We combine direct industry inputs with disciplined validation across multiple sources. Since 2014, this has helped us build market intelligence that is dependable, consistent, and accurate.
We size each market using three separate approaches and reconcile the results before publication. If the estimates differ beyond the stated threshold, we revisit the model instead of basing our results on averages.
Primary interviews and surveys form the starting point for every dataset. On top of that, regulator and institutional records are added alongside trade series, production data, logistics records, and tender filings. Developer and app telemetry is included where it is relevant to the market being sized. Company filings and plant registries are part of the standard input set. We do not scrape e-commerce sources unless the data comes through a licensed feed.
Primary research is fielded in structured waves. Samples are stratified by region, company size, channel, and product family before fieldwork begins. Institutional datasets come in through public APIs or licensed feeds, and each one is logged with its date and license terms.
Ecosystem signals from B2B marketplaces, distributor portals, and tender boards are captured under applicable terms of service. Company masters and product masters are maintained throughout the process. Where records need to be linked, deterministic keys are applied first. Fuzzy matches that remain are reviewed and adjudicated by analysts.
Prices, shipping patterns, and outage data are refreshed weekly. Trade flows, production activity, and category mixes follow a monthly cycle. Capacity figures, installed base data, and baseline metrics are reviewed each quarter. If a market shock occurs, an ad hoc update is triggered outside the standard schedule.
Each sector gets a focused lens applied to its data. The full detail sits in the reports. The short version is in the table below.
| Domain | Primary signals | Institutional / ecosystem | Cadence |
|---|---|---|---|
| Automotive & EV | Production, registrations, Tier-1/2 shipments, charging sessions, battery MWh | OICA; ACEA/JAMA/SIAM; IEA EV; NHTSA/KBA; UN Comtrade HS 84–87 | Monthly–Quarterly; EV weekly when available |
| Chemicals & Materials | Capacity, utilization, spreads, HS flows, turnarounds | IEA/EIA; USGS; Eurostat/PRODCOM; UN Comtrade HS 25–40; CEFIC; IFA | Weekly–Monthly–Quarterly |
| Healthcare | Approvals, UDI, trials, procedure volumes, payer schedules | FDA/EMA/EUDAMED/PMDA/CDSCO; ClinicalTrials.gov; WHO ICTRP; CMS DRG | Weekly–Monthly–Quarterly |
| Technology & Semis | Filings, deployment telemetry, app installs, fab/node | EDGAR; SEMI; HS 8542; GSMA/3GPP; spectrum auctions | Quarterly + monthly capex |
| Packaging | Substrate flows, converter capacity, OEE, format adoption | UN Comtrade HS 39–48, 76; FAOSTAT; PRODCOM/NAICS | Monthly–Quarterly |
| Food & Beverage | Farm-to-fork balance, price/mix, claim shares | FAOSTAT; USDA WASDE/ERS; HS 02–24; FAO FishStat | Monthly (+ weekly for volatile) |
| Oil & Gas | Capacity, production, storage, trade, emissions | IEA/EIA; OPEC; HS 27; Baker Hughes | Weekly–Monthly |
| Industrial Automation | Shipments, installed base, retrofit cycles, tenders | PRODCOM/NAICS; EU TED; SAM.gov; HS 84–85 | Monthly–Quarterly |
| Sources | UN Comtrade; ITC Trade Map; Eurostat/PRODCOM; US Census; BEA; BLS; OECD; IEA/EIA; OPEC; USGS; FAOSTAT; USDA; FAO FishStat; FDA/EMA/EUDAMED/PMDA/CDSCO; ClinicalTrials.gov; WHO ICTRP; OICA; ACEA; SIAM; national statistics; tender boards. |
|---|---|
| Access | Public / Licensed / Consent (logged with pull dates and licenses) |
| Transforms | Unit conversions; currency basis; geo normalization; seasonality method |
| Confidence | A/B/C by metric, with reasons |
| Caveats | Re-exports and intra-company transfers are handled conservatively where final use is uncertain |
Public filings and trading updates form the base. Store-locator counts and opening or closure announcements are tracked alongside retailer circulars and newsletters. App store pages and reviews are included. Public social content from Instagram, TikTok, YouTube, and X is pulled in, as are search-interest signals. Supplier and distributor anecdotes are used as guardrails, not as primary inputs. No private or proprietary data is accessed.
At FMI, EPOS and panel datasets are not used. Shopping cart scraping is avoided. Private pages are never accessed, and PII is not collected. No surprises (or shocks) here.
| Sources | Data is drawn from retail filings and trading updates, store-locator pages, retailer circulars and newsletters, public social posts, app store public pages, and national retail indices. |
|---|---|
| How the data is processed | Weekly data is rolled up to monthly before analysis. Engagement figures are normalized across platforms so they can be compared on the same scale. Duplicate records are filtered out. All currency figures are converted to constant USD |
| Confidence levels | Metrics taken directly from company filings are rated Tier A. Estimates built from two or more independent signals are rated Tier B. A reading that rests on a single signal is rated Tier C and treated with caution |
| What to watch for | Viral content can push short-term momentum figures higher than the underlying trend justifies. Some retail chains also post less promotional content publicly than others, which means their social footprint will look smaller than it is. |
| Transforms | Winsorized ASP; state-space elasticities; survival-curve retirements; constant-FX forecasts |
|---|---|
| Confidence | Tier A for audited + multi-source; Tier B when partial; Tier C when proxy-heavy |
| Caveats | Re-export uncertainty; EPOS gaps where proxies are used; vendor fiscal shifts around year-end |
| Lane | Examples |
|---|---|
| Trade & Production | UN Comtrade; ITC Trade Map; Eurostat/PRODCOM; US Census; BEA; BLS; OECD |
| Energy & Commodities | IEA; EIA; OPEC; USGS; UNCTADstat |
| Agri & Food | FAOSTAT; USDA WASDE/ERS; FAO FishStat |
| Healthcare | FDA (Drugs@FDA, PMA/510k, GUDID); EMA; EUDAMED; PMDA; CDSCO; ClinicalTrials.gov; WHO ICTRP; CMS DRG |
| Automotive & Mobility | OICA; ACEA; JAMA; SIAM; NHTSA; KBA; IEA Global EV |
| Technology & Telecom | EDGAR; SEMI; GSMA; 3GPP; spectrum auctions; HS 8542 |
| Procurement & Tenders | EU TED; US SAM.gov; UK Find a Tender; India GeM |
| Retail Public | Filings; store-locator pages; circulars/newsletters; app-store pages; public social posts |
From scope to deliverable, FMI follows a multi-step approach to ensure our findings are highly accurate. Depth is adjusted to the complexity of the market and the strength of the available evidence.
The market is defined before analysis begins, including product boundaries, geographic coverage, and the time period under review. This creates a clear base for the analysis that follows.
The research base is built from a wide range of public, proprietary, and company-level sources. This includes industry publications, trade data, company disclosures, and subscription databases.
Each report is supported by an interview program with industry experts and value-chain participants. The primary research is used to test assumptions and close information gaps.
The model is built company by company, with each revenue estimate linked to that participant's activity. The final figure emerges from the underlying estimates built across the market.
The bottom-up estimate is reviewed against the wider market context, including trade flows and adjacent market indicators. If the gap falls outside the established tolerance range, the model is refined before the final figure is confirmed.
Key assumptions are tested against a minimum of two independent inputs. Where direct evidence is hard to get, estimates are kept conservative, with assumptions documented clearly as part of the model record.
Three scenarios are built using clearly defined assumptions and documented market drivers. The assumption base is structured so clients can test how changes in key inputs affect the final view.
Market shares are estimated by comparing company-level activity with the overall market size. Each participant is assessed through a common framework, and shares are expressed as ranges where the evidence supports a range.
The framework is applied across all industries FMI caters to. The scope and depth is adjusted to market complexity, while the underlying structure remains consistent.
| Coverage Area | What Is Delivered | Validation Method |
|---|---|---|
| Market sizing | Historical five-year and forecast ten-year data in value and volume | Supply-side estimation, demand-side estimation, trade data |
| Segmentation | Product type, end-use, technology, material, distribution channel, nature, demographics, region, and more | Buyer interviews, OEM catalogs |
| Regional breakdown | Country-level data for key markets; state-level where commercially relevant | Trade flows, production data |
| Competitive landscape | Twenty or more company profiles, market share ranges, SWOT analysis, production capabilities, global and regional footprint | Relative benchmarking, pricing validation |
| Pricing analysis | Price ranges by tier, region, and channel; ex-works and delivered; contract and spot pricing | Distributor quotations, buyer procurement interviews |
| Demand drivers | Policy-demand linkages, technology shift timelines, driver impact rankings | Regulatory review, primary research |
| Value chain | Bargaining power mapping and operating pain points across participants | Primary interviews |
Each market model is reviewed through four defined cross-checks before publication.
Buyer procurement volumes are compared with supply-side totals. Gaps are addressed through follow-up research or revision of utilization and pricing assumptions.
Import and export data is used to review domestic sales against local production, imports, exports, and inventory changes.
The final estimate is tested against the output that the installed production base can support at stated utilization levels.
Final totals are reviewed against capital expenditure trends, output growth in consuming industries, and historical demand relationships.
Standard depth across industries. The scope and depth is adjusted to market complexity, while the underlying structure remains consistent.
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Pricing analysis Average selling prices mapped across products, customer groups, channels, or regions, with clear assumptions on how pricing moves over time. |
Cross-segment analysis The model can be cut across multiple dimensions such as product, end use, channel, customer type, or geography to show where growth is coming from. |
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Historical and forecast market model Fully populated historical and forecast data for 10 years, with no placeholders or undisclosed values. |
Scenario-based forecasts Optimistic, base case, and conservative paths, each supported by stated assumptions and documented market drivers. |
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Assumption tables Conversion ratios, penetration rates, and utilization assumptions are listed with their stated basis. |
Supply-demand view Where relevant, the analysis brings together supply-side capacity and demand-side consumption to test whether the market structure is realistic. |
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Country-level segmentation Regional analysis is carried down to country level for commercially significant markets. |
White-space identification FMI research methodology highlights underpenetrated segments and revenue pockets that may not be obvious in a top-line market view. |
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Competitive share estimates Twenty or more company profiles mapped to the same framework used in the market model. Shares are expressed as ranges where appropriate. |
Interactive dashboards Outputs delivered through interactive dashboards that allow users to filter, compare, and review market cuts without rebuilding the model manually. |
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Editable Excel model Provided in Excel format with linked tabs, transparent calculations, and structured tables that teams can use internally. |
Executive summary pack A presentation-ready summary with strategic takeaways tailored for leadership review. |
The mix varies by market. It typically covers manufacturers, distributors, buyers, and sector specialists. Participants are sourced through FMI's research panel and direct outreach. Names are kept confidential, but the split by role and region is disclosed in the report.
We do not average conflicting estimates. The variance threshold is stated upfront, and if any two approaches fall outside it, the assumption that is driving the gap gets investigated before the number is published.
A single number implies a precision the data rarely supports, especially for private companies. A range is more honest. Where the evidence is strong enough to narrow it, we narrow it. Where it is not, we say so.
Through peer benchmarking. We compare operating footprint, capacity, and pricing against similar participants where data does exist, then apply weighted assumptions. The basis for each estimate is documented in the model.
It comes from client feedback across purchased reports, not from internal validation. Clients who have used the data operationally and reported back on fit. It is a stated rate, not a modelled one.
Yes, through a custom engagement. The standard report includes base case, optimistic, and conservative paths. If your decision depends on a specific input moving differently, that can be modelled separately.
It means direct contact with the research team after delivery. The analyst who built the model can walk you through the assumptions or answer follow-up questions on the data.
Methodology
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