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Reliability Score

64

3164(+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%.

Illustrative Example

BayesIQ Data Reliability Audit

SaaS

Audit Period: December 2025

64

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.

4 findings4 high

Key Metrics: Reported vs Audited

MetricPeriodReportedAuditedDelta
active_users2025-121,7949,583understated 434.2%
churn_rate2025-1230.6%4.8%overstated 84.4%

Top Findings

high

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

high

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%

high

Near-duplicate rows detected

102 duplicate user records inflating MRR calculations and distorting cohort analysis

102 rows affected

high

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. 1

    Investigate duplicate records. Add dedup logic keyed on non-key fields.

    Owner: Data Engineering|Effort: Mediumhigh
  2. 2

    Fix null values in required column 'user_id' at the source.

    Owner: Data Engineering|Effort: Mediumhigh
  3. 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. 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
Prepared by BayesIQ · 2025-12

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