Reducing Customer Churn The Silver Bullet to Reducing Customer Churn

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 - 2010 Page 2

“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” - 2010 Page 3

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 Page 4

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 Page 5

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.”   Page 6

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 Page 7

% 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 Email 0 10 20 30 40 50 60 70 80 90 100 % Churners Identified % Subscribers Contacted Guess Line (statistically)‏ 360% Increase Over Guess Line Web Phone Chat Email 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 Page 8

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 Page 9

The Solution @ Cincinnati Bell Analyze and categorize interactions to reveal insights and root-causes Email 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 Page 10

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 Page 11

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%) Page 12

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 Page 13

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 Voicemail notification Page 14

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 Page 15

Future Plans Enhancing Multi-Channel Interaction Analytics Voice of the Customer across all channels: phone, email, 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 Page 16

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