1. Reducing Customer Churn
The Silver Bullet to
Reducing Customer Churn

2. US Telecom Market Overview
Industry churns around 8% per year (LL)
We fare slightly better: between 6-7%
Major Trends:
Wireless substitution
VOIP offerings from across all businesses, LL, Wireless
Source: Wireless Intelligence
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3. “We weren’t able to quickly identify customer issues or trends that might be developing”
Jeff Baker, Director of Customer Retention – Cincinnati Bell
Source: Peppers&Rogers White Paper “Hear Now or Gone Tomorrow”
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4. About Cincinnati Bell Officially incorporated on July 5, 1873
First company in the city to provide direct communication between the city’s homes and businesses
Today, provides modern telecommunications products and services in a three-state area
Landline, mobile, TV, Internet and more
750k+ Landline Customers, 350k+ Wireless Subs’
20 Owned Retail Stores + Hundreds of Distributors
6 Contact Centers
700 CSR’s
1.3 Million inbound calls for all products/centers
Facebook Social Media & some online chat functionality
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5. A Customer Centric Strategy
Our initiatives almost all center around VOC
CSAT review on daily & weekly basis
key initiatives: churn reduction & call deflection
We have a Call Center Ops Dept solely dedicated to CSAT, VOC, call deflection, online interactivity, etc
Main achievements so far and main gaps
We have identified the root sources for our issues
Still haven’t employed a desktop analytics solution - investigating this possibility
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6. What do Customers Say About Us?
“I am a smart phone user at Cincinnati Bell. I think Cincinnati bell does a great job with customer service. They are willing to negotiate on the bill as well, unlike other phone companies. Their people at the stores are very knowledgeable and helpful. I think they offer quality in their devices and afford-ability.”
“I have had a Cincinnati Bell home phone account long before the cell phone was invented.  I now have both a home phone and cell phone through Cincinnati Bell.  I even have internet service through Cincinnati Bell.”
“The most powerful form of Marketing is Word of Mouth marketing.  By creating a remarkable service experience, one that is so different from the competition, Cincinnati Bell Wireless gave me something to talk about.  I am now spreading the word for them – free of charge and in a way that resonates more than any advertisement.  Bottom line.  If you are shopping for cell phone providers and customer service is important to you, I encourage you to check out Cincinnati Bell Wireless.”  
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7. Traditional Approach to Predicting Churn
Why transactional analytics is not enough
Reactive process, based on:
Customers’ past transactions
Transfers to a “Retention Team”
Taking too long
By the time it takes to identify a likely churner, it may already be too late to retain him/her…
An early-detection retention solution is required
Proactively go after customers at risk before they cancel
In addition, a method is required to accurately identify the root cause for attrition,
Optimal retention offer customization per customer
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8. % Subscribers Contacted
The Silver Bullet:
Leveraging Cross-Channel Interaction Analytics to Complete the Picture
Capturing Interactions from all channels – Voice of the Customer Analytics
Web
Phone
Augments transactional analytics for churn prediction
Enables Measurement of CSAT:
Creation of categories around VoC -> enhance the churn prediction model
Same VoC categories utilized to better understand Customer Dissatisfaction and increase First Contact Resolution
Social Media
Phone Survey
Chat
% Churners Identified
% Subscribers Contacted
Guess Line (statistically)‏
360% Increase
Over Guess Line
Web
Phone
Chat
First we had a churn prediction model based on transactional data (what phone you have, how much are you using it, demographics, lots of parameters they find in the customer DB) which already provides some level of churn prediction of course.
By adding to this prediction model, inputs from interaction analytics (i.e. churn signals from customer calls) you improve the prediction accuracy.
About the diagram: (very theoretical)
first is the guess line. Say there is x% of customers about to churn, and your goal is to find them so you can retain them. If you proactively call all your customers and have a ‘retention discussion’ with them, you will find 100% of the churners. Of course this is not practical, because its too expensive (not only to call them, but to make retention offerings to 100% of your customers while only 2% are at risk). If you only sample customers, and you know nothing about the churn distribution, you are on the guess line. E.g. if you sample 10% of the call, statistically you’ll find 10% of the churners
The idea is to lift this curve and find more churners with the same amount of contacts.
Transaction-based model = the green line. At the best point, which happened to be 20% contacts, it increased churn prediction by 360% (from 20% to more than 70%)
The dotted red line shows what happens when you add NICE to the existing model. The curve lifts even more, so you get the same prediction accuracy (above 70%) with only 10% contacts (down from 20%).
Again, this is all a theoretical way to model the churn prediction and show the contribution of interaction analytics churn prediction on top of transactional churn modeling.
Social Media
Phone Survey
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9. Combined Solution Process
Retention CSR
Customer
CRM
Customer Call
5-10% Customers
Record
Interaction Analytics
IA Churn & Risk Score
Call Center
Integrated Calculation of churn score
Integrated Prediction Model
Automated Ticket Creating in CRM
Transactional-based Churn Prediction Model
CSR
AO Churn Score & MDOO Offers
Transactional Warehouse
Prediction Model
100% Customers
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10. The Solution @ Cincinnati Bell
Analyze and categorize interactions to reveal insights and root-causes
and Chat Analysis
Emotion Detection
Speech Analytics
Talk Over Analysis
Desktop Analytics
Call Flow Analysis
got married
why should I switch
I am moving
not interested
Categorization
Root Cause Analysis
Actions 25 75
100
Pricing
Features
Competition
Online
Proactive Campaign
Process Correction 20 40 60 80
Alert
Coaching
RT decisioning & guidance
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11. Key Findings & Business Impact
Customer Satisfaction –
Correlation reports show the relation all other categories have to the selected
Over 28% of all Repeat Callers expressed dissatisfaction due to calling back for resolution
For Post Paid, over 20% of the customers are stating their dissatisfaction when they are calling with regard to billing issues
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12. Key Findings & Business Impact
Customer Dissatisafaction –
Root-cause and drivers
22% of the dissatisfaction expressed by the customers was due to billing issues
As with repeat calls, the most common driver for dissatisfaction was due to a lack of agent follow through and communication (48%)
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13. Key Findings & Business Impact
Customer Dissatisafaction –
Common drivers for billing issues include:
Account changes and adjustments not performed as promised
Third party issue: premium text messaging being charged
They feel they did not sign up
They feel feature was not canceled
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14. Key Findings & Business Impact
Customer Dissatisafaction –
Common drivers for equipment issues include:
Phone is not working, cannot get anything to function correctly
Certain attributes of the phone not working correctly
Voic notification
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15. Key Findings & Business Impact
Customer Churn
10-15% more customers in “Danger Clusters” were identified
10-20% of newly identified customers churned in following 3 month window
14% customer churn reduction, which exceeded the targeted goal of 10%
Additional two million dollar annual revenue from customer retention
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16. Future Plans Enhancing Multi-Channel Interaction Analytics
Voice of the Customer across all channels: phone, , online chat
Focus on Real-time Impact
Up-sell and Cross-sell effectiveness
Desktop analytics
Non-Value Added Calls
New Cost of Poor Quality Internal Metric Reduction - a $10 million in potential savings through Analytics were identified
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17. Thank You