary across runs — and why the answer is a probability, not a single number.
Apply a Market Dominator scan and these ranges automatically shift to match the niche difficulty, using the curve in Step 1.
The Monte Carlo engine
For each of the 10,000 simulated launches:
- Sample one value each for ramp days, Advertising Cost of Sale, and velocity from your ranges.
- Sample one value each for niche-specific seasonality multipliers and conversion noise.
- Walk forward day by day for 365 days: compute organic + paid units sold, revenue, ad spend, Amazon fulfillment + storage fees, cost of goods, reorders, payout lag, and aged-inventory penalties.
- Track cash position, profit, and break-even day.
After 10,000 runs the simulator reports the distribution of outcomes — not the single "expected" answer. The typical (median), worst-case and best-case outcomes are the points you'll see on the cash-journey chart.
Reading the outputs
Headline cards (the top row)
- Verdict card — one-line summary plus % of simulations that turned profit by year-end. This is the headline number. 70%+ is robust; 50% is a coin flip; under 30% means your plan only works in the lucky scenarios.
- Cash needed — peak capital required (best case — survives 9 of 10 simulated runs). This is the budget you'd actually have to put up to launch this product safely.
- Break-even — the typical day your cumulative net worth crosses zero. Best/worst case shown below.
- Profit probability — the percentage of simulated runs that ended Year 1 in the black.
- Typical Year 1 profit — median final cash position after 12 months, with best/worst cases.
- 12-month Return on Investment — final profit ÷ peak capital deployed. This is how serious sellers compare opportunities — $50k profit on $200k capital is a different decision than $50k on $30k. Color-coded green ≥30%, gold 0–30%, red below 0.
Stockout impact panel
Shown whenever any simulated run hit a stockout. Tells you what fraction of runs stocked out, the median + worst-case stockout duration, and the typical $ value of demand you missed. Stockouts hurt you twice — directly (lost sales) and indirectly (rankings drop, reviews dry up, the launch loses momentum). The simulator only models the direct hit, so the real-world cost is usually worse than the number shown. Bigger inventory buffers shrink this gap.
Holiday-quarter dependency one-liner
How much of your typical Year 1 profit comes from Nov + Dec. Three message tones based on share:
- ≥40% — "holiday-fragile": miss the holiday wave and Year 1 looks very different. Launch by September or front-load inventory.
- 25–40% — "meaningful contributor": protect inventory through Nov/Dec.
- 15–25% — "helps but not load-bearing": the holiday quarter is upside, not the plan.
Charts
- Cash journey over 12 months — your net worth across the year. Solid line = typical, shaded band = best/worst case range. The marked dot is the lowest cash point across the typical run — that's the budget you actually need to survive launch. Vertical guide lines mark seller milestones (📦 order placed, 🚢 shipped, 🏢 arrived at Amazon, 🚀 launch), Amazon sales days (Prime Day, Black Friday / Cyber Monday, Christmas), and reorder cycles.
- Sales velocity over 12 months — daily units sold, split into organic (green, bottom) and paid ads (gold, stacked on top). Dashed lines show the best/worst case totals across all 10,000 runs. Reads like a textbook Amazon launch — paid ads carry you on day 1 while organic ramps slowly, organic gradually overtakes paid ads by month 5–6, and the holiday quarter produces the seasonal spike.
- Monthly profit / loss — net profit & loss for each calendar month. Different from cumulative break-even: this shows when you stop bleeding cash and start banking it month-to-month. Bars are typical; whiskers show the worst/best case range. Useful for spotting the cash-flow inflection point.
- Review accumulation over 12 months — when you typically cross 10, 50, 100, and 500 reviews. Reviews are your single biggest moat in private-label, so this chart is about when your moat materializes. Wider spread between worst/best lines means review velocity is the most uncertain part of your plan.
- Break-even histogram — month distribution of when launches turned cash-flow positive. A wide spread means timing is volatile; a tight peak means break-even is reliable.
- Sensitivity tornado — which inputs move the verdict the most. Auto-runs after the main simulation. Use it to decide where to tighten your estimates first.