Evaluation of eCommerce Search Performance

In this study, we present a comprehensive evaluation of eCommerce search performance across various online retail stores. Our methodology is designed to assess the effectiveness of search engines in handling queries of varying complexity, from simple keyword searches to more intricate user intents. By leveraging a large language model (LLM) for automated browsing and query generation, we simulate realistic user interactions and capture detailed performance metrics. This approach allows us to categorize and rank stores based on their search capabilities while identifying patterns in performance across different query complexity levels.

1. Introduction

The effectiveness of search functionality represents a critical component of the online shopping experience. As eCommerce continues to expand globally, retailers face increasing pressure to deliver search results that accurately match user intent across a spectrum of query types. Despite this importance, comparative analyses of search performance across multiple retailers remain limited in the literature.

This research addresses this gap by developing and implementing a standardized methodology for evaluating eCommerce search capabilities. By simulating realistic user interactions through automated browsing and query execution, we provide insights into how effectively different online retailers handle search queries of varying complexity.

Our approach examines not only the technical performance of search algorithms but also their practical utility from a user perspective, measuring precision, recall, and relevance of results. This multi-dimensional evaluation framework offers a comprehensive view of search performance that goes beyond simple binary assessments of result accuracy.

2. Methodology

Our methodology provides an end-to-end framework for evaluating the search performance of eCommerce stores across varying levels of query complexity. The approach is structured, reproducible, and aimed at profiling the capabilities of eCommerce search systems in handling increasingly nuanced user intent.

2.1 Industry and Store Selection

We begin by identifying the specific industry vertical and product category under investigation (Fashion & Apparel, in this study). This ensures that comparisons are meaningful across similar types of online retail experiences.

For each selected industry-category pair, we then construct a set of target stores. These can either be:

• Dynamically sourced from public rankings, such as top-performing or popular stores within the domain, or
• Loaded from a predefined and vetted list of eCommerce sites known to operate in the target segment.

This step ensures that the evaluation covers a representative and diverse sample of stores relevant to the user shopping behavior within that industry.

2.2 Query Generation

Once stores are identified, we generate a fixed set of 25 evaluation queries per store. These queries are not generic; rather, they are personalized to the product taxonomy and offerings of each individual store. This personalization increases the validity of the evaluation by ensuring the queries are realistic and grounded in each store's product catalog.

The queries are evenly distributed across five levels of query complexity, with five queries per type:

1. Lexical: Simple keyword matches (e.g., "blue shirt")
2. Semantic: Queries requiring understanding of meaning (e.g., "business casual attire")
3. Facet: Queries specifying multiple product attributes (e.g., "women's waterproof winter boots under $100")
4. Intent: Queries expressing user needs without specific product terms (e.g., "vacation outfits for tropical weather")
5. Multimodal: Queries requiring interpretation of visual elements (e.g., "dresses with floral patterns")

This design allows us to assess not only raw performance but also the depth and adaptability of the store's search engine.

2.3 Automated Store Search via Browser Agent

To simulate a realistic user experience, we use a large language model (LLM)-driven automated browser agent to perform the search on each store's front-end interface. For each query, the agent navigates to the store's search page, enters the query string, and extracts the top 10 product results.

During this process, the system captures:

• Product titles
• Product URLs
• Representative product images

Stores that present CAPTCHA challenges or encounter unexpected errors during browsing are excluded from evaluation to maintain consistency and fairness.

2.4 Scoring the Search Results

Each query result is then evaluated along three core dimensions, scored independently on a scale from 0 to 10:

• Precision: Assesses how closely the returned products match the specific query intent in both textual and visual dimensions.
• Proxy Recall: Measures the breadth and coverage of relevant results—i.e., how comprehensively the query is represented.
• Relevance: Evaluates whether the most relevant results appear at the top of the list, reflecting a good ranking mechanism.

These scores are computed based on a rubric that standardizes interpretation across different queries and complexity levels (see Table 1).

To consolidate these metrics, we compute a combined score per query using a weighted combination of the F1 score (harmonizing precision and recall) and the relevance score. This captures both retrieval quality and ranking effectiveness in a single measure.

Combined Score Formula

Let:

• P: precision score
• R: recall score
• $\text{Rel}$: relevance score
• $\alpha \in [0, 1]$: tunable weighting factor

We compute:

$\text{F1} = \frac{2 \cdot P \cdot R}{P + R}$

$\text{Combined Score} = \alpha \cdot \text{F1} + (1 - \alpha) \cdot \text{Rel}$

This combined score is calculated at three granularities:

• Query level: score for an individual query
• Query type level: average score across all queries of a given complexity
• Store level: aggregated profile of the store's ability to handle search tasks across complexity levels

2.5 Identifying Best-Supported Query Types

To understand what kinds of user queries a store can handle most effectively, we analyze the average combined score for each query complexity type. We define a thresholding strategy to avoid over-relying on raw maximum values, instead focusing on robustness across high-performing types.

Specifically, for each store:

• Let $C_i$ be the average combined score for query type i
• Let $C_{\text{max}} = \max(C_0, C_1, …, C_4)$
• Define the threshold as: $\text{Threshold} = 0.95 \cdot C_{\text{max}}$

We then select the most complex query type i that satisfies:

$C_i \geq \text{Threshold}$

This ensures we reward stores that perform well not just on a single easy query type but can sustain high performance at higher complexities. By selecting the most complex qualifying type, we effectively characterize the upper bound of the store's search capability.

3. Evaluation Rubric

Our scoring methodology employs a standardized rubric to ensure consistency across all evaluations. Table 1 presents the detailed criteria for each scoring dimension.

Table 1: Evaluation Scoring Rubric

4. Results

Our evaluation covered 15 major fashion retailers, assessing their search performance across the five complexity levels. Table 2 summarizes the overall scores and best-supported query types for each store.

Table 2: Search Performance by Store

4.1 Key Findings

Several notable patterns emerged from our analysis:

1. Performance Range: There is significant variation in search performance across retailers, with scores ranging from 2.49 to 7.88 on our 10-point scale. This suggests substantial differences in search technology implementation and effectiveness.

2. Query Complexity Handling: The most advanced stores demonstrate capabilities for handling complex intent-based and even multimodal queries, while lower-performing stores struggle with anything beyond basic lexical matching.

3. Intent Query Prevalence: Intent-based queries appear as the best-supported query type across 7 of the 15 stores, suggesting that many retailers either rely on traditional metadata enrichment or have invested in advanced methods to generate product understanding beyond explicit product specifications.

4. Multimodal Search Excellence: Only one retailer (Neiman Marcus) demonstrated excellence in multimodal search capability, indicating this remains a frontier area in eCommerce search.

5. Performance Clustering: The results reveal natural groupings of stores with similar performance characteristics, with clear delineation between high performers (scores >7), mid-range performers (scores 5-7), and lower performers (scores <5).

5. Discussion

5.1 Architectural Implications

Our findings suggest that effective eCommerce search implementations likely share certain architectural components. High-performing systems demonstrate capabilities that extend beyond traditional keyword-based search, suggesting the integration of:

1. Semantic Understanding: The ability to interpret user queries beyond literal keyword matching.
2. Faceted Search Infrastructure: Robust handling of multiple product attributes simultaneously.
3. Intent Recognition Systems: Mechanisms to translate user needs into relevant product categories.
4. Advanced Image Recognition: For top performers, the integration of visual search capabilities.

5.2 Business Impact

The substantial performance gap between top and bottom performers (5.39 points) suggests potentially significant business implications. Stores with more effective search functionality likely provide superior customer experiences, potentially leading to higher conversion rates and customer satisfaction.

Our research did not directly measure business outcomes, but previous industry studies have established correlations between search effectiveness and key performance indicators such as time-on-site, conversion rate, and average order value. This suggests that the performance differences we observed may translate to material business impact.

5.3 Implementation Strategies

An interesting observation is that some stores achieve strong performance on intent-based queries without necessarily excelling at less complex query types. This suggests alternative implementation strategies focused on enriching product metadata and taxonomies rather than sophisticated query understanding.

For example, a store might compensate for limitations in query processing by extensively tagging products with use cases, occasions, and style descriptors that allow matching to intent-based queries through conventional search mechanisms.

6. Limitations and Future Work

While our methodology provides valuable insights, several limitations should be acknowledged:

1. User Perspective: Our evaluation focuses on search result quality rather than the holistic user experience, which would include factors such as search interface usability and result presentation.

2. Query Selection: Despite efforts to personalize queries to each store's catalog, there remains potential for bias in query selection.

3. Temporal Sensitivity: Search implementations may change over time, making this evaluation a snapshot that requires periodic updating.

7. Conclusion

This study provides a comprehensive framework for evaluating eCommerce search performance across multiple dimensions and query complexity levels. Our findings reveal substantial variations in search capabilities among major fashion retailers, with clear differentiation between leading implementations and those that struggle with anything beyond basic search functionality.

The results highlight the technical gap between implementing basic keyword search and developing systems capable of understanding complex user intent or multimodal queries. They also suggest that different retailers have prioritized different aspects of search functionality, with some focusing on advanced query understanding while others emphasize product metadata enrichment.

These insights offer valuable guidance for eCommerce operators seeking to benchmark and improve their search implementations, as well as for researchers exploring the intersection of information retrieval and online retail. As eCommerce continues to evolve, the ability to effectively match user search intent with relevant products remains a critical competitive differentiator worthy of continued research attention.