What effect does interaction history have when added to traditional predictive churn data?

1. Using Interaction Analytics
To Reduce Customer Churn

2. Why Churn?

3. WhoToTarget
LimitedResource

4. demographics
transactions
interactions

5. PROCESS
• Agent Tech
Troubleshooting
• Appointments
• Offer Expiration
• Service not restored
• Equipment not
received
• Service not activated
• Installation pending
• Technical Issues
• Outage
• Broken Appointments
• Service Restricted
• Number not ported
INTERACTIONS EARLY INDICATORS PRODUCT/PRICE
• Call Reason
Categorisation
• Average Handle
Time/Reason
• Non-Talk Time/Holds
• Long/Short Call Reason
• First Call Resolution
• Transfers
• Escalation Reason
• Agent Performance
• Channel Drivers
• Agent Tools/Downtime
• Disconnects
• Escalation Requests
• Customer Satisfaction
• Offers Made/Offers
Taken
• Retention Saves
• Appropriate &
Bad Credits
• Downgrades
• Regulatory/Compliance
• Competitive Discussion
• Billing Line Item
Review
• Rude Behavior
• Residential Move
• Service Performance
• Competitive Pricing
• Equipment Failure
• Ease of Use/Navigation
• Features and Setup
• Usage
• Speed
• Fees
Interaction Clues
From
Triple Play
Customers

6. THE INTERACTION
Why Calling
Issue
Product/Price/Offer
Sentiment
THE CUSTOMER
Billing
Product Holding
CRM
Payment Method
Promotion
The Customer Lifecycle
Data Collected
MARKET SELL BILL SERVE MAINTAIN
CUSTOMER INTERACTIONS
O v e r t i m e w e c o l l e c t a l o t o f u s e f u l i n s i g h t

7. Making Churn Modeling 25-30% More Accurate
Run established churn models
Identify the interaction characteristics of previously churned customers
Run that profile against all interactions to identify those at risk
Export into suitable format for data warehouse
Match with transactional and other metadata
The Customer Lifecycle
Data Collected

8. Page 8
Predictive Churn Advanced AnalyticsMaking Churn Modeling 25-30% More AccurateThe Customer Lifecycle
Data Collected
Interactions
CONTINUALLY
REFINED
Queries
Defined Words and Phrases
Combined with Operators
Provides Rich Meaning
Utilises Machine Learning to
Identify Non-Intuitive Words
and Phrases to Improve Results
Neural Phonetic Speech Analytics
Metadata
Datasets are Combined,
Transformed to Account Level,
and Joined with Metadata
What
Was Said
What
Was
MeantWhat
Was
Done
ρX,Y = corr
Algorithm
Development
PEOPLE WHO WILL
STATISTICALLY CHURN Based
on their interactions and basic
customer data
(LIST, INTERFACE…)
Focusing on transactional data
combined with what they said in
their most recent interactions

9. Making Churn Modeling 25-30% More Accurate
So far tested with Comcast, Time Warner, Verizon and BT
Improvement to accuracy of existing churn model 25-30%
Example Comcast have 20m customers. 0.5 m churn every month
Predictive Churn Advanced AnalyticsMaking Churn Modeling 25-30% More AccurateThe Customer Lifecycle
Data Collected

10. Combined Model
Interaction Data Model
Traditional Data Model
Percentage of Non-Churn
TruePositiveRate
Case Study: The impact of adding Speech Analytics data to a large set of
Traditional data for Churn Prediction in a mitigation program with ~20 FTEs.
• The team would be able to contact ~450k accounts/year, equivalent to ~.74% of all unique accounts
that call each year. Results assume a $25 Cost per account contacted for cost of call and offers.
.74% of
Population
Interaction Data Only Result
Structured Data Only Result
Combined Data Result Identified = 45%
Cost = $10M/Annually
Opportunity = $187M/Annually
At 20% Save Rate = $37M/Annually
Profit At 20% = $27M/Annually
Identified = 31%
Cost = $10M/Annually
Opportunity = $120M/Annually
At 20% Save Rate = $26M/Annually
Profit At 20% = $16M/Annually
Identified = 27%
Cost = $10M/Annually
Opportunity = $114M/Annually
At 20% Save Rate = $23M/Annually
Profit At 20% = $12M/Annually
0 50,000 100,000 150,000 200,000 250,000 300,000
0 50,000 100,000 150,000 200,000 250,000 300,000
0 50,000 100,000 150,000 200,000 250,000 300,000
Identified (113,784)
Not Identified (308,757)
Identified (130,374)
Identified (186,826)
Not Identified (291,311)
Not Identified (230,807)
$
Conservatively
$11M in Straight Operating Income
(with significantly less effort / cost)
=Correctly identified CHURN accounts
Identified accounts that Will Not Churn
Making Churn Modeling 25-30% More AccuratePredictive Churn Advanced AnalyticsMaking Churn Modeling 25-30% More AccurateThe Customer Lifecycle
Data Collected

11. Add Interaction Data
Improve Predictive Modelling
25-40%

12. Keep More Customer
For Longer
-:)