Getting answers without asking questions: Using social media netnography with patients (by Robert Dossin), presented at the BHBIA Workshop in London (UK) on Thursday January 17, 2013.

1. Getting answers without
asking questions
Using social media netnography with
patients
January 17th 2013
Robert Dossin – Director Life Sciences & Healthcare
www.insites-consulting.com
Ghent I Rotterdam I London I Timisoara I New York

2. Today’s session
 Introduction
 Social Media Netnography: Methodology
 Applications
 Cases (4)
 FAQ (10)
Today’s
programme

3. Listening online -
helping patients
In this session InSites Consulting will show how
observational methods can lead to new patients
insights for pharmaceuticals teams.
3

4. User-
generated-
content
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5. Observational research
1. Social media netnography
2. Multimedia ethography
Netnography

6. Where does observing fit in?
% Surveys Discussions Observing
≠ clinical observation
Traditionally used most in Not yet fully exploited
the pharmaceutical industry in the pharmaceutical
industry
6 6

7. Who has
experience
with
observational
research?

8. 8 3 pillars
Observational
research

9. Tapping into online conversations
Social Media Social Media
Nethnography Dashboards / Monitoring
exploratory for
enhanced quality
Observational
research 9
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10. Social media netnography
= research methodology
that makes use of publicly
available user-generated-
content in order to answer a
research question
Observational
research 10

11. The process
Ethnography
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12. Exercise 3
As with any innovation and new method there are advantages as well as
concerns. Please take 5 minutes and write down your concerns, the reasons why,
the applications you think of and what you really would want to know
CONCERNS APPLICATIONS
REASONS WHY I WOULD WANT TO KNOW
Observational
research

13. Play by the book!
Respect key MR principles
ESOMAR & BHBIA guidelines
PHARMA specific
requirements and drug
safety/pharmacovigilance
Even AGENCY-CLIENT
specific processes
13 13

14. Social media netnography
Award winning methodology
When pioneering novel techniques it is important to rely
on a partner who can go along with you, follow you in
your reflections, bring in suggestions and adapt
accordingly.
We have appreciated InSites’ flexibility, the stimulating
discussions we had, their drive to transform the “golden
nugget” into a workable approach, … finally their
commitment to move forward and to deliver.
Rudi Van Campenhout, UCB pharma

15. Reversed research process
Traditional research Social media nethnography
SURVEY/ TOPIC GUIDE SAMPLING
DEVELOPMENT UNIVERSE DETECTION
SAMPLING PASSIVE
RESPONDENTS DATA COLLECTION
ACTIVE FRAMEWORK
DATA COLLECTION DEVELOPMENT
ANALYSIS TOP-DOWN & BOTTOM-UP
CONTENT & TEXT ANALYTICS
Observational
research

16. 1. Universe detection
Observational
research
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17. Representativeness
‘It’s the end of the world as we know it’
Observational
research
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18. Conversations are the new unit of analysis

19. Selecting the right keywords
Keywords for
sampling
Conversations on Conversations on
epilespy Epilepsy brands
Observational
research
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20. Selecting the right keywords
Conversations on Conversations on
epilespy Epilepsy brands
Analysis
Conversations on
doctor
Observational
research
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21. Good definition of a
universe
Observational
research
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22. Google keywords
Thesaurus
Keywords for search engine optimization
Observational
research
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23. Sampling is still key
Observational
research
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24. Visitors
Small #
Large influence
Medium influence
Long tail
Specialized in topics
# sources
Observational
research
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25. Visitors
http://www.healthboards.com/
http://www.coping-with-epilepsy.com/ http://bschool.pepperdine.ed
http://scott-lwe.blogspot.com/ u/studentblog/index.php/201
0/07/freedom-from-epilepsy-
doesnt-come-free/
# sources
Observational
research
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26. About profile information
 Profile information on poster / user level
– Internet has no boundaries
– Only information available that is spontaneously shared
– Representativity: what do we know about profile on social media?
 Profile information on source level
– Type of social media source
– Meta data
 About influentials
– On person level
– On source
Observational
research

27. 2. Data collection
Observational
research
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28. Observational
research
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29. All doubles, headers & footers eliminated
One post can only appear once!
Data restructuring
= maximum error reduction
Observational
research

30. Historical data
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
week week week week week week week week week week week week
1 2 3 4 5 6 7 8 9 10 11 12
brand 1 65% 82% 64% 63% 50% 25% 23% 35% 40% 45% 48% 49%
Market 75% 55% 55% 48% 29% 54% 58% 60% 65% 63% 68% 70%
Observational
research
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31. Public & fan pages only
Becoming friends
Observational
research
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32. 3. Framework development
Observational
research
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33. Two core principles
Top down
analysis
Bottom-up
analysis
Observational
research

34. The power of text analytics
Text analytics is the process of
extracting knowledge and
information from text
Stage 1: Extraction of terms Stage 2: Group terms in higher level
What do I want to use? concepts or categories
Observational
research

35. Analysis process
Analyse original verbatims via qualitative
3.Qualitative content analysis
analysis
2.Associative
pattern Which terms often co-occur? Which
recognition
concept are often mentioned bottom-
up?
1. Descriptive
taxonomy
detection
To what extent do we find our research
topics back?
Observational
research

36. Sentiment analysis
Sentiment of the Sentiment of a
conversation brand / topic
Conversation level Sentence level
'I had a terrible day. I used 'I had a terrible day. I used
brand x and felt better' brand x and felt better'
1 positive – 1 negative 1 positive
Observational
research

37. Happiness Excitement Elated, Excited, Enthusiastic
Happy Joyful
Cheerful Satisfied Relaxed Contented, Relaxed, Satisfied
Proud
Social Trust, Love, Respect
Surprise Beauty Beautiful, Admired, Attractive
Emotions
Amazed Surprised
Astonished
Fear
Afraid Nervous
Tense Worried
Insecure
Anger
Violence
Angry Furious Aggressive, Abusive, Dominating
Mad Irritated
Swear words
Resentful
Sadness Despair Agony, Despair, Ashamed
Sad Disappointed Weak Tired , Bored, Weak
Regretful Gloomy
Pessimistic Loneliness Lonely, Rejected, Alienated
Disgust
Disgust Contempt
Nasty
Observational
research

38. 4. Analysis
Observational
research
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39. Main conversation themes
Quartile 1 Quartile 2 & 3 Quartile 4
 Methodology
– Different clusters of words in the buzz are found that refer
to different themes
Quartile 4
– For each theme, we determine the size of the cluster and
the sentiment of the cluster 2
 Interpretation
– The themes are divided into different quadrants, based
on quartiles in volume and sentimeter.
– The different quadrants thus need to be interpreted in a
Quartile 2 & 3
relative way, and within a certain language. E.g. themes
in quadrant 2 are relatively more positive than themes in
quadrant 1 but can still have a negative sentiment. 4
– Meaning of different quadrants
• 1: Act: themes that are often mentioned with a negative
sentiment
• 2: Develop: themes that are often mentioned with a positive
sentiment
Quartile 1
• 3: Threats: themes with negative sentiment that are
currently not often mentioned but that are explicitely
negative for certain niches
3 1
• 4: Potential: themes with mixed sentiment that are often
mentioned. In the future, we can try to influence the
sentiment of those themes positively
Observational
research

40. Main conversation themes in epilepsy
diet
Sentimeter surgery
family
social impact
children
Success
Polytherapy treatment
diagnosis
Treatment
epilepsy
(no) control
seizures
Other treatments budget
Molecules stakeholders
knowledge
medication brands
Medication medication
unloyalty
Frequency and
symptoms
duration seizures types epilepsy
administration
Specific triggers medication
side effects
Alternative generic medication
treatment Seizure triggers
Volume
Observational
research

41. What symptoms are most often mentioned in
online conversations?
sleep issues and fatigue 21%
stress and anxiety 15%
emotional and mood 9%
dizziness 9%
pain 7%
other psychological 7%
other 6%
headache 6%
memory issues 6%
symptoms 6%
cardiovascular 5%
confusion 4%
weight issues 4%
muscles 4%
vision 3%
respiratory 3%
gastro 3%
tumor 1%
Observational
research

42. Product evaluation
&
branding
Observational
research
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43. Share of brands in total amount of
conversations
Total sample
N = 42469
Brands for epilepsy
treatment are Total sample
100%
mentioned in about N = 42469
14% of online
conversations 30%
N = 12754
Pharmaceutical treatment
Brands
14% 2% (832 conversations)
N = 5836
are about generic
products
Observational
research

44. What is the share of voice for the different
treatments for epilepsy?
10,00%
9,00% The number of conversations about
8,00% Keppra and Vimpat decreased over the
last half year.
7,00%
6,00%
5,00%
4,00%
3,00%
2,00%
1,00%
0,00%
June July August September October November
Keppra 5,74% 4,73% 5,81% 4,92% 4,49% 3,45%
Lamictal 4,90% 4,33% 5,02% 3,91% 3,54% 3,40%
Topamax 2,23% 1,58% 1,29% 1,45% 0,86% 1,57%
Depakote 0,95% 1,10% 1,47% 1,76% 1,72% 0,83%
Trileptal 1,96% 1,47% 1,41% 1,45% 0,77% 0,61%
Vimpat 1,61% 1,72% 1,01% 0,86% 0,41% 0,31%
Observational
research

45. Sentimeter
Brands for treatment of Epilepsy (top 10 brands with most buzz)
Sentiment about Depakote is significantly more positive in comparison with other brands.
Comparing the current results with the results of last year (different calculation method),
we observe that sentiment on Keppra decreased relatively compared to other brands.
Depakote Vimpat Epilim Trileptal Lamictal Topamax Lamotrigine Keppra Diastat Dilantin
90 90 90 90 90 90 90 90 90 90
80 80 80 80 80 80 80 80 80 80
70 70 70 70 70 70 70 70 70 70
60 60 60 60 60 60 60 60 60 60
50 50 50 50 50 50 50 50 50 50
40 40 40 40 40 40 40 40 40 40
30 30 30 30 30 30 30 30 30 30
20 20 20 20 20 20 20 20 20 20
10 10 10 10 10 10 10 10 10 10
0 0 0 0 0 0 0 0 0 0
-10 -10 -10 -10 -10 -10 -10 -10 -10 -10
-20 -20 -20 -20 -20 -20 -20 -20 -20 -20
-30 -30 -30 -30 -30 -30 -30 -30 -30 -30
-40 -40 -40 -40 -40 -40 -40 -40 -40 -40
-50 -50 -50 -50 -50 -50 -50 -50 -50 -50
0,41 0,38 0,33 0,28 0,25 0,25 0,23 0,21 0,19 0,14
N = 306 N = 281 N = 156 N = 221 N = 1037 N = 341 N = 138 N = 1341 N = 129 N = 192
Comparison with sentiment across brands vs top 10
Significantly higher than average Observational
No significant difference with average research
Significantly lower than average

46. Patient
language
Observational
research
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47. Petit mal
 Also called ‘absence’
 People just stand and stare vacantly
 My mind was wandering, blank, in a daze
 When asked to speak, would talk then just go blank and lose my
entire train of thought and having no idea what I had implied
 My eyes would blink rapidly and then I would go blank as if I were
daydreaming and my mind would be shot
Observational
research

48. Applications
Observational
research
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49. Typical management objectives
InSites Consulting has performed many Social Media Netnography studies in several
therapeutic areas: Parkinson’s disease, Restless Leg Syndrome, Epilepsy and
Rheumatoid Arthritis.
The main objectives of these studies were to…
1. …get insights directly from patients based on their spontaneous online discussions
2. …increase the effectiveness of the brand plans

50. Typical research questions
Get insights directly from patients based on their spontaneous online discussions
1. Which are the main topics that are discussed by patients and their caregivers?
2. Which language do patients use when talking about these themes?
3. What is the emotionality of the different topics? How can these emotions be explained?
4. What are the main information sources consulted by patients & caregivers?
Increase the effectiveness of the brand plans
1. How big is the buzz about product brands (you and competitors)? How did this change over time?
2. What is the tone of voice of the buzz about product brands ? Which sentiments are used?
3. Is this in line with the desired brand positioning?

51. All Applications
Digital strategy
Netnography study that results in an integrated communication & conversations
strategy in terms of who to target, with which content, on which platform.
(Reporting will be based on our activation model.)
Consumer insightment
Netnography study that results in insights and ideas on unmet consumer needs,
buying decision processes & product category usage.
Campaign evaluation tracker
Netnography study that results in an ad-hoc evaluation or tracking of a campaign
Online Brand audit Netnography study that results in a 360° view on your brand
and its main competitors
Product evaluation netnography / tracker
Netnography study that results in ad-hoc evaluation or tracking of a product
Definitions

52. ………………………………………………………………………………………………….…….
In Summary
………………………………………………………………………………………………….…….

53. Social media netnography: benefits
Access to highly involved patient
and difficult topics
Many conversations
Most on relevant topics
Ideal to explore new
TAs/topics/countries
53 53

54. Social media netnography: limitations
Less of the uninvolved
patient
No probing
Only what is relevant for them
Internet has no boundaries
54 54

55. Cases
UCB – Social Media as the central nervous system for learning about epilepsy
Patients at the Heart
The next slides illustrate cases to demonstrate different area‟s of patient research
CASE 2 – Janssen – Diabetes (II)
3 steps to get to a new strategy using a mix of contemporary observation methods with patients
CASE 3 – Danone – Breast Feeding Community
Drivers, barriers & opportunities for formula feeding
CASE 4 – Janssen – And they lived happily ever after
Analysing user generated content on social media to increase the elderly‟s quality of life
CASE 5 – Janssen - Schizophrenia 24 x 7
How understanding patients helped Janssen to improve its website using Social
Media Netnography Research in 5 languages
Cases

56. Thoughts to hold on to
Patients are consumers, “consumers of health” ; their discussions
are what they perceive the truth
Netnography is both a quantitative as well as a qualitative method
that requires domain knowledge to mine the user generated content
from patients
Patients want to be engaged, „cause they are engaged!
We should reinvent ourselves, engage with them where they are
& support Pharma companies in their patient conversations
Not just send our tradional messages via new channels
Not just by becoming a new channel checklist company

57. Stay tuned!
We believe in sharing knowledge with Connecting on LinkedIn
all of our stakeholders.
Connecting on Facebook
Become friends with us, visit our blogs,
download our papers and presentations, Free content on Slideshare
order our books…
InSites Consulting blog
The Conversation Manager blog
How cool brands stay hot blog

58. Robert@insites-consulting.com
@Robert_Dossin
http://uk.linkedin.com/in/RobertDossin

63. Robert Dossin
Managing Director UK
Robert’s experience:
Robert is MD in the UK and Global head of Life Sciences & Healthcare at InSites Consulting and is
responsible for the market research and consulting offerings for all clients in the Healthcare sector
Before joining InSites Consulting, Robert worked at IMS Health, the worlds leading information provider in
the pharmaceutical industry. In the six years that he worked at IMS, as VP Marketing he has been
responsible for raising awareness of IMS high value products & services
Robert started his career at Nissan, at the European HQ of the Japanese car manufacturer as Market
Researcher and has had various marketing roles with increasing levels of responsibility.
Also, he was a marketing director at Forrester Research for 5 yrs, the European HQ of the worlds leading
Internet Technology Research firm and thought-leader in Information Technology advice.
Robert’ expertise and project experience:
Robert is used to partner with commercial teams, business units heads and chief marketing officers on
strategy development and implementation – and is an expert in launching new products, (digital) marketing
and customer understanding. He works with many Pharma & Health companies such as AstraZeneca,
GSK, J&J and is a regular speaker at Healthcare and marketing conferences..
Educational background:
Robert studied Business Economics with a major in Market Analysis and Business Statistics at the Erasmus
University, Netherlands, hold a Masters in Science (MSc) degree in Marketing and a Postgraduate Diploma
in Marketing (DipM) from the Chartered Institute of Marketing in the UK, where he is also a Chartered
Marketer and elected Fellow. In addition, he sits on the medical marketing industry group of the CIM and on
the editorial board of the Journal of Medical Marketing.
Bio