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Product Information Management: 4 Key Principles of Good Product Data

In a slight departure from our previous article on proactively (and tactically) preparing for a product data transformation, let’s explore some foundational principles to consider when thinking about the evolution of your product data landscape.

Your product data taxonomy is the critical scaffolding that drives the end user experience for both internal users of a Product Information Management (PIM) system, as well as external customers who may be looking to transact with you via your website.

That said, a successful product data approach will align with the following four principles:

1. Product Data Should be Intuitive

Well-architected product data can make navigation, search, and comparison functions easy and logical for the end user to navigate. While this can be accomplished multiple ways, two key areas stand out as foundational when it comes to your architecture:

Classification Taxonomy: The baseline classification taxonomy should be used to manage product data through global and category specific attributes alike.

Attributes: Considering your attribution approach is also essential. The most consistent product data will have a robust attribution strategy in place.

2. Product Data Should be Findable

When was the last time that you visited a retail or manufacturer’s website to search for a product by keyword or name only to be taken to a list of irrelevant search results? This is typically the result of a poorly architected product taxonomy, which can make products invisible to online customers and difficult for third party trading partners to locate and publish them into syndication. Easily findable products and a well-structured site navigation can be accomplished with a tidied-up product taxonomy.

A poorly architected product taxonomy can make your products invisible to customers.

3. Product Data Should be Usable

A well-architected product taxonomy will enable your customers to see across your complete product assortment and menu of solutions. It should also clearly call out key product adjacencies, a best practice when it comes to leveraging product data to drive product adoption and top-line revenue growth.

A well-architected product taxonomy can drive top-line revenue growth.
 

4. Product Data Should be Adaptable

To foster growth activities and expansion efforts that are driven via sales, marketing and digital merchandising teams, product data should be flexible and designed to support multiple processes across business functions and enterprise operations.

In other words, to properly focus externally on the end goal of customer engagement, you must first take an inward look at the product data architecture. While not always glamorous, product data efforts can make the difference between a mediocre approach to customer engagement and a wildly successful one.

For a deeper dive into how we use customer data models, product data models, content models, and knowledge architecture to create a framework for unified commerce download our whitepaper: Attribute-Driven Framework for Unified Commerce

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