Definition
Assortment analytics is the analysis of a catalog's breadth, gaps, overlaps, and performance across categories — helping teams decide what to add, drop, or promote to build a stronger, more competitive assortment.
Key points
- It examines breadth, gaps, overlaps, and performance by category.
- It turns the catalog into a range-planning decision tool.
- It depends on consistent categorization and attributes to be reliable.
- It often compares your range against demand and competitors.
How is assortment analytics used in practice?
Teams use it to find under-served categories worth expanding, overlapping products worth rationalizing, and weak performers worth cutting. Because the analysis runs on categorization and attributes, a clean taxonomy and consistent data are what make the conclusions trustworthy.
Common pitfalls
- Analyzing assortment on top of inconsistent categorization.
- Mistaking data gaps for genuine assortment gaps.
- Optimizing breadth without regard to catalog quality.
FAQ
What questions does assortment analytics answer?
Where are the gaps in our range? Where do we overlap or cannibalize? Which categories underperform relative to demand? What are competitors carrying that we aren't? It turns the catalog into a decision tool for range planning.
How does data quality affect assortment analytics?
Assortment analysis depends on consistent categorization and attributes. If products are miscategorized or inconsistently tagged, gaps and overlaps are miscounted — so clean structure and taxonomy are prerequisites for reliable analytics.