Original Research
Beyond Relevance
The Data-Backed Case for Attractiveness as the New Standard for Ecommerce Search Performance
Ecommerce is a results-driven business that hinges on connecting shoppers with the right products at the right time. And nowhere is this connection more critical than in site search, where shoppers often arrive with clear intent and high expectations. When shoppers use the search bar, they're not looking for just relevant results - they're looking for products they actually want to buy.
Yet most retailers still measure search success through the lens of relevance alone, a metric that fails to capture the complexity of modern shopping behavior. This report introduces a more sophisticated approach: attractiveness, a comprehensive metric that goes beyond simple keyword matching to predict and measure what truly matters in ecommerce — a shopper's likelihood to purchase.
Constructor’s analysis of more than 609 million shopper searches across 100+ leading retail sites reveals why this distinction matters. Searchers account for:
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44% of total site revenue (with searchers being just 24% of shoppers)
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2.5x higher conversion rates compared to nonsearchers
Sites optimizing for attractiveness in search results consistently outperform those using traditional relevance metrics, delivering:
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Nearly double the click-through rates on search results
The Evolution of Search Metrics: From Relevance to Attractiveness
For decades, the gold standard for evaluating search results has been keyword relevance. This approach made sense in the early days of digital search, when the primary challenge was finding documents that matched user queries.
However, ecommerce search presents a fundamentally different challenge. When customers search on an ecommerce site, finding products that match their keywords is just the beginning — the real goal is connecting them with products they're likely to purchase. Consider a shopper searching for "jeans" on an apparel website. A relevance-focused search engine performs a straightforward task: It finds all products labeled as jeans and returns them as results. While this approach might seem logical, it fails to address the complex reality of how people actually shop for jeans.
Imagine two different customers searching for "men’s jeans" on the same website. The first customer consistently purchases slim-fit styles in dark washes, typically browses premium brands, and often filters for items under $200. The second customer prefers relaxed fits, gravitates toward medium washes, and has recently been viewing sustainable and eco-friendly clothing options. A relevance-only search would show identical results for both customers — every pair of jeans that matches the keyword — likely arranged by basic factors such as text match strength or popularity.
This is where attractiveness metrics demonstrate their superior value. An attractiveness-optimized search understands that these two customers, despite using the same search term, are looking for fundamentally different items. It considers their individual shopping histories, preferences, and behaviors to prioritize results that are not just relevant, but genuinely appealing to each specific customer. The first customer might see
premium slim-fit options with his usual size in stock prominently displayed, while the second customer's results would prioritize sustainable, relaxed-fit styles — dramatically increasing the likelihood of a purchase in both cases.
Attractiveness goes beyond relevance to evaluate search results based on their likelihood to convert for a specific shopper. This metric incorporates crucial factors that relevance ignores:
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Individual shopper preferences (e.g., for brands, styles, colors, etc.) and behavior patterns (both of themselves and others with similar profiles)
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Product performance data, including conversion rates and reviews
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Current inventory status and availability
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Real-time, in-session shopping context (e.g., what the shopper is engaging with, ignoring, what’s already in their cart, etc.)
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Why Attractiveness Matters Now More Than Ever
The stakes for getting search right have never been higher. Nearly 7 in 10 shoppers (68%) report that retail website search needs improvement. And 42% of shoppers say that although their search results seem technically relevant to their queries, the products aren't what they're actually hoping to see.
The industry stands at a crossroads: Retailers can either continue with relevance-based approaches that frustrate customers, or embrace attractiveness as the new standard — one that better serves today's sophisticated shoppers (and better serves retailers themselves).
This report examining search attractiveness reveals how this metric is reshaping our understanding of search performance and setting new standards for ecommerce success. Through detailed analysis of real-world search data, we'll demonstrate why attractiveness should be the north star metric for any retailer serious about optimizing their search experience.
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The research establishing attractiveness as the new standard for ecommerce search success draws from comprehensive data across the industry, including:
Activity from Q4 2024
This extensive dataset allows us to quantify not just how search experiences influence conversions and revenue, but specifically how the shift from relevance to attractiveness metrics drives superior performance.
Searchers Are Your Most Valuable Shoppers — And They Need More Than Relevance
Site search isn't just a tool for navigation — it's a strong intent signal that demands a sophisticated response. While searchers account for 24% of online shoppers, they drive 44% of ecommerce revenue and convert at 2.5x the rate of shoppers who only browse. This outsized impact makes it crucial to move beyond simple relevance to ensure these high-value customers receive truly attractive results.
Attractive Search Results = Higher Engagement & Conversions
The way search results are presented matters significantly. Our analysis shows that more attractive and engaging results generate click-through rates that are nearly twice as high as those from less attractive results. This dramatic performance difference occurs even when all results are technically "relevant" to the search query.
Search Behavior Varies by Industry, But Attractiveness Impact is Consistent
While search usage levels differ across retail verticals, the underlying trend remains the same — shoppers who engage with search consistently drive a disproportionate share of revenue. The potential impact of attractiveness optimization is particularly pronounced in industries with larger catalogs, where the gap between relevant and attractive results grows larger.
Shoppers Who Search: Understanding High-Intent Customer Behavior
The data consistently reveals a fundamental truth across all retail verticals: Shoppers who search demonstrate remarkably high purchase intent, making them the most valuable customers on any ecommerce site. This heightened intent creates both an opportunity and a challenge. While these customers are more likely to make a purchase, they also arrive with specific needs and expectations that basic keyword matching cannot satisfy.
Our analysis shows that roughly a quarter of shoppers (24%) engage with site search, yet they contribute 44% of total site revenue. This outsized impact stems from a crucial characteristic of search behavior: When customers use the search bar, they're not just browsing — they're actively seeking specific products with the intention to buy. This intention manifests in concrete behavioral differences throughout the shopping journey.
Search engagement foreshadows conversion. Searchers don't just browse — they act, demonstrating nearly double the add-to-cart rate of non-searchers. This behavior pattern highlights why traditional relevance metrics fall short. When customers are this motivated to purchase, showing them merely relevant results isn't enough. They need results that are genuinely attractive — aligned with their preferences, shopping history, and current context – or they’ll take their wallets elsewhere.
The impact of search becomes even more pronounced at the conversion stage. Searchers convert at nearly three times the rate of browsers, a multiplier effect that demonstrates how crucial it is to optimize the search experience. This dramatic difference in conversion rates reveals an important truth about search behavior: These customers aren't just more likely to buy — they're actively trying to buy. The barrier to purchase isn't usually product availability; it's the ability to quickly find the most attractive options among all relevant choices.
This pattern of heightened engagement extends across the entire customer journey. From initial search to final purchase, searchers consistently demonstrate higher engagement rates at every stage. However, this engagement isn't guaranteed — it depends heavily on the quality of the search experience. When searchers encounter results that are merely relevant rather than truly attractive, engagement drops sharply. This explains why nearly 7 in 10 shoppers report that retail website search needs improvement: Relevance alone isn't meeting their needs.
Understanding these behavior patterns reveals why attractiveness must replace relevance as the primary metric for search success. Searchers arrive with high intent and clear purpose — they're ready to buy if shown the right products. Traditional relevance metrics might ensure these customers see products that match their search terms, but attractiveness metrics ensure they see products they actually want to buy. In an era where customer expectations continue to rise, this distinction becomes increasingly crucial for ecommerce success.
Search Result Attractiveness: A New Standard for Measuring Search Success
Understanding what makes search results truly effective requires moving beyond traditional metrics. While conventional relevance measures how well a result matches keywords in a search query, attractiveness measures something far more valuable: how likely a product is to convert for a specific customer. This shift from simple matching to conversion probability represents a fundamental evolution in how we evaluate search performance.
The evidence for attractiveness as the superior metric for search performance is clear in the data. Our analysis reveals the influence that highly attractive results have on business outcomes across all major performance indicators.
The Performance Impact of Attractive Results
Attractiveness scores are measured over a 7-day period, and clickthrough rates from the next 7 days to ensure that results reflect shopper behavior without being biased by later activity.
Search results with higher attractiveness drive significantly more engagement, revealing a clear pattern in shopper behavior. High-attractiveness results — those in the top quarter of all results — achieve a 7% click-through rate, nearly doubling the engagement seen with lower-attractiveness results. This dramatic difference shows that when shoppers see results that genuinely match their preferences and intent, they're far more likely to click and ultimately purchase.
For retailers, this means that improving attractiveness scores isn't just about incremental gains — it's about fundamentally transforming how effectively their search function serves customers.
The relationship between attractiveness and performance reveals one of the most compelling findings in our research. Our analysis shows that for every 1-point increase in attractiveness score, click-through rates improve by 3.8%. This impact is extraordinary in the world of ecommerce, where improvements often come in unpredictable bursts. Instead, attractiveness provides a reliable lever for improving search performance: Retailers can confidently invest in attractiveness optimization knowing that each incremental improvement will drive measurable results. When we consider that higher click-through rates typically lead to more conversions, this predictable relationship becomes even more valuable — it creates a clear, reliable path to revenue growth through search optimization.
Consider a retailer with 1 million monthly searches and a 10% click-through rate. A 5-point increase in attractiveness provides 19,000 more product views each month. This isn't just about better metrics – it's about creating significantly more opportunities for purchases with every search.
Implementing Attractiveness Optimization
To fully capture these benefits, retailers must ensure their search systems are capable of considering the full context of each shopping interaction.
This means:
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Understanding individual customer preferences and shopping patters
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Analyzing product performance data across different customer segments
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Incorporating real-time factors like inventory and seasonality
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Continuously learning from customer interactions to improve result ranking
The data makes the business case clear: Optimizing for attractiveness directly drives revenue growth through better search experiences.
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About the Report
This comprehensive analysis of ecommerce search behavior represents a significant advance in our understanding of what drives search success. Constructor’s research examines actual search activity on retail websites from October through December 2024, focusing exclusively on website interactions rather than app-based search behavior.
To ensure the validity of attractiveness measurements, we employed a rigorous twophase analysis approach. Attractiveness scores were calculated based on a 7-day period, with click-through rates collected over the subsequent 7 days. This methodology prevents any overlap between training and evaluation data, ensuring that the attractiveness metrics genuinely predict future performance rather than simply reflecting past behavior.
For analytical purposes, we grouped attractiveness scores into percentage-point increments, allowing us to identify clear patterns in how increasing attractiveness correlates with improved performance metrics.
The scope of this research encompasses:
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609 Million Searches
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244 Million Unique Searches
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$9.8 billion in search-driven revenue
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113 leading ecommerce websites
To maintain data integrity and ensure reliable conclusions, we implemented strict aggregation measures. These include specific requirements for analysis set size, diversity of data sources, and consistency of measurements. All data presented has been rounded where appropriate to maintain precision while ensuring clarity.
Our analysis employs robust statistical methods to ensure that the relationship between attractiveness and performance metrics is genuine and significant. While the research includes this comprehensive dataset, it's important to note that these metrics are specific to the analysis set and should not be interpreted as direct indicators of Constructor's operational performance or as guarantees of future ecommerce performance.
The findings presented in this report reflect actual customer behavior and demonstrate the concrete impact of moving beyond simple relevance to embrace attractiveness as the key metric for search optimization.