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 By Chantal Schweizer

Strong data is essential for a positive B2B customer experience. What does strong data mean? Adherence to the 4 C’s – complete, concise, clean, and correct. Failure in any of these areas leads to customer confusion and frustration, a trend increasingly visible in B2B. A solid 53% of consumers say that experience matters just as much as the products or services a brand provides. And product data content is a fundamental building block for customer experience. 

Product data must flow downstream to distributors, marketplaces, and websites for accessibility and purchase. Customers rely on comprehensive product information originating from manufacturers and syndicated across channels to make confident decisions. While managing this data may seem daunting, it’s not impossible to ensure that your data lives up to the 4 C’s to enhance customer experience and drive revenue.

Here’s how you achieve the 4 C’s

1. Complete Data

Using an attribution taxonomy to establish category-specific schema sets ensures that data is comprehensive for each product category. For example, a lightbulb requires attributes like ‘Lumens,’ ‘Voltage,’ and ‘Color Temperature,’ while a bearing requires attributes like ‘Ring Material,’ ‘Bore Diameter,’ and ‘Number of Rows.’ This taxonomy and schema organization ensures accurate data entry for each product type. Furnishing this data thoroughly is crucial for downstream applications like filters, product comparisons, and search to enhance findability.

When data is shared with a distributor without essential attributes (like ‘Lumens’ for light bulbs), the product won’t appear when customers filter their searches by ‘Lumens.’ Similarly, if specific details such as ‘Voltage’ aren’t included, the product may not show up in searches tailored to those criteria, like “120V lightbulb.” Moreover, incomplete data in product comparisons reduces the likelihood of a product being chosen, especially if the missing information is critical for decision-making.

💡 Attribution taxonomy: A structured framework for categorizing product attributes.

💡 Schema: A blueprint that organizes these attributes for data entry and retrieval.

2. Concise Data

Keeping data easily readable is crucial, and this means keeping things concise. The use of clear attribute and value pairs with concise information for quick comprehension is crucial. Similarly, establishing accurate attribute labels and setting length limits helps data serve as a quick reference point. Attribute values must directly relate to their associated attributes, leaving no room for confusion. If more detailed distinctions are necessary, refine the attributes accordingly. 

A good example of this is the attribute ‘Material.’ If you have a product that has multiple materials, it’s likely that the attribute is populated with messy data. For example, in the case of shoes, it could be populated as ‘Leather Upper Material, Rubber Outsole Material, PVC Midsole Material.’ This makes it terribly hard to read and nearly impossible to compare to other products. To improve customer experience, it would be much better if the data was broken down into separate attributes. If different downstream applications use this data in different ways, the data must then be adjusted accordingly. With syndication mapping, the data can then be powered with “one-to-one” or “one-to-many” associations that define relationships in how the data is shared. This helps the manufacturer control their data better, becoming a reliable source for customers and building loyalty.

💡 Attribute: An attribute is a characteristic or quality that describes a product or entity.

💡 Attribute value: An attribute value is the specific piece of information associated with an attribute.

💡 Syndication mapping: Syndication mapping refers to the process of organizing and mapping data to be shared across various platforms, channels, or applications. It involves defining relationships between data elements and determining how they will be transformed or represented in different contexts.

3. Clean Data

The best way to avoid messy data is to use a well-governed List of Values (LOV) with the attributes.  The list of values should be concise and specific to the attribute. If shared across multiple attributes, it should have a common value set. If the value set is overly differentiated between two or more attributes, it’s advisable to split the LOV to avoid the use of a wrong value and a confusingly long LOV, which can be cumbersome to manage. There is nothing worse for a customer experience than a messy filter value set, and can be easily avoided if the data is well managed. Using a style guideline to establish formatting rules also ensures that data is consistent across the product breadth. How the data uses capitalization, acronyms, and special characters should be defined and used with the data set. 

Imagine you’re shopping online for a new smartphone, and you want to filter the options by price. However, when you try to use the price filter, you see a messy list of values like “$200 – $399,” “Over $1000,” and “Under $50.” This inconsistency and clutter make it frustrating to find the right price range. With well-managed data, the filter values would be clean and organized, like “Low Range,” “Mid Range,” and “High Range,” making it much easier for customers to navigate and find what they’re looking for.

💡 List of Values (LOV): A predetermined list of options or choices available to users when interacting with the e-commerce platform. For example, when selecting a product attribute like color or size, users may be presented with a list of predefined options to choose from. 

4. Correct Data

Ensuring data accuracy is critical. A single mistake in attribute values could lead to serious consequences, including legal action or product disasters if users rely on inaccurate information. For instance, if the Rated Pressure for an actuator is mistakenly recorded as much higher than actual, it could cause catastrophic problems during product use. To prevent such issues, implement a robust data governance program with clear roles and approval processes. This ensures necessary data checks are in place. Inaccurate data sent to distributors not only damages relationships but also erodes customer trust in the product.

💡 Data governance: The overall management framework and practices put in place to ensure that data assets are properly managed, protected, and utilized within an organization. It involves defining policies, procedures, roles, and responsibilities related to product data management, quality, security, and compliance.

Strategic data content plays a crucial role in elevating B2B customer experience. Ensuring data completeness, clarity, cleanliness, and correctness is central to this endeavor. This can be achieved by employing attribution taxonomies, maintaining concise information, and implementing robust data governance practices are key strategies. Similarly, syndication mapping and a well-governed List of Values (LOV) further streamline data management efforts. Put this all together, and you get accurate and relevant data across all touchpoints – a crucial factor in building customer loyalty and driving revenue growth.

Learn more about our data content and governance solutions. Contact us today at [email protected].

Chantal Schweizer is the Practice Director of Data Services at Pivotree where she helps a wide variety of clients optimize their product information for successful UX and data management. Chantal has been working in the product data management industry for 18 years including designing cross platform information architectures, ontologies, testing navigation and UX, as well as, training and socializing data management tools and best practices.

About Pivotree: Pivotree designs, builds, and manages frictionless commerce experiences for brands and their customers around the world. We provide end-to-end solutions and services in Commerce, Data Management, and Supply Chain for hundreds of brands globally.


1. How can businesses effectively measure and track the success of these efforts? Are there any key performance indicators (KPIs) or metrics recommended for evaluating the quality and impact of product data management on B2B customer experience and revenue?

Evaluating the success of efforts to ensure complete, concise, clean, and correct product data requires a nuanced approach. Key performance indicators (KPIs) could include metrics like data completeness rates, error reduction rates, customer satisfaction scores related to data quality, and revenue growth attributed to improved data-driven customer experiences. Businesses may also track metrics such as time-to-market for new products or the frequency of data-related customer complaints. By analyzing these KPIs over time, businesses can gauge the effectiveness of their data management strategies and identify areas for further improvement.

2. How businesses have successfully implemented these practices to improve their B2B customer experience and drive revenue growth?

Implementing syndication mapping and maintaining a well-governed List of Values (LOV) can yield tangible benefits for B2B businesses. For example, a manufacturing company successfully streamlined its product data syndication process by mapping product attributes consistently across various sales channels. This resulted in improved product discoverability and accuracy, leading to increased sales conversions and customer satisfaction. Similarly, a distributor enhanced its customer experience by implementing a standardized LOV for product specifications, reducing confusion and simplifying the purchasing process for buyers. Case studies like these highlight the practical impact of effective data management practices on B2B customer experience and revenue growth.

3. What are the best practices for developing and enforcing such guidelines, especially in complex B2B environments with multiple stakeholders involved in data management and content creation?

Developing and enforcing style guidelines for data presentation is crucial for maintaining consistency and clarity in B2B environments. Best practices include involving key stakeholders from marketing, sales, and product teams in the development of style guidelines to ensure alignment with business objectives and customer needs. Guidelines should specify formatting rules for attributes such as capitalization, acronyms, and special characters, with examples provided for clarity. To enforce adherence to guidelines, businesses can implement automated data validation tools and provide ongoing training for employees involved in data management and content creation. Additionally, establishing a governance structure with clear roles and responsibilities for maintaining and updating style guidelines can help ensure their continued effectiveness in driving data quality and enhancing the B2B customer experience.