Please enjoy this second installment of Overcoming Year Make Model Challenges. Part 1 can be found here. In this four part series, Sitation’s Director of Automotive Solutions, Steve Bach explains how you can overcome difficulties, as well as leverage the value of mastering this segment of automotive data.

III. Top Challenges in Managing Year Make Model Data with PIM or MDM Systems

A. Inconsistent Data Formats: Addressing Issues Across Multiple Sources and Systems

One of the primary challenges in YMM data management is dealing with inconsistent data formats from various suppliers and systems. Each supplier might use different structures for YMM data, making it difficult to consolidate and standardize the information. Without a centralized approach, the data integration process becomes time-consuming and error-prone. Some suppliers may provide data in ACES & PIES standards, while others will only offer their own custom and non-standard data. 

ACES stands for “Aftermarket Catalog Exchange Standard,” and it is an industry standard used in the automotive aftermarket. It is developed and maintained by the Auto Care Association (formerly known as the Automotive Aftermarket Industry Association or AAIA) and is used to exchange product information, vehicle fitment data, and digital assets between manufacturers, distributors, retailers, and other participants in the automotive aftermarket supply chain.

The ACES standard aims to streamline and improve the accuracy of product data and fitment information for automotive parts and accessories. It defines a structured format for cataloging product attributes, including Year, Make, Model, and Engine (YMM) data, as well as other relevant product details such as part numbers, descriptions, images, and specifications. This standardized format enables seamless communication and integration of data across different systems and platforms.

Implementing a PIM or MDM system helps address this issue by providing a single repository to harmonize YMM data from multiple sources. These systems offer data mapping and transformation capabilities, allowing businesses to convert diverse data formats into a unified standard. This ensures consistent and accurate YMM data throughout the entire product catalog, streamlining the shopping experience for customers.

B. Data Quality and Completeness: Ensuring Accuracy for a Wide Range of Products

Maintaining data quality and completeness for a vast range of products, each with specific YMM attributes, can be a daunting task. Inaccurate or incomplete YMM data can lead to incorrect product recommendations, negatively impacting customer trust and satisfaction.

PIM and MDM systems play a crucial role in improving data quality. They offer validation mechanisms to identify and rectify data inconsistencies, such as missing or erroneous information. Additionally, these systems can enforce data governance rules, ensuring that YMM data meets predefined standards before being published. Regular data audits and data quality checks further help businesses maintain the accuracy and completeness of YMM data.

C. Frequent Product Updates: Managing Year Make Model Data with Constant Changes

The automotive industry sees regular updates, with new vehicle models being introduced and existing ones undergoing modifications. For ecommerce businesses, keeping up with these changes and promptly updating YMM data is vital to prevent outdated or incompatible product listings.

PIM and MDM systems excel at managing frequent updates efficiently. With automation features, businesses can schedule and automate YMM data updates based on manufacturers’ release schedules. Real-time integration with industry databases and suppliers’ feeds ensures that the latest YMM data is always available to customers, reducing the chances of selling outdated products.

D. Cross-Reference Management: Maintaining Accurate Product Attribute References

Cross-referencing YMM data with product attributes is critical to enable precise fitment and compatibility information. However, managing these cross-references can become challenging, especially when dealing with a large number of products and their corresponding YMM combinations.

PIM and MDM systems simplify cross-reference management by providing intuitive tools to associate products with their compatible vehicles. These systems can create relationships between products and YMM attributes, allowing customers to find the right products for their specific vehicles effortlessly. Additionally, automated cross-referencing ensures that updates to YMM data propagate to relevant product listings in real-time.

By leveraging PIM and MDM systems to address these top challenges, ecommerce businesses can streamline their YMM data management, enhance data accuracy, and provide customers with a seamless shopping experience. In the next section, we will explore the benefits and best practices of using these systems to optimize ecommerce product data. Let’s dive in.

Please join us for Part 3, focused on overcoming YMM data management challenges with PIM or MDM Systems.

Contact us to be connected to our team of automotive experts and begin overcoming your data challenges right away.