Pimcore for Sportswear – a New Data Problem Every Season, Not Just a New Collection.
Size grids, color variants, material combinations, and two to four collections a year: sportswear data models have to survive short cycles without getting rebuilt every season. We know the patterns from projects in the sportswear industry.
Few industries combine as much variant depth with as short a product cycle as sportswear. A single garment typically exists in several colors, several sizes, and sometimes several material versions, multiplied by the fact that most of the range gets replaced by new collections after one or two seasons. A data model that isn't built for that pace forces the team to manually rebuild large parts of the product structure with every new season, instead of deriving it from a stable base.
Typical PIM Patterns in Sportswear
Size grids across multiple markets. EU, US, and UK sizing systems differ, sometimes further split by audience (women's, men's, kids'). A data model with structured, market-specific size-table logic prevents mis-purchases and manual conversion tables living outside the system.
Variant combinations of color, size, and material. The same silhouette quickly branches into hundreds of sellable combinations. Inheritance keeps shared collection properties centrally maintained, so only actual differences per variant need to be captured.
Seasonal collection cycles as a structural pattern, not an exception. Two to four collections a year mean a significant share of the range regularly phases out and gets replaced. A data model with a clear collection structure makes that phase-out and succession plannable instead of chaotic.
Multi-channel sales across your own shop, retail, and marketplaces. Size charts, care instructions, and marketing copy have to stay consistent across every channel, with different text length and attribute depth depending on channel requirements.
How We Work
The starting point is a data model with clean collection and variant structure: a collection carries the shared properties, each color, size, and material variant inherits from it, the same principle as Enterprise Data & MDM. Size charts are kept as structured, market-dependent data instead of a static conversion table living with the marketing team. For seasonal catalogs, lookbooks, and dealer materials that should come from the same product data as the online shop, we use our Web2Print setup, so print and digital output never drift apart.
Frequently Asked Questions
How do you cleanly model different sizing systems (EU/US/UK) in the data model?
Through structured, market-specific size tables attached to the product as an attribute group, not as a separate spreadsheet conversion table outside the system. That reduces mis-purchases and keeps conversion consistent at every point of sale.
Does the data model need to be rebuilt from scratch for every new season?
No, that's exactly the problem a clean collection structure prevents. A new season inherits the existing structure and adds new variants instead of starting from zero.
How quickly can a new collection be entered into the system?
That depends on scope and supplier data quality, but is significantly faster with a standing data model than a structural rebuild per season. Effort concentrates on genuinely new attributes, not repeating existing ones.
Can we generate lookbooks and printed catalogs from the same data as the online shop?
Yes, that's a standard case for our Web2Print setup: both outputs come from the same data model, so product information never drifts between print and digital.
Ready for Your Next Season Rollout?
We build a collection structure that every new season inherits instead of rebuilding.
Discuss Your Season Rollout