Brand Website Activity Impact Analysis:

Transcription

1 Brand Website Activity Impact Analysis: Do Page Views Drive Rx Outcomes? Jane Portman & Lynda Gordon, Merkle

2 Overview: A Paradox An argument that produces inconsistency Search optimization is designed primarily to drive traffic to a brand website Marketing mix models in the pharmaceutical industry typically show a strong, positive relationship between investment in search optimization and resulting Rx or sales outcomes digital is ROI-positive However most brand website data also shows only a weak relationship between website visits or page views and Rx or sales Why this paradoxical result? Our team set out to discover the differences between types of web visits to add new insights into the evaluation of search 2

3 Brand Website Analytics: Data-Rich but Causality Uncertain A wealth of web activity metrics: # of visitors to website, by day, by zipcode # of page views per visit Pages viewed / duration of view Sequence or path of page views, by visit Activity or registrations per visit And insights into source of web traffic: Referring source to brand website Keyword search term used to get to site Clicks / Click-Through Rate / Cost-per-Click Great data for optimizing volume of web traffic and engagement with the brand site But does it causally impact brand Rx? 3

4 4 Web Analytics Processes Typically Focused on Optimizing Web Traffic Impressions Clicks Click-Thru-Rate Paid Media Website/ Landing Page Visits Page Views Path Analysis Marketing Mix: Optimize Paid Media ROI Conversion Optimize Traffic to Site

5 5 Page Views and Rx Typically Low Predictive Relationship Website/ Landing Page Impressions Clicks Click-Thru-Rate Visits Page Views Path Analysis Paid Media Conversion Why Not Just Measure Here and Then Optimize Traffic to Site Via Paid Search? Typically, low correlation observed between page views or visits and NRx volume

6 6 Digital Analytics Case Study Started as a Request for a path analysis Path analysis loses analytic power quickly due to number of possible paths: 1,956 paths alone for a 6-page website Analytics proved that content viewed was more relevant than order of page views AND more relevant than quantity of pages viewed

7 7 SAI Methodology Linking Content Viewed to Rx Outcomes Develop Site-Specific SAI Metric 1) Categorize Site 2) Assign an Initial Value to Each Asset 3) Score Each Site Visit Collect Data 4) Aggregate Scores by Geography 5) Aggregate Sales, Market Events, Other Promotions by Geography Create Model 6) Quantify Impact on Sales (Rx) Over Time

8 8 Steps 1-2: Scoring Methodology High-level Site Audit 1) Categorize Site: Understand drivers influencing key site metrics Using unique visit-level data from web tracking tool, identify the specific site assets used each session 3 4 Identify the order of events Track whether call to action is performed 1 2 Create profiles based on site activity

9 9 Steps 1-2: Scoring Methodology Develop a Site Specific SAI Metric 2) Assign value/weight to each page and site action Visitor A visit 1 visit 2 Understanding Resistance: Learn About Causes: Watch Demo: Homepage Bounce: 1 pt 1 pt 3 pts 0 pts M O D E L visit 1 View ISI: Brand Support Enrollment: 1 pt 2 pts SAI Score by Geography Visitor B Treatment Goals: 2 pts visit 2 Prints Treatment Companion: Site scoring process highly iterative and requires input from brand & marketing teams 4 pts

10 10 Average Site Path? In general, the order in which a site is consumed is less important than the specific content explored The SAI is not impacted by the order of specific pages viewed Each of the visit paths below generated an SAI score of 3 Home Page (0) Disease Information Page: What is Disease? (1) Patient Experience Feedback Page: Share Your Story (2) Possible Side Effects Page (1) Alternate Disease Information (1) Tests to Monitor Disease (1)

11 11 Step 3: Score Visits Weighted web visits = total visits 3) Score each visit Home page visits with no additional page views or site activity generate an SAI score of 0 and account for over 1/3 of all site visits and 20% of all page views This explains the low correlation between Rx and web activity large amount of non-productive activity on the site Total Web Visits and Visits*QPA SAI Score, Average per Day, April-September 2011 Web Visits SAI Web Visits * QPA Score 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 # of Web Visits April-Sept 2011 by SAI QPA Score SAI QPA Score

12 Steps 4-5: Aggregate Data Zip-level data proves to be valid in the model 4) Aggregate Scores by Geography Since the visitors are generally anonymous, aggregating at the SCF (3-digit zip) provides an acceptable way to correlate with sales 72% of patients fill their prescriptions within the 3 digit SCF of their home* 5) Aggregate Sales, Market Events, Other Promotions by Geography We will model SAI vs. sales and require this at the SCF-level The model will control for market events and other promotional activity * Internal Merkle research 12

13 13 Step 6: Create Model Calculate Rx Impact of SAI-Scored Site Visits Details SAI Score* Visits Vouchers Market Events All Other Promo REGRESSION MODEL Registrations 6) Quantify Impact on Sales (Rx) Over Time Calculate appropriate time lag between activity and Rx in a longitudinal mixed model Create a regression model to measure the impact to sales following the site activity using the weighted site visits variable as the predictor Control for other promotion and activity, including personal details, co-pay / voucher registrations & activations, market events, and all other promotion

14 Exploratory Analysis SAI Weighted Visits Stronger Correlation to Rx Brand Rx SAI * Visits Brand Rx Weak relationship between visits & Rx (correlation <0.5) Stronger relationship between SAI-scored visits and Rx (correlation 0.71) Strongest relationship when accounting for 1-month lag from visits to Rx outcomes June Rx SAI * Visits August Rx Brand Rx Forward

15 15 Model Data Level of Analysis: 3-digit zipcode, month 3-digit zip code (SCF) Month Brand-R TRx (based on physician office zip) (Month t ) Web visits Month t-1 SAI Scored visits Month t-1 Physician Details, Samples, Tele-Details (Month t-1 ) Physician Marketing , DM, Mobile (Month t-1 ) Patient Marketing Touches including Display impressions, Paid search clicks (Month t-1 ) Patient Support Program Registrations (Month t ) Data size: 8 months * 414 SCF = 3,312 observations

16 16 Model Structure and Output SAS PROCEDURE RANDOM MODEL Functional Form PROC MIXED INTERCEPT TRx = SAI Visits, Details, Direct Mail Touches, Sent, Display Impressions, Paid Search Clicks, Co-Pay Card Program Registrations Quadratic (sample output below) Predictor First-Order Second-Order Sample Units Predicted TRx SAI * Visits ( ) Details ( ) Direct Mail ( ) Display Impressions ( ) 7, Hypothetical Data

17 17 Quantity vs. Quality Same # of Visits Can Have Different Rx Impact SAI scored visits yield a different impact than visits alone At 1,000 visits, depending on quality of the visit, Rx impact ranges from However, at 5,000 visits of lower content value, Rx impact is only 13.2 Sample Application of the Predictive Equation of Impact of Visits*SAI on TRx Visits Average SAI Score Per Visit Visits*SAI = SAI Scored Visits TRx Resulting from SAI 1 Value of Initial Rx due to SAI 2 1, , $1,077 1, , $1, , $1, , $1,318 Implication: A search strategy that drives high volume of lower-sai traffic may not be as desirable as search that drives less volume of higher-sai traffic 1: based on parameter estimates from model output 2: assumes $/Rx of $100 (hypothetical example)

18 Insights Predictive model shows impact of SAI on Rx Key Finding SAI score is highly predictive of future BRAND-R TRx volume Type of website activity has varying impact on future Rx Paid and organic search drove high number of visits to BRAND-R.com Details Higher the SAI score of visits, the more Rx generated in the following 4 week period Higher SAI scores reflects types of pages viewed Disease information and payment assistance searches drive the highest level of overall Rx, including new Rx Re-contact and stay-connected page activity resulted in growth in TRx, but lower than disease information search BRAND-R treatment and education page views had the lowest impact on future Rx Paid search involving the term cost or payment assistance generates the highest SAI score Searches that comes through needymeds.com or disease-specific organization sites resulted in higher SAI scores 18

19 19 New Metrics for Search When Rx Linked to Content Viewed Evaluating SAI by keyword, for example, allows us to predict and optimize based on projected ROI (not just click through or registration goals) Traditional Web Optimization Merkle s Way of Optimization Actions Taken in Web Visits April-September '11 QPA SAI Scored Visits from Source and ROI Estimate Click Through QPA* SAI Avg TRx Keyword Channel Impressions Clicks Rate Cost Visits SAI QPA Generated Value ROI chronic disease Google 166,893 1,273 1% $11, $19,040 $2 1.7:1 chronic disease MSN 146,614 2,934 2% $7, $1,808 $0 0.2:1 chronic disease #2 Google 89, % $3, $10,393 $3 2.9:1 chronic disease #3 Google 37, % $3, $6,163 $2 2:1 chronic disease #3 MSN 69, % $2, $2,472 $1 1:1 abbreviated Google 276,435 1,636 1% $17, $62,504 $4 3.7:1 abbreviated MSN 183, % $1, $7,294 $4 3.8:1 abbreviated disease Google 40, % $3, $5,313 $2 1.7:1 disease treatment Google 25, % $4, $13,383 $3 2.7:1 brand chemical name Google 131,441 3,658 3% $14,119 1, $125,070 $9 8.9:1 brand package insert Google 2, % $1, $26,560 $21 21:1 brand Google 67,608 7,979 12% $9,477 6, $307,990 $ :1 brand cost Google 2, % $ $18,486 $ :1 brand with Google 6, % $1, $34,968 $ :1 competitor Google 42, % $1, $9,174 $8 7.9:1

20 20 Application to Search Focus on driving traffic from higher SAI sites Within paid search, the treatment and disease state related referring sources, e.g. needymeds.org, webmd.com and righthealth.com had high SAI scores per visit Brand-R should secure additional visits coming from these sites Organic Paid

21 21 Discussion and Implications Analysis demonstrates that web visit quality, as defined by a scoring algorithm specific to a brand site, impacts the outcome of web visits on resulting brand sales Traditional web metrics of clicks and click-thru-rate effectively capture traffic generation but may not pullthrough to Rx outcomes By identifying the referring sources and keyword searches that ultimately result in the most valuable website engagements, search can be further optimized to drive higher-value traffic to the website

22 22 THANK YOU! Jane Portman Lynda Gordon