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Reliability Score
64
31 → 64(+33 pts)
Is our reported MRR of $2.4M accurate or inflated by ghost accounts?
BayesIQ's audit surfaced 4 findings and enabled targeted remediation that improved the reliability score from 31 to 64.
Leadership approved Series C deck showing 1,794 active users — actual count is 9,583. Investor materials contained a 434% undercount
Reported
1,794
active users · 2025-12
Audited
9,583
Understated 434.2%
Decision Exposure
Leadership approved Series C deck showing 1,794 active users — actual count is 9,583. Investor materials contained a 434% undercount
Reported
30.6%
churn rate · 2025-12
Audited
4.8%
Overstated 84.4%
Decision Exposure
Churn rate reported to board at 30.6% triggered emergency retention spend — actual churn is 4.8%
Reported active_users for 2025-12 is 1,794, but recomputed value from raw events is 9,583. Discrepancy of -434.2%.
BayesIQ Data Reliability Audit
SaaS
Audit Period: December 2025
Reliability Score
Fair — significant data quality issues detected
Illustrative example — representative of BayesIQ audit deliverables
BayesIQ audits a SaaS company's core reporting pipeline covering active users, churn rate, and MRR calculations. The audit surfaces 4 critical issues — including a 434% active user undercount caused by deduplication failures and a churn rate overstated by 84% due to denominator drift. Leadership had been making expansion and retention decisions based on fundamentally wrong numbers. After remediation, the reliability score improves from 31 to 64.
Key Metrics: Reported vs Audited
| Metric | Period | Reported | Audited | Delta |
|---|---|---|---|---|
| active_users | 2025-12 | 1,794 | 9,583 | understated 434.2% |
| churn_rate | 2025-12 | 30.6% | 4.8% | overstated 84.4% |
Top Findings
active_users misreported for 2025-12 (off by -434.2%)
Leadership approved Series C deck showing 1,794 active users — actual count is 9,583. Investor materials contained a 434% undercount
churn_rate misreported for 2025-12 (off by +84.4%)
Churn rate reported to board at 30.6% triggered emergency retention spend — actual churn is 4.8%
Near-duplicate rows detected
102 duplicate user records inflating MRR calculations and distorting cohort analysis
102 rows affected
Null values in required column: user_id
40 records missing user_id — these accounts are invisible to retention tracking
40 rows affected
Recommended Actions
- 1
Investigate duplicate records. Add dedup logic keyed on non-key fields.
Owner: Data Engineering|Effort: Mediumhigh - 2
Fix null values in required column 'user_id' at the source.
Owner: Data Engineering|Effort: Mediumhigh - 3
Investigate root cause of active_users discrepancy for 2025-12. Check for duplicate events, missing data, or filter logic differences.
Owner: Data Engineering|Effort: Mediumhigh - 4
Investigate root cause of churn_rate discrepancy for 2025-12. Check for duplicate events, missing data, or filter logic differences.
Owner: Data Engineering|Effort: Mediumhigh
Deliverables
Book a SaaS Data Diagnostic
A focused $7,500 engagement. We audit your saas data, score your metrics, and deliver a remediation roadmap in 2 weeks.
Book a diagnosticMonthly Metric Reliability Program
Ongoing monitoring, governed corrections, and executive-ready reporting. Starting at $2,500/month.
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