TARGET WEEKLY — INTERNAL
Overview
Month forecast ?
Projected month-end based on trailing 4-week velocity
Total Weekly $ Sales (all SKUs) ?
Sum across all selling stores, by week
Velocity ($PSPW) by SKU ?
Trailing 12 weeks, $ sold per store per week
Anomaly watch
Stores, SKUs, and regions that broke trend last week
By SKU
Velocity, OOS, store distribution
SKU
$ per Store per Week ($PSPW) ?
Total dollars ÷ stores selling, by week
Units per Store per Week (UPSPW) ?
Total units ÷ stores selling, by week
Total $ and Average Retail Price ?
Total dollars (left axis) · ARP = total $ ÷ total units (right axis) ·
Total $ — Co-Space stores only ?
Stores flagged "Apr Co-Space = Yes" in the raw data
Out-of-Stock % ?
Stores with EOH=0 and zero units sold, divided by all stores carrying the SKU
Stores Selling ?
Distribution by week
By Region
Regional Performance ?
Trailing 4 weeks, transition stores excluded
RegionStoresSales $ Sales U$PSPW
Stores
StoreRegion $PSPW (4wk) $ (4wk) $ (12wk) Flags
Store map
Every Target store color-coded by trailing 4-week velocity
Mode SKU Window
Top 10 by velocity · in current view
StoreCityState $PSPW$ (window)
Lift Analysis
What's actually working at shelf — methodologically honest
Persistent placements — Cross-section ?
Within-region matched comparison, trailing 12 weeks
FlagSKU Flagged stores$PSPW (flagged) Control stores$PSPW (control) Lift
Method. For each SKU and flag, we compare $PSPW of stores with the flag = Yes vs stores with the flag = No, only matched within the same region, over the trailing 12 weeks. This controls for regional taste differences and store density. Read this as association, not causation — a sidecap store probably also has more foot traffic. Sample under 30 stores per side is tagged Directional.
Demo events — Before/After (region-controlled) ?
Compare 4 weeks before vs 4 weeks after demo date, net of the same-region non-demoed control
FlagSKU Stores Raw lift Net lift
Method. For each demoed store, calculate weekly $ in the 4 weeks before vs 4 weeks after the demo date (Mar 30 / Sep 13, 2024). Subtract the same delta computed on non-demoed stores in the same region — this isolates the demo from seasonal trend. Net lift is the cleaner read.
Reset cohort comparison ?
Stores grouped by which reset cycle added them. Trailing 4-week store-level $PSPW.
CohortSKU Stores$PSPW (4wk)
Method. Reset? is not a marketing flag — it's a cohort indicator. Jan'24 stores joined in the January 2024 reset; Aug'24 stores joined in August 2024; Pre-Jan'24 stores were already in distribution. Compare current velocity across cohorts to see how each wave is performing.
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Chat With This Data
Plain-English questions, answered against the raw weekly rows
Open in Cowork

All 227,557 raw rows are loaded into Gentic as target_weekly + target_stores. Cowork can query them directly. Ask in plain English — Cowork will write the SQL.

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Example prompts
Drop any of these into a Cowork conversation
  • Using the target_weekly + target_stores tables, what are our 10 highest-velocity stores for Cacao in the last 4 weeks?
  • From target_weekly, how does freezer-side display lift on Cacao compare to Matcha?
  • Which states had the steepest week-over-week drop in Matcha velocity last week?
  • For the Aug'24 reset cohort, what's the average $PSPW by region for the trailing 4 weeks?
  • Find stores carrying Cacao that had zero EOH for 3+ consecutive weeks
Schema reference (paste into Cowork if it asks). target_weekly: wk_end, year, wk_num, loc, state, region, sku, sales_d, sales_u, eoh, sidecap, demo_mar, demo_sep, phase1_geo, freezer_side, reset_cohort
target_stores: loc, name, city, state, region, market, in_may26_transition
SKUs: cacao, matcha, hot_choc, coffee, vanilla_chai, mocha, strawberry
Region codes: NE, MW, GP, SA, MS, SW, PN, PW, MT