GOODYEAR
How a global tire manufacturer brought product data under control across North America with an enterprise Akeneo PIM implementation.
Visit website →A global manufacturer that needed to get product data under control
Goodyear needs no introduction. A global tire manufacturer with operations spanning dozens of countries, hundreds of product lines, and thousands of SKUs — each with deep technical specifications, vehicle fitment data, performance ratings, regional certifications, and marketing content that varies by market and channel.
Enterprise PIM implementations fail for the same reason most large technology projects fail: not because the software does not work, but because the organization’s product data is far messier than anyone realized, the stakeholders have competing requirements, and nobody has agreed on what “good” product data actually looks like.
Goodyear’s environment amplified every one of these factors. Product data lived across multiple systems and teams. Marketing, product engineering, regional sales organizations, and dealer networks all needed product information — but in different formats, at different levels of detail, and with different update cadences.
The PIM had to become the single source of truth for product data — the place where it is governed, enriched, and prepared before flowing downstream to commerce platforms, ERP, marketing channels, and dealer networks. Getting the data model and governance right inside Akeneo was the foundation everything else depended on.
Enterprise Akeneo PIM architecture and data governance
We were brought in as Akeneo PIM specialists, partnering with the agency leading Goodyear’s broader digital commerce program across North America. The scope was the PIM layer — not the full commerce stack.
Product data modeling. Designed the Akeneo data model to accommodate Goodyear’s tire catalog — product families, attribute sets, variant structures, and classification systems that reflect how tires are actually specified, sold, and serviced. Tire data is unusually complex: size, speed rating, load index, tread pattern, season classification, vehicle fitment, warranty terms, and regional availability all need to be structured, enriched, and distributed consistently.
Data governance and workflows. Established enrichment workflows, completeness rules, and quality gates within Akeneo to ensure that product data meets a defined standard before it flows downstream. In an organization the size of Goodyear, data quality cannot rely on individual discipline — it has to be enforced by the system.
Catalog enrichment strategy. Defined the processes and standards for enriching product content within Akeneo — ensuring that marketing descriptions, technical specifications, imagery, and regional attributes are complete and consistent before activation.
Multi-market and localization support. The implementation covered North American operations, with product data structures designed to support localization requirements across markets, languages, and regional regulatory standards.
Goodyear is the kind of engagement that validates everything we advocate for when it comes to product information management. At enterprise scale, bad product data is not an inconvenience — it is a revenue problem, a compliance risk, and an operational drag that compounds across every system and channel it touches. The Akeneo implementation was not a software installation. It was a data architecture and governance project that happened to run on Akeneo.
"At enterprise scale, bad product data is not an inconvenience — it is a revenue problem, a compliance risk, and an operational drag that compounds across every system and channel it touches. The Akeneo implementation was a data architecture and governance project that happened to run on Akeneo."
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