Omantel B2B Retention Flow

Customer Flow from Outlet through CRM+ | Current Process Workflow

Ready to execute

0 / 7 stages completed

Initial
Check

AON
Check

CLV
Segment

Customer
Reason

Condition
Evaluation

Offer
Hierarchy

Final
Outcome

Initial Check - Customer Entry via CRM+

Customer arrives at outlet through CRM+ | Staff performs blacklist and eligibility verification

1,247

Daily Check-ins

97.3%

Pass Rate

Blacklisted

2.3m

Avg Check Time

Verification Process

Staff Check

Verify staff credentials and authorization level for retention process handling

Blacklist Check

Cross-reference customer against fraud database and previous violations

Identity Verification

Validate customer identity documents and account ownership

AON Check - Age on Network

Determine customer eligibility based on network tenure | Threshold: 90 Days

1,213

Customers Checked

1,089

AON ≥ 90 Days

124

AON < 90 Days

237

Avg AON (Days)

Decision Flow

🔍

Check AON

→

AON ≥

90 Days?

YES

→

✅

Continue to

CLV Segment

→

🛑

Process Ends

Not Eligible

CLV Segment - Customer Lifetime Value Classification

Categorize customers by value: Low | Medium | High

287

High CLV

523

Medium CLV

279

Low CLV

$2.4M

Total CLV

💎

High CLV

Premium Customers

Avg Monthly Spend: $450+

Contract Type: Business/Enterprise

Priority: Maximum Retention

🥈

Medium CLV

Standard Business

Avg Monthly Spend: $150-$449

Contract Type: SME/Small Business

Priority: Standard Retention

🥉

Low CLV

Basic Business

Avg Monthly Spend: $50-$149

Contract Type: Micro Business

Priority: Basic Retention

Customer Reason - Churn Attribution

Agent captures reason for potential churn from dropdown list

34%

Price/Competition

28%

QoS Issues

19%

Low NPS

19%

Other Reasons

Churn Reason Categories

Price Sensitivity

Competitor offering better rates
Budget constraints
Price increase concerns

Quality of Service

Network coverage issues
Frequent connection drops
Slow data speeds

Low NPS / Satisfaction

Poor customer service
Billing issues
Unresolved complaints

Competition

Better promotional offers
Enhanced features elsewhere
Brand preference

Low Usage

Service no longer needed
Business downsizing
Changed work patterns

Other Reasons

Relocation/moving
Business closure
Contract fulfillment

Condition Evaluation - Offer Eligibility

System evaluates if retention offers are available for customer based on rules

892

Eligible (Available)

197

Not Eligible

81.9%

Eligibility Rate

1.8s

Avg Eval Time

Eligibility Decision Flow

⚙️

Evaluate
Rules

→

Offers

Available?

YES (A)

→

🎁

Continue to

Offer Hierarchy

NO (N)

→

📋

Route to

Dropdown List

Eligibility Rules

Account in good standing

No active promotional offers

Within retention budget limits

CLV meets minimum threshold

No recent churn attempts (90 days)

Eligible contract type

Offer Hierarchy - Three-Tier Retention Strategy

Sequential offer presentation: Floor → Middle → Ceiling | Multiple retention outcomes

68.4%

Retention Rate

Offer Tiers

$487K

Value Saved (MTD)

1.8

Avg Offers/Customer

🎁

Floor Offer

Initial Retention Attempt

Typical Offers:

5-10% discount (3 months)
Free service upgrade (basic)
Bonus data/minutes package

✓ Saved without offer

✓ Floor offer accepted

✗ Not retained → Escalate to Middle

Success Rate: 42%

🎖️

Middle Offer

Enhanced Retention Attempt

Typical Offers:

15-20% discount (6 months)
Premium service upgrade
Waived activation/setup fees

✓ Saved without offer

✓ Middle offer accepted

✗ Not retained → Escalate to Ceiling

Success Rate: 34%

👑

Ceiling Offer

Final Retention Attempt

Typical Offers:

25-30% discount (12 months)
Premium+ service tier
Custom business bundle

✓ Saved without offer

✓ Ceiling offer accepted

✗ Not retained → End process

Success Rate: 24%

Offer Escalation Path

Floor

→

Middle

→

Ceiling

→

Add Offer

Final Outcome - Process Completion

Customer retained or churned | Add offer to account and complete retention flow

610

Customers Retained

282

Customers Churned

68.4%

Retention Rate

8.3m

Avg Handling Time

✅

Customer Retained

Successfully saved through retention flow

Next Actions:

Add approved offer to account

Update customer status to "Retained"

Send confirmation SMS/email

Log interaction in CRM+

Schedule follow-up check (30 days)

Process Status

COMPLETED ✓

❌

Customer Churned

All retention attempts exhausted

Next Actions:

Update customer status to "Churned"

Document churn reason in CRM+

Initiate account closure process

Send exit survey request

Add to win-back campaign (90 days)

Process Status

CLOSED

Today's Performance Summary

1,089

Total Processed

610

Retained

$487K

CLV Saved

8.3m

Avg Handle Time

Notebook Configuration

Notebook Type

ML Framework

Scikit-learn XGBoost PyTorch

Include Sections

Exploratory Data Analysis Feature Engineering Hyperparameter Optimization Model Explainability (SHAP)

Generated Code Preview

# Configure options above and click "Generate Notebook" to create code

AWS Nova-Powered Feature Importance Analysis

Advanced AI explanations using SHAP (SHapley Additive exPlanations) values + AWS Bedrock insights

Features Selected

Initial Features

61.7%

Reduction Rate

92.1%

Model Accuracy

3.2x

Speed Improvement

Top 10 Features - Ranked by SHAP Importance

usage_decline_percentage

Behavioral Signal · Structured Data

23.5%

SHAP Importance

AWS Nova Pro AI Explanation:

Why it matters: Declining usage is the #1 predictor of churn. Customers who reduce their monthly voice/data consumption by >20% are 4.7x more likely to churn within 90 days. This behavioral shift signals dissatisfaction, financial constraints, or migration to competitors.

Business Impact: Early detection allows proactive retention campaigns. A 10% improvement in identifying usage decline can save $2.4M annually by preventing high-value customer churn.

Model Interpretation: SHAP values show this feature contributes most to churn probability. When usage drops >30%, model confidence increases by 18 percentage points.

complaint_count_90d

Service Quality · Structured Data

18.2%

SHAP Importance

AWS Nova Pro AI Explanation:

Why it matters: Customer complaints are direct signals of dissatisfaction. Each additional complaint in a 90-day window increases churn probability by 12%. Customers with 3+ complaints have a 67% churn rate vs. 8% baseline.

Business Impact: Complaint resolution within 48 hours reduces churn risk by 45%. Prioritizing high-complaint customers for proactive outreach can recover 32% of at-risk revenue.

Model Interpretation: This feature shows strong interaction effects with NPS score and sentiment. Combined low NPS + high complaints = 89% churn prediction accuracy.

sentiment_score_avg

NLP-Derived · Unstructured Data (AWS Nova)

15.3%

SHAP Importance

AWS Nova Pro AI Explanation:

Why it matters: NLP sentiment analysis from call transcripts reveals emotional state. Negative sentiment (score < -0.5) correlates with 73% churn probability. This unstructured data captures dissatisfaction that structured metrics miss.

Business Impact: Combining sentiment with structured features improves model accuracy by 8.4%. Real-time sentiment monitoring enables same-day intervention for highly negative interactions.

Model Interpretation: AWS Nova Pro extracts emotional context from 127K call transcripts. SHAP shows sentiment is critical for borderline cases where traditional metrics are neutral.

Features 4-10: Summary

4. customer_tenure_months 14.1%

Demographic · New customers (<6 months) churn 3.2x more

5. payment_delay_days 12.3%

Financial · Payment issues indicate affordability problems

6. competitor_mention_count 10.7%

NLP-Derived · Mentions of du, etisalat indicate switching intent

7. nps_score 9.8%

Service Quality · Detractors (NPS<7) have 54% churn rate

8. connection_drop_rate 8.9%

Network Quality · Drop rate >5% drives service quality complaints

9. avg_bill_amount 7.6%

Financial · High bills (>500 AED/mo) correlate with pricing churn

10. support_call_frequency 6.4%

Behavioral · >3 support calls/month indicate unresolved issues

Multi-Method Feature Selection Approach

SHAP Values

Game theory-based feature importance

Random Forest

Feature importance from tree splits

XGBoost Gain

Feature gain and coverage metrics

Mutual Info

Statistical dependency analysis

Consensus-Based Selection

Features selected only if ranked in top 20 by at least 3 out of 4 methods, ensuring robust and reliable feature set with minimal redundancy and maximum predictive power.

Omantel AI Churn Retention

Omantel B2B Retention Flow

Initial Check - Customer Entry via CRM+

Verification Process

Staff Check

Blacklist Check

Identity Verification

AON Check - Age on Network

Decision Flow

CLV Segment - Customer Lifetime Value Classification

High CLV

Medium CLV

Low CLV

Customer Reason - Churn Attribution

Churn Reason Categories

Price Sensitivity

Quality of Service

Low NPS / Satisfaction

Competition

Low Usage

Other Reasons

Condition Evaluation - Offer Eligibility

Eligibility Decision Flow

Eligibility Rules

Offer Hierarchy - Three-Tier Retention Strategy

Floor Offer

Middle Offer

Ceiling Offer

Offer Escalation Path

Final Outcome - Process Completion

Customer Retained

Next Actions:

Customer Churned

Next Actions:

Today's Performance Summary

Dataset Profile

Information

Code Generator

Notebook Configuration

Generated Code Preview

Code Ignite

Interactive Code Viewer

Execution Output

Jupyter Integration

SHAP Feature Analysis - AI-Powered Explainability

AWS Nova-Powered Feature Importance Analysis

Top 10 Features - Ranked by SHAP Importance

usage_decline_percentage

complaint_count_90d

sentiment_score_avg

Features 4-10: Summary

Multi-Method Feature Selection Approach