1 Session 3: The World of Big Data from the Evolving Digital World Laura Jenkins Jirele, Jeff Kirsch, Tom Jirele
2 PMSA Virtual University PMSA Virtual University is conducting this four part webinar series focused on the introduction and understanding of the current and evolving data resources available in the Life Sciences industry. Each session in this webinar series is designed to build on the prior session to both expand and explore the evolving data available to the Pharmaceutical industry: Session 1: Learn about core pharmaceutical datasets retail and non retail. Session 2: Dig deeper into analytics with APLD, EMR, and Specialty data. Session 3: The world of big data coming from the evolving digital world. Session 4: Understanding data behind the complex new world of healthcare involving IDNs and ACOs. With a solid foundation of data resources, PVU s goal is to establish a venue for discussion and collaboration on best practices in analytics, marketing, and sales operations.
3 Agenda Big Data Pharma s View The Media Data Landscape The Online Data World Data Linkage and Analytics Q&A Goal: Right Data + Right Analytics + Right Answer 3
4 In the prior sessions, we have focused on more traditional and broadly utilized resources Session I Session II Big Data Media/Digital
5 Big Data is multi structured, rapidly changing, and tends to be large, often necessitating new tools and processes for efficient management and analytics Complexity of processing, analysis, and deriving insights Complexity Variety Structured and unstructured data of all types Velocity Real time, continuous, and streaming data Traditional Approach Structured & Repeatable Analysis Business Users determine what question to ask IT structures the data to answer that question Big Data Approach Iterative & Exploratory Analysis IT delivers a platform to enable creative discovery Very large data sets in the order of 100s of Terabytes to Petabytes Volume Business explores what questions could be asked 5
6 While there are differences between traditional data sources, similarities exist as well Similarities Differences Available for patients, payers, and prescribers Much greater variation in structure of the data Known cadence for collection Alignment of tools to source and type of data Linkage exists to traditional pharma views (sales, territories, disease states) Unique & known nuances by source Much more pre processing needed to get data ready for use Multiple sources needed to provide comprehensive & stable views 6
7 Pharma s View The Media Landscape Brave New Worlds
8 Today s world of media: trends, consumption, and the creation of data galore
9 The world of media (and what media represents) is evolving at a rapid pace
10 Today s media take various forms
11 Media buying versus consumption Because the media world is shifting so quickly, Pharma is not adapting their spending habits to where the consumers are spending their time. And they are doing multiple things simultaneously.
12 The Online Data World
13 The Pharma industry has several emarketing channels to engage customers, but no single data source to capture all activities + E- samples Sends, Opens, Click-throughs, and Orders (conversion) Mobile Apps for Patient Education Starts, Completes, Registrations, Secondary Engagements Banner Ads Impressions and Click-throughs Website/Web Portal Total Views, Unique Visitors, Engagement Level, Most-valued Action, Who is On Search (SEM & SEO) Keywords Information, Views (Available in clickstream data) Social Media/ Networking Posts, Unique Posters, Conversation Velocity & Virality, Social Linkage, Social Influencers
14 The coordinated use of this data can help teams design more effective sales strategies and gain competitive advantage Digital Data Management Behavior Analytics Digital Channel ROI Analytics Other Digital Analytics Solutions Solutions Digital Data Collection & Tracking Digital Data Integration Data Cleaning & Validation Data Mart Creation Reporting & Dashboarding Customer Activity tracking Navigation Pattern Analysis Anonymous User Profiling Segmentation Key Driver Identification Customer Relationship & Engagement Digital Channel Mix Optimization Quantification of the drivers of engagement and sales (Connect the Cookies) Forecasting & Regression Analysis for Digital Channel Program Input and Evaluation Social Media Analytics (Tracking, Listening, Behavior Analysis, Network Analysis) Digital Campaign Management (Targeting, Response Modeling, Execution) Search Engine Optimization Providers Coremetrics Terradata Cognizant Accenture Epsilon ZS Coremetrics Omniture SAS Cognizant Accenture Epsilon Epsilon MMA Synovate Cognizant Accenture Epsilon ZS Sysomos SAS Cognizant Accenture MMA Synovate Radian 6
15 What is click stream data? Definition: A record of what content is shown and where a user clicks on while web browsing or using another software application Originally website & server based Then came cookies and tagging Track people over time ISP based Referring pages and search capture Geo accuracy Segmentation Behavioral Analysis Marketing Effectiveness Site Optimization Digital Measurement
16 The social data world adds complexity and changing velocity Source: Business2Community.com
17 You must be aware of social data restrictions All social media data is subject to Data Protection laws, meaning that it should only be obtained for a specific purpose and should not be kept for longer than is necessary Organizations must find out whether there is any intellectual property contained within the data, and if so, whether it is subject to copyright or confidentiality regulations Respect social networkers' privacy, even if their profiles are publicly visible HIPAA restrictions Examples of regulations and policies: The Russian parliament has passed a law mandating that social networks and other similar services be required to warehouse data on citizens within Russia New Twitter policy around the deletion of tweets 17
18 The world of Wearables brings the Internet of Things to Pharma Wikipedia: Miniature, body born electronic devices that are worn by a person under, with, or on top of clothing that collect and transmit data in real time
19 The composite framework below provides the direction for a company to achieve desired outcomes from its digital marketing strategy Brand/Disease Sites HCP Portals Disease State Awareness & Discussion Forums Medical Knowledge Center E-Sampling Coupons/Vouchers Patient Wellness Patient Compliance Web 2.0 Interactions Channels Physician/Patient Communities Tablet Solutions Interactions Physician/Patient Information Desk Adverse Event Reporting edetailing Outcomes Social Reputation Brand Awareness Mobile Solutions KOL Webcasts/Podcasts Brand Acceptance Contact Center KOL Events/Events/Workshops Informed Medical Treatment Health Applications Physician Association
20 Linkage and Analytics The Future is Here: Case Studies
21 A solid understanding of data allows you to start to use digital and media data to complement your traditional approaches Prescriber Payer Patient What frequency of non personal touch points are optimal to increase prescribing behavior? What digital tactics best complement sales force activity? How do prescriber views on disease state influence their treatment decisions, and how can you tell when views are changing? What percentage of your potential scripts are being lost due to payer decisions? How to therapy pathways change by payer? What payers are moving your product up in the treatment cycle, and which ones are moving it down? How can you track a patient from being informed about your product to being a loyalist? What patient behaviors predict a change in therapy? What media channels are most effective in driving a patient to the prescriber? The linkage of traditional data with media and online data can help you answer these and many other questions.
22 Patient Pathing Approach to Media Measurement Case Study 1 Transition in Approach Old Approach: Single Point of Insight Limited insights available at end of the study period, making them non actionable CAMPAIGN EXPOSURE Ad Campaign, Website, Microsite PATIENT Rx Patient Pathing In Action 249,000 Consumers exposed to media intervention 87,000 were being treated in the category of interest 4,900 patients received or had the diagnosis of interest 5,800 patients went to the MD 3,500 patients received a prescription for Brand A 1,960 patients filled Rx for Brand A for the first time New Model: Patient Pathing More actionable, continuous receipt of patient data + AUDIENCE QUALITY + PHYSICIAN OFFICE VISIT & DIAGNOSIS + PATIENT Rx + ADMINISTRATIONS + PAYOR CONTROL + ADHERENCE TRACKING 280 patients got an Rx but didn t go to the pharmacy (estimated) 300 patients Rxs were rejected by their payer 450 patients Rxs were substituted with a different brand or generic 510 patients abandoned Rx at the pharmacy TRACKING Barriers prevented 1,540 new patient starts for Brand A
23 Patient Pathing Approach to Media Measurement Case Study 1 Transition in Approach Old Approach: Single Point of Insight Limited insights available at end of the study period, making them non actionable Patient Pathing In Action 4,900 patients received or had the diagnosis of interest 1,960 patients filled Rx for Brand A for the first time CAMPAIGN EXPOSURE Ad Campaign, Website, Microsite PATIENT Rx 5,800 patients went to the MD 3,500 patients received a prescription for Brand A New Model: Patient Pathing More actionable, continuous receipt of patient data + AUDIENCE QUALITY + PHYSICIAN OFFICE VISIT & DIAGNOSIS + PATIENT Rx + ADMINISTRATIONS + PAYOR CONTROL + ADHERENCE TRACKING TRACKING 249,000 Consumers exposed to media intervention 87,000 were being treated in the category of interest 280 patients got an Rx but didn t go to the pharmacy (estimated) 300 patients Rxs were rejected by their payer 450 patients Rxs were substituted with a different brand or generic 510 patients abandoned Rx at the pharmacy Barriers prevented 1,540 new patient starts for Brand A
24 We built a model to recognize if the visitor on the brand website is a Using prescriber Online or Behavior a consumer to Recognize the Anonymous Visitor Case Study 2 Web Page 1 Time Spent 1 Source We developed models based on the behavior of known physicians on the client brand s professional site. Page 2 Time Spent 2 And applied the models to two other groups of physicians using the site. Brand Website Page 3 Time Spent 3 Page 4 Time Spent 4 Sept Oct Nov Dec Jan Validate 88% correct Develop Model Validate 80% correct Web Exit Page Time Spent Exit Using Weblog information for the brand, we analyzed the path used and content accessed by a visitor to build a logistic regression model. We were able to predict with over 80% accuracy whether the visitor was a physician. This allowed the customer to differentiate between a prescriber and a non prescriber, thereby enabling them to tailor their content according to visitor type. 24
26 With media and digital data, the language has changed Media Electronic Health Records Move to appendix Laura will update The Cloud Social Media Agency Machine Learning Impressions Internet of Things Data Scientist Big Data Weblogs Streaming Data
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