Where Product Language Meets Shopper Intent

Online retail intelligence at scale

Products are invisible to the people trying to find them

A catalog lists "Carbon-Fiber Road Frame." A user asks, "Hey Siri, what's the best bike for steep hills?" This mismatch creates a silent revenue leak, leaving high-intent customers stranded and ad campaigns underperforming. As conversational search grows, these gaps are only multiplying. We analyzed the vocabulary of 370 million product titles and descriptions and resolved it against 55 million search phrases, voice queries, and buyer intent signals. Updated weekly, the intelligence reflects what's happening in the market right now, not last quarter.

Two vocabularies. Zero overlap. Until now.

There is a widening gap between how brands describe products and how humans actually search for them. A retailer lists a "Carbon-Fiber Road Frame". Precise, technical, correct. But a user asks, "Hey Siri, what's the best bike for steep hills?" Those are the same product. Two completely different vocabularies. The platform has no way to connect them, so the sale never happens. This isn't an edge case. It's the default. Across 370 million product titles, the merchant vocabulary and the shopper vocabulary almost never overlap. As voice search and conversational queries grow, the gap widens. Our engine was built specifically on commercial vocabulary, trained to understand what merchants mean when they write a product listing and what shoppers mean when they type or speak a search query. That specificity is what makes the intersection visible.

Intent Mismatches

"Carbon-Fiber Road Frame" vs "best bike for steep hills." Product vocabulary and shopper vocabulary don't overlap, and conversational search is making the gap wider every day.

Ghost Products

High-demand items with strong semantic signals but zero advertising coverage. High-intent customers stranded, ad campaigns underperforming, and nobody knows why.

Catalog Coverage Gaps

Missing products, categories, or attributes that shoppers expect but can't find. Standard taxonomies answer one question. Our engine answers hundreds.

Sparse Product Density

Optimize for fuller, intent-aligned catalogs. Each cluster is enriched across 5 semantic dimensions simultaneously: categories, demographics, occasions, use cases, and seasons.

Inefficient Ad Spend

Keywords with high intent but low matching products and low competition go undetected. Our opportunity scoring combines demand, competition, and seller density into a single 0-100 score.

Untapped Voids

Uncover trends, pricing signals, and competitive weaknesses. 95M price events across 2 years of tracking reveal rising, falling, and volatile markets before competitors react.

The full vocabulary of online retail. Updated weekly.

We examine and screen over 1.5 million online stores every week. Only retailers that pass our data integrity filters make it into the graph, including major department stores, publicly traded DTC brands, to specialty retailers and custom cart platforms.

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Titles Analyzed
Growing weekly
Product titles, descriptions, and naming structures screened from 1.5M+ stores, filtered to 714K that pass data integrity checks. Decomposed, resolved, and refreshed weekly.
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Product Clusters
Growing weekly
Semantically grouped product neighborhoods. 3.6M enriched across all 5 dimensions, covering 192M individual products. New clusters form as new products enter the graph.
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Price Events
Tracked continuously
Individual price changes tracked continuously since Feb 2024. Weekly dimension snapshots capture trends, momentum, and 13-week rolling medians per cluster.
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Keywords
Growing weekly
22.3M intent keywords plus 30.5M voice and conversational search queries. CPC, competition, and search volume from Google Ads. 82% carry a demand signal.

Coverage spans 188 countries and 36,551 cities. We screen 1.5M+ online stores and exclude nearly half. 687K stores are filtered out as spam, dropshippers, or failing data integrity checks. The 714K that remain include major department stores, publicly traded brands, specialty retailers, and 100M+ product listings from custom shopping cart platforms. Every title, description, and naming structure is linguistically decomposed, not just counted. Pricing data reflects actual storefront prices: the brand's own pricing strategy, not marketplace algorithm adjustments.

Two engines, one enrichment platform

Two products that work the intersection from different angles. One enriches your catalog, the other explores the full graph.

Taxonomy Engine Core

Your product titles and descriptions meet your buyers' search language here. Upload your catalog or sync your Shopify store. The engine resolves your product vocabulary against 29.6M canonical neighborhoods and 22.3M buyer intent keywords, assigns high-value keywords with CPC and competition data, and generates gap analysis reports that show exactly where your listings are invisible to the people trying to find them.

  • Catalog enrichment across 5 semantic dimensions (categories, demographics, occasions, use cases, seasons)
  • 22.3M intent keywords plus 52.7M voice and conversational search queries
  • 223M keyword-to-cluster relevance connections
  • Opportunity scoring: demand vs supply vs competition per cluster
  • Google Ads Editor export and campaign generation
  • Budget optimization with ROAS-driven allocation
Open Taxonomy Engine
Apparel > ShoesSV: 12,400
running shoes womentrail running sneakerslightweight marathon shoe
CPC $1.42Competition: 0.73

Data Explorer Graph

Navigate the full intersection visually. Start from any node: a keyword, a product cluster, a store, a demographic, an occasion. Traverse the connections between product vocabulary and buyer vocabulary. Every cluster node displays 18 financial attributes on hover: price, demand, sellers, CPC, competition, opportunity score, and more.

  • Interactive force-directed graph with 15 traversable entity types
  • Financial overlays: median price, demand/seller ratio, opportunity score, price momentum
  • 52.7M voice and conversational search queries with per-cluster dimensional context
  • AI-powered chat interface for conversational graph queries
  • CSV/JSON export and "Reproduce in Snowflake" SQL generation
Explore the Graph
Solid Color Midi Dress (450 products)
Median: $34.99 +8.2%Demand: 45,200/moSellers: 127
Opportunity: 72/100CPC $0.45

Once you can see the intersection, everything opens up

Skip brute-force data cleaning. Jump straight to high-value discovery.

Market Sizing

How big is the wireless earbuds market? Count titles, clusters, search volume, price bands, and seller density in a single query across 372M analyzed listings.

Gap Analysis

High-SV keywords with few competing products. Surface ghost inventory and untapped voids with demand-supply gap scoring across 29.6M clusters.

Competitor Research

Which stores dominate your category? Compare seller density, intent breadth, category distribution, and geographic coverage across 714K data-integrity-screened retailers in 188 countries.

Price Intelligence

Track price trends across product clusters over 2 years. Weekly snapshots capture rising, falling, and volatile markets as they shift, not months after the fact.

Keyword & Ad Research

Target keywords and conversational queries with high intent and low competition. CPC tiers, revenue potential, and opportunity scores per cluster. Export directly to Google Ads Editor.

Seasonal & Demographic Intelligence

"What do young women buy for New Year's Eve?" Cross-dimension queries spanning categories, demographics, occasions, seasons, and use cases. Weekly snapshots track how the market evolves in real time.

Decode both vocabularies. Find the intersection.

The engine deconstructs product vocabulary and buyer vocabulary independently, then resolves the intersection into a connected, queryable graph.

01 / Product Vocabulary

Titles Decoded

367M product titles and descriptions analyzed, decomposed, and resolved into 29.6M semantic neighborhoods. "Women's Boho Floral Maxi Dress" and "Ladies Bohemian Long Flower Print Dress" collapse into the same node. Each enriched across 5 dimensions: who buys it, when, why, what for, and what season. Sourced from 1.5M+ stores screened down to 714K that pass data integrity filters.

02 / Shopper Vocabulary

Intent Resolved

1M seed keywords from Google Ads and Amazon expand into 22.3M intent nodes. 52.7M voice and conversational search queries generated, including the "Hey Siri" vocabulary that's growing fastest. Resolved against 127M cluster centroids into 223M connections. 82% of all keywords carry a demand signal.

03 / Market Signals

Intelligence Layer

1.5M+ stores screened, 714K pass integrity filters, spanning 188 countries. 95M price events across 2 years. CPC, competition, demand scoring, opportunity tiers, price momentum, and demand velocity, all connecting vocabulary to dollars. The entire graph refreshes weekly.

Your data, your platform

Access the enrichment graph wherever you already work.

BigQuery

Same enrichment graph, native on Google Cloud, updated weekly. BigQuery Partner Connect for zero-setup access to the full taxonomy dataset.

BigQuery Partner Connect →

Shopify App

Install the Taxonomy Engine directly from the Shopify App Store. Sync your catalog and get enriched keywords, gap analysis, and ad campaigns.

Shopify App Store →

Snowflake

1.07B rows across 15 tables + 2 views. Full SQL access to the semantic graph, updated weekly. CLUSTER_FULL view for one-query enriched cluster data.

Snowflake Marketplace →

Two vocabularies. One intersection. Your advantage.

Conversational search is growing. The gap between product vocabulary and shopper vocabulary is widening. Reach.dog sits at the intersection, updated weekly, so you're making decisions on this week's market, not last quarter's. Get started via reach.dog, BigQuery, Snowflake Marketplace, or the Shopify App Store.