Unlocking the Power of Product Data with Attribute Enrichment

For eCommerce retailers, Product Information Management (PIM) systems like Akeneo play a pivotal role in centralizing and standardizing product data. This consolidation creates a single source of truth, streamlining operations for businesses dealing with large volumes of SKUs. However, as with any data management solution, data accuracy and integrity issues are inevitable. 

In this blog post, we explore the challenges associated with product data, focusing on the common problems retailers face and how Constructor’s Attribute Enrichment addresses these issues, resulting in more accurate and usable product data and a better customer experience.

Big Data, Big Problems

Managing product data is no small feat. Product information originates from various sources such as supplier spreadsheets, ERPs, eCommerce platforms, and Product Lifecycle Management (PLM) systems, where product teams input data during the design phase. As a result, data completeness and standardization can vary significantly between different vendors and product categories. This not only contributes to a less-than-ideal customer experience, but can also result in missed revenue.

For example, a general merchandise retailer may carry both electronics and clothing. As you might imagine, the product data for these two categories will differ significantly. This is complicated by the fact that electronics often require highly specific technical attributes like screen type, memory capacity, resolution, and special features whereas clothing might have general attributes for categories like tops, bottoms, and footwear. 

And when it comes to B2B commerce, these challenges are multiplied. It’s not uncommon for B2B eCommerce catalogs to contain millions of SKUs — in many cases with numerical data like measurements or part numbers. When that data is inaccurate or missing, the outcomes can be devastating: both for the B2B companies that rely on high-volume bulk orders and for their customers, for whom speed and accuracy is critical to deliver on time.

With thousands or millions of products from hundreds of different brands in hundreds of different categories, how can retailers make sure that their product data is accurate and complete for the category of product and for the attributes that matter most to customers?

Filling the Gaps

What if a shirt is manually tagged as purple when it’s actually blue, or what if a soup is tagged as vegan or gluten-free when it’s actually not? 

Modern PIM systems like Akeneo can detect and alert eCommerce teams when product data is missing, inconsistent, or inaccurate. However, fixing the issue effectively is another story. Especially with large volumes of data, allocating resources to manually fix missing or inaccurate data is expensive. Human intervention can also introduce errors, making the process unreliable. A recent MIT study confirmed that human data tagging contains errors to the effect of, on average, 3.5%.

When it comes to product discovery, the challenge also goes beyond data completion; it’s about understanding your customers’ preferences and language and making sure your product data reflects them accurately. Without a clear understanding of which attributes matter most to shoppers, manual efforts to correct data could end up being a waste of time.

Enter Attribute Enrichment

It’s here where Constructor’s Attribute Enrichment comes into play. Revolutionizing the way product data is enriched, Attribute Enrichment combines Constructor’s existing AI capabilities with innovative Machine Vision and Text Classification algorithms. 

The result? An automated system that tags products with new, relevant attributes and categories daily, enhancing data accuracy and maintaining a structured and consistent product catalog. This is a game-changer for retailers, as it significantly improves customer experiences and drives sales and competitiveness in the eCommerce landscape.

The advantages of Attribute Enrichment extend across various aspects of your business. Here are some of the key benefits:

• • Increase Revenue per Visitor and Average Order Value: With improved product attributes, your entire product discovery offering, including search, recommendations, and quizzes, becomes more relevant. This translates to higher revenue per visitor (RPV) and increased average order value (AOV).
  • Reduce Returns and Refunds: High data quality leads to improved customer satisfaction, as the right products match customers’ preferences in terms of colors, flavors, sizes, and other attributes. 
  • Enhance Operational Efficiencies: Attribute Enrichment automates the process of enhancing product information, saving eCommerce teams time and money, and reducing human error. This newfound efficiency allows your merchandisers to focus on high-value strategic contributions, ultimately improving overall operations. 

• Maintain Control: Don’t blindly trust the AI. Your team has the final say on what attributes are added to the catalog so they can refine and tweak as needed.

• Stay Current with Market Trends: Attribute Enrichment leverages customer behavioral data to support the latest search trends. By creating trending attributes based on actual customer behavior, you can keep your product catalog up-to-date and competitive.

The Power of Composable eCommerce Solutions

Constructor’s Attribute Enrichment is a specific solution to address the challenges of managing product data in today’s complex eCommerce environment. By combining Akeneo’s PIM capabilities with Constructor’s AI-driven Attribute Enrichment, businesses can achieve standardized, complete, and highly accurate product data. This partnership offers a composable solution that is API-first, making it easier to integrate across various platforms. The result is a more streamlined, efficient, and competitive ecommerce presence — with the power of product data at its core.