Copyright 2009 SAS Institute Inc. All rights reserved. Success With Business Analytics in the New Pharmaceutical Commercial Model.

Transcription

2 Emerging Commercial Models: Rethinking Analytics Deloitte Consulting LLP April 22, 2010

3 Hang on Here we go Eli Lilly & Co.'s (LLY) reported a 5% decline in first-quarter earnings and lowered its financial forecasts due to costs related to the new U.S. health-care overhaul. (WSJ) Abbott Laboratories said costs of the U.S. health-care law contributed to a drop in first-quarter profit and will reduce earnings and sales in (Bloomberg) Johnson & Johnson this week also projected reduced sales because of President Barack Obama s health-care overhaul. (Bloomberg) -3- Copyright 2009 Deloitte Development LLC. All rights reserved.

4 Increased pressure on margins Maturing & Evolving Markets, More Competition, More Products Changing Influence among an increasing number of stakeholders Need for Enhanced Analytics to Meet Changing Dynamics Complexities in Reaching Customers As a result, Life Sciences companies need to identify potential efficiencies in commercial practices and to better utilize available information -4-

5 Reach and Frequency is coming to an end... # of US Pharma Sales Reps Decline in US Pharmaceutical Reps Q 2008 Impact of reps making fewer calls: Represents a theoretical decline of 18.4% in rep-physician interaction over the same period Reps are Making Fewer Calls: average calls per rep declined 11% from 2006 to 2007 (impact shaded at left) Reps are Not Getting Detail Time: 93% of physician details last less than two minutes, Reps are Leaving Without Detailing: 65% of sales calls result in samples being dropped off by reps without them ever speaking to the physician More Cuts to Come: the economic crisis and patent expiry and health care reform are accelerating the pace of sales force reductions Ultimately, we see a trend of fewer reps having less impact while making fewer calls to fewer physicians -5- Copyright 2009 Deloitte Development LLC. All rights reserved.

6 Stakeholders are evolving Physician Payers Patients Government No Longer the Unilateral Decision-Maker Increased Bargaining Power Empowered and Informed Consolidated Buying Power Shifting Influence on the Prescribing Decision Focus on cost of care shifting physician decision criteria PhRMA code restricts interactions with prescribers HIT to drive convergence around standards of care Increased competition in crowded therapeutic areas combined with payer consolidation increases bargaining power Successfully shaping the market by shifting costs to patients Rising consumerism with increased desire to direct care More knowledgeable patients due to greater access to information (e.g., WebMD) Changes in Medicare and Medicaid policy increases state and federal influence Constricted budgets necessitate a health economics argument to justify inclusion on state formularies Commercial organizations must continue to demonstrate value to physicians, while also allocating resources to other important stakeholders -6- Copyright 2009 Deloitte Development LLC. All rights reserved.

7 New Commercial Models are Emerging Create a seamless customer experience across all touch-points Organization Fewer reps Redefined rep profile More clinical expertise Shift from product/ta to customer alignment Flexibility skills and capacity Leverage technologies to improve efficiency and manage metrics Strategy Multi-stakeholder management Customer centricity B2B models Alliances / Bus. Models Geographic customization Technology Integration of data silos Mobile sales support e sales and marketing Real-time reporting Align around customer segments rather than product groups Capabilities Local Decision making Managing to ROMI SMART/closed-loop marketing Optimized Resource allocation Enhanced segmentation Use analytics to identify segments, drive targeting, and influence product development Core Objectives Customer Centricity Flexible and Responsive Tailored to Regional Variations Customized Interface Streamlined Decision Making Accountability at Lower Levels Organizing Dimensions Customer / stakeholder Centralized vs. Decentralized Internal / External Resources Sales Reps vs. Alternative Channels Product Characteristics Promotional Mix -7- Copyright 2009 Deloitte Development LLC. All rights reserved.

8 From Retrospective Analysis to Exploratory Information Insight Foresight Action What to do differently Value What Happened? Most Companies Tracking/ Monitoring Trending Why Did it Happen? Comparison Visualization Multi-Dimensional Opportunity Exploration What Will Happen? Predictive Statistical Modeling - Correlation Forecasting - Regression - Multi-Factorial Targeting Messaging Contracting Channel Mix Analytical Maturity Continuum Retrospective Exploratory Prospective Actionable Routinely Understand Why things are happening in the marketplace versus just knowing that they happened retrospectively Systematically Have Easy Access to multiple sets of integrated information (internal and external) and Business Intelligence and data visualization tools Quickly Turn Insights into Action e.g., course correcting the HCP call list and delivering targeted messages in sync with a contract formulary tier change and HCP preferences -8- Copyright 2009 Deloitte Development LLC. All rights reserved.

9 Success With Business Analytics in the New Pharmaceutical Commercial Model. Patrick Homer, Global Life Sciences Sales and Marketing Practice Principal Copyright 2009 SAS Institute Inc. All rights reserved.

10 Predictive Analytics Faster Analysis As near real time results SaaS

11 Recent Results: Large Pharma 24.2% Lift Brand A $77 million in additional revenue potential on 2009 sales 15.9% Lift Brand B $19 million additional Rx potential Opportunity to significantly reduce promotion (38% reduction in targets) $97 million in Potential Revenue

12 The Perfect Storm.

13 The Planets are aligning for a new commercial model Reality Less Sales/Marketing Resources & sub optimal targeting strategies Challenge - How to be more effective with existing resources Next Generation BI for Pharma Increasing Salesforce Effectiveness Via Improved Physician targeting Improve Allocation of Marketing Spends

15 Predictive Analytics in Physician Targeting Addressing the problem of suboptimal targeting strategies ISSUES... Avg. Total Prescriptions Per Physician Per Segment Traditional Target Zone Past Performance is no guarantee of future results Unchallenged is the greater potential elsewhere? Segment No competitive advantage

16 Predictive Analytics Predictive analytics encompasses a variety of techniques from statistics and data mining that analyze current and historical data to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data to identify opportunities and risk.

17 Components of A Predictive Analytics Solution Predictive Analytics Physician Targeting Target Lists Promotional Response Call Plan Forecasting Data Integration Advanced analytics typically incorporate such functionality as data mining, descriptive modeling, econometrics, forecasting, operations research, optimization, predictive modeling & simulation, Data exploration Data mining Access Components Model Manager Operations Research Forecast Server Enterprise BI

18 The Predictive Analytics Difference in Targeting Know where Physicians are going, not just where they have been... Today Rear view mirror perspective Historical, not predictive Many Islands of data Tomorrow Future perspective Predictive based on historical patterns Considers all physician data Rx (NRx/TRx) Other APLD CRM Influencer/ Payor

19 Components of Predictive Analytics For Physician Targeting Why Predictive Analytics enables more effective targeting Data Integration Predictive Models Deliver Intelligence (Data Quality) Prediction to Rx Prediction for Profit Prediction for Churn/Loyalty to brand Prediction for Lifetime Value

21 Proof Of Value Timeline The Proof-Of-Value is executed over a 30 day period to deliver real results Week 1 Week 2 Week 3 Week 4 Goals & Objectives Data Requirements Variables Case Prep Data Prep Assure Quality Integrate Rx, CRM Data Make data analysis ready Load Data Select approach Create segments Create response curves Refine Model Scoring Curves Variables Findings Document

22 Recommended target list actually prescribed an additional 630,000 scripts per 6 month, with year value of $40mm Not targeting 26 % of Top Prescribers -Low deciles and a source of distinct competitive advantage? Copyright 2009, SAS Institute Inc. Source: All rights reserved. SAS Research, 2009

23 Turning Water into Wine... High Value Physicians from Low Value Deciles Deciles less than 3.5 Average 1.8 Rx Yield increased to 6.8 Rx First split Physicians started to Rx our product in first 3 months Second split Did Physicians continue Rx our product in next 3 months? Yield increased to 9.6 Rx Yield increased to 65.5 Rx Third split Physicians high Rx of Competitor One Total RX Yield increases to 113 Rx on high Rx of Competitor 6 Fourth split Physicians Rx of Competitor Six Total RX

24 Understanding Why Predictive Models increase Target Yields Behavior Model Identified Future High Prescribing Characteristics in Low Deciles MOMENTUM VARIABLE Identify importance of physicians who started and continued prescribing over 6 month time period Deciles less than 3.5 Average 1.8 Rx Yield increased to 6.8 Rx Identified prescribing volume of Competitor 1 First split Physicians started to Rx our product in first 3 months Second split Did Physicians continue Rx our product in next 3 months? Yield increased to 9.6 Rx Yield increased to 65 Rx Identified prescribing volume of Competitor 6 Third split Physicians high Rx of Competitor One Total RX Yield increases to 113 Rx on high Rx of Competitor 6 Fourth split Physicians High Rx of Competitor Six Total RX

26 Call Plan Optimization Solution Methodology Flow Predicted set of Targets Subset Data based on key factors Build Response Models Solve Integer Optimization Problem What-if Analysis YES Enter Call and Sample Volume Ranges Output Efficiency Surface: TRx v/s Calls&Samples Implement Call Plan Select Suitable Call Plan

27 Optimizing on Calls & Samples at Representative Level Doctor ID Location Ashville NC Optimal Call Plan 10 Calls over 4 months Optimal Sample Plan 6 Samples per call Predicted Rx 10 Rx per Month

28 Case Study : Increased TRx with Call Plan Optimization Call Plan Optimization TRx: 778,582 15% increase in TRx Prior Targeting TRx: 676,968 Calls: 952,804 Samples: 460,549

30 Next Generation BI & Marketing Mix Addressing the most complex marketing questions 1. What s the optimal spend across the portfolio? 2. How should funds be allocated by franchise and brand? 3. What specific tactics deliver the greatest ROI? Whilst overcoming current limitations of existing multichannel analytics 1. Predictive 2. Fragmented 3. Real Time In the campaign

31 Sensitivity Analysis Identify optimal points of investments based on historic spending across each activity Validate intuition for individual media

32 Marketing Mix Optimization Determine optimal marketing mix plan to achieve specific objective

33 Closing thoughts Nothing in the world is so powerful as an idea whose time has come Victor Hugo

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