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
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MARKETING 2.0 CONFERENCE
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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
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
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
