Search and Discovery Optimization: A Practical Playbook for Marketplaces

Consider the numbers: on most marketplaces, 40–60% of GMV is directly touched by search or filters. A 10% uplift in search conversion can translate into millions in annualized revenue, depending on your scale. Yet many teams still treat their site search as a cosmetic feature rather than a core revenue infrastructure.

Why Search Is Your Highest-Leverage Conversion Lever

The gap is striking. A typical marketplace might see 2–3% overall conversion, but search users often hit 5–8%. Amazon has reported a browse conversion rate of around 2%, compared to 12% after search engagement. Etsy shows similar patterns—1–2% for browse versus approximately 5% from search. When customers search, they’re signaling intent. When you fail to deliver relevant search results, you’re actively burning money.

This article will show you concrete fixes and experiments, not vague theory. Here’s the roadmap:

• Fix taxonomy: Establish the deterministic backbone that makes facets and filters actually work

• Add AI tagging: Augment your product data with machine learning models that fill gaps and add semantic richness

• Tune your search engine: Configure relevance, behavioral signals, and safeguards in Algolia, Elasticsearch, or Bloomreach

• Run experiments: Design controlled tests that isolate the impact on search conversion

• Track the right KPIs: Measure what matters so you can justify investment and iterate

Search is a revenue infrastructure. Treat it accordingly.

Common Search & Discovery Failures in Marketplaces

Before you can fix your search function, you need to understand what’s breaking it. Here are the failure modes marketplace leaders encounter constantly in 2024–2025.

Inconsistent Seller Attributes

When sellers populate product data, chaos follows. One seller lists “colour: Navy,” another uses “Color: Midnight Blue,” and a third enters “shade: dk blue.” These aren’t the same attributes in your system, which means faceted filters become unreliable and ranking algorithms can’t normalize matches.

The same pattern appears everywhere: sizes listed as “XL,” “extra large,” “size 7,” or left blank. Product categories are assigned based on what sellers think will get more visibility rather than where items actually belong.

Overloaded or Flat Taxonomies

A single “Home & Kitchen” leaf with 250,000 SKUs and no usable subcategories is effectively useless for navigation. Similarly, “Electronics > Accessories” holding chargers, adapters, USB cables, phone cases, and screen protectors together doesn’t help anyone find what they need.

When your taxonomy lacks hierarchical depth, you force the search bar to do all the work. And keyword-only matching can’t save you.

The Limits of Keyword-Only Search

Standard search systems in Algolia, Elasticsearch, or Solr struggle when queries don’t precisely match product titles. A user searching for “kids winter waterproof boots” may not find results for “children snow boots” because the engine lacks normalized attributes and semantic tags to bridge the gap.

This is where customers search with natural language queries and get frustrated when the system can’t understand user intent beyond exact matches.

Manual Ranking Rules That Don’t Scale

You’ve seen these rules: “if query contains ‘sale’ then boost discount > 20%” or “always pin house brands to positions 1–3.” These work at a small scale but become brittle once:

• Your product catalog passes 100,000 SKUs

• Daily queries exceed a few thousand

• Seasonal trends shift faster than your merchandising team can update rules

What This Looks Like in Dashboards

Marketplace ops teams see the same patterns across their analytics:

• 10–20% zero-result rate on long-tail queries

• High search exit rates after generic terms like “black dress” or “office chair”

• Support tickets are flooding in about “can’t find my product”

• Filter usage is dropping because facets return unreliable results

• Search performance degrades as the catalog grows

These aren’t theoretical problems. Their revenue leakage is happening right now on your site.

Structured Taxonomy: The Non-Negotiable Foundation

Taxonomy is not optional. It’s the governed hierarchy of product categories plus a schema of standard product attributes per category that makes everything else possible.

What Taxonomy Actually Means

For a marketplace, taxonomy means “Shoes > Running Shoes” must have required fields: size, gender, surface type, cushioning level, and pronation support. “Laptops” must have RAM, storage, and screen size. This structure enables:

• Deterministic navigation through product categories

• Reliable facets and filters

• Compliance checks during seller onboarding

• Templates that constrain catalog chaos before it starts

What Taxonomy Is Good At

When properly implemented, taxonomy powers 60–70% of successful discovery paths. Site visitors rely on faceted navigation to narrow results—filtering by “waterproof” or using price sliders—and this only works when product attributes are standardized.

Consider how marketplaces that cleaned up “Electronics” in 2023–2024 saw measurable impact. By splitting generic headphone categories into “Noise-cancelling headphones” versus “Gaming headsets” with distinct attribute schemas, they achieved double-digit improvement in filter usage and lower search exit rates.

The Critical Limitation

Here’s what you must understand: taxonomy creates structure, not relevance.

A correct category and complete attributes tell the search engine what a product is. But ranking relevance still depends on query understanding, behavioral signals, and business logic. Taxonomy alone cannot solve the semantic gap between how sellers describe products and how customers search for them.

Where Taxonomy Breaks at Scale

Even perfect taxonomy hits walls:

• Long-tail products: Niche automotive parts, handmade items on Etsy-style platforms, and specialty goods where seller-provided data deviates from standards

• Mis-categorization for visibility: Sellers listing products in higher-traffic categories regardless of actual fit

• Regional language variance: US vs UK vs EU marketplaces with different terminology (sneakers vs trainers, pants vs trousers)

Operationalizing Taxonomy Cleanup in 2025

You can’t fix everything at once. Here’s a realistic approach:

• Run a 60–90 day program focused on your top 20 revenue categories

• Define canonical category trees with required and optional attributes per node

• Enforce attribute requirements in seller tools with validation at submission

• Implement bulk cleanup of historical data for priority categories

• Create documentation and training for ops teams managing ongoing compliance

Taxonomy is the foundation. But it’s not the complete solution. That’s where AI tagging enters.

AI Product Tagging: Where ML Actually Adds Value

AI product tagging uses machine learning models to infer attributes and semantic tags from titles, descriptions, images, and seller metadata. This is where you address the 40–60% of product data that’s incomplete or inconsistent.

Attribute Enrichment

Models can extract and add missing fields that sellers left blank:

• Color and pattern from product images

• Neckline type and sleeve length for apparel

• Compatibility information (e.g., “fits iPhone 15 Pro”) from descriptions

• Material composition from text analysis

This automated product tagging runs at scale across millions of SKUs, filling gaps that would take years to address manually.

Normalizing Seller-Generated Content

Natural language processing NLP enables mapping seller variants to canonical values:

• “Navy,” “midnight blue,” “dk blue” all map to “Blue”

• “XL,” “extra large,” “size 7” convert to standardized size grid entries

• “Synthetic leather,” “faux leather,” “vegan leather” unify under a consistent material attribute

This normalization makes filters actually work. When a user searching for blue products uses the color filter, they get accurate results regardless of how individual sellers entered the data.

Semantic Tagging for Intent

Beyond structured attributes, AI can apply use-case and intent-based tags:

• “Back-to-school” for relevant supplies and apparel

• “Work from home” for office equipment and furniture

• “Travel-friendly” for compact and portable products

• “Eco-friendly” is based on materials and certifications detected in text and images

These semantic tags enable product discovery for intent-led queries that don’t match traditional category structures.

AI Augments Taxonomy—It Doesn’t Replace It

Be explicit about this: taxonomy gives the schema and rules. Machine learning models fill and normalize fields and add softer tags that don’t fit neatly into rigid attribute lists.

Without taxonomy, you have no structure for AI outputs to populate. Without AI, you have incomplete data and no semantic enrichment. You need both.

Quality Is Iterative

Expect initial precision around 70%, improving to 90%+ over training cycles. The process requires:

• A labeled seed set for initial model training

• Human review on samples (500–1000 SKUs per category monthly)

• Feedback loops tied to click and conversion outcomes

• Retraining cycles every 3–6 months based on accumulated data

The techniques involved—natural language processing for text, computer vision for images, entity normalization using ML—are well-established. The business impact is what matters: higher recall, usable filters, and fewer zero results.

Practical AI Tagging Use Cases by Category

Different product categories benefit from different enrichment approaches:

Fashion & Apparel

• Detect sleeve length, fit type, and neckline style from images

• Infer occasion (casual, formal, athletic) from product descriptions

• Extract pattern types (solid, striped, floral) via computer vision

• Enable queries like “casual summer dress with pockets”

Furniture & Home

• Tag room type (bedroom, living room, office)

• Identify style (mid-century, farmhouse, industrial)

• Extract material from descriptions and images

• Power queries like “mid-century coffee table for small spaces” or analyze the market context to see what competitors offer

Electronics

• Detect connector types and supported standards (HDMI 2.1, USB-C)

• Infer compatibility with specific devices

• Extract technical specifications from unstructured descriptions

• Enable “wireless earbuds compatible with Android” searches

Grocery & Food

• Apply dietary tags (vegan, gluten-free, organic, keto)

• Detect allergen information

• Identify serving size and nutritional claims

• Support “vegan snacks under $10” queries

Before vs After Example

Before AI tagging: A query for “office chair for back pain” returns generic chairs sorted by popularity or keyword frequency.

After tagging: Products with inferred tags for lumbar support, ergonomic design, and high customer satisfaction ratings surface first. The search engine now understands user intent beyond the literal search terms.

Combining Taxonomy + AI Tagging: The Hybrid Model

The hybrid model represents the current best practice for search and discovery optimization. Taxonomy provides the deterministic backbone. AI tags add probabilistic enrichment. Together, they solve problems neither can address alone.

Think of it like roads and traffic. Taxonomy is the road network—the fixed infrastructure that determines what paths exist. AI tagging is real-time traffic optimization—dynamic adjustments that route queries to relevant products even when the “roads” weren’t built for that specific journey.

How the Hybrid Improves Recall

Even when sellers mis-categorize a “gaming monitor” under “Accessories,” AI tags like “144Hz,” “27 inch,” and “G-Sync” allow semantic search to recover it for relevant queries. The engine can find products based on their actual characteristics, not just their assigned categories.

How the Hybrid Improves Ranking

Enriched product attributes give ranking algorithms more signals to work with:

• More precise matching against user queries

• Additional features for machine learning models to weight

• Ability to boost products that match more inferred intent attributes

• Better differentiation between products with similar titles

Impact on Filters and Navigation

AI-inferred attributes can become experimental filters. You might A/B test new facets like “eco-friendly” or “pet-safe” before promoting them into your core taxonomy. This lets you validate that new filters actually improve user engagement before committing to permanent schema changes.

Implementation Advice

Start with 3–5 critical GMV categories where the taxonomy is already cleaned up. Deploy AI tagging as a layer on top of that foundation. Monitor changes across:

• Search conversion rate for queries in those categories

• Zero result rate for long-tail queries

• Filter usage rates for enriched attributes

• Revenue per search session

Scale to additional categories only after validating impact in the initial set.

Architecture Pattern for Hybrid Discovery Systems

At a high level, the pipeline flows like this:

Raw seller feed → Validation + taxonomy assignment → AI enrichment service → Normalized product index → Search engine (Algolia, Elasticsearch, OpenSearch, Bloomreach) → Analytics events

Key architectural considerations: For more insights, explore our Natural Language Processing Services.

• Data storage separation: Maintain a governed “product master” store (PIM or internal service) that holds taxonomy attributes and AI tags. Search indexes consume a denormalized view optimized for fast queries.

• Feedback loops: Search clicks and conversion logs feed back into AI training and taxonomy refinement. Identify attributes that strongly correlate with purchase and promote them as first-class required fields.

• Configuration-driven rollout: Use feature flags to control when new tags or attributes appear in search results. Avoid entangling taxonomy changes with search configuration releases.

• Versioning and auditability: Track which models produced which tags, enabling rollback if quality degrades.

This architecture supports continuous improvement without requiring risky big-bang deployments.

Search Engine Tuning: Making Algolia, Elasticsearch & Bloomreach Actually Work

Modern search engines are powerful. But simply “turning them on” with default settings won’t tap their revenue potential. These tools require tuning and good data to deliver relevant results.

Three Main Tuning Levers

Relevance configuration: Field weighting determines which product attributes matter most for matching. Query rules handle exceptional cases. Synonym sets normalize vocabulary.

Behavioral signals: Click-through rates, add-to-cart actions, and conversion data can boost popular products. But these signals require careful implementation.

Safeguards against overfitting: Caps, decay functions, and guardrails prevent short-term spikes from permanently distorting rankings.

Concrete Relevance Examples

• Boost exact title matches heavily—users who type a specific product name should find it

• Weight critical attributes (brand, category, gender, size) to ensure basic matching works

• Configure synonym sets for category-specific vocabularies (“trainers” vs “sneakers” in UK vs US)

• Use query expansion for head queries to capture related terms

• Improve typo tolerance and fuzzy matching, especially for mobile users with small keyboards

Using Behavioral Data Responsibly

Incorporate popularity and conversion as soft boosts, not hard overrides. Key practices:

• Use time-decay (e.g., 30-day rolling window) to avoid locking in historical winners

• Ensure new SKUs get exposure even without historical signals

• Normalize for browsing history effects—repeat visitors shouldn’t distort the overall ranking

• Segment behavioral signals by device and user type

Risks of Overfitting to Short-Term Signals

Seasonal surges like Black Friday or Prime Day in 2025 can distort rankings if signals aren’t normalized. Social-media-driven spikes create temporary demand that shouldn’t permanently boost products. Internal promotions artificially inflate metrics for promoted items.

Build decay functions and caps that prevent any single event from overwhelming your ranking algorithms.

Practical Configurations

• Query expansion for head queries: “running shoes” should also consider “jogging sneakers,” “athletic footwear”

• Better typo tolerance: Mobile queries have higher error rates

• No-result fallbacks: When accurate matches don’t exist, show close matches or popular products in relevant product categories

• Autocomplete suggestions: Guide users toward queries that will return relevant products

Patterns for Avoiding Zero-Result and Dead-End Searches

Zero results pages are profit killers. When site visitors search and find nothing, they leave; 2024 benchmarks show 10–15% zero-result rates correlate with significant revenue leakage and higher bounce rates.

Strategies to minimize zero results:

• Aggressive typo tolerance that catches common misspellings

• Synonym expansion is maintained by category

• Auto-redirect to the closest matching category when exact matches fail

• Curated fallback collections (“Bestsellers in [top category]”) when accurate matches are impossible

• Natural language queries handling that extracts intent from verbose searches

Operational practices:

• Set a target band: keep zero-result rates between 2–5% for logged queries

• Monitor weekly by device and category

• Review queries with chronically high zero-result rates monthly

• Fix via taxonomy changes, AI tagging rules, or handcrafted synonyms

• Track mean reciprocal rank and normalized discounted cumulative gain as quality metrics

This is a KPI that Marketplace Ops can own directly, with clear actions to improve it within 4–8 weeks.

Experiments That Actually Move Conversion

Search and discovery optimization must run as an experiment program, not one-off tuning. For mid- to large-scale marketplaces in 2025, A/B testing and multi-armed bandit approaches are essential.

Experiment Archetypes

Before/after AI tag enrichment: Enable enriched tags for a subset of categories and compare search conversion against control categories.

Ranking changes: Increase weight for behavioral signals or adjust boosting rules, then measure the impact on the quality of results pages.

Filter UX adjustments: Simplify filter sets on mobile, reorder filters, or add new facet options based on AI-inferred attributes.

Custom ranking experiments: Test boosting products with high conversion rate but low impressions to surface hidden gems.

Basic Experiment Setup

Every experiment needs:

• Clearly defined control vs variant: What exactly is different?

• Traffic split: 50/50 for standard tests, 10/90 for risky changes

• Minimum runtime: Often 2–4 weeks to smooth weekly patterns and capture enough data

• Simple success criteria: Primary KPI with guardrail metrics

Sample Size Intuition

For search experiments, you usually need tens of thousands of search sessions per variant to detect a 3–5% relative uplift in search conversion with statistical confidence. Reciprocal rank improvements may require even larger samples.

The variance in search behavior is high. Users search for different things, at other times, with a different intent. Patience is required.

Why Search Experiments Need Patience

Seasonality, promotions, and external traffic changes can mask the signal. A test run during a sale event will show different patterns than a test during regular periods.

Best practices:

• Tie experiments to stable periods when possible

• Use stratified analysis to control for known confounders

• Run longer than you think necessary

• Watch guardrail metrics (overall revenue, add-to-cart rate) to catch unintended harm

Design an experiment backlog and governance process. This is how Heads of Marketplace Ops and VPs of Product & Engineering turn intuition into validated improvement.

Example Experiment Designs for Taxonomy & AI Improvements

Taxonomy Experiment: Office Chair Schema

• Change: Re-categorize all “office chairs” with new schema (ergonomic vs basic, with/without headrest, lumbar support level)

• Traffic split: Route 50% of search traffic for relevant queries (“desk chair,” “office chair for back pain”) to the enriched catalog

• KPIs tracked: Search conversion rate, filter usage rate, revenue per search session; consider supplementing with user research to inform decisions

• Time frame: 28 days

• Guardrails: Overall revenue, add-to-cart rate, search exit rate

• Decision rules: Ship if search conversion improves >5% with no guardrail degradation; iterate if mixed results; roll back if conversion drops

AI Tagging Experiment: Men’s Footwear Use Cases

• Change: Enable inferred “use case” tags (running, trail, gym, casual) for the men’s footwear category

• Traffic split: 50/50 between tagged and control indexes

• KPIs tracked: Search conversion for running-related queries, filter usage for new facets, zero-result rate

• Time frame: 21 days

• Guardrails: Category revenue, overall search exit rate

• Decision rules: Ship if search conversion improves >3%; iterate by refining tag models if results are flat

Search Tuning Experiment: Boosting High-Conversion Low-Impression Products

• Change: Adjust ranking to boost products with a conversion rate >5% but < 1000 impressions in the past 30 days

• Traffic split: 10/90 (conservative due to ranking impact)

• KPIs tracked: Revenue per search session, search exit rate, product diversity in top 10 results. For experiments relying on well-categorized or labeled data, see data labeling best practices.

• Time frame: 14 days

• Guardrails: Overall conversion rate, top-seller revenue

• Decision rules: Ship if revenue per session improves without harming top-seller performance

These blueprints are designed for teams to replicate and adapt to their specific business priorities and catalog characteristics.

KPIs That Matter for Search & Discovery

If you can’t measure search impact, you can’t justify investment. Here are the core key metrics every marketplace should track.

Core KPIs Defined

Search conversion rate: Purchases divided by search sessions. Target: 4–6% for well-optimized marketplaces. Compare against your overall site conversion to quantify the search user premium.

Zero-result search rate: Percentage of searches returning no results. Target: <5%. Baseline for many marketplaces is 15–25%. Every point reduced here recovers revenue.

Search exit rate: Percentage of sessions where users leave after viewing search result pages without clicking. Target: <20%. High exit rates signal poor search relevance.

Revenue per search session: Total revenue attributed to search divided by search sessions. This connects search improvements directly to business outcomes.

Filter usage rate: Percentage of search sessions where users engage with facets. Higher usage indicates filters are useful; declining usage may signal noisy data in attributes.

Segment Your KPIs

Global averages hide real problems. Segment by:

• Device: Desktop vs mobile web vs app (mobile optimization matters—mobile users often have different search patterns)

• Traffic source: Paid vs organic vs direct (user behavior varies by acquisition channel)

• Category: Some categories inherently have different search patterns and conversion rates

Benchmark Ranges (2024–2025)

Research shows top-performing marketplaces achieve:

• Search users convert 2–3× higher than non-searchers

• Zero-result rates <3% correlating with 2.5× overall conversion

• Search conversion rates of 4–6% (versus 1–2% for browse-only)

• Revenue per search session is $2–5 higher than non-search sessions

Make Search Visible in Dashboards

If search is not measured separately from overall site performance, investment cases will be weak. Add a dedicated “Search & Discovery” section to your core business dashboards showing:

• Time series of key metrics

• Breakdowns by top 100 queries

• Category-level views

• Week-over-week and month-over-month trends

A 1% increase in search conversion can translate to significant incremental GMV. For a marketplace doing $100M in annual search-attributed revenue, that’s $1M in additional revenue from a single percentage point.

Instrumentation and Analytics for Search Measurement

Minimal event tracking required:

• Query issued: Timestamp, query text, session ID, user ID (if logged in)

• Results shown: Number of results, products in positions 1–10, any filters applied

• Product clicked: Position, product ID, time since results shown

• Add-to-cart: From search results vs browsing history vs other sources

• Purchase: Attribution to originating search session

• Filter usage: Which facets were selected, in what order

• Pagination: How deep users scroll through results pages

• Search refinement: New queries or filter changes within the session

Tools and patterns for 2025:

• Use data warehouses (Snowflake, BigQuery) for comprehensive analytics

• Event pipelines (Segment, Kafka-based custom solutions) for real-time data

• Build daily search KPI reports with automated alerting

• Create a “search health” dashboard for weekly exec reviews

Design should be simple enough that your Head of Marketplace Ops can review it in 5 minutes and identify where to focus improvement efforts.

Implementation Roadmap: Phased Approach Instead of Big-Bang Rewrites

Avoid risky complete rewrites. Here’s a realistic 3–4 phase roadmap executable over 6–18 months.

Phase 1: Taxonomy and Data Hygiene (0–3 Months)

Objective: Establish the foundation that makes everything else possible.

Key activities:

• Audit the top 20 categories by GMV to assess current taxonomy quality

• Define canonical attributes per category with clear required vs optional fields

• Implement seller validation rules in product submission tools

• Clean historical data for priority categories (expect 10–20% duplicate or invalid SKU waste)

• Create documentation for merchandising and ops teams

Owners: Marketplace Ops (taxonomy definitions), Engineering (validation tools)

Success metrics: Attribute completeness >80% in top categories, duplicate SKU rate <5%

Phase 2: AI Tagging Pilot (3–9 Months)

Objective: Prove that ML enrichment improves search performance.

Key activities: See the essential components for efficient mobile app architecture for a comprehensive overview.

• Select 3–5 categories where Phase 1 cleanup is complete

• Deploy machine learning models for attribute enrichment and semantic tagging

• Integrate enriched data with PIM or product master

• Run controlled experiments comparing enriched vs baseline search

• Establish a human review process for tag quality (500–1000 SKUs per category monthly)

Owners: Data Science/ML (models), Product (experiments), Ops (quality review)

Success metrics: 15–25% reduction in zero-result rate for pilot categories, measurable lift in search conversion

Phase 3: Search Engine Tuning at Scale (6–12 Months)

Objective: Optimize relevance across the full catalog.

Key activities: Leverage clickstream data to improve customer experience and optimize sales.

• Refine relevance configuration based on Phase 2 learnings

• Deploy synonym management with category-specific vocabularies

• Add behavioral signals with appropriate decay and safeguards

• Build automated alerting on key search KPIs

• Implement visual search capabilities where applicable for your ecommerce site

Owners: Engineering (search infrastructure), Product (relevance strategy)

Success metrics: Search exit rate <20%, filter usage up 15%, revenue per search session increase

Phase 4: Experimentation and Continuous Optimization (9–18 Months)

Objective: Establish ongoing improvement as standard practice.

Key activities:

• Create a permanent experiment pipeline with transparent governance

• Form a cross-functional Search Council (Ops, Product, Engineering, Data)

• Prioritize experiment backlog based on KPI gaps

• Establish quarterly goals for search improvement

• Build feedback loops from conversion data back to AI models and taxonomy

Owners: Product (experiment backlog), all functions (council participation)

Success metrics: At least one measurable KPI improvement per quarter, documented in business reviews

Change Management Essentials

Technology alone won’t succeed without:

• Documentation that ops teams can actually use

• Training for merchandising staff on taxonomy requirements

• Clear ownership matrix for taxonomy, AI models, and search configurations

• Regular review cadence to catch drift and degradation

Organizational Roles and Ownership

Clear accountability prevents the search from becoming everyone’s problem and no one’s priority.

Marketplace Ops: Owns taxonomy definitions, seller policies, and product data quality standards. Reviews search health dashboards weekly. Escalates data quality issues.

Product: Owns site’s search function UX, experiment backlog prioritization, and search features roadmap. Defines success criteria for experiments. Balances user engagement with business priorities.

Engineering: Owns search infrastructure, index management, and relevance configuration. Implements feature flags and monitoring. Maintains SLAs for search latency and availability.

Data Science/ML: Owns AI tagging models, training pipelines, and quality metrics. Iterates models based on feedback loops. Reports on tag coverage and accuracy.

Recommended governance structure:

• Form a “Search & Discovery working group” meeting bi-weekly

• Review KPI dashboards and experiment results

• Decide on rollouts, rollbacks, and following experiments

• Maintain a shared backlog visible to all stakeholders

Establish clear SLAs and escalation paths:

• What happens when zero-result rate spikes after a catalog change?

• Who gets paged when search latency exceeds thresholds?

• How quickly must the store’s search bar issues be resolved?

Without governance, search optimization becomes a series of ad hoc fixes rather than a systematic program that delivers sustained customer satisfaction and conversion improvements.

Conclusion: Discovery Is a Data Problem, Not a UI Problem

Winning marketplaces in 2025 aren’t distinguished by prettier search bars or fancier visual search features. They win because they have disciplined product data, hybrid taxonomy and AI models, tuned search engines, and a rigorous experimental culture.

You should now understand:

• What to fix: Taxonomy and data quality form the non-negotiable foundation

• What to add: AI tagging and semantic enrichment fill gaps and unlock intent-based discovery

• What to test: Ranking changes, filter improvements, and enrichment impact through controlled experiments

• How to measure: Search-specific KPIs that connect improvement to revenue and prove ROI

Every quarter should deliver at least one measurable uplift in a core search KPI. This is not a one-off project—it’s an ongoing program that compounds value over time.

Marketplaces that win search don’t just have better algorithms—they have better product intelligence and the discipline to iterate on it.

Start your taxonomy audit this quarter. Set up your search KPI dashboard. Run your first experiment. The buying journey of your online store depends on how well customers can discover relevant products—and that discovery starts with the data behind your search.