Independent synthetic B2B analytics system
Case study / Revenue & Retention Analytics
RenewalOS
Revenue Quality & Account Health
Business question
Can revenue KPIs be trusted before Customer Success teams prioritize accounts?
- Data status
- Synthetic
- KPI status
- Gated
- Decision output
- Diagnostic scenarios
Dataset disclosureSynthetic B2B data only. No production customer data, production deployment, observed intervention result or real business impact is claimed.
Executive decision brief
The decision, before the documentation.
A concise chain from the commercial question to the recommended action.
- Question
Can revenue KPIs be trusted before Customer Success teams prioritize accounts?
- Observed signal
KPI outputs stay gated until data-quality exceptions are reviewed.
- Data status
- Synthetic
- Interpretation
Decision outputs are restricted until source-data exceptions and reconciliation gaps are visible and reviewed.
- Recommended decision
Review quality exceptions before treating ARR, churn or renewal metrics as management KPIs
Primary evidence
Inspect the analytical exhibit.
The dashboard is presented as reviewable evidence, with the full analytical context preserved.
RenewalOS / primary analytical output
RenewalOS Control Tower — synthetic data disclaimer and KPI reporting restrictions
Synthetic B2B data only. No production customer data, production deployment, observed intervention result or real business impact is claimed.Supporting diagnostic view
Data Trust diagnostics make source-data exceptions visible before KPI or prioritization outputs are reviewed.
Synthetic B2B data only. No production customer data, production deployment, observed intervention result or real business impact is claimed.Analysis and diagnosis
From signal to commercial meaning.
B2B teams often act on ARR, churn, renewal and account-health signals before source-system issues are visible. RenewalOS shows a synthetic analytics workflow where data exceptions, reconciliation gaps and decision rules are exposed before Customer Success prioritization is reviewed.
KPI trust gate
Exceptions visible before decision output- Gate 01
Data status
Synthetic - Gate 02
KPI status
Gated - Gate 03
Decision output
Diagnostic scenarios
- Interface
- Local Streamlit
- Deployment
- Not production
- Impact claim
- None
Decision outputs are restricted until source-data exceptions and reconciliation gaps are visible and reviewed.
Finding / interpretation
Decision outputs are restricted until source-data exceptions and reconciliation gaps are visible and reviewed.
Decision layer
Recommended business action
Recommendations follow the evidence in this independent case study; no tested uplift is implied.- 01
Review quality exceptions before treating ARR, churn or renewal metrics as management KPIs
- 02
Use reconciliation gaps as blockers that require evidence rather than manual smoothing
- 03
Treat CSM prioritization output as simulated scenario planning until validated on real data
- 04
Keep excluded records visible so capacity decisions do not hide data-trust issues
Method and quality
How the conclusion was built.
The technical record stays inspectable without displacing the business question.
Methodology
- 01
Generated synthetic source data for contracts, billing, usage, support and Customer Success activity
- 02
Loaded untrusted records into DuckDB and modeled warehouse layers with dbt
- 03
Applied data-quality controls and revenue reconciliation checks before KPI reporting
- 04
Built explainable account-health diagnostics with source exceptions still visible
- 05
Produced capacity-constrained CSM prioritization scenarios with explicit exclusions
Architecture
- 01
Synthetic source domains feed a local DuckDB warehouse modeled with dbt.
- 02
Quality controls and revenue reconciliation checks surface source-data exceptions before KPI-facing views are used.
- 03
Account-health diagnostics explain risk signals while preserving blocked or excluded records.
- 04
OR-Tools applies simulated CSM capacity limits to scenario recommendations, not production decisions.
Evidence and quality controls
- DuckDB warehouse modeled with dbt
- Data-quality and revenue-reconciliation controls
- Explainable account-health and prioritization workflow
- Public Streamlit demonstration and GitHub repository
Tools in service of the question
- DuckDB
- dbt
- SQL
- Python
- Streamlit
- OR-Tools
Transparency record
What this work does—and does not—claim.
Dataset origin, ownership and material limitations remain part of the main narrative.
- 01Project type
- Independent synthetic B2B analytics system
- 02Ownership
- Individual end-to-end project
- 03Dataset origin and boundary
- Synthetic B2B data only. No production customer data, production deployment, observed intervention result or real business impact is claimed.
Material limitations
- Uses synthetic data only.
- Outputs are diagnostic and are not trusted management KPI reporting.
- CSM prioritization is simulated scenario analysis, not observed intervention evidence.
- No observed business impact, customer outcome or model-accuracy claim is made.
- No production deployment is configured or claimed.
Evidence handoff
Inspect the work.
Open the underlying repository, methodology and analytical artifacts.
- 01Synthetic source generation(opens in a new tab)
Reproducible source-data generation and validation layer that injects and detects controlled quality incidents.
- 02KPI trust gate(opens in a new tab)
dbt model that blocks, caveats or marks revenue metrics as not assessable based on quality and reconciliation evidence.
- 03Quality-control validation(opens in a new tab)
Validation layer checking incident coverage, exception metadata, quality statuses and reconciliation gaps.
- 04Account-health methodology(opens in a new tab)
Documented quality gates, scoring components, simulated thresholds, explanation logic and limitations.
- 05Capacity-constrained optimizer(opens in a new tab)
OR-Tools scenario optimizer selecting eligible synthetic account priorities under CSM hour and account-capacity constraints.
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