Marketing-Driven Measurement of Internet Sites and Online...
Transcript of Marketing-Driven Measurement of Internet Sites and Online...
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Ira Haimowitz
Director / Team Leader, Data Innovation
INFORMS NYC, February 2006
Ira Haimowitz
Director / Team Leader, Data Innovation
INFORMS NYC, February 2006
Marketing-Driven Measurement of Internet Sites and Online Media
Marketing-Driven Measurement of Internet Sites and Online Media
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Ira Haimowitz Personal BackgroundIra Haimowitz Personal BackgroundIra Haimowitz Personal Background
Ph.D. Computer Science, M.I.T. 1994Artificial intelligence for medical diagnosis
General Electric R&D 1994-1998Data mining for financial services, retail
Software for “film-less radiology”
At Pfizer U.S. Pharmaceuticals 8 years:Director/ Team Leader, Data Innovation
President, Pharmaceutical Management Science Assn.www.pmsa.net
Ph.D. Computer Science, M.I.T. 1994Artificial intelligence for medical diagnosis
General Electric R&D 1994-1998Data mining for financial services, retail
Software for “film-less radiology”
At Pfizer U.S. Pharmaceuticals 8 years:Director/ Team Leader, Data Innovation
President, Pharmaceutical Management Science Assn.www.pmsa.net
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Measurement of TV ad impactMeasurement of TV ad impact
Optimization of media spending vs. goalsOptimization of media spending vs. goals
ResultsResults
Redesigned web site helps people explore content in-depthRedesigned web site helps people explore content in-depth
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First Click
Seco
nd C
lick
Before After
Content Explored Deeply in Re-designed SiteContent Explored Deeply in Re-designed Site
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Motivations
Prerequisites
Data mining
Marketing-driven measurements
Evaluating media
Motivations
Prerequisites
Data mining
Marketing-driven measurements
Evaluating media
OutlineOutline
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Complete multimedia DTC tracking analytics
Feedback to marketers in terms they understand“Is the web site working?”
“Who are the customer segments visiting?”
“What is the quality of traffic?”
“What are the predominant paths to goals?”
Continual web site improvementSystemic analytics for continuous discovery and improvement
Complete multimedia DTC tracking analytics
Feedback to marketers in terms they understand“Is the web site working?”
“Who are the customer segments visiting?”
“What is the quality of traffic?”
“What are the predominant paths to goals?”
Continual web site improvementSystemic analytics for continuous discovery and improvement
MotivationsMotivations
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No personally identifiable informationHIPAA compliant
Results viewed in aggregate
Analysis – no DB scoring
Strict opt in policy
No personally identifiable informationHIPAA compliant
Results viewed in aggregate
Analysis – no DB scoring
Strict opt in policy
DisclaimerDisclaimer
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Print, TV, Radio Promotion
Search Engines
Banner Ad
Prescription or Sample Package
RxDrug.com
Request for Information
On-line Survey
Loyalty Program
SourceSource Web SiteWeb Site GoalsGoals
Data Mining:Is the Website Really Working?Data Mining:Is the Website Really Working?
Informative Page
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Hits, visits/sessions, page views, top pagesDo not capture behavior
Minimal insight for improvement
Largely measure the quantity from a promotion, not the quality of lead
Hits, visits/sessions, page views, top pagesDo not capture behavior
Minimal insight for improvement
Largely measure the quantity from a promotion, not the quality of lead
The Beef With Traditional MetricsThe Beef With Traditional Metrics
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RelevantRelevantDiscrete EventsDiscrete Events
InsightsInsightsAssociationsAssociations FunnelsFunnels
Actionable AnalyticsActionable Analytics
Marketing GoalsMarketing Goals
Market SegmentsMarket Segments
DimensionsDimensionsSite Im
provement
Site Improvement
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PrerequisitesPrerequisites
Truly integrated approach to relationship marketing across all media
Goal-oriented designEngineered for measurement
Cross-functional, Interdisciplinary teams (Statisticians, Marketers, DB Managers, Customer Response Managers, Creative Agencies)
Truly integrated approach to relationship marketing across all media
Goal-oriented designEngineered for measurement
Cross-functional, Interdisciplinary teams (Statisticians, Marketers, DB Managers, Customer Response Managers, Creative Agencies)
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Discovering knowledge
that is actionable
automatically
from large databases
Multidisciplinary nature
Artificial intelligence
Data warehousing
Statistics, operations research
Overview of Data MiningOverview of Data Mining
DataDataSourcesSources
SurveyResponses
Business Rules
Web Logs
Media Spend
Ext. Data
DataDataPreparationPreparation
Extraction
Cleansing
Enrich
Aggregate
Impute
Transform
Extraction
Cleansing
Enrich
Aggregate
Impute
Transform
Data AnalysisData Analysisand Data Miningand Data Mining
ReportsReports
MiningMining
ReportingReportingOLAPOLAP
DeviationsDeviationsTrendsTrends
SegmentationSegmentationClusteringClusteringSimulationSimulation
OptimizationOptimization
Changed Market Conditions
Changed Market Conditions
The Data-Mining ProcessThe Data-Mining Process
Insights andDeploymentInsights andDeployment
Redesign site or Change CampaignRedesign site or
Change Campaign
Assistance: Alexander Linden, Gartner GroupAssistance: Alexander Linden, Gartner Group
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Data Preparation for Web MiningData Preparation for Web MiningPreparing web analytical data mart :
Cleaning up web logs / tags
Dividing into sessions
Determine relevant time span of analysis
Fields related to behavior– Referring page– First few pages visited– Indicators of visiting certain sections– Indicators of achieving goals
Other Data Sources Registration database
Survey results
Media spending
Preparing web analytical data mart :Cleaning up web logs / tags
Dividing into sessions
Determine relevant time span of analysis
Fields related to behavior– Referring page– First few pages visited– Indicators of visiting certain sections– Indicators of achieving goals
Other Data Sources Registration database
Survey results
Media spending
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Data Mining Yields More Relevant On-Line MetricsData Mining Yields More Relevant On-Line Metrics
Segment distributions Do site visitors correspond to marketing objectives?
AssociationsCan we “cross market” different aspects of our site?Are there unforeseen correlations?
Path AnalysisAre visitors taking the paths we intended?
Predictive modelingWhat are the key drivers of reaching goals?
Segment distributions Do site visitors correspond to marketing objectives?
AssociationsCan we “cross market” different aspects of our site?Are there unforeseen correlations?
Path AnalysisAre visitors taking the paths we intended?
Predictive modelingWhat are the key drivers of reaching goals?
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Web Site Business SegmentsWeb Site Business Segments
Unbranded Interest
Unbranded Interest Branded
InterestBranded Interest
NeitherNeither
16%16%
37%37%47%
Interest
RegistrantsRegistrants
Non-RegistrantsNon-Registrants
Registrants
95%
5%5%
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Traffic behaviorTraffic behavior
Content Areas ViewedContent Areas Viewed
Goal PagesGoal Pages
C1C1
CkCk
Web Data Mining: Behavior-Based SegmentationWeb Data Mining: Behavior-Based Segmentation
C2C2
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Customer transactional DBCustomer transactional DB
Association Rules: Retail Association Rules: Retail
Customer ID Product123 Cola123 Pretzels123 Chips134 Diapers134 Cola134 Bandaids134 Apples245 Pretzels245 Cola367 Pretzels367 Hotdogs367 Cola…
Rule:
If Customer buys Cola, then also buys Pretzels
Confidence = 35%
Support = 6%
Rule:
If Customer buys Cola, then also buys Pretzels
Confidence = 35%
Support = 6%
Re-arrange shelf space to leverage associationsRe-arrange shelf space to leverage associations
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Customer transactional DBCustomer transactional DB
Association Rules: Web Sites Association Rules: Web Sites
Rule:
If Session includes Condition page,
then session also includes screening page
Confidence = 35%
Support = 6%
Rule:
If Session includes Condition page,
then session also includes screening page
Confidence = 35%
Support = 6%
Session ID Page URL 123 Condition_home.htm123 See_doctor.htm123 Branded_drug_home.htm134 Side_effects.htm134 Branded_drug_home.htm134 See_doctor.htm134 Screening.htm245 See_doctor.htm245 Condition_home.htm367 See_doctor.htm367 Screening.htm367 Condition_home.htm…
Re-arrange links to leverage visitor associationsRe-arrange links to leverage visitor associations
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Path AnalysisPath Analysis
Home
Product.htm
Condition.htm45%
25%
Screening.htm
Comorbid.htm
35%
30%
PI.htm
Brochure.htm
10%
20%
Thousands of paths, few desirableThousands of paths, few desirable
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Visualizing Associations and SequencesVisualizing Associations and Sequences
Page associations
Session sequence
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Total Sessions: 100% (10,000)
Material Request: 10.7% (1,070)
Form for Materials 8.5% (850)
Materials Receipt: 0.7% (70)
Total Sessions: 100% (10,000)
Material Request: 10.7% (1,070)
Form for Materials 8.5% (850)
Materials Receipt: 0.7% (70)
Funnels: Drop-offs to Goal Pages Funnels: Drop-offs to Goal Pages
Material Request Conversion FunnelMaterial Request Conversion Funnel
Simplify pathway to widen the funnelSimplify pathway to widen the funnel
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Optimizing On-Line Media TacticsOptimizing On-Line Media Tactics
How to allocate a fixed on-line media budget?
Banner Ads, Sitelets, Search Engine Optimization, Content Integration, Sponsorship, etc…
How to allocate a fixed on-line media budget?
Banner Ads, Sitelets, Search Engine Optimization, Content Integration, Sponsorship, etc…
What is the volume of visitors each tactic sends to the site(s)?
What is the value of the visitors once they arrive?
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Value of a Web Site VisitorValue of a Web Site VisitorRates of meaningful marketing behaviors
Goal page completion
Registrant
Desirable segment
Likelihood of NRx
Measure promotional cost per behavior for each campaign tactic
Rates of meaningful marketing behaviorsGoal page completion
Registrant
Desirable segment
Likelihood of NRx
Measure promotional cost per behavior for each campaign tactic
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Extending to Integrated Media AnalyticsExtending to Integrated Media Analytics
Use web site to measure the effectiveness of a complete DTC campaign
TV, Print, Banner Ads, Patient Samples
Challenge: identify visitors to site as from specific campaign
Subdirectories-specific to campaign
Isolated media expansion
Use web site to measure the effectiveness of a complete DTC campaign
TV, Print, Banner Ads, Patient Samples
Challenge: identify visitors to site as from specific campaign
Subdirectories-specific to campaign
Isolated media expansion
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Modeling Websites as Markov Processes: Research with Bronx Science Intel studentsModeling Websites as Markov Processes: Research with Bronx Science Intel students
Transition MatrixTransition Matrix
Click VectorsClick Vectors
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SummarySummary
Data Mining of DTC Web sites enable:complete multimedia DTC tracking analytics
feedback to marketers in terms they understand
Process improvement framework is critical
Software vendor selection is only part of the effort
Discoveries have impact
Data Mining of DTC Web sites enable:complete multimedia DTC tracking analytics
feedback to marketers in terms they understand
Process improvement framework is critical
Software vendor selection is only part of the effort
Discoveries have impact