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Using Interaction Analytics
To Reduce Customer Churn
Why Churn? Price CX
Who
To T
arge
tLim
ited Resource
demographics
t ransactions
inte
racti
on
s
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
THE INTERACTION
Why CallingIssueProduct/Price/OfferSentiment
THE CUSTOMER
BillingProduct HoldingCRMPayment MethodPromotion
The Customer LifecycleData Collected
MARKET SELL BILL SERVE MAINTAIN
CUSTOMER INTERACTIONS
O v e r ti 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
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 LifecycleData Collected
Page 8
Predictive Churn Advanced AnalyticsMaking Churn Modeling 25-30% More AccurateThe Customer LifecycleData Collected
Interactions
CONTINUALLY REFINED
Queries
Defined Words and PhrasesCombined with Operators
Provides Rich Meaning
Utilises Machine Learning toIdentify 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
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 LifecycleData Collected
0%
20%
40%
60%
80%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Page 10
Combined Model
Interaction Data Model
Traditional Data Model
Percentage of Non-Churn
True
Pos
itive
Rat
e
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/AnnuallyOpportunity = $187M/AnnuallyAt 20% Save Rate = $37M/Annually
Profit At 20% = $27M/Annually
Identified = 31%Cost = $10M/AnnuallyOpportunity = $120M/AnnuallyAt 20% Save Rate = $26M/Annually
Profit At 20% = $16M/Annually
Identified = 27%Cost = $10M/AnnuallyOpportunity = $114M/AnnuallyAt 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 LifecycleData Collected
Add Interaction DataImprove Predictive
Modelling25-40%
Keep More CustomerFor Longer
-:)