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BSI Mining Word of Mouth Communities in 3G Mobile Networks

Marketing 2.0 Conference, Hamburg 2005
BSI




         Join the conversation
  MARKETING 2.0 CONFERENCE
        www.marketing2conference.com
Suresh Sood, School of Marketing, University of Technology, Sydney




Mining WOM Communities
           in
   G3 Mobile Networks
                October 7, 2005




                       Send Correspondence to: ssood@uts.edu.au
AGENDA

•   The Problem
•   Content Specific WOM communities
•   Backgrounder (data set)
•   Visualisation of calling data
•   Train of Thought Analysis
•   Seeing the WOM communities
•   Influencers
•   Steps to Achieving Enterprise Success
•   Marketing to WOM Communities
•   Future Research Considerations
The Problem




How do you identify the natural WOM
communities in mobile (3G) networks ?
Content Specific WOM Communities
•   Buying a new golf club or computer ?

•   Complaining about customer service

•   Friends or experts

•   Advice or just information sharing

     –   Fishing hole
     –   School fete preparation
     –   Fund raising
     –   School reunion
     –   Soccer
     –   Buying a new car
     –   Retirement planning

•   Storytelling
Backgrounder – Data Set
       Service
        Video                          A-B Call
      Activation
     Cancellation                     Date/time
                                      Fault code
                                    Calling number
  Customer                          Dialed number                City
                                     Call Duration             Post code
Date of Birth                                                    State
Martial status                                                    …
  Gender

             Customer Type
               Consumer
              Commercial

                             •   April – July 2003
                             •   Key cities Sydney-Brisbane-Melbourne-Brisbane-Perth
                             •   65,536 calls +
                             •   Key fields encrypted and/or reformatted
Visualisation of A-B video calling data


Dialing Numbers (A)




                                       Called Numbers (B)
Node Link Visualisation of A-B video calling data
Train of Thought Analysis

•      A bottom-up approach using Nodes & Links
•      Origins in intelligence and “bad guy” investigations
•      Perceptual process of discovery to uncover structure
•      Distinguish patterns, structure, relationships and anomalies
•      Knowledge is colour coded
•      Marketing Analyst can spot the WOM communities
•      Not sure why but where does this lead
•      Harnesses the power of the human mind

    Marketing           Marketing                   Marketing
      Data              Information                 Knowledge
WOM communities in A-B party call records
WOM communities in A-B party call records
WOM communities in A-B party call records
WOM communities vary over time
Influencers
People with                         Roles

  – broad and wide social             – Information/knowledge
    networks                            sources and dissemination

  – frequently communicate            – Social pressure in creating
                                        group norms
  – credible influencers
                                      – Social support in trying and
  – Interested in discovering and       using new things.
    telling other about relevant
    new ideas


                                              Tipping Point (Gladwell 2000)
Can you see what I see ?



  Potential Influencers
The Product Adoption Curve and Influencers


                             • Different than Early Adopters

                             • High Influence in Product Adoption
% of Population Adopting




                                               Time
Steps to achieving enterprise success

•   Identify key communities & influencers

•   Maximize budgets e.g. preview advertising, special
    offers can be made or tested with influencers first at
    much lower costs than comparable advertising
    experiments.

•   Precision targeting can be achieved around the
    communities and influencers

•   Fine tune CRM around the communities and influencers.
Marketing to Communities
1.       Identify WOM communities

2.       Assign profitability

     –      Customer probability of buying service
                  = f { product, influence in community }

     -      Customer network value is based on influencing
            sales to other customers

3. Efficient and intelligent marketing to
     communities using viral techniques
Future research considerations
                    Issue                                          Action

Investigating WOM communities can be very         Exploration of automating the visualisation
expensive by virtue of the labour required to     process as well as other techniques e.g.
identify them using visualisation alone.          CART decision tree technique for
                                                  classification of a dataset.

Privacy concerns prevent the ability to readily   Simultaneously provide blogging capability
correlate WOM communities with the subject        to share stories using 3G service of
matter of the information being transferred       customer experiences, luxury brands &
between parties.                                  country destinations.



Identification of influencers and other key       Follow up interviews with individuals
members of WOM communities                        identified as influencers.
Conclusions

• New insights from mobile calling data by identifying
  patterns of WOM communities not previously
  known can be identified

• Opportunity to maximise marketing expenditure,
  increase precision of targeting and create
  competitive advantage
When we dream alone, it is only a dream.
 When we dream together, it is no longer
 a dream but the beginning of reality.


     Adapted from a Brazilian proverb

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Mining Word of Mouth Communities in 3G Mobile Networks

  • 1. BSI Mining Word of Mouth Communities in 3G Mobile Networks Marketing 2.0 Conference, Hamburg 2005
  • 2. BSI Join the conversation MARKETING 2.0 CONFERENCE www.marketing2conference.com
  • 3. Suresh Sood, School of Marketing, University of Technology, Sydney Mining WOM Communities in G3 Mobile Networks October 7, 2005 Send Correspondence to: ssood@uts.edu.au
  • 4. AGENDA • The Problem • Content Specific WOM communities • Backgrounder (data set) • Visualisation of calling data • Train of Thought Analysis • Seeing the WOM communities • Influencers • Steps to Achieving Enterprise Success • Marketing to WOM Communities • Future Research Considerations
  • 5. The Problem How do you identify the natural WOM communities in mobile (3G) networks ?
  • 6. Content Specific WOM Communities • Buying a new golf club or computer ? • Complaining about customer service • Friends or experts • Advice or just information sharing – Fishing hole – School fete preparation – Fund raising – School reunion – Soccer – Buying a new car – Retirement planning • Storytelling
  • 7. Backgrounder – Data Set Service Video A-B Call Activation Cancellation Date/time Fault code Calling number Customer Dialed number City Call Duration Post code Date of Birth State Martial status … Gender Customer Type Consumer Commercial • April – July 2003 • Key cities Sydney-Brisbane-Melbourne-Brisbane-Perth • 65,536 calls + • Key fields encrypted and/or reformatted
  • 8. Visualisation of A-B video calling data Dialing Numbers (A) Called Numbers (B)
  • 9. Node Link Visualisation of A-B video calling data
  • 10. Train of Thought Analysis • A bottom-up approach using Nodes & Links • Origins in intelligence and “bad guy” investigations • Perceptual process of discovery to uncover structure • Distinguish patterns, structure, relationships and anomalies • Knowledge is colour coded • Marketing Analyst can spot the WOM communities • Not sure why but where does this lead • Harnesses the power of the human mind Marketing Marketing Marketing Data Information Knowledge
  • 11. WOM communities in A-B party call records
  • 12. WOM communities in A-B party call records
  • 13. WOM communities in A-B party call records
  • 14. WOM communities vary over time
  • 15. Influencers People with Roles – broad and wide social – Information/knowledge networks sources and dissemination – frequently communicate – Social pressure in creating group norms – credible influencers – Social support in trying and – Interested in discovering and using new things. telling other about relevant new ideas Tipping Point (Gladwell 2000)
  • 16. Can you see what I see ? Potential Influencers
  • 17. The Product Adoption Curve and Influencers • Different than Early Adopters • High Influence in Product Adoption % of Population Adopting Time
  • 18. Steps to achieving enterprise success • Identify key communities & influencers • Maximize budgets e.g. preview advertising, special offers can be made or tested with influencers first at much lower costs than comparable advertising experiments. • Precision targeting can be achieved around the communities and influencers • Fine tune CRM around the communities and influencers.
  • 19. Marketing to Communities 1. Identify WOM communities 2. Assign profitability – Customer probability of buying service = f { product, influence in community } - Customer network value is based on influencing sales to other customers 3. Efficient and intelligent marketing to communities using viral techniques
  • 20. Future research considerations Issue Action Investigating WOM communities can be very Exploration of automating the visualisation expensive by virtue of the labour required to process as well as other techniques e.g. identify them using visualisation alone. CART decision tree technique for classification of a dataset. Privacy concerns prevent the ability to readily Simultaneously provide blogging capability correlate WOM communities with the subject to share stories using 3G service of matter of the information being transferred customer experiences, luxury brands & between parties. country destinations. Identification of influencers and other key Follow up interviews with individuals members of WOM communities identified as influencers.
  • 21. Conclusions • New insights from mobile calling data by identifying patterns of WOM communities not previously known can be identified • Opportunity to maximise marketing expenditure, increase precision of targeting and create competitive advantage
  • 22. When we dream alone, it is only a dream. When we dream together, it is no longer a dream but the beginning of reality. Adapted from a Brazilian proverb