Improving Pharmaceutical marketing performance
using big data solutions
Paul Grant Chief Innovation Officer
@paulgrant
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A sizeable proportion of consumers are happy for companies to use their
personal data, providing they benefit through more targeted marketing
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“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
'don’t care – big data is a pointless marketing term’
Online Measurement and Strategy Report 2013 by Econsultancy, July 2013
8% Marketers say…
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
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What about Big Data in Pharma^?marketing
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Opportunities for ^ improvement1. Observational (Passive) inputs
– Non-solicited, non-structured, non-validated– Basis of a hypothesis – indicative insights,
trends2. 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
70M+ websitesFull 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
89,824,885processed social profiles
377,744algorithm selected profiles
88,569human validated profiles
24,519HCP authored blogs & sites
208million tweets
152thousand tweets per day
Source: Creation Pinpoint, data correct at Jan 2014
Photo credit: https://creationpinpoint.cartodb.com/viz/24b4934a-adb1-11e3-9ea7-0edbca4b5057/public_map
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
HCP community networkCardiologist
Academic PhysicianAcademic Surgeon
AnesthetistDental surgeon
Hospital DirectorMedia PhysicianMedical BiologistMedical student
NeurologistNeurosurgeon
NurseOncologist
Orthopedic registrarPediatricianPharmacistPhysician
PsychiatristNeurolaw
RheumatologistSports Therapist
Trauma AnesthetistTrauma 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)
Detailed HCP profile information
Creation Pinpoint sample study, inflammation among conversations of UK healthcare professionals 01 Dec 2012-30 Nov 2013
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Proof-of-concept real-time NLPA: Initial data insights B: Future strategic approachAnalysis 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
28Drill-down by area of interest i.e pharmacist
Four+ clear ‘problem’ products for pharmacists
What happens if we focus on a word like ‘fridge’
30
Clear issue already detectable week one, escalation within business to avoid week two peak
Normal
Issue
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
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
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-time2. Create content (dynamic?) for specific segments/needs3. Allow customers to set their own preferences (then learn!)4. 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 Pinpoint2. Social Network Analysis: Gephi/Anaconda3. Integrations and data scraping: Import.io4. Location visualization: CartoDB5. Natural language processing: Brandwatch, Lexalytics, Semantria, Clarabridge6. Structured and unstructured data: Omniscope