Utilizing Big Data to Optimize Customer Value Management Strategies


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How can big data help us look differently at our customer base? A presentation by Elan Rosenberg, Business Development Director, Marketing Analytics at cVidya

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Utilizing Big Data to Optimize Customer Value Management Strategies

  1. 1. BUSINESS GROWTH © 2014 – PROPRIETARY AND CONFIDENTIAL INFORMATION OF CVIDYA Utilizing big data to optimize customer value management strategies Elan Rosenberg Business Development Director Marketing Analytics
  2. 2. 2 A leading supplier of Revenue Analytics solutions to communications and digital service providers Founded: 2001 300 employees in 15 locations worldwide Deployed at 7 out of the 10 largest operators in the world 150 customers in 64 countries Processing 2.45 Billion subscribers in deployments globally Saving over $12 Billion to providers annual revenue Partnering with world leading vendors What You Should Know ABOUT US 2
  3. 3. 3 How can big data help us look differently at our customer base? What if you identify that these are all one family with different kind of data users? Daughter Mother FatherSon 3
  4. 4. 4 And what if you knew that they are mainly interested in Football? 4
  5. 5. 5 So, how can this optimize our marketing activities? 5
  6. 6. 6 The Williams Family 6
  7. 7. 7 Profession Freelance architect Hobbies Fashion, sports (tennis), news (business, entertainment) Profession Marketing professional in an int’l firm Main Usage Patterns Voice, WhatsApp, Skype, frequent roamer, news apps Devices Laptop, tablet, iPhone5s Devices Laptop, tablet, Nexus 5 Main Usage Patterns Voice, internet browsing, tethering Hobbies Sports (football, basketball) and cooking Debra George
  8. 8. 8 University student Hobbies – music, sports (rock climbing, scuba diving) Devices – laptop, tablet, iPhone 4s Main Usage Patterns – Voice, Facebook, Skype, WhatsApp High school student Hobbies – movies, sports (dancing, swimming) Device – Galaxy S2 Main Usage Patterns – Voice, WhatsApp, Instagram, YouTube Elementary school student Hobbies – Reading, sports (biking, skateboarding) Device – low-end smartphone Main Usage Patterns – Voice, WhatsApp, Facebook, internet browsing Mike 16 11 JessicaColin 19
  9. 9. 9 Back to the CSP’s reality…
  10. 10. 10 Tools to support a non-technical marketer with quick path from ideation to actionable results Complexity of getting near real-time data insight supporting informed decisions Lack of subscriber insight for personalized user experience Multiple and disparate data sources Access, collection, enrichment, analysis Quick, relevant and cost-effective launch of new services and propositions Base Management Challenges & Needs 10
  11. 11. 11 How does the CSP see the Williams family today?  Debra − Private account − Plan: bundle of 3 GBs data, unlimited nat’l/int’l voice/sms − Silent roamer (mainly WiFi)  Colin − On a student plan in a competitor network  Mike – Prepaid SIM – No visibility on demographics – Plan: recurrent bundle of 500MBs data, 500 minutes, 500 SMS – Occasionally exceeds data allowance  George – SOHO account – Plan: bundle of 5 GB data, unlimited nat’l voice/sms – Never exceeds data allowance ?  Jessica − On the same account as Debra − Plan: bundle of 1 GB data, unlimited nat’l voice/sms − Regularly exceeds data allowance ? Debra Jessica George Mike Colin
  12. 12. 12 Top-up stimulation offers  Mobile data dongle  Cloud storage  Standard roaming package  Extra SIM for a tablet  Bridge data bundle  Data bundle upsell …and what can it offer them? ? Debra Jessica George Mike
  13. 13. 13 Utilizing big data analytics Data Available Customer attributes, XDRs, DPI, device, location, data bundle utilization, point of sale, invoice, top- ups, etc… Insights Correlations, relationships, patterns, habits  Correlations – social circles, families, SMBs  Patterns of use – profile enrichment  Interests  Gender and age groups  Influencers (new offers, retention)  Needs and communication habits as individuals and as a group/segment 13
  14. 14. 14 What can big data analytics reveal about the Williams family? ? ? Family Circles
  15. 15. 15 What can big data analytics reveal about the Williams family? ? Age Group (8-13)
  16. 16. 16 What can big data analytics reveal about the Williams family? ? Gender
  17. 17. 17 What can big data analytics reveal about the Williams family? Interests Family Circles ? ? Age Gender Devices
  18. 18. 18 What can big data analytics reveal about the Williams family? ? ? Now what can we offer them?  Shared, multi-device, data family plan  Acquisition campaign – add another family member  Migration of prepaid to post-paid  Special data roaming rates  Device upgrade supporting LTE *  Promotions on a special occasion to a sports event  1 month free offer for a Mobile HDTV sports pack * “Apple to be the most desired brand among American teenagers” (Piper Jaffray’s 25th bi-annual teen survey)
  19. 19. 19 Let’s zoom out to a full customer base family analysis Tethering and multi-device usage Correlation between # data users and family ARPU/Usage Families data usage characteristics Family size distribution Influencers
  20. 20. 20 cVidya Enrich – Your Guided Path to Actionable Insights  Self-service environment for Telecom marketers  Pre-modeled customer data analytics with use cases focusing on different business objectives  Identifies potential target micro-segments for different marketing activities  Impact analysis of potential offers on targeted segments  Combines advanced analytical models, based on machine learning sophisticated algorithms Greater visibility of meaningful data
  21. 21. THANK YOU! www.cvidya.com Elan Rosenberg Marketing Analytics Business Development Director Email: elan.rosenberg@cvidya.com Mobile: +972.54.561.5661
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