Chapter 9: Business Alignment¶

Purpose: Align data exploration with business objectives and constraints.

Outputs:

  • Business context documentation
  • Success metrics definition
  • Constraints and requirements

9.1 Setup¶

In [1]:
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from customer_retention.analysis.notebook_progress import track_and_export_previous

track_and_export_previous("09_business_alignment.ipynb")

import pandas as pd

from customer_retention.analysis.auto_explorer import ExplorationFindings
from customer_retention.core.config.experiments import (
    FINDINGS_DIR,
)
In [2]:
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from customer_retention.analysis.auto_explorer import load_notebook_findings

FINDINGS_PATH, _namespace, _ = load_notebook_findings("09_business_alignment.ipynb")
print(f"Using: {FINDINGS_PATH}")

findings = ExplorationFindings.load(FINDINGS_PATH)
print(f"\nLoaded findings for {findings.column_count} columns")

Using: /Users/Vital/python/CustomerRetention/experiments/runs/retail-e7471284/datasets/customer_retention_retail/findings/customer_retention_retail_aggregated_findings.yaml

Loaded findings for 407 columns

9.2 Business Context¶

Define the business context for this project.

In [3]:
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BUSINESS_CONTEXT = {
    "project_name": "Customer Churn Prediction",
    "business_objective": "Reduce customer churn by 20% through proactive retention campaigns",
    "stakeholders": ["Marketing Team", "Customer Success", "Data Science"],
    "timeline": "Q1 2025",
    "budget_constraints": "$50k for retention campaigns per month"
}

print("Business Context:")
for key, value in BUSINESS_CONTEXT.items():
    print(f"  {key}: {value}")

Business Context:
  project_name: Customer Churn Prediction
  business_objective: Reduce customer churn by 20% through proactive retention campaigns
  stakeholders: ['Marketing Team', 'Customer Success', 'Data Science']
  timeline: Q1 2025
  budget_constraints: $50k for retention campaigns per month

9.3 Success Metrics¶

In [4]:
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SUCCESS_METRICS = [
    {
        "Metric": "Model AUC",
        "Target": ">= 0.80",
        "Priority": "High",
        "Rationale": "Need strong discrimination to prioritize high-risk customers"
    },
    {
        "Metric": "Precision at 20%",
        "Target": ">= 0.60",
        "Priority": "High",
        "Rationale": "Limited budget means we can only target top 20% of predictions"
    },
    {
        "Metric": "Churn Rate Reduction",
        "Target": "20%",
        "Priority": "High",
        "Rationale": "Primary business objective"
    },
    {
        "Metric": "Model Latency",
        "Target": "< 100ms",
        "Priority": "Medium",
        "Rationale": "Required for real-time scoring"
    },
    {
        "Metric": "Fairness (Demographic Parity)",
        "Target": "Ratio >= 0.8",
        "Priority": "Medium",
        "Rationale": "Ensure equitable treatment across segments"
    }
]

metrics_df = pd.DataFrame(SUCCESS_METRICS)
print("Success Metrics:")
display(metrics_df)

Success Metrics:
Metric Target Priority Rationale
0 Model AUC >= 0.80 High Need strong discrimination to prioritize high-...
1 Precision at 20% >= 0.60 High Limited budget means we can only target top 20...
2 Churn Rate Reduction 20% High Primary business objective
3 Model Latency < 100ms Medium Required for real-time scoring
4 Fairness (Demographic Parity) Ratio >= 0.8 Medium Ensure equitable treatment across segments

9.4 Deployment Requirements¶

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DEPLOYMENT_REQUIREMENTS = {
    "scoring_mode": "Both batch and real-time",
    "batch_frequency": "Daily",
    "real_time_latency": "< 100ms p99",
    "infrastructure": "Databricks",
    "model_registry": "MLflow",
    "monitoring": "Required - drift detection and performance tracking",
    "retraining": "Monthly or on significant drift"
}

print("Deployment Requirements:")
for key, value in DEPLOYMENT_REQUIREMENTS.items():
    print(f"  {key}: {value}")

Deployment Requirements:
  scoring_mode: Both batch and real-time
  batch_frequency: Daily
  real_time_latency: < 100ms p99
  infrastructure: Databricks
  model_registry: MLflow
  monitoring: Required - drift detection and performance tracking
  retraining: Monthly or on significant drift

9.5 Data Constraints¶

In [6]:
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DATA_CONSTRAINTS = [
    {
        "Constraint": "PII Handling",
        "Requirement": "No direct PII in features (names, SSN, etc.)",
        "Status": "To verify"
    },
    {
        "Constraint": "Data Freshness",
        "Requirement": "Features must be available within 24 hours",
        "Status": "To verify"
    },
    {
        "Constraint": "Historical Depth",
        "Requirement": "Minimum 12 months of history for training",
        "Status": "To verify"
    },
    {
        "Constraint": "Protected Attributes",
        "Requirement": "Age, gender, race should not be direct features",
        "Status": "To verify"
    }
]

constraints_df = pd.DataFrame(DATA_CONSTRAINTS)
print("Data Constraints:")
display(constraints_df)

Data Constraints:
Constraint Requirement Status
0 PII Handling No direct PII in features (names, SSN, etc.) To verify
1 Data Freshness Features must be available within 24 hours To verify
2 Historical Depth Minimum 12 months of history for training To verify
3 Protected Attributes Age, gender, race should not be direct features To verify

9.6 Intervention Strategy¶

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INTERVENTIONS = [
    {
        "Risk Level": "High (>0.8)",
        "Intervention": "Personal call from account manager",
        "Cost": "$50/customer",
        "Expected Effectiveness": "40% retention"
    },
    {
        "Risk Level": "Medium (0.5-0.8)",
        "Intervention": "Personalized email + discount offer",
        "Cost": "$10/customer",
        "Expected Effectiveness": "20% retention"
    },
    {
        "Risk Level": "Low (<0.5)",
        "Intervention": "Automated engagement email",
        "Cost": "$0.50/customer",
        "Expected Effectiveness": "5% retention"
    }
]

interventions_df = pd.DataFrame(INTERVENTIONS)
print("Intervention Strategy:")
display(interventions_df)

Intervention Strategy:
Risk Level Intervention Cost Expected Effectiveness
0 High (>0.8) Personal call from account manager $50/customer 40% retention
1 Medium (0.5-0.8) Personalized email + discount offer $10/customer 20% retention
2 Low (<0.5) Automated engagement email $0.50/customer 5% retention

9.7 Save Business Context to Findings¶

In [8]:
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findings.metadata = findings.metadata or {}
findings.metadata["business_context"] = BUSINESS_CONTEXT
findings.metadata["success_metrics"] = SUCCESS_METRICS
findings.metadata["deployment_requirements"] = DEPLOYMENT_REQUIREMENTS

findings.save(FINDINGS_PATH)
print(f"Business context saved to: {FINDINGS_PATH}")

Business context saved to: /Users/Vital/python/CustomerRetention/experiments/runs/retail-e7471284/datasets/customer_retention_retail/findings/customer_retention_retail_aggregated_findings.yaml

Next Steps¶

Continue to 10_spec_generation.ipynb to generate production specifications.

Save Reminder: Save this notebook (Ctrl+S / Cmd+S) before running the next one. The next notebook will automatically export this notebook's HTML documentation from the saved file.