Best available evidence
Reconcile earnings releases, presentations, structured facts, and narrative 10-Q or 10-K disclosures instead of assuming a single feed is complete.
01 / Fundamentals
Source-backed SaaS operating KPIs for software investors.
Connect contracted demand, retention, customer cohorts, workforce investment, and management guidance to the best available company disclosure and the definitions behind each observation.
The SoftwareIQ difference
Resolve the disclosure—not just the document.
A filing is one part of the evidence set, not a finished dataset. SoftwareIQ evaluates issuer materials together, selects the most specific supportable observation, and preserves the evidence and qualifiers required to use it.
Reported before calculated
Prefer a qualifying direct disclosure. When a maintained calculation is required, keep its formula and inputs distinguishable from the company-reported series.
Comparable without flattening
Standardize periods, units, and metric families while retaining customer thresholds, lower bounds, company-defined scope, and reporting cadence.
Leading-indicator analysis · MongoDB
Which operating measures are moving before reported revenue?
Triangulate demand across the operating evidence set.
Leading-indicator work requires several related measures with different timing and economic meaning. The MongoDB exhibit places cRPO, current bookings, calculated billings, and revenue on a consistent growth basis so a demand inflection can be evaluated before it is fully visible in reported revenue.
SoftwareIQ keeps reported and calculated measures distinct, resolves each observation to its best available disclosure, and preserves the source context. The analysis gains comparability without presenting backlog, bookings, billings, and revenue as interchangeable facts.
Research outputDemand-inflection screens, quarterly model updates, and source-verifiable leading indicators.
MongoDB · Eight reported quarters
Contracted demand reaccelerated ahead of revenue
Year-over-year growth by fiscal quarter
cRPOCurrent bookingsCalculated billingsRevenue
Q1 FY27cRPO 70.6%Current bookings 23.6%Calculated billings 25.7%Revenue 25.2%
Guidance calibration · Okta
How consistently does management convert an outlook into reported results?
Build guidance histories against the outcome they forecast.
Guidance becomes analytically useful when issuer, metric, basis, and target period are aligned with the eventual actual. The Okta exhibit applies that structure to final cRPO guidance and six completed quarterly results.
SoftwareIQ links each range to the matching realized period instead of the next document in time. That creates a reusable calibration history for forecast formation, guide conservatism, and earnings preparation.
Research outputResults-versus-guidance models, forecast calibration, and management forecasting analysis.
Okta · Six completed target periods
Final cRPO guidance versus reported actual
Guidance and actuals matched to the same fiscal quarter
Q1 FY27Guide $2440–$2450mActual $2,499m+2.2% vs midpoint
$2,100m$2,600m
Operating-input analysis · Elastic
What commercial evidence is available when conventional bookings are incomplete?
Use sales-commission accounting as a complementary operating input.
Capitalized commissions and deferred-commission balances can add evidence about compensated sales activity and contract acquisition costs. The Elastic exhibit compares eight quarters of commission growth with reported revenue while retaining the different accounting basis of each series.
This is particularly relevant for consumption businesses, where bookings may not align with high-value customer additions or consumption expansion in the same period. When those outcomes influence compensation, commission data supplies another model input without being treated as bookings or revenue.
Research outputCommercial-investment diligence, consumption-model context, and revenue-growth calibration.
Elastic · Eight reported quarters
Sales commissions as an operating signal
Commission growth compared with reported revenue growth
Capitalized commissionsDeferred commissionsRevenue
Q4 FY26Capitalized commissions 59.7%Deferred commissions 26.2%Revenue 16%
Customer-mix analysis · monday.com
How is growth changing across disclosed customer populations?
Analyze customer scale without collapsing unlike thresholds.
Customer counts, retention, and ARR mix can reveal whether growth is broadening, concentrating, or moving upmarket when each measure stays attached to its disclosed population. The monday.com exhibit provides 14 quarters of ARR composition plus threshold-specific retention and customer cohorts.
SoftwareIQ retains the reported thresholds and derives mutually exclusive ARR bands by subtraction, so the composition sums to 100% without double-counting nested disclosures. The same structure supports comparison without erasing company-specific scope.
Research outputCustomer-cohort models, enterprise-mix analysis, and retention benchmarking.
monday.com · Detailed operating KPIs
ARR contribution from larger customers
Threshold-specific metrics remain distinct
<$50K ARR$50K–$100K ARR>$100K ARR
Q2 FY26 · 100% of ARR<$50K ARR 57%$50K–$100K ARR 13%>$100K ARR 30%
Resource-allocation analysis · Snowflake
Which functions are driving changes in operating capacity?
Connect disclosed workforce allocation to the operating model.
Functional headcount supplies a direct input for evaluating commercial capacity, product investment, service delivery, and expense intensity. The Snowflake exhibit separates Sales & Marketing, R&D, G&A, and cost-of-revenue employees across eight reported quarters.
SoftwareIQ retains company-disclosed functions and reconciles them to total headcount. This distinguishes an observed workforce measure from estimates based on open positions or expense ratios.
Research outputFunctional expense models, investment-mix analysis, and workforce productivity research.
Snowflake · Disclosed workforce
Headcount investment by function
Eight quarterly observations
Sales & MarketingR&DCost of RevenueG&A
Q1 FY27 · 9,250 totalSales & Marketing 4,304R&D 2,499Cost of Revenue 1,273G&A 1,174
Standardized peer screening · Selected software cohort
Can company-defined operating metrics support a comparable research panel?
Standardize the metric while preserving its disclosure context.
Peer analysis requires a canonical metric family without hiding differences in scope, threshold, period, or reporting cadence. The interactive exhibit ranks six software companies by NRR, current billings growth, current bookings growth, or disclosed customer count and shows the observation period beside each value.
SoftwareIQ searches both recurring KPI materials and narrative 10-Q or 10-K text, then retains the latest distinct disclosure rather than silently forward-filling it. MongoDB's customer observation is displayed as 67,700 while its underlying source qualifier remains available in the maintained record.
Research outputPeer screens, comp-sheet construction, and auditable operating benchmark panels.
Selected software companies
Net revenue retention
A standardized comparison across the latest available periods
Methodology
From heterogeneous disclosure to comparable evidence.
Resolve
Evaluate filings, filing narrative, earnings releases, and presentations for the most specific supportable observation.
Normalize
Map the fact to a canonical metric, fiscal period, unit, and basis while preserving thresholds, qualifiers, and company-defined scope.
Validate
Reconcile calculations and components, link guidance with realized results, and retain evidence identifiers for downstream review.
Research delivery
Use the same definitions in the platform, API, and data warehouse.
Analyst research
Move from company history and source evidence into peer comparison, guidance review, functional headcount, and SBC-adjusted P&L analysis.
Systematic research
Build panels using stable company and metric identifiers while preserving qualifiers, periods, and null semantics.
Data science
Query normalized facts, definitions, source references, and guidance outcomes through documented delivery interfaces.
Make the next decision with more context