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Eight Countries, One PIM: What the Numbers Said

· 2026-03-06 · Updated 2026-10-23 · 4 min read

One global PIM beat a local PIM in each country, on the retailer's own numbers. The client is a European retailer and wholesaler of caravan and camping accessories, in 8 countries, with more than 300,000 SKUs. PIMvendors compared two setups and built the ROI from the bottom up. The global PIM came out above €5 million over three years. The local option returned about 40 percent of that, with a higher combined licence and maintenance cost. The board approved a global implementation. Vendor selection and implementation planning are still underway.

Client Introduction

Each of the 8 countries had run its own product data. Local ERPs, spreadsheets and supplier feeds, and no shared model, no shared process, no single version of a product. The same item from the same supplier could sit under a different name, with different specs, in a different system.

The Challenge

Attribute definitions, category trees and quality standards differed by country. Cross-border assortment decisions leaned on gut feel. Suppliers delivered the same product information several times, in different formats. E-commerce teams reworked content by hand for each channel and each country. Nobody could measure product data quality for the group. The central team already wanted a PIM. The decision in front of them was which shape: a PIM per country, or one PIM for the group.

The Solution

PIMvendors started by mapping how product data was sourced, created, enriched and published in all 8 markets, and marked the overlaps, the gaps and the bottlenecks. The target model had to serve every market and still carry local language, local regulation and local channels.

With people from each country, the process was drawn as one flow: onboarding, enrichment, validation, translation and publication. Global rules were tight enough to reuse a product, and loose enough that a country team could still do its job. Roles were written down.

Two scenarios then got a full ROI model. Licence, implementation effort, FTE time, time to publish, and revenue from better product content all went in. Local finance and operations checked the models.

A local PIM in each country is faster to start and leaves each market in charge of its own tool. It also limits reuse, raises the combined cost of ownership, and makes cross-border work harder. One global PIM needs more agreement up front and a phased rollout. It reuses product data across the 8 countries, cuts duplicate effort, and gives the group one base for reporting and for later AI work.

The Results

The global scenario showed an estimated ROI above €5 million within three years. The drivers in the model were less duplicated work, faster product launches across markets, and better content tied to higher online conversion. The local scenario was positive too, at roughly 40 percent of the global figure, and the combined licence and maintenance cost ran higher over time.

Leadership took the strategy, the process and the comparison to the board and got approval for a global PIM. PIMvendors is still supporting vendor selection and the implementation plan. The software is not chosen yet. The shape is.

The trade-off sits in that approval. One PIM means a country team does not pick its own system, and the first markets wait while the shared model is agreed. Eight local PIMs would have moved a single country sooner and left the group paying eight times for the same product record.

The comparison is the part worth copying, including the fact that it was done before a vendor was in the room. How to select a PIM is the vendor step this retailer has not taken yet, and it only works if the global-versus-local call is already made. Product data quality and governance is the shared definition of a complete product, which the 8 countries did not have. The PIM implementation guide is the phased rollout the global scenario depends on. AI-ready product data is the later use of that shared model, which is why the global scenario treats one PIM as the base for reporting and for AI.

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