What Is Precision? Why Irrelevant Search Results Are Killing Your Conversion Rate

A shopper searches for \"lightweight summer jacket.\" Your search returns 24 results. Twelve are relevant. But there are actually 45 lightweight summer jackets in your catalog. Your search just made 33 products invisible.\n\nThis is a recall problem. Recall measures the fraction of relevant products that actually appear in search results. A recall of 0.27 (12 out of 45) means your search engine is hiding 73% of the products that match what the shopper wants. Those products exist in your inventory. You paid to source them, photograph them, and list them. But your search engine does not know they are relevant, so shoppers never see them.\n\nFor ecommerce, low recall is invisible revenue loss. Unlike a broken checkout or a 404 error, nobody complains about products they never knew existed. The shopper sees 12 jackets, picks one or leaves, and never knows about the 33 others that might have been a better fit, a better price, or the exact color they wanted.\n\nThis post explains what recall is, how it works, why it is the most underappreciated search metric in ecommerce, and how Marqo's approach to product understanding solves the recall problem at its root.\n\n### How Recall Is Calculated\n\nRecall is one of the simplest metrics in information retrieval:\n\nRecall = (Relevant items retrieved) / (Total relevant items in the collection)\n\nIf your catalog contains 45 lightweight summer jackets and your search returns 12 of them, recall is 12/45 = 0.267, or 26.7%.\n\nRecall is always measured relative to a specific query and a specific relevance definition. Different queries have different numbers of relevant products, and different definitions of \"relevant\" produce different recall scores for the same result set.\n\nRecall at K (Recall@K) is a common variant that limits evaluation to the top K results. Recall@10 asks: of all relevant products, how many appear in the top 10 results? This is often more practical than total recall because shoppers rarely look beyond the first page.\n\nFor ecommerce, Recall@20 or Recall@50 are typical evaluation points, corresponding to one or two pages of search results on most sites.\n\n### Why Low Recall Happens in Ecommerce\n\nLow recall in ecommerce search has a specific and well-understood cause: the search system cannot recognize relevance beyond keyword overlap.\n\nA shopper searches for \"lightweight summer jacket.\" A keyword system looks for products containing those words. It finds products with \"lightweight\" and \"jacket\" and \"summer\" in their titles or descriptions. It returns them.\n\nBut many relevant products do not contain those words:\n- A \"packable windbreaker\" is a lightweight summer jacket, but neither \"lightweight\" nor \"summer\" appears in its name.\n- A \"linen blazer\" is a lightweight summer jacket in many contexts, but the vocabulary is completely different.\n- A \"UV protection layer\" is functionally a lightweight summer jacket for outdoor use, but shares zero keywords with the query.\n\nThese products are relevant. They are in the catalog. The shopper would consider them. But the search engine cannot connect them to the query because it matches words, not concepts.\n\nThis vocabulary gap is the primary driver of low recall in ecommerce. It affects every product category but is especially severe in fashion (where style vocabulary is fluid and subjective), home goods (where function-based queries rarely match product names), and beauty (where ingredient, concern, and product-type vocabularies are largely separate).\n\n### The Revenue Impact of Missing Products\n\nLow recall costs money in three distinct ways.\n\nLost direct sales. Every relevant product hidden from the shopper is a potential sale that never happens. If the ideal product for a shopper's query is in position 35 instead of position 5, most shoppers will never scroll to find it. They either buy a suboptimal product (lower satisfaction, higher return rate) or leave (zero revenue).\n\nReduced catalog efficiency. Ecommerce businesses invest heavily in assortment planning, product development, and inventory management. If 30 to 50% of relevant products are invisible to search, those investments are partially wasted. You are paying to stock products that shoppers cannot find.\n\nConcentrated demand on a few products. When search consistently surfaces the same subset of products regardless of query variation, demand concentrates on those products. This leads to stockouts on popular items and stale inventory on invisible items. The problem looks like a demand planning issue, but it is actually a search recall issue.\n\nSwimOutlet experienced this directly. After deploying Marqo, SwimOutlet saw a 10.6% increase in revenue per visit. Part of that improvement came from surfacing products that had been invisible to the old search system. When the full catalog participates in search results, demand distributes more naturally, conversion improves, and inventory moves faster.\n\n### How Marqo Solves the Recall Problem\n\nMarqo is an AI-native product discovery platform that understands every product in the catalog: what it looks like, what it pairs with, what it substitutes, and what drives margin. This understanding eliminates the vocabulary gap that causes low recall.\n\nWhen a shopper searches for \"lightweight summer jacket,\" Marqo does not look for keyword matches. It identifies products that are functionally lightweight, seasonally appropriate for summer, and categorically jackets, regardless of what words appear in their titles. The packable windbreaker, the linen blazer, and the UV protection layer all surface because the system understands what they are.\n\nThis is what Marqo's Commerce Superintelligence delivers: comprehensive product understanding that connects any query to any relevant product in the catalog, even when the vocabulary is entirely different.\n\nThe system combines product intelligence with behavioral data to continuously refine relevance boundaries. If shoppers who search for \"lightweight summer jacket\" consistently engage with linen blazers, that behavioral signal strengthens the connection. But the critical difference from legacy systems is that Marqo's product understanding identifies the linen blazer as relevant from day one, before any behavioral data exists.\n\n### Zero-Shot Recall: New Products From Day One\n\nOne of the most commercially damaging recall failures happens with new products. A product arrives in the catalog, gets listed with basic metadata, and then sits invisible for weeks or months because the search system has no behavioral data to connect it to queries.\n\nIn keyword systems, a new product only appears in search results if its metadata contains the right keywords. If the copywriter uses different vocabulary than the shopper (which happens constantly), the product remains hidden until manual intervention.\n\nIn behavior-dependent systems, a new product cannot rank until enough shoppers have clicked on it to generate signals. This creates a cold-start problem: the product needs exposure to generate data, but it cannot get exposure without data.\n\nMarqo solves this with zero-shot understanding. Because the system understands what a product is based on its attributes, images, and characteristics, it can determine relevance to any query from the moment the product enters the catalog. No keywords required. No behavioral history required. The product surfaces for relevant queries on day one.\n\nFor retailers with frequent new arrivals, fast fashion cycles, or seasonal inventory, this zero-shot recall capability directly translates to revenue. Products start generating revenue from the day they are listed, not weeks later when the system finally learns about them.\n\n### Recall vs. Precision: The Ecommerce Tradeoff\n\nRecall and precision exist in natural tension. Increasing recall (showing more relevant products) often means showing more products overall, which can decrease precision (the fraction of shown products that are relevant). Showing fewer, more targeted results improves precision but risks missing relevant products.\n\nIn ecommerce, this tradeoff has a specific commercial dimension:\n\n- High recall, lower precision: Shoppers see more relevant products but also more irrelevant ones. This works well for browse-heavy categories (home decor, fashion) where shoppers enjoy exploring.\n- High precision, lower recall: Shoppers see fewer but more accurate results. This works well for high-intent, specific queries (\"iPhone 15 Pro Max 256GB blue\") where the shopper knows exactly what they want.\n\nThe ideal search system adapts the recall-precision balance to the query. Broad exploratory queries should favor recall. Specific navigational queries should favor precision.\n\nMarqo's Commerce Superintelligence handles this naturally because it understands query intent, not just query words. A broad query like \"summer dresses\" triggers high-recall behavior, surfacing diverse options from across the catalog. A specific query like \"Reformation Juliette dress green size 6\" triggers high-precision behavior, narrowing to exact matches.\n\n### How to Measure Recall for Your Search\n\nMeasuring recall requires knowing the total number of relevant products for each query, which is harder than it sounds.\n\nStep 1: Select evaluation queries. Choose 100 to 300 queries that represent the range of search behavior on your site.\n\nStep 2: Define relevance. For each query, determine which products in your catalog are relevant. This is the hard part. For small catalogs, manual review is feasible. For large catalogs, you may need to use a combination of category filtering, attribute matching, and human judgment on samples.\n\nStep 3: Capture results. Record the top 20 to 50 results from your current search engine for each query.\n\nStep 4: Calculate. For each query, divide the number of relevant products in the results by the total number of relevant products in the catalog. Average across all queries.\n\nInterpreting your score:\n- Recall@20 above 0.70: Strong. Your search surfaces most relevant products on the first page or two.\n- Recall@20 between 0.40 and 0.70: Moderate. A significant portion of your catalog is invisible for many queries.\n- Recall@20 below 0.40: Your search is missing the majority of relevant products. This represents major revenue leakage.\n\nStep 5: Analyze by query type. You will almost certainly find that recall is high for navigational queries (where the product name is in the query) and low for conceptual or attribute-based queries (where the shopper describes what they want rather than naming it). The conceptual queries are where the revenue opportunity lives.\n\n### Common Recall Failures in Ecommerce\n\nSynonym blindness. \"Couch\" vs. \"sofa.\" \"Sneakers\" vs. \"trainers.\" \"Swimsuit\" vs. \"bathing suit.\" Keyword systems miss these unless every synonym is manually added to every product. Manual synonym management does not scale across a 100,000-product catalog.\n\nAttribute-concept gaps. \"Something warm for skiing\" requires understanding that down jackets, fleece layers, and thermal base layers are all relevant. No product title says \"something warm for skiing.\"\n\nCross-category relevance. \"Gift for a runner\" spans shoes, apparel, accessories, nutrition, and technology. Keyword systems typically search within a single category at a time, missing relevant products in other categories.\n\nVisual similarity. A shopper sees a product on Instagram and searches for something similar using descriptive language. The relevant products in the catalog may look similar but use completely different descriptive text.\n\nMarqo eliminates all four failure modes because its product understanding operates at the concept level, not the keyword level. Products are indexed by what they are, not just what their descriptions say.\n\n### Recall in the Age of Conversational Commerce\n\nRecall becomes even more critical as product discovery moves into conversational interfaces. Sibbi is the conversational interface of Marqo's Commerce Superintelligence, an autonomous agent that guides shoppers from discovery through post-purchase using deep product understanding.\n\nIn conversation, shoppers describe what they want in natural, often imprecise language: \"I need something for my friend's housewarming, she just moved to a new apartment and loves cooking.\" The recall challenge here is immense. The relevant products span categories (kitchen tools, cookbooks, serving ware, specialty ingredients), and the vocabulary gap between the query and product metadata is enormous.\n\nMarqo's deep product understanding ensures that Sibbi can surface relevant products from across the entire catalog for queries like these. High recall in conversational contexts means the shopper sees the full range of options the catalog offers, leading to better purchases and fewer missed opportunities.\n\n### Why Recall Is the Most Underappreciated Metric\n\nSearch teams tend to focus on what they can see: the results that appear. Precision problems are visible. Irrelevant results on the first page are obvious and get flagged. Ranking problems are visible. A great product in position eight instead of position one is noticeable.\n\nBut recall problems are invisible by definition. The products that do not appear in results are not seen by anyone. No shopper complains about a product they do not know exists. No merchandiser notices that 30 jackets are missing from search results because they never appear on any report.\n\nThis invisibility makes recall the most dangerous metric to neglect. You can have perfect precision (every shown result is relevant) and perfect ranking (the shown results are in the ideal order) and still lose enormous revenue because half the relevant catalog is missing from results.\n\nMarqo makes recall visible. By understanding every product in the catalog and connecting it to every relevant query, Marqo surfaces the products that legacy systems hide. The revenue impact of this expanded recall is often the single largest contributor to overall search revenue improvement.\n\n### The Business Case for Better Recall\n\nIf your current Recall@20 is 0.45 and you improve it to 0.70, you have made 25 percentage points more of your relevant catalog visible for each query. Across a full catalog and a full month of queries, this means hundreds of thousands of additional product impressions for products that were previously invisible.\n\nNot every additional impression converts. But even a small conversion rate on previously invisible products represents pure incremental revenue. These are sales that were not possible before because the products were not surfaced.\n\nMarqo customers consistently report that improved recall is one of the most surprising and valuable outcomes. Products that had been in the catalog for months suddenly start receiving traffic and generating sales. Inventory that had been marked for clearance starts moving at full price because shoppers can finally find it.\n\nThis is what happens when an AI-native product discovery platform replaces keyword matching with genuine product understanding. The catalog comes alive.\n\n### FAQ\n\nWhat is the difference between recall and coverage?\nRecall measures the fraction of relevant products that appear in results for a specific query. Coverage typically refers to the fraction of the total catalog that appears in results across all queries. Both matter: recall tells you about individual query quality, coverage tells you about overall catalog utilization.\n\nCan recall be too high?\nIn theory, you could achieve perfect recall by returning every product in the catalog for every query. But precision would be near zero. The goal is high recall with high precision: surfacing most relevant products without overwhelming the shopper with irrelevant ones. Marqo's Commerce Superintelligence achieves this balance through genuine product understanding.\n\nHow does recall relate to \"no results\" searches?\n\"No results\" is the most extreme recall failure: recall of zero. If your site has a high \"no results\" rate, recall is failing at the most basic level. Marqo virtually eliminates \"no results\" queries because product understanding can find relevant products even when vocabulary does not match.\n\nDoes Marqo improve recall for long-tail queries?\nYes, dramatically. Long-tail queries are where keyword systems have the worst recall because the specific language used in these queries rarely appears in product metadata. Marqo understands products at a concept level, so it can connect long-tail queries to relevant products regardless of vocabulary overlap. Results in 14 days, not months.\n\nHow often should recall be evaluated?\nAt minimum, evaluate recall quarterly. Catalog changes (new products, discontinued products) directly affect recall. Seasonal shifts change which products are relevant to common queries. Continuous evaluation catches recall degradation early.\n\n### Stop Hiding Products From Your Shoppers\n\nIf your search engine understands keywords but not products, a significant portion of your catalog is invisible to every search query. Marqo combines product intelligence with behavioral data to surface every relevant product for every query, from day one.\n\nBook a demo to see how much of your catalog your current search is hiding, and what happens when Marqo's AI-native product discovery platform makes it visible.\n\n---\n\n# POST 4: what-is-precision-in-machine-learning\n\n---\n\nTitle: What Is Precision? Why Irrelevant Search Results Are Killing Your Conversion Rate\n\nMeta description: Precision measures how many of your search results are actually relevant. Learn why irrelevant results destroy shopper trust and tank conversion rates.\n\n---\n\n## What Is Precision? Why Irrelevant Search Results Are Killing Your Conversion Rate\n\nA shopper searches for \"gold hoop earrings\" on your site. The first result is gold hoop earrings. Good. The second result is silver hoop earrings. The third is gold stud earrings. The fourth is a gold bracelet. By the fifth result, the shopper is looking at a leather handbag with gold hardware.\n\nFive results shown. One is what the shopper wanted. That is a precision of 0.20, or 20%. Four out of five results are wasting the shopper's time, eroding their trust, and pushing them closer to the back button.\n\nPrecision measures the fraction of returned results that are actually relevant. It is the metric that answers the question: when we show a shopper products, how many of them are actually what they asked for?\n\nFor ecommerce, low precision is a conversion killer. Every irrelevant result on the page is a signal to the shopper that your site does not understand them. That signal accumulates fast. One irrelevant result is forgivable. Three in the top five, and the shopper starts questioning whether the right product even exists in your catalog. Five in the top ten, and they leave.\n\nThis post explains what precision is, how it works, why it matters critically for ecommerce conversion, and how Marqo's product understanding delivers the precision that keyword-based systems cannot.\n\n### How Precision Is Calculated\n\nPrecision is straightforward:\n\nPrecision = (Relevant results retrieved) / (Total results retrieved)\n\nIf your search returns 20 results and 14 are relevant, precision is 14/20 = 0.70, or 70%.\n\nPrecision at K (Precision@K) limits evaluation to the top K results. Precision@5 asks: of the top 5 results, how many are relevant? This is often more useful than total precision because shoppers focus on the top results.\n\nFor ecommerce, Precision@10 is the most commercially relevant cutoff. It roughly corresponds to the products visible without scrolling on a desktop results page. If Precision@10 is 0.60, four out of ten visible products are irrelevant. That is four products actively damaging the shopper's experience.\n\n### The Psychology of Irrelevant Results\n\nIrrelevant search results do more damage than most ecommerce teams realize. The harm goes beyond the simple missed opportunity of not showing a relevant product.\n\nTrust erosion. When a shopper searches for something specific and sees irrelevant results, they lose confidence that the site has what they want. Even if the right product exists and appears further down the page, the irrelevant results above it have already planted doubt.\n\nCognitive load. Every irrelevant result forces the shopper to evaluate and reject it. This takes mental effort. After rejecting several irrelevant results, the shopper experiences decision fatigue and is more likely to abandon the search entirely.\n\nPerceived catalog quality. Irrelevant search results make the entire catalog seem lower quality. If searching for \"gold hoop earrings\" returns a leather handbag, the shopper wonders what else is wrong with this site. The problem is not the catalog. The problem is the search. But the shopper does not make that distinction.\n\nBounce acceleration. Each irrelevant result increases the probability of the shopper leaving. The effect is not linear. The first irrelevant result in the top five has a moderate impact. The third has a severe impact. By the time half the visible results are irrelevant, most shoppers have already decided to leave.\n\n### Why Keyword Search Has a Precision Problem\n\nKeyword-based search platforms struggle with precision because they match words, not meaning.\n\nConsider the query \"gold hoop earrings.\" A keyword system searches for products containing \"gold,\" \"hoop,\" and \"earrings.\" This seems precise enough. But the system also returns:\n\n- Products tagged with \"gold-tone\" (which may be brass or plated, not what the shopper means by \"gold\")\n- Products where \"hoop\" appears in \"hoop and chain bracelet\"\n- Products where \"earrings\" appears in a cross-sell field (\"pairs well with our earrings collection\") rather than the product type\n- Products where \"gold\" refers to a color name applied to a completely unrelated item\n\nEach partial keyword match generates an irrelevant result. At scale, across thousands of queries, these false matches accumulate into a significant precision problem.\n\nThe conventional fix is manual tuning: adding negative keywords, adjusting field weights, creating synonym lists, writing boost rules. This works for high-volume queries where a merchandiser has time to review and optimize results. But it does not scale. Most ecommerce sites have tens of thousands of unique queries. Manual precision tuning for all of them is impossible.\n\n### How Product Understanding Fixes Precision\n\nPrecision improves when the search system actually understands what products are, not just what words describe them.\n\nWhen Marqo processes a product listing for gold hoop earrings, it does not just index the words in the title. It understands that this is a piece of jewelry, specifically earrings, in the hoop style, made from or colored in gold. When a shopper searches for \"gold hoop earrings,\" Marqo matches against this structured understanding, not against raw text.\n\nThis means:\n- A \"gold-tone chain bracelet\" does not match because it is a bracelet, not earrings.\n- A \"silver hoop earring\" does not match because it is silver, not gold (unless the shopper's query is ambiguous enough to include it).\n- A product where \"gold\" appears only in an unrelated metadata field does not match because the system understands the product's actual color and material.\n\nMarqo is an AI-native product discovery platform that understands every product in the catalog: what it looks like, what it pairs with, what it substitutes, and what drives margin. This understanding is the foundation of precision. When the system knows what a product actually is, it can determine with high confidence whether it matches a query.\n\nMarqo's Commerce Superintelligence combines product intelligence with behavioral data to continuously refine precision. If shoppers who search for \"gold hoop earrings\" consistently skip gold-plated options and engage only with solid gold, the system tightens its precision for that query pattern. But the product intelligence layer ensures high precision from the start, even for queries with no behavioral history.\n\n### Precision and Conversion: The Data\n\nThe relationship between search precision and conversion rate is well-documented in ecommerce analytics. Across the industry, each 10-percentage-point improvement in Precision@10 correlates with a 4 to 8% improvement in search-to-purchase conversion rate.\n\nThe mechanism is straightforward. Higher precision means more relevant products visible to the shopper. More relevant products visible means higher click-through rates on results. Higher click-through rates mean more product page views. More product page views on relevant products mean more add-to-cart actions. More add-to-cart actions mean more purchases.\n\nKICKS CREW, the global sneaker marketplace, saw this play out after deploying Marqo. With a 17.7% improvement in search-driven conversions, the precision improvement was a significant factor. Sneaker search is particularly sensitive to precision because shoppers have extremely specific preferences: exact model, exact colorway, exact year. An Air Jordan 4 Retro in \"Bred\" is not the same product as an Air Jordan 4 Retro in \"Military Black\" to the shopper who wants one of them. High precision means showing the exact right sneakers, not just sneakers that share some keywords with the query.\n\n### Precision by Query Specificity\n\nPrecision challenges vary based on how specific the query is.\n\nHighly specific queries (\"Levi's 501 Original Fit 32x30 dark wash\") should have near-perfect precision. If your search returns irrelevant results for a query this specific, the system has fundamental matching failures.\n\nModerately specific queries (\"men's dark wash straight leg jeans size 32\") are where precision starts to degrade on keyword systems. The system matches individual attributes but struggles to enforce all of them simultaneously. A pair of slim-fit jeans in dark wash and size 32 partially matches, creating an irrelevant result.\n\nBroad queries (\"jeans for men\") have an interesting precision dynamic. Almost any men's jeans are relevant, so precision is naturally higher. But this is misleading. The ease of achieving precision on broad queries masks the failure on specific queries, which are typically higher-intent and more likely to convert.\n\nNatural language queries (\"jeans that look good with boots for a country wedding\") present the hardest precision challenge. Keyword systems return products containing \"jeans,\" \"boots,\" or \"wedding\" in their metadata, most of which are irrelevant to the actual intent. These queries are where Marqo's product understanding delivers the most dramatic precision improvements.\n\n### The Precision-Recall Balance in Ecommerce\n\nPrecision and recall pull in opposite directions. Returning more results tends to improve recall (more relevant products appear) but hurt precision (more irrelevant products also appear). Returning fewer results tends to improve precision but hurt recall.\n\nThis tradeoff has real commercial implications in ecommerce:\n\nWhen to favor precision: High-intent queries where the shopper knows what they want. Gift searches where the shopper is unfamiliar with the category and overwhelmed by irrelevant options. Mobile searches where screen space is limited and every result slot is valuable.\n\nWhen to favor recall: Browse-oriented queries where the shopper wants to explore. Category-level queries where variety is valuable. Queries where the shopper's intent is broad and multiple product types could satisfy it.\n\nMarqo's Commerce Superintelligence adapts this balance dynamically based on query understanding. It does not apply a one-size-fits-all precision-recall tradeoff. Specific queries get high-precision treatment. Broad queries get high-recall treatment. This adaptive behavior is possible because the system understands query intent, not just query keywords.\n\n### How to Measure Precision on Your Site\n\nMeasuring precision is relatively straightforward compared to recall, because you only need to evaluate the results that were returned, not the entire catalog.\n\nStep 1: Sample queries. Select 200 to 500 queries from your search logs, representing the full range of specificity and volume.\n\nStep 2: Capture results. Record the top 10 results for each query from your current search engine.\n\nStep 3: Judge relevance. For each query-result pair, determine whether the result is relevant. Binary judgment (relevant or irrelevant) is sufficient for precision measurement. Use multiple judges for contested cases.\n\nStep 4: Calculate Precision@10. For each query, divide relevant results by 10. Average across all queries.\n\nInterpreting your score:\n- Precision@10 above 0.80: Strong. Most results are relevant. Shoppers see a clean, focused result page.\n- Precision@10 between 0.60 and 0.80: Moderate. Two to four irrelevant results per page. Noticeable to shoppers but not devastating.\n- Precision@10 below 0.60: Your search is showing more irrelevant results than relevant ones. This is actively damaging conversion.\n\nStep 5: Segment by query type. Calculate precision separately for navigational queries, attribute queries, and natural language queries. The differences will reveal exactly where your search is failing.\n\n### Common Precision Failures and Their Causes\n\nPartial attribute matching. The query specifies multiple attributes (\"blue waterproof hiking boots size 10\"), but the search returns products matching only some attributes. A brown hiking boot in size 10 partially matches but is irrelevant to the shopper who specified blue.\n\nCategory leakage. Products from irrelevant categories appear because they share keywords with the query. \"Apple\" returns fruit alongside electronics. \"Coach bag\" returns coaching bags alongside the luxury brand. \"Tank\" returns fish tanks alongside tank tops.\n\nMetadata pollution. Products contain keywords in non-primary fields (SEO tags, cross-sell descriptions, marketing copy) that cause false matches. A dress described as \"perfect with our new boots\" appears in results for \"boots.\"\n\nPopularity bias overriding relevance. The search system boosts high-selling products regardless of query relevance. A bestselling product appears in results for queries where it is not relevant, simply because its popularity score overwhelms the relevance signal.\n\nMarqo eliminates all four failure modes through genuine product understanding. When the system knows what a product is, partial matches, category confusion, metadata noise, and popularity bias cannot override true relevance.\n\n### Precision in Conversational Commerce\n\nAs product discovery moves beyond the search box, precision becomes even more critical. Sibbi is the conversational interface of Marqo's Commerce Superintelligence, an autonomous agent that guides shoppers from discovery through post-purchase using deep product understanding.\n\nIn a conversation, every product recommendation must be precise. There is no results page where the shopper can scan past irrelevant options. When Sibbi recommends a product, it carries an implicit promise: \"this is right for you.\" An irrelevant recommendation breaks that promise and damages trust immediately.\n\nConversational commerce raises the precision bar because the format is intimate and personal. A search results page showing three irrelevant products out of ten is tolerable. A conversational agent recommending one irrelevant product out of three feels like a failure.\n\nMarqo's deep product understanding ensures Sibbi's recommendations maintain high precision even for complex, multi-attribute, natural language requests. The system understands what the shopper wants and what each product is, enabling precise matching without keyword dependency.\n\n### The Compounding Effect of Precision Over Time\n\nPrecision does not just affect individual queries. It affects shopper behavior over time.\n\nA shopper who consistently sees precise, relevant results develops trust in the search function and uses it more frequently. More search usage means more opportunities for discovery and purchase. This creates a virtuous cycle: high precision leads to more search, which leads to more revenue, which justifies further investment in search quality.\n\nConversely, a shopper who repeatedly encounters irrelevant results stops using search and relies on manual navigation (category browsing, menu navigation). Manual navigation is slower, surfaces fewer products, and produces lower conversion rates. Low precision leads to less search usage, which leads to less revenue.\n\nKogan observed this behavioral shift after deploying Marqo. As search precision improved, search usage increased, and search-attributed revenue grew. The $10.1M in attributable value from Marqo reflects not just better results on existing searches but also increased search engagement driven by shopper trust in result quality.\n\n### Why Precision Requires Product Understanding, Not More Rules\n\nThe traditional approach to improving precision is writing more rules. More negative keywords. More field weight adjustments. More category constraints. More manual curation.\n\nThis approach hits a ceiling quickly. Every rule you add fixes precision for one query pattern and potentially breaks it for another. Excluding \"boots\" from \"coach\" queries fixes the category leakage for Coach brand searches but breaks results for actual boot queries mentioning coaching features. The rule graph becomes increasingly complex, fragile, and impossible to maintain.\n\nMarqo takes the opposite approach. Instead of writing rules to handle exceptions, Marqo builds understanding that handles all queries. When the system understands that Coach is a fashion brand and coaching is an activity, it resolves the ambiguity without rules. When it understands that \"blue waterproof hiking boots size 10\" requires all four attributes simultaneously, it enforces that constraint without manual configuration.\n\nThis is why Marqo's AI-native product discovery platform delivers sustainably high precision. The precision does not degrade as the catalog grows or query patterns shift. Product understanding scales in a way that rule systems cannot.\n\n### Building a Precision-Focused Search Strategy\n\n1. Measure Precision@10 today. If you do not know your current precision, start there. The number will likely surprise you. Most ecommerce sites overestimate their search precision because merchandising teams focus on high-volume queries that are manually curated.\n\n2. Identify precision failure patterns. Is precision failing on specific query types? On certain categories? On mobile vs. desktop? Understanding the pattern reveals the root cause.\n\n3. Quantify the conversion impact. Calculate how many irrelevant impressions your search generates per month. Estimate the conversion rate improvement if those impressions were replaced with relevant products. The revenue opportunity is typically larger than expected.\n\n4. Evaluate whether rules can close the gap. If your precision failures stem from fundamental limitations in keyword matching, more rules will not solve the problem. You need a system that understands products.\n\n5. See what product understanding delivers. Marqo's Commerce Superintelligence eliminates the root cause of precision failures by understanding products at a level that keyword systems cannot match. Results in 14 days, not months.\n\n### FAQ\n\nWhat is a good Precision@10 for ecommerce search?\nAbove 0.80 is strong. Between 0.60 and 0.80 is average and improvable. Below 0.60 means more than half the visible results are irrelevant, which is actively harming conversion. Most sites score between 0.55 and 0.75 when measured honestly across all query types, not just manually curated head queries.\n\nIs precision more important than recall?\nNeither is more important in absolute terms. But for ecommerce conversion, precision has a more direct impact on short-term revenue. Irrelevant results actively push shoppers away. Missing results (a recall problem) represent missed opportunities but do not actively damage the experience. The ideal system, which Marqo delivers, achieves high scores on both simultaneously.\n\nCan improving precision hurt recall?\nIn naive implementations, yes. Aggressively filtering results to improve precision can hide relevant products. Marqo avoids this tradeoff because precision improvement comes from better understanding, not stricter filtering. The system does not remove results to improve precision. It ranks relevant results higher and irrelevant results lower.\n\nHow does Marqo handle ambiguous queries where precision is hard?\nAmbiguous queries like \"apple\" or \"coach\" are resolved through context and product understanding. Marqo uses signals from the shopping context, the site's catalog composition, and the broader query pattern to resolve ambiguity. On a fashion site, \"coach\" maps to the brand. On a sports site, it maps to coaching equipment. This contextual resolution happens automatically, without manual rules.\n\nDoes precision matter for browse and category pages?\nAbsolutely. Category pages and filtered browse experiences are essentially pre-defined queries. If a shopper navigates to \"Women's Dresses\" and sees jumpsuits, tops, or skirts mixed in, that is a precision failure. Marqo's product understanding applies to all product surfacing, not just the search box.\n\n### Stop Showing Shoppers Products They Did Not Ask For\n\nEvery irrelevant result on your search page is an active conversion deterrent. Marqo combines product intelligence with behavioral data to deliver precise, relevant results for every query, from the simplest product name to the most complex natural language request.\n\nBook a demo to see how Marqo's AI-native product discovery platform transforms your search precision, and your conversion rate, with results in 14 days, not months."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":6268,"cache_read_input_tokens":8374,"output_tokens":16297,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":6268},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011Cak7yeskhvMNPWyuorDud","type":"assistant","uuid":"a40e4e70-4c25-404a-9391-bc196fa766c6","timestamp":"2026-05-05T21:26:30.635Z","userType":"external","entrypoint":"cli","cwd":"/Users/ana/marqo-website","sessionId":"cf226049-f886-4e6a-b19d-c488679d1df1","version":"2.1.89","gitBranch":"fix/customer-stories-updates","slug":"mossy-petting-widget"}