Sell-through analysis, SKU rationalization, store clustering, shelf space decisions, and the data habits that let a two-person category team run a sharper assortment review than teams twice the size. No software pitches — just the methodology.
Most category reviews collapse to one of three calls per SKU. Here is how to make each one faster, grounded in sell-through rate and shelf margin — not last season's plan.
Aggregating sell-through across 40 stores hides the stores where a SKU is a hero and the stores where it is dead weight. A guide to reading store-level sell-through without a data warehouse.
Store clustering is not new. But most chains cluster once at reset and forget until next year. Here is what happens to margin when you let store performance re-cluster your assortment mid-season.
A SKU with 6 facings and 40% sell-through is not underperforming. A SKU with 2 facings and 40% sell-through might be your best item. This distinction changes every cut-keep-reorder call you make.
You have access to more data than you can act on. A practical guide to the four data pulls that drive the highest-leverage assortment decisions each week — and the five reports you can safely stop running.
Planograms encode last year's decisions. When current sell-through data points a different direction, most teams defer to the plan. Here is why that deference is costing you turns — and how to build the case for an intra-season assortment update.