When Over-Attribution Can Become a Problem

Adding a lot of attributes might seem like a good way to give shoppers more personalization and enhance search precision. However, when there are excessive or overly specific attributes, it can actually create noise, making it harder for customers to find the products they want. Here’s how over-attribution affects the customer experience:

1. Overwhelming Filters and Options
   When customers search for a product, an excess of filter options (e.g., dozens of fabric types, overly specific color variations like “midnight teal”) can overwhelm them. Instead of helping customers narrow down options, it clutters the interface, making it confusing and time-consuming to find the right product.
2. Inconsistent Attribute Importance
   Some attributes are more relevant than others depending on the product. For instance, color and size may be essential for clothing, but less so for tech products like USB cables. Over-attributing less relevant details leads to inconsistency and can distract from the core attributes that actually help customers make purchasing decisions.
3. Increased Decision Fatigue
   When too many details are provided, customers may experience decision fatigue, where the abundance of choices or overly complex information leads to frustration or abandoned searches. Simplicity often makes for a smoother shopping experience.

Effects on Keyword-Based Search vs. Vector-Based Search

Over-attribution affects search algorithms in different ways depending on the type of search: keyword-based search or vector-based search.

Impact on Keyword-Based Search

In keyword-based search, which matches products with exact or similar keywords in customer queries, over-attribution can lead to:

• Search Result Dilution: The more attributes there are, the more keywords are added to the product data. This can create irrelevant or overly broad search results when customers enter simple terms. For example, if a product has highly specific attributes like “moisture-wicking,” “breathable,” and “quick-dry” added to “athletic shirt,” a customer searching for “shirt” could get results that include items meant for very specific use cases, even if they’re irrelevant.
• Inaccurate Filtering: Excessive attributes create a high volume of filters, which can cause misalignment with customer search intent. A customer looking for “cotton T-shirt” could be presented with several options for “cotton blends,” “organic cotton,” “lightweight cotton,” and more, which clutters the results rather than helping them locate a basic cotton T-shirt.

Keyword-based search thrives on simplicity and relevance; adding too many specific attributes can create noise that makes it difficult to match the search query precisely.

Impact on Vector-Based Search

Vector-based search uses machine learning to understand the context and relationship between words, matching queries based on meaning rather than exact keyword matches. Over-attribution impacts vector-based search in different ways:

• Dilution of Contextual Meaning: In vector-based search, too many specific attributes can dilute the “essence” or overall meaning of a product. For instance, if a product is tagged with dozens of technical or descriptive terms, the system may have difficulty understanding which attributes are most important. This can lead to misinterpretation of the customer’s intent, with results that are less contextually accurate.
• Reduced Recommendation Relevance: Vector-based systems often use attributes to make recommendations. When products are over-attributed, recommendations may become overly specific or overly broad, as the algorithm tries to connect multiple meanings. For example, if a catalog has numerous niche attributes, a customer who searched for “running shoes” might receive recommendations for trail-specific, eco-friendly, or waterproof shoes that don’t match their initial intent.

Vector-based search is designed to understand nuanced context, but over-attribution can make the model overthink the product’s intended use or core attributes, leading to less relevant results.

Balancing Attribution to Enhance Search Performance and Customer Experience

To avoid the pitfalls of over-attribution:

• Prioritize Core and Relevant Attributes: Focus on attributes that are essential to the product and its search performance (e.g., material and color for clothing).
• Limit Overly Specific Attributes: Avoid adding niche descriptors that only apply to a small segment of your catalog unless they provide essential filtering.
• Test for Customer Relevance: Run A/B tests to see if adding or removing certain attributes affects search relevance and conversions. This can help identify attributes that truly enhance the customer experience versus those that add unnecessary complexity.

By keeping the attribute list balanced and relevant, you create a catalog that aligns with both keyword and vector-based search algorithms, ensuring customers have a smooth and efficient shopping experience.