Maximizing Customer Retention with Data Analytics

2. Customer Retention Through Analytics Paul King, President & COO Aperio CI September 18, 2008

3. Economic Pressures Require Service Providers to Realize New Revenue Streams • Churn is increasing… • …While ARPU is declining • Revenue and profits are squeezed • Customers demand more and better service… • …While brand loyalty becomes minimized

4. Acquisition-centric Has Economic Challenges • Pressure to reduce Capex and Opex • “Doing More With Less” is a mantra heard world-wide by network operators • IT constraints • Internal Systems are over-taxed • Requirement to spend marketing monies effectively • Outspending the competition is no longer an option; Providers demand tangible ROI • Building brand loyalty is a necessity

5. Operators Respond to Market Conditions • Significant, and continuing, investments in CRM systems, practices, procedures and segmentation strategies to improve customer service and quality of offers • Intense focus on churn propensity and market segmentation models to help address loyalty and reward programs • Every mobile operator is looking for differentiators, but finding them is proving difficult, complex and expensive • Content • Data Services • Devices

6. Operators Respond to Market Conditions • Frequent price reductions and increased bundles are utilized in complicated, and in many cases, less than optimal ways, to both attract and retain customers. • Front-loaded promotions (handset subsidies, additional free minutes, etc) exacerbate profit dilution and reinforce “more for less” customer expectations. • Complicated plans invite customer skepticism of pricing strategies, practices, policies and execution methods.

7. Truths About Retention • It is more cost effective to retain a customer than it is to acquire a new one. • The presentation of the ‘right’ offer at the ‘right’ time and via the ‘right’ method can only be enacted if the customer’s usage and history are analyzed in detail. • To create the required offer necessitates analysis of usage data such as: • Account information • Billing data • Customer history

8. A Different Approach Using Existing Customer Data To Break The Cycle • Increasing ARPU • Better Up- and Cross-sell Opportunities • Enhanced Customer Satisfaction • Improved Profitability

9. Effective Use of Customer Data TheBasic Tenets • Operational proficiency • Consistently accurate • Timeliness • Flexibility In order to provide profit-focused decision making, intelligence, and delivery systems must be tailored to meet each customers’ specific needs.

10. Required Data Functions Software-based data analytics: • Gather, mine, and categorize billing records tightly woven with current user information • Customer history • Calling pattern analysis • Competitive price positioning • Bundling analysis • Loyalty and reward history • Competitor offerings • Social Network indicators

11. Required Data Functions • Expert interpretation and analysis: • Cross-referencing critical identifiers • Behavior changes • Patterns of promotions • Take-up rates • Rewards and sales --Identify patterns and trends that accurately forecast customer behavior trends

12. Required Data Functions • Tailored, near real-time delivery: • Well-defined, fast and flexible execution processes • Integrated offer delivery and reporting that works across all customer channels (fully integrated marketing) • Contact center • Web portal • Text message • Voice mail • Direct mail • Email

13. Imperatives for Successful Execution To execute effective loyalty programs, there are fundamental bits of knowledge that you need to know: Know your customer: • Who are they right now, at this moment? • What tariff are they on? What handset do they have? (Do we know?) What options, bundles and services do they have? • How do they actually use our products and services? (As opposed to what they have purchased from you.) • Who do they call…when…how often?

14. Imperatives for Successful Execution Propensities, preferred method of communications, applied loyalty and reward offers: What has worked before? Will it work now? • How much they’ve spent (basic LTV) • Tenure and contract status (where applicable) • How do they actually use your products?… as opposed to what they have purchased from you

15. Imperatives for Successful Execution Know your product: • Know the tariffs, handset, offer and product/service positioning in detail: • How do they fit within our product and service portfolio? • How are they positioned relative to competitor portfolios and programs? Know whether or not the proposed product, service, or reward is useful to that particular customer based upon factoring all critical attributes.

16. Imperatives for Successful Execution Know how your customer fits your products • Assess all possible options, apply only what fits, when it fits and the channel by which the customer prefers to communicate This is where the rubber meets the road!

17. Execution Requirements End-to-end delivery requires: • Systems and hardware • Software applications • Integrated access and delivery • People and expertise • Security • Quality assurance • Process…Process…and did we mention?…Process ….. to meet customer’s specific preferences and needs.

18. Analytics in Action…A Quick Case Study Large Western European Mobile Operator • Challenge • Saturated market • Commoditized pricing • Multiple plans & bundles for voice/data • High annual churn rate—32%

19. The Analysis Highlights High annual churn rate – 32% overall • 73% of customers not on optimal plan • Customers spending 30% or more above optimal plan churned at more than 60% per year!! • Customers spending close to lowest priced churned at less than 15% per year • Social network members churned completely within 3 weeks of first disconnect

20. The Solution Solution: Targeted “Best Plan Advice” Offer • Proactively notified customers spending 30% or more above lowest price when a better plan was available—regardless of contract stage • Focus on Social Network members • Reactive plan support at the call center • Delivered side-by-side plan comparisons • Allowed operator to focus on delivering value as opposed to cutting price • Minutes • Data bundles/content • Enabled operator to offer more personalized offers that meet specific customer needs, multiple plans & bundles for voice/data

21. Analytics in Action…A Quick Case Study • Results • Churn was reduced by nearly 50% • Social Network churn reduced by more than 60% • Contact center processes more calls with improved resolution • Improved morale • Resources are optimized • Up-selling and cross-selling improves • ARPU increased by 4.2% (net!!), improving profitability

22. Conclusion • Proper use of analytics allow service providers to improve operational performance • Reduces churn • Increases ARPU • Reduces margin erosion through accurate pricing actions and effective competitive response • Increases cross and up-sell take-up rates • Enhances customer care efficiencies • Increases staff morale • Builds customer and brand loyalty • Positively impacts profitability