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The Largest Meta analysis of Pharma Messaging

This comprehensive meta-analysis examines the effectiveness of pharmaceutical messaging across multiple studies and platforms.

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The Largest Meta analysis of Pharma Messaging

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  1. 11 Lessons learned from testing 10,000s of pharma messages Busting myths about the dos and don’ts of pharma messaging

  2. These messages are unnecessary. Just show me the clinical data. That’s all doctors need to know about your product! How many of us have been in this situation before? 2

  3. Myth vs. Reality Do we really know what kinds of messages are preferred by HCPs and patients or do we subscribe to industry myths that just get passed around? 3

  4. No more anecdotal stories. Learn from a large-scale meta-analysis! 75+ studies 34,000+ respondents 16,000+ Messages 64 Brands 57 Disease States 4

  5. Messaging hypotheses tested 1 better? 2 difference?3 BRAND NAME?4 appeal?5 Does EMOTION improve message Does Should messages feature the Do messages with DATA perform Does LENGTH of messages make a CUSTOMER CENTRICITY matter in messaging? better?7 6 scores?8 appeal?9 difference?10 Does Does Do Does Is including a REFERENCE SOURCE add to message STATEMENT VS. QUESTION phrasing make a COMPARATIVE messages perform SUPERLATIVE language improve READABILITY important? 5

  6. Hypothesis # 1: Do messages with DATA perform better? Statistical impact Impact by Attribute Impact by Disease State Incidence With Data (28%) Without data (72%) YES (+38%) (+48%) Headlines Ophthalmic (+28%) (+39%) Legacy Dermatologic (+22%) (+34%) Overall impact Impact by Stakeholder Access Pain (+13%) (+15%) Efficacy Oncology +16% HCPs Patients (+6%) (+20%) (+14%) (+13%) Dosing Infectious 6

  7. DATA in Messages: Key Findings 1 2 3 4 Contrary to popular belief that oncologists just want to see data, the improvement in oncology from adding data to messages is not as high as many other disease states As expected, messages with data perform significantly better Adding data improves message appeal more than any other variable studied (+16%) Surprisingly, the biggest beneficiary of data is not efficacy messages 7

  8. Hypothesis # 2: Does LENGTH of messages make a difference? Statistical impact Impact by Attribute Impact by Disease State Incidence Short Medium Long (30%) (33%) (37%) (-52%) (-36%) QOL CV YES, but (-40%) (-36%) Patient Type Infectious (-28%) (-31%) Overall impact Impact by Stakeholder Guidelines Immunization (-27%) (-30%) Legacy Pain HCPs (-20%) Patients (-21%) Longer messages are more preferred (-26%) (-15%) Headlines Oncology 8

  9. Length of Messages: Key Findings Average Message Motivation Index Score Counterintuitively, longer messages perform better than shorter ones. 90 60 Once the word count gets past a threshold number (~40 words), then length of message does not correlate with message appeal. 30 As word count goes up, message appeal also goes up As word count goes up, message appeal fluctuates wildly When message length is high, there is more variability in appeal of the messages 0 1 21 41 61 101 WORD COUNT 9

  10. Hypothesis # 3: Should messages feature the BRAND NAME? Statistical impact Impact by Attribute Impact by Disease State Incidence With name (46%) Without name (54%) YES (+37%) (+49%) QOL Ophthalmic (+35%) (+44%) Support CV (+25%) (+40%) Overall impact Impact by Stakeholder Legacy Respiratory (+12%) (+19%) CTA Urologic +5% HCPs (+6%) Patients (+4%) (+10%) (+15%) Trial Design Neurologic 10

  11. BRAND NAME in Messages: Key Findings 1 2 3 Having the brand name in Patient Support and Legacy messages is expected, but QOL messages is the biggest beneficiary of brand name inclusion, which is surprising. Adding brand name to messages provides a small, but statistically significant improvement in message appeal. It’s also surprising that inclusion of brand name didn’t have a different impact on HCPs vs. Patients 11

  12. Hypothesis # 4: Does EMOTIONAL LANGUAGE improve message appeal? Statistical impact Impact by Attribute Impact by Disease State Incidence With emotion (11%) Without emotion (89%) YES (+42%) (15%) Patient type Respiratory (+21%) (12%) Headlines Immunization (+18%) (12%) Overall impact Impact by Stakeholder Guideline Gastro (+18%) (10%) Support Neurologic +6% HCPs (+4%) Patients (+7%) (+13%) (8%) Legacy Psychiatric 12

  13. EMOTION in Messages: Key Findings 1 Messages with emotional language had statistically higher appeal, but the differential was smaller than expected at 6% 2 3 4 HCPs messages with emotional language produced even smaller improvement (4%) and it wasn’t statistically significant A very small % of messages analyzed had any emotional language in them (11%) Messaging attributes that benefited the most having emotional language were unexpected – Patient type, Patient Support, Guidelines, Headline 13

  14. Hypothesis # 5: Does CUSTOMER CENTRICITY matter in messaging? Statistical impact Impact by Attribute Impact by Disease State Incidence Customer Centric (57%) Manufac Centric (43%) YES (23%) (32%) Patient type CV (14%) (19%) MOA Pain (11%) (13%) Overall impact Impact by Stakeholder DSE Respiratory (11%) (10%) Legacy Urologic +6% HCPs (+6%) Patients (+4%) (5%) (8%) Safety Oncology 14

  15. CUSTOMER CENTRICITY in Messages: Key Findings 1 Messages with customer centricity are significantly more appealing, but the upside is not as high as it can or should be and there is room for improvement 2 3 4 Patient Type and DSE attributes benefit from customer centricity, which is somewhat expected. MOA messages also benefit from customer centricity when MOA is connected to Efficacy Over 40% of messages analyzed were manufacturer centric, which is surprising for an industry focused on customer centricity CV and Pain disorders benefit the most from customer centricity, perhaps because it helps bring more empathy into the message 15

  16. Hypothesis # 6: Do COMPARATIVE MESSAGES perform better? Statistical impact Impact by Attribute Impact by Disease State Incidence With Without Comparison (43%) YES (57%) (85%) Headline Ophthalmic Comparison (57%) (53%) (32%) DSE Infectious (49%) (30%) Overall impact Impact by Stakeholder CTA Immunization (25%) (24%) Dosing Musculoskeletal +12% HCPs (+9%) Patients (+12%) (10%) (24%) Efficacy Neurologic 16

  17. COMPARISON in Messages: Key Findings 1 2 3 Typically, comparisons are featured in efficacy messages, but the biggest impact of comparative language was observed in Headline, DSE and CTA messages Next to DATA, adding COMPARATIVE language in messages leads to the biggest improvement in scores (12%) Counterintuitively, patient messages benefit more from comparative language than HCP messages 17

  18. Hypothesis # 7: Does SUPERLATIVE LANGUAGE improve scores? Statistical impact Impact by Attribute Impact by Disease State Incidence With Without Superlatives (90%) NO (+35%) (+22%) CV Patient Profile Superlatives (10%) (+24%) (+22%) MOA Ophthalmic (+23%) (+22%) Overall impact Impact by Stakeholder QOL Urologic (+17%) (+16%) Guidelines Musculoskeletal +1% HCPs (+1%) Patients (+9%) (+9%) (+13%) Unmet Need Pain 18

  19. SUPERLATIVES in Messages: Key Findings 1 2 3 Superlatives are often used in Efficacy, Dosing, and Legacy attributes but none of them show statistical improvement Counterintuitively, superlative words like First, Only, Best, Largest, etc. don’t make messages more appealing Patients are much more likely to respond to superlatives than HCPs 19

  20. Hypothesis # 8: Does a REFERENCE SOURCE add to message appeal? Statistical impact Impact by Attribute Impact by Disease State Incidence With Without Reference (87%) YES (+53%) (+46%) Headline Ophthalmic Reference (13%) (+26%) (+20%) Legacy Musculoskeletal (+19%) (+14%) Overall impact Impact by Stakeholder Trial Design Pain (+12%) (+13%) MOA Infectious +6% HCPs (+8%) Patients (+3%) (+12%) (+12%) CTA Respiratory 20

  21. REFERENCE in Messages: Key Findings 1 2 3 Referring to a credible source in the message does add to appeal, surprisingly at a level similar to adding emotion Counterintuitively, Headline and Legacy messages benefit the most from addition of reference sources As expected, reference sources carry more weight with HCPs than patients 21

  22. Hypothesis # 9: Does STATEMENT vs. QUESTION phrasing make a difference? Statistical impact Impact by Attribute Impact by Disease State Incidence Statement Phrasing (95%) Question Phrasing (5%) NO (+10%) (+23%) QoL Pain (+9%) (+15%) Safety Oncology (+7%) (-17%) Overall impact Impact by Stakeholder Efficacy Respiratory (-13%) (-20%) Headline Immunization +2% HCPs (+7%) Patients (-12%) (-33%) (-23%) Support Infectious 22

  23. Question Phrasing in Messages: Key Findings 1 2 3 Interestingly, coverting statements to question phrasing has a backfire effect for many attributes like Headline and Patient Support Phrasing a message as a question instead of a statement does not improve the appeal meaningfully Question phrasing has a negative affect in several disease states also like Respiratory, Infectious, etc. 23

  24. Hypothesis # 10: Is READABILITY of a message important? Statistical impact Impact by Attribute Impact by Disease State Incidence Low (>12 G) Med (8-12 G) High (<8 G) (33%) (34%) (33%) (+7%) (+9%) NO Safety Gastro (+5%) (+3%) Support CV (+5%) (+3%) Overall impact Impact by Stakeholder MOA Endocrine (-10%) (-13%) Access Pain +0.2% HCPs (+1%) Patients (-2%) (-12%) (-31%) Guidelines Respiratory 24

  25. READABILITY Level of Messages: Key Findings 1 2 3 Messages with higher readability surprisingly don’t perform any better and there is no statistical differences in Low/Med/High readability levels Counterintuitively, improving the readability of messages has a slight negative effect for patients, likely because the messages become less informative Higher readability messages have a negative effect for several attributes and in many disease states also 25

  26. Bonus Hypothesis #11 Do ON-LIST vs. OFF-LIST HCPs like different messages? 26

  27. No difference in preference share of the winning message storyflow between On-List and Off-List HCPs! Preference share of best LONG STORYFLOW Preference share of best SHORT STORYFLOW 60% 60% 50% 49% 49% 50% 50% 41% 41% 40% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% All HCPs On-List HCPs Off-List HCPs All HCPs On-List HCPs Off-List HCPs 27

  28. Very high consistency in message hierarchy between On-List and Off-List HCPs 100% 88% 87% 90% Top 10% of Messages 81% 78% 80% 70% Top 25% of Messages 60% 50% Bottom 25% of Messages 40% 30% 20% Bottom 10% of Messages 10% 0% 28

  29. Newristics is the market leader in pharma messaging related services, including content development, market research, messaging analytics and more! Combining the power of behavioral science and messaging AI, Newristics optimizes omni-channel messaging for Top 20 out of 20 pharma companies and 100s of pharma brands. About Newristics www.Newristics.com 29

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