TL;DR:
- Structured market data is organized into five key domains that support strategic analysis. Most enterprise data remains unstructured, causing gaps and errors in market intelligence. Proper schema design, regular refreshes, and domain integration are essential for effective decision-making.
Structured market data is defined as any dataset organized into a fixed schema, stored in relational databases, and queryable with SQL or equivalent tools. The industry term for this category is "structured data," and it sits at the core of every serious market intelligence program. Approximately 80% of enterprise data is unstructured, leaving only 20% in queryable form. That gap is where most competitive intelligence failures originate. Professional programs organize the types of structured market data into five core domains: competitor, customer, product, market and industry, and intent data. Each domain answers a different strategic question, and knowing which to prioritize is the difference between analysis and guesswork.
1. Types of structured market data: the five core domains
Professional market intelligence programs classify structured market data into five distinct categories. Each category maps to a specific business question and requires its own collection cadence, schema design, and refresh cycle. Analysts who treat all market data as interchangeable end up with stale pricing next to fresh intent signals, which produces contradictory outputs. The five domains are competitor intelligence, customer intelligence, product intelligence, market and industry data, and intent and signal data.
2. Competitor intelligence data
Competitor intelligence is the most time-sensitive of all market data categories. Competitor data covers pricing, product features, hiring patterns, M&A activity, and positioning changes. Pricing can shift weekly, so the standard refresh cadence is under 30 days. Letting that window slip turns decision-grade data into historical noise.
Key structured data examples in this domain include:
- Pricing tables: SKU-level price points, discount tiers, and promotional windows
- Feature matrices: Side-by-side capability comparisons updated per product release
- Hiring signals: Job posting volume and role type by department, indicating strategic direction
- M&A records: Acquisition dates, deal values, and target company profiles
Collecting this data at scale requires structured extraction from job boards, review platforms, and public filings. Raw HTML scraping introduces schema drift and missing fields. APIs for structured signals from job boards and review platforms outperform raw scraping for reliability and AI readiness.
Pro Tip: Set a calendar alert for your 30-day competitor pricing refresh. Stale pricing data fed into a pricing model produces compounding errors that are hard to trace back to the source.
3. Customer intelligence: structured data examples and applications
Customer intelligence covers three structured data types: demographic and firmographic data, behavioral data, and voice-of-customer data. Firmographics include company size, industry vertical, revenue band, and geographic location. Behavioral data captures product usage frequency, feature adoption rates, and support ticket volume. Voice-of-customer data includes NPS scores, CSAT ratings, and structured survey responses.
Core structured data examples in this domain:
- Firmographic records: Industry, headcount, revenue, and technology stack
- NPS scores: Numeric ratings segmented by cohort, product line, or acquisition channel
- Churn indicators: Days since last login, support escalation count, and contract renewal date
- Segmentation tags: Persona labels assigned through rule-based or model-driven classification
53% of consumers expect brands to anticipate their needs before they express them. That expectation is only achievable when customer data is structured, current, and connected to a BI tool that can run segmentation queries in real time. Analysts who rely on spreadsheet exports miss the behavioral signals that predict churn three to six weeks out.
4. Product intelligence and market and industry data
Product intelligence
Product intelligence answers the question: where does your product stand relative to the market? Structured data examples here include feature gap matrices, review sentiment scores (converted to numeric ratings), pricing elasticity curves, and return rate tables. Source data comes from app store reviews, support ticket categories, and structured feedback forms. The key is converting qualitative reviews into numeric fields before storage, not after.
- Identify the product attributes that matter most to buyers (speed, integrations, price, support quality).
- Build a scoring rubric that converts review language into a 1–5 numeric field.
- Refresh the dataset with each major competitor release or quarterly, whichever comes first.
Market and industry data
| Data type | Example metric | Primary source |
|---|---|---|
| Macroeconomic indicators | GDP growth rate, inflation index | Government statistical agencies |
| Industry CAGR | Segment revenue growth over 5 years | Analyst firm databases (Gartner, IDC) |
| Technology adoption | Percentage of firms using a given platform | Survey-based research panels |
| Financial market data | Quote, trade, depth-of-book, index, reference | Exchange feeds and normalized providers |
Financial market data is strictly categorized into quote, trade, depth-of-book, index, and reference data. Quote updates outnumber trade updates in liquid instruments by a wide margin. That volume difference matters when you are designing a data pipeline: quote data requires higher throughput and more aggressive deduplication logic. Exchange-specific protocols like ITCH and PITCH complicate direct feed integration, making normalized third-party providers the practical choice for most analytics teams.
5. Intent and signal data: the fastest growing category
Intent and signal data is the newest and fastest growing domain in structured market insights. It captures behavioral signals that indicate a company or individual is actively researching a purchase or strategic move. Intent data shortens sales cycles by surfacing in-market accounts before they raise their hand.
Structured signal examples include:
- Website visit data: Page-level visit counts from B2B intent platforms, normalized by company and topic cluster
- Content download records: Gated asset downloads tied to a company domain and timestamp
- Job postings: Role type and volume signals indicating a company is building toward a new capability
- Funding announcements: Round size, investor, and date, structured into a queryable record
- Technology install data: Third-party detection of software stack changes, formatted as a boolean or version field
The real power of intent data comes from triangulation. A single signal is noise. A funding announcement plus a spike in job postings for a specific role plus a competitor content download is a pattern. Matching intent signals with firmographic records using frameworks like SWOT or PESTLE converts raw signals into a ranked account list.
Pro Tip: Do not buy intent data as a standalone feed. Layer it against your firmographic database first. Signals without firmographic context produce account lists that your sales team will ignore after the first bad call.
6. How to choose and combine market data categories
Selecting the right data domains starts with the strategic question, not the data source. Analysts who start with the source end up with data that answers questions nobody asked.
A practical selection framework:
- Define the decision first. Pricing decisions need competitor and customer data. Market entry decisions need industry CAGR and intent signals. Product roadmap decisions need product intelligence and voice-of-customer data.
- Set refresh cadences by domain. Competitor pricing refreshes every 30 days. Firmographic data refreshes quarterly. Macroeconomic indicators refresh monthly or annually depending on the metric.
- Design schemas before collection. Schema design for analysts determines whether your data is queryable or just stored. A field that holds "large enterprise" in one record and "Enterprise (1000+)" in another is not structured data. It is a string problem.
- Integrate domains through a shared key. Company domain or a normalized company ID lets you join competitor, customer, and intent records in a single SQL query.
- Audit for staleness quarterly. Data that is 90 days old without a refresh flag is a liability, not an asset.
Structured data enables SQL querying and BI tool integration, which makes automation and forecasting possible. Unstructured formats cannot support that pipeline without a conversion step first.
Key takeaways
Structured market data organized into five queryable domains gives analysts the foundation for competitive analysis that unstructured formats cannot support.
| Point | Details |
|---|---|
| Five core domains | Competitor, customer, product, market and industry, and intent data each answer a distinct strategic question. |
| Refresh cadence matters | Competitor pricing requires a sub-30-day refresh; stale data produces compounding analytical errors. |
| Intent data needs context | Triangulate intent signals with firmographic records before acting on them. |
| Schema before collection | Define fields and normalization rules before ingesting data, not after. |
| Financial data complexity | Exchange protocols like ITCH require normalized providers for most analytics teams. |
The case for treating data domains as infrastructure, not reports
The biggest mistake I see market intelligence teams make is treating structured data as a reporting input rather than a queryable infrastructure layer. A team that exports a competitor pricing table to a spreadsheet every month has not built structured market data. They have built a manual process that breaks the moment the analyst who owns it goes on vacation.
The shift that actually changes outcomes is moving from static document attachments to structured, queryable assets that live in a database and connect directly to a BI tool. Transitioning to structured document intelligence is not a technology project. It is a schema discipline project. The technology is the easy part.
My second observation: teams that rely solely on public data sources consistently underestimate their competitors. A tiered data strategy that combines public sources, analyst databases like Gartner and IDC, and primary research produces a materially different picture than public data alone. The primary tier is where the real differentiation lives, and most teams skip it because it requires effort. That is exactly why it is worth doing.
The analysts who get this right are the ones who treat their data domains as a product with an owner, a schema, a refresh SLA, and a deprecation policy. Everything else is just files.
— Glen
Gyrence and structured data extraction for BI teams
Market analysts who need to pull structured signals from the web at scale face a consistent problem: the web does not serve data in the format your database expects. Gyrence solves that directly.
Gyrence is a web data API built for AI agents and data teams. Its five primitives, Search, Traverse, Fetch, Extract, and Map, return typed, structured JSON responses that plug directly into BI pipelines. The Extract primitive accepts a schema or prompt and returns a discriminated-union response, including failure cases, so your pipeline knows exactly what it received. Spending caps mean your data acquisition costs stay predictable. For teams building structured web data extraction into a competitive intelligence workflow, Gyrence removes the schema drift and silent failure modes that make raw scraping unreliable.
FAQ
What are the main types of structured market data?
The five core types are competitor intelligence, customer intelligence, product intelligence, market and industry data, and intent and signal data. Each domain is stored in a queryable schema and refreshed on a cadence matched to its rate of change.
How is structured data different from unstructured data in market analysis?
Structured data is organized into fixed fields and queryable with SQL. Unstructured data, such as PDFs, emails, and free-text reviews, requires conversion before it can support automated analysis or forecasting.
What are examples of structured market data in financial markets?
Financial market data includes quote data, trade records, depth-of-book data, index values, and reference data. Quote updates outnumber trade updates in liquid instruments, and exchange-specific protocols require normalized feed providers for most teams.
How often should structured market data be refreshed?
Competitor pricing data requires a refresh under 30 days. Firmographic and customer data refreshes quarterly. Macroeconomic and industry data refreshes monthly or annually depending on the specific metric and its volatility.
What is intent data and why does it matter for competitive analysis?
Intent data captures behavioral signals, such as website visits, content downloads, and job postings, that indicate a company is actively researching a purchase or strategic move. Triangulating intent signals with firmographic records converts raw signals into ranked, actionable account lists.