Improving Product Data Quality at Kramp

As a technical wholesaler with a broad product catalogue and an ambitious data strategy, Kramp was reaching the limits of its existing product data. Across three ongoing initiatives, the company faced challenges with data quality: improving product findability, conducting assortment analyses and exploring AI applications.

To address this, Kramp needed an integrated approach to map out both the strengths and the improvement areas of its data. They turned to Squadra for help. We executed a Master Data Management (MDM) assessment, working together with Kramp on a future-proof improvement plan.

Customer Profile: Kramp  

Kramp is Europe’s largest technical wholesaler of parts for the agricultural sector, garden and park machinery. With locations in more than 20 countries and an assortment of over 500,000 products, Kramp offers its customers fast delivery, expert advice and a user-friendly webshop. Digitalization and data-driven operations are central to Kramp’s strategy.

The Challenge: Data Quality Limiting Growth  

Kramp had already implemented the STEP system from Stibo as its new PIM system. With technically everything in place, data quality issues still persisted. This impacted three key initiatives:

• Product findability in Kramp’s webshop
• Quality of assortment analyses
• Usability of data for AI strategies

Inconsistencies in the data stemmed from earlier implementation choices. At the same time, there was no clear understanding of the scale of the issues, nor a structured plan to address them effectively. The separate initiatives within Kramp required an integrated approach, with focus on structure, processes and governance.

The Solution: MDM Data Quality Assessment Brings Insight and Structure  

Kramp opted for Squadra’s proven Master Data Management (MDM) approach. The assessment focused on six core areas: Vision & Strategy, Organization & Data Governance, Processes, Data Architecture & Model, Data Quality, and Technology. The research combined both qualitative and quantitative analyses:

• Stakeholder interviews revealed bottlenecks in processes and governance.
• A data analysis identified issues in the quality of around 450,000 products.

The assessment deliberately examined the customer experience in the webshop: which choices in data structures and data quality influence the search and filter process? This customer-centric perspective gave extra direction to the improvement proposals.

The Result: Concrete Actions To Improve Data Quality  

The MDM assessment provided Kramp with a clear overview of the main improvement areas and practical actions:

• Restructuring of product hierarchy and web taxonomy to help customers find the right products faster.
• Cleansing and deduplication of attributes to enable better use of features as webshop filters.
• Assigning ownership of product data to the product management team.
• Structuring data cleansing as a programmatic improvement process, with clear goals and planning.

Thanks to Squadra’s MDM assessment, Kramp has regained control over its product data. The improvement actions enable the company to increase product findability, conduct reliable assortment analyses and take the right steps towards a future-proof AI strategy.

Ready to structurally improve your product data quality?

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