How this workbook was built.

3b. The monthlySold badge.

Amazon's "X bought in past month" badge is reported in bucket-aligned tiers (50, 100, 200, 300, ..., 1,000, 2,000, ..., 100,000+). We use the badge value as-is. We do not synthesize sales from BSR.

A widely cited critique from r/FulfillmentByAmazon[1]: "all sales rank based sales estimators are inherently inaccurate to the same degree. Think of it this way; you can't measure the average speed of your car just by looking at the speedometer once."[2]

We agree. Rank is a relative position, not a sales count, so we sampled actual sales-revealing badge values rather than inferring sales from rank.

3c. The badge floor.

The badge is shown only for products selling 50 per month or more. Roughly 60% of the Amazon marketplace is unbadged and structurally invisible to this sample. Cells dominated by the badge floor get explicit two-tier flags (see §5d).

Through v2 the guidance for hard-floor cells was "rule out, do not decide." v3 supersedes that: hard-floor cells are now exactly where the Demand Read (§8) earns its keep. A product hiding behind the badge floor still leaves two footprints the badge cannot censor, its rank drops and its added reviews, and the Demand Read sizes the slot from those instead of the censored median.

3d. Variation handling.

Amazon's variation-parent listings aggregate child-variant sales into a single monthlySold value while reporting only the parent's own rank drops. A parent with 80,000 monthly sold and 50 drops can show a per-drop ratio two orders of magnitude above the category typical. We detect and remove suspect rollup samples in stage 2 (see §5b).

Mid-BSR rows use four drops bands (0-15, 16-40, 41-100, 101+) because there is more drop variance to resolve in that range. Head and tail BSRs use coarser splits.

4b. Sampling.

For each category in Keepa's depth-≤2 taxonomy (775 categories in the US tree, 696 in CA), we draw a stratified sample across the cell grid. The sampler:

• Targets ~50 products per cell where possible.
• Walks the full BSR by drops grid per category, so a category can contribute up to ~1,400 ASINs.
• Pulls only badged products: products where Keepa reports a non-null monthlySold value. This is the single largest methodological constraint and we surface it on every sheet. Roughly 60% of Amazon products are unbadged and structurally invisible to us.
• Reaches depth 2 in the category tree so subcategory data is captured, not just root level.

After sampling: 307,127 observation rows from 254,407 unique ASINs across both markets (some overlap between US and CA samples, since multi-marketplace ASINs can earn badges in both).

The Canadian market has a markedly higher floor concentration: over 70% of (category by cell) tuples are dominated by 50/mo products. We surface this honestly rather than smoothing it over. CA cells reading "100-500/mo" on Strong confidence are still meaningful; cells at the badge minimum tell you the slot is structurally below the measurement floor.

One vocabulary change ships with v3: in the tier map and evidence table, hard-floor cells are now labeled "50+/mo" instead of "~50/mo". The old label asserted a number we now know is a censoring artifact; the new one is open-ended on purpose. Legend wording: badge floor, true rate unresolved, size it with the Demand Read (§8), not the badge.

5e. Per-cell confidence chips.

Confidence is driven by n_unique, the unique-product count after the rollup filter:

Cells flagged Thin are excluded from tier classification entirely. They read as "No data" in the workbook rather than pretending to a number we can't support.

8c. How they combine: gates and a geometric average.

Each signal first passes a usability gate. The gates are deliberately simple on-or-off rules, not tuned weights:

• The lookup is ignored when its cell is hard-floor (half or more of the sample at the badge minimum), or has fewer than 5 unique products, or has no data. It counts at half strength on soft-floor cells (30 to 50% at the minimum). Otherwise it anchors the read.
• Rank velocity counts when the category has a measured sales-per-drop rate (30 or more qualifying products) and the product shows at least 5 drops.
• Review velocity counts when at least 3 reviews were added; at half strength when the category's rate is a fallback rather than measured. It is discarded entirely when the review count looks like a variation-family rollup (50 or more reviews added AND more than 5 times the category's typical review velocity). When that screen trips, the sheet says so.

The surviving signals are combined as a weighted geometric mean (an average of logarithms). Two reasons, both standard statistics rather than invention:

• Sales volumes are multiplicative quantities with long right tails. For numbers like that the geometric mean is the correct center; an ordinary average would let one high signal drag the estimate up.[6]
• Decades of forecast-combination research show that simple, equal-style weights beat cleverly tuned ones out of sample.[7][8] We cannot measure each signal's precision (there is no ground truth below the badge floor), so we do not pretend to.

The floor behavior falls out on its own, with no special-casing. In a hard-floor cell the lookup's gate is zero, so the velocity signals carry the estimate, and it lifts off 50 exactly when the drops and reviews justify it. A genuinely slow product has few drops and few reviews, so its velocity estimates are small and the read stays near 50. A clean cell keeps the lookup as the anchor and nothing changes.

Two final guards, both conservative:

• The estimate is never allowed below the cell's observed median (de-censoring only ever raises a floor, it never wishes one away).
• A rank ceiling: no estimate may exceed the most we have ever observed sell at that rank in that category (the maximum visible badge value among rollup-filtered samples in the category-by-BSR-band). Rank is the one signal a variation rollup cannot inflate, so it is the contamination-proof upper bound.

8d. The Signal chip: what corroboration means here.

Each Demand Read carries a Signal label: High, Medium, or Low. This is a different concept from the sample-size chips (Strong / Limited / Thin sample, §5e) that grade how much data sits under each evidence cell. Signal grades whether independent signals corroborate this particular read.

Only one pair is genuinely independent: rank velocity versus review velocity. They come from different Keepa fields tracking different physical traces of a sale. The lookup is not independent of rank velocity (the cell is defined by the same drop count), so lookup-velocity agreement earns nothing. Corroboration is counted only between independent signals; correlated signals do not get to vote twice.

• High: rank velocity and review velocity are both live and land within 2x of each other, and the category's sales-per-drop rate is not wide-spread. Two independent measurements agree.
• Medium: one trustworthy signal carries the read (typically rank velocity alone), or the pair is live but 2x to 4x apart.
• Low: only the weakest configurations: a lone review-velocity read, a lone lookup, or signals spread more than 4x apart.
• No data: nothing usable. The sheet says so instead of inventing a number.

8e. What gets displayed: a range, a worst case, and the arithmetic.

The Demand Read never prints a single point. It prints a range whose width is set by the Signal (tighter at High, wider at Medium), both ends snapped down to a coarse display grid (50, 100, 150, 200, 300, 500, 700, 1,000, 1,500, 2,000). Snapping always rounds in the conservative direction.

Display rules, all locked:

• The 2K+ cap. Nothing anywhere in the Demand Read displays above 2,000 a month; higher values render as the open bucket "2K+ /mo". A sourcing decision saturates around one to two thousand units a month; resolution above that adds risk, not information. The cap also structurally bounds the damage of any contaminated input: the worst possible failure is a wrong "2K+", never a wrong "52,000".
• Low prints a lower bound only. At Low signal there is no range and no upper number, just "sells above the badge floor, treat as ~N+ /mo and verify". A printed 20x-wide range invites anchoring on its top, which is the exact over-buy failure the product exists to prevent. We print only the bound the data can defend.
• The worst case is always shown. Under the hero range sits the conservative floor figure ("worst case supported by the data"), visually primary. Size the buy against it.
• The why-line shows the arithmetic. Every read explains itself in one line, for example: "36 drops x ~9 sales/drop (category rate) = ~320/mo; reviews: 12 x 14 = ~170/mo (agrees)". When the review screen tripped, or the rank ceiling clamped the number, the line says that too. You are the last filter; the sheet gives you what you need to overrule it.
• An AGREE marker appears only when the two velocity reads actually converge. It is suppressed otherwise.

8f. What we can and cannot claim.

There is no ground truth below the badge floor. The badge is the only sales figure that exists, and it is the censored one, so the cases where the Demand Read matters most are exactly the cases no one can verify without buying inventory and selling it. The Demand Read is therefore a velocity-implied estimate, not a verified sales figure. The deliverable is restored discrimination and internal coherence, not proven accuracy.

The one external check available is the above-floor back-test: on products whose badge shows a real number (above 50), does rank velocity predict it? It does, out of sample. Tracking real sales above the floor is the evidence the same arithmetic extrapolates below it; it is evidence, not proof. The numbers below apply to products whose true badge value is visible:

Known failure mode, disclosed on-sheet: listings whose drops and reviews accrue at the variation-family level while the badge is per-variation can read high. The review screen, the rank ceiling, and the 2K+ cap each exist to bound this; they bound it, they do not eliminate it.

Direction-of-error policy: a false low costs a missed opportunity; a false high causes the over-buy. Every tie-break (gate, snap, cap, ceiling, Low's lower-bound-only rule) is chosen to make false highs the rarer error.

9b. Category-level filters.

Two filters decide which categories earn a dedicated sheet:

1. Cell-coverage floor. A category must have at least 10 cells at Strong or Limited confidence. Below that, the category sheet would be more gaps than data.
2. Hub-name filter. Amazon's category tree contains depth-1 navigation scaffolding (Categories, Departments, Products, Subjects, Styles): these are not real categories, they are internal navigation hubs. We drop them by name. US drops 15 hubs (231 category sheets ship). CA drops 2 (140 ship).

9c. Final composition.

• 231 US category sheets + 1 Overview + 1 Sales per Drop. (full list)
• 140 CA category sheets + 1 Overview + 1 Sales per Drop. (full list)
• Each sheet contains: three numeric inputs (BSR, 30-day drops, optional reviews added; no dropdowns), the Demand Read hero with its Signal chip, worst-case line, and why-line (§8), the demoted evidence block beneath it ("what sampled products at this slot showed": typical / range / best with sample size), a tier-map grid, and a full Evidence table showing every observed cell.

9d. Notes column vocabulary.

The Notes column on each cell uses a fixed vocabulary so a workbook user can decode any caveat at a glance, and so AI agents lifting workbook screenshots can map notes back to triggers:

9e. Reproducibility and determinism.

The pipeline (stage 1 → 2 → 3a) is fully deterministic on a fixed input. The Excel output is byte-reproducible: re-running the workbook builder on the same parquets produces an identical file. Verified by 201 automated tests including a byte-equality parity check against a locked oracle workbook. Re-runs cannot drift; only a fresh quarterly sample can change a number.