1.
Improving
Pharmaceutical marketing performance
using big data solutions
Paul Grant
Chief Innovation Officer
@paulgrant

2.
Photo credit: pasukaru76 / Foter / Creative Commons Attribution 2.0 Generic (CC BY 2.0)

3.
Photo credit: Eva Rinaldi Celebrity and Live Music Photographer / Foter / Creative Commons Attribution-ShareAlike 2.0 Generic (CC BY-
SA 2.0)

4.
Photo credit: NASA Goddard Photo and Video / Foter / Creative Commons Attribution 2.0 Generic (CC BY 2.0)

5.
Photo credit: josullivan.59 / Foter / Creative Commons Attribution 2.0 Generic (CC BY 2.0)

6.
Photo credit: AndYaDontStop / Foter / Creative Commons Attribution 2.0 Generic (CC BY 2.0)

7.
A sizeable proportion of consumers are
happy for companies to use their
personal data, providing they benefit
through more targeted marketing
Photo credit: http://www.ey.com/Media/vwLUExtFile/BigData/$FILE/ey-bigdata_v3.png

8.
“There are a lot of
small data problems
that occur in big data.
They don’t disappear
because you’ve got
lots of the stuff.
They get worse.”
David Spiegelhalter
Winton Professor of the Public Understanding of
Risk at Cambridge University

9.
'don’t care – big data is a
pointless marketing term’
Online Measurement and Strategy Report 2013 by Econsultancy, July 2013
8%Marketers say…

10.
Time available to analyse data in Google Analytics is too
little, so adding more data to the 'pile' to analyse will only lead
to less insight, not more.
Little to none. We know we need to gather and analyse the
available date to run our marketing and our business better,
but 'big data' is not the driver of this.
We have tonnes of data and sometime it's difficult to analyse,
but this has always been a problem and always will be as
data acquisition will keep growing.
Not sure what "big data" means.
Online Measurement and Strategy Report 2013 by Econsultancy, July 2013

11.
Photo credit: http://engagementstrategy.com/articles/diffusion-of-pharma-digital-innovators/
What about Big Data in Pharma^?
marketing

12.
Photo credit: http://calibergroup.files.wordpress.com/2010/10/traditionaldigitalscale.jpg

13.
Photo credit: j.reed / Foter / Creative Commons Attribution-ShareAlike 2.0 Generic (CC BY-SA 2.0)

14.
Opportunities for ^ improvement
1. Observational (Passive) inputs
– Non-solicited, non-structured, non-validated
– Basis of a hypothesis – indicative insights,
trends
1. Direct engagement (Active) inputs
– (somewhat) structured, solicited etc.
– Tied (hopefully) to business questions
– Still has a ‘human’ component
In both cases we are exploiting real-time data
marketing

15.
70M+ websites
Full Twitter ‘Firehose’ feed
All major social media
• >100,000 verified
healthcare professional
(HCP) sources covering
websites & social media
Typically 2-5% of all public social media
conversation for a health topic coming from HCPs

16.
89,824,885
processed social profiles
377,744
algorithm selected profiles
88,569
human validated profiles
24,519
HCP authored blogs & sites
208million tweets
152thousand tweets per day
Source: Creation Pinpoint, data correct at Jan 2014

17.
Photo credit: https://creationpinpoint.cartodb.com/viz/24b4934a-adb1-11e3-9ea7-0edbca4b5057/public_map

18.
The digital world changes the
model of influence
Traditional KOL model: Emerging DOL model:
KOL relationships are different to digital opinion leader (DOL) relationships
Hierarchy typically based on seniority,
experience, publications etc.
Collaborative ‘flattened’ relationships,
not ordinarily common in real-world

19.
HCP community network
Cardiologist
Academic Physician
Academic Surgeon
Anesthetist
Dental surgeon
Hospital Director
Media Physician
Medical Biologist
Medical student
Neurologist
Neurosurgeon
Nurse
Oncologist
Orthopedic registrar
Pediatrician
Pharmacist
Physician
Psychiatrist
Neurolaw
Rheumatologist
Sports Therapist
Trauma Anesthetist
Trauma Physician
Various
Nodes: 13,781(4.35%)
Edges: 35,886(9.05%)
Note:
This diagram represents ~10% of the HCPs connected to those
talking about the study topics (shown as colored circles)

20.
Detailed HCP profile information

21.
Creation Pinpoint sample study, inflammation among conversations of UK healthcare professionals 01 Dec 2012-30 Nov 2013

22.
Photo credit: National Library of Ireland on The Commons / Foter / No known copyright restrictions

23.
Proof-of-concept real-time NLP
A: Initial data insights B: Future strategic approach
Analysis of an anonymized sample dataset
to determine the visual outputs and
information insights that are possible.
An exploratory exercise to find ways that
medical information can potentially service
commercial strategy development.
Key components include:
Based on learning from the sample data
set, and the evaluation of various tools and
processes for developing these insights,
recommendations to be made for how
PharmaCo might use this type of data in an
on-going implementation.
Key components include:
• Assessing data opportunities • Pricing and feature comparison
• Analysis and experimental approaches • Handling of languages other than English
• Types of outputs possible • Metrics and potential success indicators
• Presentation of findings • Potential real-time integration

24.
28Drill-down by area of interest i.e pharmacistDrill-down by area of interest i.e pharmacist
Four+ clear ‘problem’ products for pharmacistsFour+ clear ‘problem’ products for pharmacists

25.
What happens if we focus on a word like ‘fridge’What happens if we focus on a word like ‘fridge’

26.
30
Clear issue already
detectable week one,
escalation within business
to avoid week two peak
Clear issue already
detectable week one,
escalation within business
to avoid week two peak
Normal
Issue

27.
Rank City Population MI requests Ratio
1&2 London/City of London(England) 7556900 818 0.011%
3 Birmingham(England) 984333 216 0.022%
10 Manchester(England) 395515 177 0.045%
4 Glasgow(Scotland) 610268 159 0.026%
6 Leeds(England) 455123 143 0.031%
22 Nottingham(England) 246654 141 0.057%
5 Liverpool(England) 468945 139 0.030%
18 Belfast(Northern Ireland) 274770 135 0.049%
9 Bristol(England) 430713 115 0.027%
31 Newcastle upon Tyne(England) 192382 108 0.056%
8 Edinburgh(Scotland) 435791 105 0.024%
44 Dundee(Scotland) 151592 77 0.051%
7 Sheffield(England) 447047 66 0.015%
62 Newport(Wales) 117326 65 0.055%
12 Leicester(England) 339239 63 0.019%
16 Cardiff(Wales) 302139 62 0.021%
23 Southampton(England) 246201 57 0.023%
38 Walsall(England) 172141 57 0.033%
26 London Borough of Harrow(England) 216200 52 0.024%
90 Lincoln(England) 89228 52 0.058%
43 Oxford(England) 154566 47 0.030%
17 Bradford(England) 299310 45 0.015%
24 Reading(England) 244070 45 0.018%
UK population data source: http://www.populationlabs.com/UK_Population.asp

28.
Know
Know
Don’t Know
Don’t Know
What we know we know What we don’t know we know
What we don’t know we don’t knowWhat we know we don’t know
Customer
Information
Source:Adapted from http://www.doceo.co.uk/tools/knowing.htm

29.
Thoughts (and some tools)
1. Getting started: need education for marketing departments to
develop understanding of the power of indicative insights…
– what data do we already have, or could we have
– how to ‘munge’ it to answer behavioral or segmentation
questions – beyond the obvious, in real-time
1. Create content (dynamic?) for specific segments/needs
2. Allow customers to set their own preferences (then learn!)
3. Once you have the basics, start to explore machine learning
algorithms and predictive analytics
Can Pharma be as ‘clever’ as Amazon or Netflix? Of course!
1. Online HCP insights research: Creation Pinpoint
2. Social Network Analysis: Gephi/Anaconda
3. Integrations and data scraping: Import.io
4. Location visualization: CartoDB
5. Natural language processing: Brandwatch, Lexalytics, Semantria, Clarabridge
6. Structured and unstructured data: Omniscope