What this is

A browser-based diagnostic for YTD sell-out workbooks. It parses raw retail rows into a canonical record set, flags integrity issues, then turns the same dataset into focused reads: data integrity, liquidation risk, velocity, momentum, promo dependence, and collection theme authoring.

Mental model

Each read is a lens over the same normalized records. The parser standardizes headers and metrics first; the selected read then chooses which fields, thresholds, and hero KPI matter. Nothing is recomputed from hidden server logic.

Privacy

All parsing happens locally in your browser. The workbook is never uploaded to a server.

Steps

Analysis path — one normalized record set feeds multiple diagnostic reads.

raw sell-out workbook

→

header scan

metric cleanup

→

reads

velocity

liquidation

momentum

integrity flags stay attached to rows so every read can exclude bad inputs consistently.

Pick a read. The cover lists every diagnostic question the tool can answer. Choose the read that matches the decision you are trying to make.
Check the column contract. The prelude lists required and optional fields for the active read. Paste mode includes a synthetic sample that matches the chosen read.
Load or paste data. Drop an Excel workbook, paste a table, or load the built-in sample. The parser searches for the strongest header row and maps aliases into canonical fields.
Review vitals. KPI tiles summarize records, rows excluded by integrity rules, and the hero metric for the active read.
Use the story body. Each lens renders tables, matrices, flags, or copy-ready theme language from the normalized records.
Move across reads. Footer navigation keeps the same loaded data while switching the diagnostic lens.

Read catalog

Read	Question answered
Integrity	Can this workbook be trusted for analysis?
Liquidation	Where is inventory aging faster than sales?
Velocity	Which items are stars, sleepers, slow-bleeders, or dogs?
Momentum	What part of YoY movement is new, dropped, or continuing product?
Promo	Which brands or products depend most on markdown/promo depth?
Theme	Which product attributes can become a collection story?

Required workbook shape

The tool scans the top of the workbook for the best header row, including clustered cadence rows above metric headers. Each story runs only when its required fields are present.

Schema area	Fields
Identity	`Retailer`, `Location`, `Brand`, `Collection Release`, `Frame Material`, `MSRP`
TY metrics	`TY YTD Units Sls`, `TY YTD Rtl Sls`, `TY WTD Units OH`, `TY YTD ST%`
LY metrics	`LY YTD Units Sls`, `LY YTD Rtl Sls`, `LY WTD Units OH`, `LY YTD ST%`
Attributes	`Newness`, `Brand Category`, `Material Code`, `Price Range`
Optional UPC grain	`UPC`, `Style`, `Lens Color`, `Frame Color`, `Temple Color`, `Theme`

Canonical record

The normalized row keeps identity fields wide and metric fields numeric. Percent fields accept whole-number or decimal input; currency and comma formatting are stripped before calculation.

Parser behavior	Detail
Header aliases	Common retailer report labels are mapped to canonical field names.
Numeric cleanup	Currency symbols, commas, and percent signs are removed before number conversion.
Blank handling	Missing optional fields stay blank; missing required fields make the active read unavailable.
Sample paste	The paste box generates only the fields needed for the active read.

Integrity flags

Rows with one of the flags below are excluded from the metrics they would distort, but stay in the integrity counts.

MSRP missing / zero — excluded from AUR, promo, and markdown depth.
Negative units / retail / on-hand — excluded from sales totals, AUR, and inventory health.
ST% out of range — excluded from the velocity matrix.
AUR above MSRP — excluded from promo penetration so above-list rows do not pull the share down.

Velocity matrix

Rows are classified by a median split on ST% and TY units. Star = high ST × high volume; Sleeper = high ST × low volume; Slow-bleeder = low ST × high volume; Dog = low ST × low volume.

Momentum decomposition

YoY unit variance per brand is split into New (TY only), Dropped (LY only), and Continuing (both years). Continuing carries the delta; New is additive and Dropped is subtractive.

Core formulas

Metric	Definition
AUR	`retail sales / units sold`, only when units are positive.
Promo depth	`1 - AUR / MSRP`, bounded by integrity checks.
Sell-through	Uses reported ST% when available; otherwise derived reads rely on unit and on-hand context.
Inventory risk	High on-hand plus low sell-through and/or aged release signals.
Momentum	`TY units - LY units`, then classified into new/dropped/continuing buckets.

Interpretation caveats

Median splits are relative to the loaded file, not an external benchmark.
Promo dependence is only as reliable as MSRP and sales-dollar quality.
Theme authoring benefits from richer attribute columns; sparse color/material fields reduce story quality.
Integrity exclusions protect metric quality but do not delete rows from the audit trail.

Fashion Toolbox

Fashion ToolboxIndex DIGI LINE SHEETProduct Review Assortment ComparisonRetailer Overlap Door TrackerDoor Distribution Retail Data StandardizerWeekly Sell-Out Selling AnalysisSell-Through Diagnostic UPC ConcatSchema Mapping

Pick the read.

Integrity Audit