Stripe Revenue Risk Analysis

Fictional Dataset

Fictional B2B SaaS company with self-serve plans, monthly subscriptions, failed payments, upgrades, downgrades, and churn events.

Filestripe_revenue_risk_demo.csv

Rows1840

SourceStripe

invoice_month customer_segment mrr failed_payment churn_flag plan_change

Month	Segment	MRR	Failed	Churn
Jan	SMB	$42,800	4.1%	2.6%
Feb	SMB	$44,900	4.8%	2.9%
Mar	Mid-market	$53,200	6.7%	4.1%
Apr	SMB	$51,700	7.4%	4.6%

MRR increased during the period, driven by mid-market expansion and upgrades.
Failed payments rose faster than revenue, creating near-term revenue leakage risk.
The highest-risk cohort is customers with repeated failures and no recent product activity.

Confirmed insight

Confidence 86%

Prioritize dunning outreach for high-LTV customers with two or more failed attempts.

Possible signal

Confidence 79%

Review churn by plan tier before increasing acquisition spend.

Confirmed insight

Confidence 88%

Package the upgrade motion into a repeatable customer success playbook.

1. Load dataCSV, Excel, database, Google Sheets, or connector.

2. Generate analysisCharts, report, data quality, and AI interpretation.

3. Review PulseExecutive signal for risks, trends, and opportunities.

4. Act on insightsAssign owners, comments, review status, and next actions.