Elevate B2B Customer Experience with Strategic Data Content

 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.

FAQs