1.
Personalized Intelligence in KOL engagement and why
it’s all about you.
Copyright 2016. rMark Bio, Inc.

2.
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Researchers produce health data
useful in KOL Identification.
+ Clinical Studies
+ Prescriber Data
+ Peer-to-Peer Reviews
+ Other
+ Publications
+ Grants
+ Lectures
+ Patents
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3.
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This global data is made
publicly available.
+ ACS
+ ADA
+ AMA
+ Editorial Boards
+ Conferences
+ NIH
+ PubMed
+ Patent
+ Medicare
+ Clinical Trials
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4.
How is big data analysis helping in
KOL identification?
01.
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5.
Very specific criteria and you
are trying to generalize to
include more individuals like
the ones that meet your
specific criteria.
(Who else is like me?)
Top Down vs. Bottom Up
start here >
Copyright 2016. rMark Bio, Inc.

6.
With a massive number of
possibilities, you are trying to
apply criteria to get to what
matters most to you.
(Filtered Search)
Bottom Up vs. Top Down
start here >
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7.
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Text Mining
Ability to search and identify key
information across hundreds of
disparate data types and sources.
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8.
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Looking at who is in a KOLs network and
the type of influence they have.
Influence
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9.
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Rate and rank potential key opinion
leaders based on criteria.
Scoring
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10.
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Automatically group KOLs
Categorization
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11.
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All of these techniques
provide similar value.
Time
Savings
Ease of
Identification
New
Opportunities
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12.
What are public data limitations and why do
you need personalization?
02.
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13.
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Analyzing global health data
provides insights that help
in KOL identification.
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14.
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But public data insights from
these approaches only
tells half the story.
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15.
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business.
needs.
goals.
strategies.
teams.
(All too unique to be simply quantified.)
Your
Your
Your
Your
Your
Copyright 2016. rMark Bio, Inc.

16.
Common needs extend beyond
what public data alone can answer.
+ Identifying one
researcher from
another beyond
publications, grants or
lectures
+ Pivoting in real-time
based on internal
needs and market
response
+ Identifying one
clinician versus another
beyond clinical trials
and perception data
Copyright 2016. rMark Bio, Inc.

17.
03.
What is personalization, how is it used, and
where does it come from?
Copyright 2016. rMark Bio, Inc.

18.
Personalization is the process of automatically
learning your individual and organization
preferences, then applying them to global data
in order to find key opinion leaders that can
positively impact your business.
Copyright 2016. rMark Bio, Inc.

19.
Personalization is used in many aspects of our lives.
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20.
Copyright 2016. rMark Bio, Inc.
Personalization is used in many aspects of our lives.

21.
Copyright 2016. rMark Bio, Inc.
Personalization is used in many aspects of our lives.

22.
What types of data
drives personalization?
+ Details personalization
engines can derive from
sources that humans can’t add
in as search keywords
+ Favorable attributes for
specific use cases. Thoughts
you didn’t even know about.
Copyright 2016. rMark Bio, Inc.

23.
Where does personalized
data come from?
+ KOL Interactions
+ Key Performance Indicators
+ Outcome Management
+ Evolving Priorities
+ Competition
+ Regulatory Changes
+ Shifting Priorities
+ Key Performance Indicators
+ Strategy
+ Budget Management
+ New Targets
+ Efficacy
+ Trials
+ 5 Year Strategy
+ Public Perception
+ Growth Goals
+ Regulatory Management
+ Cost Management
MSLs Medical Affairs R&D Corporate
Copyright 2016. rMark Bio, Inc.

24.
What types of data
help personalization?
+ CRM
+ Strategy Documents
+ User Feedback
+ Conversation Tracking
+ Drug Data
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25.
Not one database on the planet
stores all of this in one place –
every company, every Med Affairs
team and every MSL.
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26.
What does personalization intelligence
look like in action?
04.
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27.
Tailored KOL Identification
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28.
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The Challenge
Identify several dozen new KOLs in key
geographies within the United States.
EXPERIMENT #1
Personalization through KOL Analytics
Therapeutic Area
Diabetes Type II
Parameters
• Cannot be known KOLs (list provide)
• Compare results to Basic Search & Analytics
• Access to Internal Data, including CRM, MSL
Feedback, Strategy Data, Investment Data
Copyright 2016. rMark Bio, Inc.

29.
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TEST #1
Amalgamate & Search
AGGREGATE SEARCH
7,562 “POTENTIAL KOLS”
NIH
PubMED
Patents
Clinical Trials
….
“DIABETES TYPE 2”
“UNITED STATES”
Copyright 2016. rMark Bio, Inc.

30.
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TEST #2
Analytics & Filtering
ANALYTICS KOL LIST 9,889
1,311 “POTENITAL KOLS”
Published
Grants
Influence
Geography
ONLY PEOPLE
NOT
IN EXISTING
DATABASE
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31.
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Collaboration
Recommendation
Engine
(CoRE™)
Current KOLs
Feedback MSLs
Geographical
Relevance
Budget
Private Data
Strategy Data
Copyright 2016. rMark Bio, Inc.
TEST #3
Personalization

32.
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PERSONALIZED
6 different data sources on
either a one-time or on-
going basis.
Using this data, we created a
unique, multi-level profile for
the organization, Med Affairs
Division and MSLs.
Copyright 2016. rMark Bio, Inc.
TEST #1
Personalization Results
52
Personalized
KOL Recommendations

33.
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RESULTS #1
Amalgamate & Search
KOL list
RESULTS #2
Plus Analytics
KOL list results
RESULTS #3
With Personalization
7562 1311 52
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EXPERIMENT #1
Comparing Personalization to existing KOL Analytics

34.
05.
Challenges with
personalized intelligence?
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35.
How do we find the sweet spot between
too much and too little information?
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+ Frequency + Details + Decisions

36.
How do we enable feedback to be
meaningful, yet quick?
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+ User Interface + Q&A Bots + Training

37.
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How is rMark Bio
solving the problem?
+ A intelligent platform
that does the work for
you. Evolves with you
and provides instant
insights.
+ A web and mobile
application designed
by MSLs for MSLs to
find and engage with
potential KOLs.
+ Extensible system
that automatically
tailors the results, data
and applications to
your unique needs.

38.
Thank you.
Jason Smith
CEO & Co-founder
rMark Bio, Inc.
www.rmarkbio.com
@rmarkbio
Copyright 2016. rMark Bio, Inc.