improving pharmaceutical marketing using big data solutions

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

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A presentation for SMi Big Data in Pharma conference, London, 12-13th May 2014. Slides herein contain most content shown on stage.

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Page 1: Improving pharmaceutical marketing using big data solutions

Improving Pharmaceutical marketing performance

using big data solutions

Paul Grant Chief Innovation Officer

@paulgrant

Page 2: Improving pharmaceutical marketing using big data solutions

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

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Page 5: Improving pharmaceutical marketing using big data solutions

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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

Photo credit: http://www.ey.com/Media/vwLUExtFile/BigData/$FILE/ey-bigdata_v3.png

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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

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'don’t care – big data is a pointless marketing term’

Online Measurement and Strategy Report 2013 by Econsultancy, July 2013

8% Marketers say…

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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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Photo credit: http://engagementstrategy.com/articles/diffusion-of-pharma-digital-innovators/

What about Big Data in Pharma^?marketing

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

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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

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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

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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

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

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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

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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)

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Detailed HCP profile information

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

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

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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

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28Drill-down by area of interest i.e pharmacist

Four+ clear ‘problem’ products for pharmacists

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

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30

Clear issue already detectable week one, escalation within business to avoid week two peak

Normal

Issue

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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

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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

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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