This is a slide deck used for 'Netnography: Overview & How-to' presentation on Feb. 15, 2012. The presentation (watch the YouTube video below) was a part of the class assignments for "Social Media Marketing" class taught by Robert Kozinets at Schulich School of Business, York University. In this presentation, topics such as why netnography is useful for marketing research and what the researchers have to keep in mind are explored with some specific examples. The video on the first slide is a teaser for this presentation. The link to the recorded presentation: https://www.youtube.com/watch?v=UWApBu2ERTU&context=C31c1b83ADOEgsToPDskJO-DQt8ZUtzIA-tdvMiOHd

1. NETNOGRAPHY:
OVERVIEW & HOW-TO
Anferny Chen & Hiro Sudo Feb 2012

2. We will help you understand…
AGENDA
 What is ‘netnography’
 How great it is
 How it works
 Our first netnography experience
 Takeaways

3. What is ‘netnography’
Internet + Ethnography
Ethnography
• Understand culture of
a community
• Qualitative method
• Field study
(Wikipedia)

4. How does Ethnography work?
Planning Where to go? How long?
Learn culture/
Entrée Know the players rituals
Data Observation
Interview/
Collection Questionnaire
Analysis Skills Experience
Reporting Academic Conference

5. Ethnography for Marketing Research
 Knowing consumer culture provides insights about…
• Why people buy (Needs)
• How people like us (Brand perception)
• Who customers are (Segments)
• Why people choose us (Competition)
• How people respond to our ads (ROI)
Great hints for better
managerial decisions

6. Ethnography vs Well-known methods
Well-known methods Ethnography
 Artificial  Natural
 Outsider observation  Immersive
 Mostly numeric data  Descriptive
 1 perspective/time  Multi-method
 Adaptable
A window into the realities: In-depth insight

7. Netnography: Online Ethnography
 Technology makes ethnography…
• More cost-effective
• Less painstaking (automatically logged)
• Less obtrusive (more natural)
• Less time-consuming (geography)
• Accessible to various groups
• Able to observe the past

8. How does it work?
Planning
Entrée
Data Collection
Analysis
Reporting

9. Planning Entrée Data Analysis Reporting
Collection
Example Case: Listerine
 Objective : Identify Listerine’s brand personality
 Key Question : Where Listerine consumers gather?
What brand meanings has?
 Target Group : Blogs such as Lost in Laundry

10. Planning Entrée Data Analysis Reporting
Collection
 You need to…
• Know the culture of the community
• Behave as a community member
• Be accepted/credited by the community
 Don’t forget that…
• This is not an interrogation
• Someone might have done the same research
• The community knows much more than you do

11. Entrée Failure (to an activist group)
A young researcher R.K.:
I am a professor at XX University…interested in
finding out more about individual’s involvement in
boycotts… This might help make your activities
maximally effective… Thank you very much for
your participation in this ‘cyber-interview’
Sincerely,
A member:
This is fishy!! Everyone, let’s
“BOYCOTT THIS RESEARCH”!!!!!

12. Entrée Failure (to an activist group)
A young researcher R.K.:
I am a professor at XX University…interested in
finding out more about individual’s involvement in
boycotts… This might help make your activities
maximally effective… Thank you very much for
My force was not strong
your participation in this ‘cyber-interview’
Sincerely,
enough…
A member:
This is fishy!! Everyone, let’s
“BOYCOTT THIS RESEARCH”!!!!!

13. Planning Entrée Data Analysis Reporting
Collection
 Communication with members, not the website
• Copy from pre-existing communications
Archival • Cultural baseline info
Data • Copy & Paste or Archival Software e.g. Quotations
• Filter data by direct communications
Elicited • Objective-related info,
Data • Communal Interaction (postings) or Interviews (e-mail)
e.g. Answer to specific questions
• Record what you sensed/felt during the online experience
Fieldnote • Deeper insight into the culture,
Data • Note-taking e.g. Context (shocked by an event)

14. Planning Entrée Data Analysis Reporting
Collection
Archival Data Example: Listerine
 “Generally, the idea of Listerine gives me the shivers. I think of the old school
original flavor that my grandpa used to use and want to run screaming.”
 “Grandpa always made me gargle with Listerine when I had a little cough or
cold. Grandpa soaked his feet in Listerine. Coming up close for a hug, my
Grandpa would always have the slight lingering scent of Listerine about him. ”
• The brand is rooted in nostalgia
• Implications about limitations and
opportunities (such as new geriatric lines).

15. Planning Entrée Data Analysis Reporting
Collection
Archival Data Example: Listerine
 “Generally, the idea of Listerine gives me the shivers. I think of the old school
original flavor that my grandpa used to use and want to run screaming.”
 “Grandpa always made me gargle with Listerine when I had a little cough or
cold. Grandpa soaked his feet in Listerine. Coming up close for a hug, my
Grandpa would always have the slight lingering scent of Listerine about him. ”
• The brand is rooted in nostalgia
• Implications about limitations and
opportunities (such as new geriatric lines).

16. Planning Entrée Data Analysis Reporting
Collection
Elicited Data Example: Why people like Star trek
• Star Trek “was the symbol of a world where there was no racism, poverty,
deformity, idiotic nationalism, or political injustice … we fen [plural for fan]
have put much of our energy into it, and into making the world a little more
like the Federation which we admire so much” (e‐mail interview).
• “At its simplest, what Star Trek means to me—and, I think, to many fans—is
possibility. … People do want to live in the Trek universe” (e‐mail interview).
• Utopian nature is the reason
• “Fen” implies established
culture of this community

17. Planning Entrée Data Analysis Reporting
Collection
Fieldnote Example: coffee connoisseur community
• ”…I kept observational fieldnotes about my changing
coffee habits, about conversations and meals at friends’
and families’ homes, about my shopping ventures, about
my trips to Starbucks…”
• “Data about the effect that the community had on my
entire social experience…” Rob Kozinets
• Now you know the needs &
wants of the target segment
• You became a part of it

18. Planning Entrée Data Analysis Reporting
Collection
 You need to…
• Clarify strategic implications
• Assume managers don’t understand jargons
• Be convincing with solid evidence & logics

19. Ethical Concern
 You need to…
• Be respectful (introduce yourself, ask permission)
• Be legal (terms of use, human rights)

20. The Netnography Experience
 Objective: Examine if the SMM Facebook page is
enhancing peer-learning
 Audiences: Online communities at Schulich, IMBA’12,
GBC and SMM
 Time Frame: Jan 18th to Feb 8th
 Approaches:
 Quantitative: Gathering the posts and replies info
 Qualitative: Surveying the identified candidates to
explore the depth of analysis and potential
recommendation

21. Communities Background
IMBA’12: Small SMM: Mixed of GBC: Large
community provides small & large community serves
interactive activities community that aims for information &
outside of class to provide students interaction
interactive learning
Schulich Communities

22. SMM FB Activities: Jan 18th to Feb 8th
16
14
12
10
8
Posts
Replies
6
4
2
0

23. IMBA’12
 Reply rate: 85%  Total # of members: 45
 Reply-to-post ratio: 5.07
Devotee: 3 out of 5 Insider: 0 out of 5
identified identified
E-Tribal
Tourist: 73% Mingler: 2 out of 5
identified

24. GBC
• Reply rate: 25% • Total # of members: 682
• Reply-to-post ratio: 2.22 • % of one-time posters:
79%
Devotee: 2 out of 6 Insider: 0 out of 6
identified identified
E-Tribal
Tourist: 94% Mingler: 4 out of 6
identified

25. SMM
• Reply rate: 49% • Total # of members: 346
• Reply-to-post ratio: 1.42 • % of one-time posters:
60%
Devotee: 3 out of 6 Insider: 1 out of 6
identified identified
E-Tribal
Tourist: 87% Mingler: 2 out of 6
identified

26. Highlights
* 85% reply rate * 49% reply rate * 25% reply rate
* 5.07 R/OP ratio * 1.42 R/OP ratio * 2.22 R/OP ratio
* A space for class * A learning space or * Information space
and fun activities reporting duty? with sub-group
activities
Schulich Communities

27. Next Step
 “There are lies, damn lies and statistics”
 Questionnaires for qualitative analysis
 Q1. Motivation for posting/ replying
 Q2. What kind of contents you are likely to post or
reply to
 Q3. What would motivate you to post/reply more.

28. Sample Archival Data Analysis (SMM)
 Our guest today mentioned Don Tapscott - CBC Radio 1 has
broadcast 3 of a 4 part series, with the 4th next Sunday. All
available as podcasts
 Linking the in-class activity with external resource. This post
provides the additional learning opportunity and resource for
other students

29. Sample Elicited Data Analysis (SMM)
 A1 (motivation to post). I like to voice my opinion and engage
in a debate with my peers on certain topics. Plus we also
receive class participation marks for posting.
 A2 (content). I like to reply to controversial topics the most.
 A3 (motivation to post more). If more of my classmates replied
to my posts to further debate. And if some of the topics posted
were more controversial.
 Controversial topic gets people interacting. Class-participation
mark is the incentive but getting more people involved would
generate the true motivation the peer-learning and interaction.

30. The Result?
 To Be Continued!

31. Conclusion
Research Experience
• Being an anonymous is challenging for conducting
netnography research (lack of responce)
• Selection process (for identifying targets) takes time
• Need guidance and tools to stay objective
Learning Experience
• The mixed use of qualitative vs quantitative: one gets the
direction and another helps exploring the depth
• It’s fun and the observation is extensive, because there are
different angles to take and response extends the learning

32. Contributors
Social Media Marketing GBC & IMBA
Alex Athanasopoulos
Sai Ra

  Sudeep Garg
Farhang
 Alyssa Fearon 
 Suzanne Pragg
 Charmainne King 

Norman Wong
Alex Wolf
 Pratysh D  Shaun Charles
 Yvonne Chang
 Satyameet Ahuja  Sandeep Nath
 Meggie Lee
 And many others  Derek Lud
 Brian Inigues

33. QUESTIONS??
Thank you very much for listening