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Market Research Report

Big Data Analytics: How to Generate Revenue and Customer Loyalty Using Real-time Network Data

Published by Analysys Mason Product code 259777
Published Content info PPT (67 slides)
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Big Data Analytics: How to Generate Revenue and Customer Loyalty Using Real-time Network Data
Published: January 15, 2013 Content info: PPT (67 slides)

Communications service providers (CSPs) can either ride the wave of big data analytics or miss another multi-billion dollar market.

The term ‘big data' is associated with capturing and analysing consumer behaviour using the Web. The velocity and volume of digital transactions captured and processed to achieve business value require changes in the software systems to support petabytes of structured and unstructured data.

This Report provides:

  • an analysis of the market
  • a diverse set of use cases
  • an overview of vendor capabilities
  • a 5-year forecast
  • recommendations.

Company coverage

The following vendors of big data analytics solutions are analysed in this Report.

  • Alcatel-Lucent
  • Apache Software Foundation
  • Cisco Systems
  • Ericsson
  • EMC
  • Guavus
  • Hewlett-Packard
  • IBM
  • JDS Uniphase
  • NetScout Systems
  • Neuralitic
  • Nokia Siemens Networks
  • Oracle
  • SAP
  • Tektronix Communications
  • Teradata
  • The Now Factory.

The following companies are mentioned in this Report.

  • Agilent
  • Amazon
  • Ascom
  • Astellia
  • Autonomy
  • AT&T
  • Bell Mobility
  • BT
  • China Mobile
  • China Telecom
  • Comcast
  • Commprove
  • eBay
  • Empirix
  • Etisalat (UAE)
  • Ericsson
  • EXFO
  • Facebook
  • France Telecom (Orange)
  • Globul
  • Google
  • Greenplum
  • KXEN
  • Leap Wireless International
  • Level 3 Communications
  • LinkedIn
  • Microsoft
  • MicroStrategy
  • Mobile Telephone Networks (MTN South Africa)
  • Nexus Telecom
  • Polystar OSIX
  • SK Telecom
  • Sprint
  • T-Mobile USA
  • Taiwan Mobile
  • Telcordia Technologies
  • Telecom New Zealand
  • Telefónica Digital
  • Telstra
  • US Cellular
  • Vertica
  • VMware
  • Wireless City Planning (Softbank)
  • Zhilabs.

About the authors

Patrick Kelly (Research Director) sets the direction for Analysys Mason's Telecoms Software research stream, which focuses on identifying the rapidly growing segments in the telecoms software market and providing forecast and market share data by region and service type. He has produced research on policy management, cloud computing, LTE and mobile backhaul, IP next-generation service assurance, and customer experience management. Patrick is a frequent speaker at industry and user group conferences. He holds a BSc from the University of Vermont, and an MBA from Plymouth College.

Anil Rao (Analyst) is a member of Analysys Mason's Telecoms Software research team, focusing on the Service Assurance, Infrastructure Solutions, Service Delivery Platforms and Telecoms Software Strategies programmes. He has more than 10 years' experience in the telecoms industry, working in systems integration and service delivery with major Tier 1 mobile and fixed-line operators, and independent software vendors. Anil joined Analysys Mason in early 2012. He holds a BEng in Computer Science from the University of Mysore, and an MBA from Lancaster University Management School.

Table of Contents

Table of Contents

  • 5. Executive summary
  • 6. Big data comprises four key attributes
  • 7. The volume of data on telecoms networks has increased a thousand-fold in the past 20 years
  • 8. Only a fraction of data that traverses telecoms networks needs to be captured for analysis
  • 9. Big data analytics projects that generate business benefits in the short (3 - 6 months) and medium (6 - 12 months) terms
  • 10. CSPs have vast amounts of diverse data, but do not fully exploit it when making strategic business decisions
  • 11. The increase in mobile Internet penetration gives CSPs the opportunity to expand their data capturing capabilities
  • 12. What are the sources of the data that will help CSPs to understand customer behaviour and usage patterns?
  • 13. What will drive the business case for big data analytics for CSPs in the next year?
  • 14. Big data analytics can be used to improve internal decision making and could represent an independent source of revenue
  • 15. Based on a client's needs and available data dimensions, the operator can provide targeted advertising, market insights and trend analysis
  • 16. Market drivers
  • 17. Real-time network analytics market drivers [1]
  • 18. Real-time network analytics market drivers [2]
  • 19. Real-time network analytics market inhibitors
  • 20. Forecast
  • 21. Spending on IT analytic platforms, data store appliances and service monitoring will grow from USD4.1 billion in 2011 to USD6.6 billion in 2016
  • 22. Real-time network analytics definitions
  • 23. Business environment
  • 24. What will drive the business case for big data analytics for CSPs in the next year?
  • 25. Big data analytics promotes granular consumer segmentation
  • 26. Telefonica has recently become the first large telecoms multinational to use big data as a direct revenue source
  • 27. What are the fundamental building blocks of a big data strategy?
  • 28. Who are the suppliers and users of big data systems?
  • 29. Use case studies
  • 30. Use case: A CSP improves the customer experience by using real-time network analytics to troubleshoot signalling overload
  • 31. Use case: A mobile CSP saves EUR1 million with targeted network investments
  • 32. Use case: Improvements in first-call resolution yield annual cost savings of EUR5 million
  • 33. Use case: A targeted marketing campaign delivers 100% conversion rate
  • 34. Use case: A Tier 1 CSP in the USA uses real-time analytics to improve marketing effectiveness
  • 35. Use case: A European CSP uses probe-based analytics for end-to-end mobile broadband network monitoring and optimisation
  • 36. Use case: A European MVNO achieves an unified view of service usage and profitability of its fixed - mobile business customers
  • 37. Use case: An Asian CSP increases marketing effectiveness with better customer segmentation and targeted promotions
  • 38. Vendor analysis
  • 39. Vendor capabilities
  • 40. Network equipment manufacturers (NEMs)
  • 41. Big data analytics profile: Alcatel-Lucent
  • 42. Big data analytics profile: Cisco Systems
  • 43. Big data analytics profile: Ericsson
  • 44. Big data analytics profile: Nokia Siemens Networks
  • 45. IT vendors
  • 46. Big data analytics profile: Apache Software Foundation
  • 47. Big data analytics profile: EMC
  • 48. Big data analytics profile: Hewlett-Packard
  • 49. Big data analytics profile: IBM
  • 50. Big data analytics profile: Oracle
  • 51. Big data analytics profile: SAP
  • 52. Big data analytics profile: Teradata
  • 53. Specialist telecoms independent software vendors (ISVs)
  • 54. Big data analytics profile: Guavus
  • 55. Big data analytics profile: JDS Uniphase
  • 56. Big data analytics profile: NetScout Systems
  • 57. Big data analytics profile: Neuralitic
  • 58. Big data analytics profile: Tektronix Communications
  • 59. Big data analytics profile: The Now Factory
  • 60. Recommendations
  • 61. Recommendations for CSPs
  • 62. About the authors and Analysys Mason
  • 63. About the authors
  • 64. About Analysys Mason
  • 65. Research from Analysys Mason
  • 66. Consulting from Analysys Mason

List of figures

  • Figure 1: Big data attributes
  • Figure 2: Data volume on telecoms networks, worldwide, 1986 - 2013
  • Figure 3: Data on telecoms networks by type, worldwide, 1990 and 2010
  • Figure 4: Categories of data
  • Figure 5: Examples of big data projects with short- and medium-term benefits
  • Figure 6: Harvesting real-time network data to act now and predict future scenarios
  • Figure 7: Sources of network, customer and market data
  • Figure 8: Drivers behind big data analytics projects cited by Tier 1 and 2 mobile and fixed operators, worldwide, December 2013
  • Figure 9: Potential benefits of big data analytics
  • Figure 10: Product innovation using big data analytics
  • Figure 11a: Market drivers for real-time network analytics
  • Figure 11b: Market drivers for real-time network analytics
  • Figure 12: Market inhibitors for real-time network analytics
  • Figure 13: Analytics and real-time network monitoring revenue, worldwide, 2011 - 2016
  • Figure 14: Data source, storage, business intelligence and advanced analytics definitions
  • Figure 15: Drivers behind big data analytics projects cited by Tier 1 and 2 mobile and fixed operators, worldwide, December 2013
  • Figure 16: Customer segmentation approaches: the four ‘Ps' and clustering
  • Figure 17: Smart Steps product, placement and pricing
  • Figure 18: Analytics system components
  • Figure 19: Suppliers and users of analytics system components
  • Figure 20: Comparison of analytics suppliers by data source, database software and analytics software components
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