Brand Website Activity Impact Analysis:

(1)

Brand Website Activity

Impact Analysis:

(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

(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?

(4)

Web Analytics Processes

Typically Focused on Optimizing Web Traffic

Paid Media

Website/

Landing Page

Conversion

Optimize

Traffic to Site

Impressions

Clicks

Click-Thru-Rate

Visits

Page Views

Path Analysis

Marketing Mix:

Optimize Paid

Media ROI

(5)

Page Views and Rx

Typically Low Predictive Relationship

Paid Media

Website/

Landing Page

Conversion

Impressions

Clicks

Click-Thru-Rate

Visits

Page Views

Path Analysis

Why Not Just Measure Here and

Then Optimize Traffic to Site Via Paid Search?

Typically, low

correlation observed

between page views or

(6)

• 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

Digital Analytics Case Study

(7)

SAI Methodology

Linking “Content Viewed” to Rx Outcomes

Develop Site-Specific

SAI Metric

Collect Data

Create Model

1) Categorize Site

6) Quantify Impact on Sales (Rx) Over

Time

3) Score Each Site Visit

4) Aggregate Scores by Geography

5) Aggregate Sales, Market Events, Other

Promotions by Geography

(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

Identify the order of

events

Track whether call to

action is performed

Create profiles based on

site activity

1

2

(9)

Steps 1-2: Scoring Methodology

Develop a Site Specific SAI Metric

2) Assign value/weight to each page and site action

Visitor A

Understanding Resistance:

1 pt

Learn About Causes:

1 pt

Watch Demo:

3 pts

Visitor B

Treatment Goals:

2 pts

M O D E L

SAI Score by

Geography

Homepage Bounce:

0 pts

View ISI:

1 pt

Brand Support Enrollment:

2 pts

visit 1

visit 2

visit 1

visit 2

(10)

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

Average Site Path?

Home Page (0)

Disease Information Page:

What is Disease? (1)

Patient Experience Feedback Page:

Share Your Story (2)

Tests to Monitor Disease (1)

Alternate Disease Information (1)

(11)

Step 3: Score Visits

“Weighted” web visits = total visits

-50 100 150 200 250 300 350 400 450 500

Total Web Visits and Visits*QPA Score, Average per Day, April-September 2011

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 0 1 2 3 4 5 6 7 8 9 10

# of Web Visits April-Sept 2011 by QPA Score

SAI

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 –

(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

(13)

Step 6: Create Model

Calculate Rx Impact of SAI-Scored Site Visits

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

REGRESSION MODEL

SAI

Score*

Visits

All Other Promo

Details

Vouchers

Registrations

Market Events

(14)

Exploratory Analysis

SAI Weighted Visits Stronger Correlation to Rx

Brand Rx Brand Rx

Brand Rx Forward 1

SAI * Visits

• 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

(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 Email, DM, Mobile (Month

_t-1

)

–

Patient Marketing Touches including Display impressions, Paid

search clicks (Month

_t-1

)

–

Patient Support Program Registrations (Month

_t

)

(16)

Model Structure and Output

SAS PROCEDURE PROC MIXED

RANDOM

INTERCEPT

MODEL

TRx = SAI Visits, Details, Direct Mail Touches, Email

Sent, Display Impressions, Paid Search Clicks, Co-Pay

Card Program Registrations

Functional Form

Quadratic (sample output below)

Predictor

First-Order

Second-Order

Sample Units

Predicted

TRx

**SAI * Visits**

0.01166590

(0.00000090)

60 .70

Details

0.13090000

(0.00001450)

230

29.3 Direct Mail

0.02310000

(0.00006320)

90

1.6 Display Impressions

0.00015430 (0.00000000071)

7,500

1.1 Hypothetical Data

(17)

Visits

Average SAI

Score Per Visit

“SAI Scored Visits”

**Visits*SAI =**

TRx Resulting

from SAI

1

Value of Initial

Rx due to SAI

2

1,000

1.0 1,000

10.8 $1,077

1,000

2.0 2,000

19.7 $1,973

500

3.0 1,500

15.5 $1,547

5000

0.25 1,250

13.2 $1,318

**Sample Application of the Predictive Equation of Impact of Visits*SAI on TRx**

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 10.8-19.7. However, at 5,000 visits of lower content value,

Rx impact is only 13.2

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

(18)

Insights

Predictive model shows impact of SAI on Rx

Key Finding

Details

SAI score is highly

predictive of future

BRAND-R TRx volume

–

Higher the SAI score of visits, the more Rx generated

in the following 4 week period

–

Higher SAI scores reflects types of pages viewed

Type of website

activity has varying

impact on future Rx

–

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 and organic

search drove high

number of visits to

BRAND-R.com

–

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

(19)

• Evaluating SAI by keyword, for example, allows us to predict and optimize

based on projected ROI (not just click through or registration goals)

Keyword Channel Impressions Clicks

Click Through Rate Cost QPA* Visits Avg QPA TRx

Generated Value ROI

chronic disease Google 166,893 1,273 1% $11,100 203 1.66 2.33 $19,040 $ 2 1.7:1

chronic disease MSN 146,614 2,934 2% $7,599 19 1.35 0.22 $1,808 $ 0 0.2:1

chronic disease #2 Google 89,685 320 0% $3,544 110 2.41 1.27 $10,393 $ 3 2.9:1

chronic disease #3 Google 37,939 263 1% $3,088 65 2.40 0.75 $6,163 $ 2 2:1

chronic disease #3 MSN 69,851 403 1% $2,378 26 2.36 0.30 $2,472 $ 1 1:1

abbreviated Google 276,435 1,636 1% $17,022 693 2.04 7.65 $62,504 $ 4 3.7:1

abbreviated MSN 183,452 342 0% $1,906 77 2.02 0.89 $7,294 $ 4 3.8:1

abbreviated disease Google 40,117 310 1% $3,148 56 1.93 0.65 $5,313 $ 2 1.7:1

disease treatment Google 25,362 418 2% $4,962 142 1.78 1.64 $13,383 $ 3 2.7:1

brand chemical name Google 131,441 3,658 3% $14,119 1,482 1.94 15.31 $125,070 $ 9 8.9:1

brand package insert Google 2,680 284 11% $1,266 285 3.03 3.25 $26,560 $ 2 1 21:1

brand Google 67,608 7,979 12% $9,477 6,153 2.54 37.71 $307,990 $ 3 2 32.5:1

brand cost Google 2,597 244 9% $419 197 7.30 2.26 $18,486 $ 4 4 44.1:1

brand with Google 6,894 339 5% $1,235 378 3.89 4.28 $34,968 28.3:1

Actions Taken in Web Visits April-September '11 QPA Scored Visits from Source and ROI Estimate SAI

SAI

Traditional Web Optimization

Merkle’s Way of Optimization

New Metrics for Search

(20)

• Within paid search, the treatment and disease state related referring sources,