We've analyzed 47 companies through 13 lenses, producing thousands of reports. We've built a macro analysis vertical that tracks how monetary policy, trade dynamics, and global capital flows cascade into equity valuations. But there was a gap between them.
When our Moat Mapper analyzed CrowdStrike, it didn't know what the same lens found about Okta. When the Gravy Gauge assessed Salesforce's revenue durability, it had no idea that ServiceNow's renewal rate was 98%. Each analysis was excellent in isolation and blind in context.
Sector Deep-Dives fills that gap. It's our third analytical vertical — industry-level analysis that operates between companies, synthesizing what we already know about individual equities into cross-company insights that no single-stock lens can produce.
The Three Pillars
Runchey Research now operates across three analytical verticals. Each asks a fundamentally different question:
Equity Analysis — 13 lenses, 47 companies
“What is this company worth, and is the market pricing it correctly?” Deep single-company analysis from SEC filings, transcripts, and alternative data.
Macro Themes — 13 lenses, 6 themes
“How do economic forces cascade into valuations?” Causal effect deltas measuring how monetary policy, trade dynamics, and global flows affect specific companies.
Sector Deep-Dives — 6 lenses, industry groupsNEW
“Who is winning, who is losing, and what structural forces shape the industry?” Cross-company analysis using our own equity data as the primary input, supplemented by industry-level external data.
The three verticals aren't independent. They cross-pollinate: macro themes inform sector context, sector insights flow back into equity pages, and equity updates trigger sector refreshes. The result is a research platform where changing one variable propagates through the entire system.
How Sector Analysis Works
The traditional approach to sector analysis starts from raw industry data — market size reports, competitor surveys, analyst estimates. We do something different.
Track 1: Our Own Equity Analyses (Internal)
The foundation is the Equity Digest — a structured aggregation of every signal, metric, and assessment across all companies in the sector. For Enterprise SaaS, that means taking the 14 signals we've already computed for each of 8 companies and laying them side by side.
This is the artifact that no single-stock analysis can produce. When you see that 7 of 8 Enterprise SaaS companies rate as DEFENSIBLE or DOMINANT on competitive positioning, but one rates CONTESTED — that's a signal. When aggregate free cash flow is $38.4 billion at 35% margins across ~$109B combined revenue — that's a sector-level data point invisible from any single 10-K.
Track 2: Industry-Level External Data
We supplement our internal data with external industry sources: BLS sector employment from FRED, Google Trends comparing competitor mindshare, USPTO patent velocity by technology category, job posting aggregates across all sector companies, sector ETF performance, and Federal Register regulatory activity.
These two tracks — our deep equity analyses plus broad industry data — feed into a unified sector dossier. That dossier is what the 6 sector lenses analyze.
Six Lenses, Eleven Signals
Each sector lens asks a different structural question. The first five run independently (just like our equity lenses), and the sixth — the Sector Regime Identifier — synthesizes all five into a single regime classification.
Competitive Chessboard
Who is winning, who is losing, and why?
Consolidation Compass
Is M&A reshaping this sector, and who acquires vs. gets acquired?
Capital Cycle Gauge
Is the sector over-investing or under-investing relative to demand?
Value Chain Mapper
Where does value accrue in the sector's stack?
Disruption Vector Scanner
What forces could structurally reshape this sector within 3 years?
Sector Regime Identifier
What regime is this sector operating in, and is it about to shift?
Every lens goes through the same rigorous discourse process as our equity and macro analyses: dual analysts (Opus + Sonnet running in parallel), synthesizer, fact-checker, auditor, bullet hole loop, moderator convergence detection, and reporter.
First Sector: Enterprise SaaS
Our inaugural sector deep-dive covers Enterprise SaaS — 8 companies navigating the same structural question: does AI eat their lunch, or does it extend their buffet?
~$109B combined revenue · $38.4B aggregate FCF (35% margin) · 13.3% revenue-weighted growth
These 8 companies span different verticals — CRM, design tools, observability, tax software, IT workflow, e-signatures, project management — but share the same structural question about AI's impact on per-seat software pricing models.
The Signal Dashboard
Here are the 11 signals our 6 lenses produced:
7 of 10 first-order signals match the MATURE_OPTIMIZATION regime fingerprint. Three signals point elsewhere: RELATIVE_MOMENTUM (fits Growth Expansion), VALUE_CONCENTRATION (fits Structural Disruption), and COMPETITIVE_DYNAMICS (fits Contested Transition). These contradicting signals are the interesting part — they may be leading indicators of a regime shift, or legacy artifacts of adding two fast-growing constituents.
The Four Tiers
The most striking finding: these 8 companies crystallize into four distinct tiers with a 21 percentage-point growth dispersion (DDOG at 29% to ASAN at ~8%).
| Tier | Companies | Revenue | Growth |
|---|---|---|---|
| Leaders | CRM, ADBE, INTU, NOW | 86.1% | 9-21% |
| Ascending | ADSK, DDOG | 8.8% | 18-29% |
| At-Risk | DOCU | 2.7% | 8% |
| Laggard | ASAN | 0.7% | ~8% |
Platform companies with multi-module switching costs (CRM, NOW, INTU, ADBE) control 86% of sector revenue. Point-solution companies (DOCU, ASAN) are structurally disadvantaged regardless of execution quality. This is a sector-level structural insight that's invisible from any individual company's 10-K.
Three Findings Only Sector Analysis Can Produce
AI Is Accretive, Not Destructive
Three companies now have quantified AI revenue: ServiceNow (>$600M ACV from Now Assist), Datadog (12% of revenue from AI-native customers), and Salesforce (AgentForce approaching $1B ARR). The market narrative says AI destroys SaaS. The operational data says incumbents are monetizing it. This finding reduced our disruption shift probability from 30-40% to 25-35%.
The Narrative-Reality Gap Is Sector-Wide
8 of 8 companies show narrative-reality disconnects. The IGV software ETF was down ~20% YTD at the time of analysis — against 13.3% revenue-weighted growth and $38.4B free cash flow. 6 of 8 constituents trade at what our models classify as MODEST expectations pricing. The market prices AI as destructive while operational data shows AI as accretive. Only cross-company analysis makes this aggregate disconnect visible.
Platform vs. Point Solution Is the Defining Fault Line
Value is migrating from application-only companies to platform companies with multi-module switching costs. ServiceNow's $11.4B M&A spree (Armis, Moveworks, Veza) is the sector's largest capital deployment in history — a bet that platform breadth beats point-solution depth. DocuSign's IAM pivot and Asana's competitive displacement by Monday.com both trace to this structural divide. The sector isn't just growing unevenly — it's stratifying.
The Regime Framework
The Sector Regime Identifier classifies sectors into one of four operating regimes. Each regime has a characteristic “fingerprint” — a pattern of signals that historically co-occur. The classification matters because different strategies tend to succeed in different regimes:
GROWTH_EXPANSION
Rising tide. Market share investment historically outperforms margin optimization.
MATURE_OPTIMIZATION
Steady state. Disciplined M&A and operational efficiency historically outperform growth spending.← Enterprise SaaS is here
CYCLICAL_CONTRACTION
Downcycle. Cash preservation and selective distressed acquisitions historically outperform.
STRUCTURAL_DISRUPTION
Paradigm break. Business model reinvention historically outperforms incremental improvement.
Enterprise SaaS classifies as MATURE_OPTIMIZATION at moderate confidence. The regime shift probability toward Structural Disruption is 25-35% — lower than you might expect given the AI narrative — precisely because companies like ServiceNow, Datadog, and Salesforce are demonstrating that incumbents can monetize the disruption rather than be displaced by it.
Cross-Pollination: The Real Value
Sector analysis isn't a standalone product. Its value comes from how it connects back to everything else:
- Sector → Equity: Each company's equity analysis page now shows a “Sector Context” card with insights about their competitive positioning, consolidation risk, and disruption exposure relative to peers.
- Equity → Sector: When an equity analysis updates (post-earnings, for example), the sector digest becomes stale and automatically flags for refresh.
- Macro → Sector: Each sector links to relevant macro themes. When monetary policy or trade policy updates cascade down, they affect sector-level assessments of capital cycles and disruption trajectories.
- Sector → Forecast Markets: Sector analysis generates industry-outcome binary markets: “Will AI-native security tools capture>5% of enterprise endpoint spend by Q4 2026?” These use the same prediction infrastructure as our equity and macro markets.
The goal is a research system where no analysis exists in isolation. Every insight connects to every other insight, and changing one input propagates through the whole network.
What's Next
Enterprise SaaS is the first. Additional sectors in the pipeline:
- Cybersecurity (CRWD, OKTA, S) — endpoint protection, identity management, and cloud security
- Cloud Infrastructure & DevOps (SNOW, DDOG, MDB, GTLB) — data platforms and developer tools
- Payments & Fintech (V, MA, PYPL, SQ, HOOD) — payment rails, digital wallets, and trading platforms
- Healthcare (LLY, NVO, MOH, HIMS, MRNA) — GLP-1 therapeutics, managed care, and telehealth
Each sector will go through the same rigorous 6-lens analysis, producing its own signal dashboard, regime classification, and cross-pollination links. Explore the Enterprise SaaS sector deep-dive to see the full analysis.
Explore the analysis