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Automotive Product Data – Overcoming Year Make Model Challenges: Part 4

October 5, 2023

In conclusion to this four part series, Sitation’s Director of Automotive Solutions, Steve Bach shares best practices for mastering year make model data management

Best Practices for Successful Year Make Model Data Management


A. Conduct Regular Data Audits: Ensuring Data Accuracy and Completeness


Regular data audits are essential to maintain the accuracy and completeness of YMM data. Businesses should establish a systematic process of reviewing and validating YMM information at regular intervals. These audits help identify and rectify inconsistencies, errors, and missing data. By conducting thorough data checks, businesses can ensure that their product catalog remains reliable and up-to-date, bolstering customer trust and loyalty.


B. Collaborate with Suppliers: Timely and Accurate Data Updates


Collaboration with suppliers is critical for maintaining timely and accurate YMM data updates. Establish open lines of communication with suppliers to receive the latest information on new vehicle releases, updates, and product changes. Encourage them to provide data in a standardized format compatible with your PIM or MDM system. By fostering strong partnerships with suppliers, businesses can ensure a constant flow of reliable data, reducing the time between updates and improving the overall customer experience.


C. Stay Informed about Industry Changes: Keeping Up with Trends and Releases


The automotive industry is constantly evolving, with new vehicle models and updates being released regularly. Staying informed about industry changes is crucial for effective YMM data management. Keep a close eye on industry publications like Auto Care Association, SEMA, Aftermarket News, official manufacturer announcements, and industry events to stay up-to-date with the latest developments. By being proactive, businesses can swiftly adapt their YMM data to accommodate new vehicles and market trends, giving them a competitive edge.


D. Implement Data Governance: Ensuring Data Integrity


To maintain data integrity, businesses should implement robust data governance policies. Data governance establishes rules and guidelines for data quality, usage, and access. Define roles and responsibilities within the organization to ensure that YMM data is managed by designated personnel who understand the importance of accurate and standardized information. Additionally, implement data validation protocols to enforce data quality standards and prevent inaccurate or incomplete YMM data from entering the system. This becomes even more critical as product data teams create kits of existing products that have disparate fitment requirements.

By following these best practices, businesses can effectively manage YMM data and optimize their ecommerce product data management. A seamless and accurate shopping experience will lead to increased customer satisfaction, higher conversion rates, and a strengthened brand reputation.


E. Implement Data Governance: Ensuring Data Integrity

While Automotive PIES (Product Information Exchange Standard) data is designed to provide standardized product information for automotive parts and accessories, it may not always be helpful to customers shopping for auto parts online due to several reasons:


Fitment Variations: PIES data may not always capture all fitment variations, especially for older or less common vehicle models. Customers may not find the exact fitment information they need to ensure the part will work for their specific vehicle.

Inconsistent Data Quality: The quality of PIES data can vary among different manufacturers and suppliers. Inaccurate or incomplete data can lead to confusion and potential mismatches between the part and the customer’s vehicle.

Limited Cross-Sell Opportunities: PIES data might not include information about related or compatible products, limiting cross-selling opportunities for sellers and reducing convenience for customers who need multiple parts for a project.

Technical Jargon: Some translation of the PIES data may be needed in order to communicate more effectively with your end customer. Data requirements change heavily depending on your target market in a B2B Jobber site compared to a DIY B2C customer.


To overcome these limitations, automotive ecommerce platforms and sellers often supplement PIES data with additional product information, custom fitment databases, images, installation guides, and real-time inventory and pricing data. This helps improve the overall customer experience and provides shoppers with the necessary information to make informed decisions when shopping for auto parts online.


fitment MDM automotive year make model


In the dynamic realm of ecommerce, managing Year Make Model (YMM) data poses significant challenges for businesses. The complexities of dealing with inconsistent data formats, ensuring data accuracy, handling frequent updates, and maintaining cross-references can be overwhelming.

Embracing modern solutions like Product Information Management (PIM) and Master Data Management (MDM) systems can empower businesses to conquer these challenges effectively.

PIM and MDM systems serve as centralized hubs for YMM data management, offering streamlined processes for data standardization, enrichment, and automated updates. These solutions not only enhance data accuracy but also contribute to a seamless customer experience, ultimately leading to increased sales and customer satisfaction.

Unlock the true potential of your automotive ecommerce business today. Partner with Sitation and embark on a journey towards seamless data management and remarkable customer satisfaction. Get started now to create a winning customer experience that sets you apart from the competition.


This concludes our Overcoming Year Make Model Challenges series. Avoid success hinderance and embrace best practices for successful year make model data management to stay ahead in the competitive landscape.

Overcoming Year Make Model Challenges: Part 1

Overcoming Year Make Model Challenges: Part 2

Overcoming Year Make Model Challenges: Part 3

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