Improving pharmaceutical marketing using big data solutions

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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.

Text of Improving pharmaceutical marketing using big data solutions

  • 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 Photo credit: http://www.ey.com/Media/vwLUExtFile/BigData/$FILE/ey-bigdata_v3.png
  • There are a lot of small data problems that occur in big data. They dont disappear because youve got lots of the stuff. They get worse. David Spiegelhalter Winton Professor of the Public Understanding of Risk at Cambridge University
  • 'dont 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
  • Photo credit: http://engagementstrategy.com/articles/diffusion-of-pharma-digital-innovators/ What about Big Data in Pharma^? marketing
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  • 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
  • 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
  • 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
  • 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 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)
  • Detailed HCP profile information
  • Creation Pinpoint sample study, inflammation among conversations of UK healthcare professionals 01 Dec 2012-30 Nov 2013
  • Photo credit: National Library of Ireland on The Commons / Foter / No known copyright restrictions
  • 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
  • 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
  • What happens if we focus on a word like fridgeWhat happens if we focus on a word like fridge
  • 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
  • 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 Dont Know Dont Know What we know we know What we dont know we know What we dont know we dont knowWhat we know we dont 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-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