RAMP Decision System Architect & Auditor

Interactive Operational DAG Live customer record flows across RAMP nodes

CRITICAL FLAWS DETECTED

Net Economic Value

-$32,400

Margin bleed exceeds genuine churn salvage

False Alarm / Noise Rate

72.4%

Supervisory agents alerting on holiday drops & small cohorts

Strategic Churn Threat Inflation

+28.5%

Accounts deliberately threatening churn to trigger $120 coupons

Target Leakage Inflation

+14.2% Spurious

Post-facto manual cancellation tags distorting predictive validity

1. Retrieval: Reality vs Data Proxy

"Retrieval is not merely retrieving more data. It is retrieving the right reality." Investigate whether warehouse fields represent actual dissatisfaction or organizational artifacts.

Include `manual_cancellation_pending` tag

Post-facto CRM flag updated by reps after churn is communicated. Creates massive circular target leakage.

Treat High Support Volume as Churn Risk

High-value enterprise accounts file 4x tickets due to massive usage, not dissatisfaction. Confounders misclassify best customers as flight risks.

True Predictive Validity (Sound Signal vs Noise): 64% Valid (36% Spurious)

2. Agents: Supervisory Framing

"Agents can run the analysis, but somebody still has to frame the investigation." Prevent monitoring agents from drowning teams in statistically interesting trivia.

Seasonality & Calendar Dip Suppressor

Filters out 7% weekly active drops caused by regional public holidays and weekend seasonality.

Statistical Significance Threshold (α):

p < 0.05

Minimum Cohort Sample Size for Alerts:

25 accts

Actionable Alert Ratio: 28% Real / 72% False Alarm

3. Models: Decision Economics vs AUC

"When is the difference between 91% and 93% accuracy economically irrelevant?" A high AUC model with a blind discount policy destroys margin.

Predictive Model Architecture Tier:

91% AUC (Interpretable Tree) 93% AUC (Deep Tuned AutoML)

Automated Retention Incentive Value ($):

$120

Intervention Trigger Probability:

> 50% risk

Salvaged Churn Revenue: $51,600

Wasted Margin (Discounts to Retained): -$84,000

4. Proof: Feedback Loops & Causal Attribution

"The machine is partly learning from a world it helped create." Accounts learn that threatening to churn wins discounts, distorting future models.

Feedback Cycles (Model Retraining Rounds):

3 Cycles

Customer Learning Susceptibility (% exploiting policy):

35%

Enforce Randomized Holdout & Causal Control Group

Separates genuine retention lift from marketing campaign traffic and natural renewals. Halts runaway discount gaming.

Estimated Genuine Causal Lift: +3.2% Absolute

Incentive-Induced Fake Threats: 685 Accounts

RAMP Decision System Governance & Audit Log

Comprehensive system verification spec for data science leaders and risk committees.

{ "system": "RAMP Enterprise Decision Engine v1.0", "audit_status": "CRITICAL_RISK", "net_economic_value": -32400, "leakage_present": true, "false_alarm_rate": 0.724, "strategic_distortion_pct": 28.5 }