Competing On Web Analytics by Eric T Peterson, Web Analytics Demystified

Web Analytics Demystified COMPETING ON WEB ANALYTICS

Introduction • Founder, Web Analytics Demystified, Inc. • Author of three books: – Web Analytics Demystified – Web Site Measurement Hacks – Big Book of Key Performance Indicators • Focused on measurement process, organizational structure, and overall digital analytics strategy 2

@ericTpeterson 3

@ericpeterson 4

Web Analytics Demystified COMPETING ON WEB ANALYTICS

“Businesses can create sustainable and strategic competitive advantages by investing in analytics.” 8

What is Analytics? “By analytics we mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions…analytics are part of what has come to be called business intelligence: a set of technologies and processes that use data to understand and analyze business performance.” 9

What is Analytics? “By analytics we mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions…analytics are part of what has come to be called business intelligence: a set of technologies and processes that use data to understand and analyze business performance.” 10

Key Ideas • Depends on operational interpretation and visualization, not data collection and reporting • Managed globally for all processes and functions, not departmentally or in silos • Requires continual monitoring and response based on observed changes, not episodic changes and re-engineering • Powered by people and process, not just technology 12

Key Ideas • Depends on operational interpretation and visualization, not data collection and reporting • Managed globally for all processes and functions, not departmentally or in silos • Requires continual monitoring and response based on observed changes, not episodic changes and re-engineering • Powered by people and process, not just technology 13

The Big Problem? • It is very easy to profess a great love for data … • … and still fail to use that data to inform action! 14

Consider Starting Small … 17

“Businesses can create sustainable and strategic competitive advantages by investing in analytics.” 20

“Businesses can create sustainable and strategic competitive advantages online by investing in web analytics.” 21

“Businesses can create sustainable and strategic competitive advantages online by investing in web analytics.” 22

The Same Key Ideas • Depends on operational interpretation and visualization, not data collection and reporting • Managed globally for all processes and functions, not departmentally or in silos • Requires continual monitoring and response based on observed changes, not episodic changes and re-engineering • Powered by people and process, not just technology 23

The Big Problem Overly complex and expensive segmentation tools Expensive software Web analytics and audience research solutions give different numbers No real web analytics community association Data integration nightmares No standard definitions of engagement and influence No information about visitor and customer intent Crummy data export tools No standard qualitative inputs Poorly defined key performance indicators Poorly defined processes for using web analytics Lousy vendor documentation Smarmy web analytics sales people Not enough great books on web analytics Crappy implementations Too few relevant case studies No data about statistical relevance Software is too complex Too many reports, not enough information Expensive consultants No help from Information Technology (I.T.) No real tools for modeling and predictive analytics Few formal web analytics training courses No standard definitions in the industry Too many HiPPOs Contradictory explanations of what the data means Too few people who understand web analytics Cookie deletion, cookie blocking, and cookie‐less mobile browsers 24

The Real Problem Overly complex and expensive segmentation tools Expensive software Web analytics and audience research solutions give different numbers No real web analytics community association Data integration nightmares No standard definitions of engagement and influence No information about visitor and customer intent Crummy data export tools No standard qualitative inputs Poorly defined key performance indicators Poorly defined processes for using web analytics Lousy vendor documentation WEB ANALYTICS IS HARD Smarmy web analytics sales people Not enough great books on web analytics Crappy implementations Too few relevant case studies No data about statistical relevance Software is too complex Too many reports, not enough information Expensive consultants No help from Information Technology (I.T.) No real tools for modeling and predictive analytics Few formal web analytics training courses No standard definitions in the industry Too many HiPPOs Contradictory explanations of what the data means Too few people who understand web analytics Cookie deletion, cookie blocking, and cookie‐less mobile browsers 25

The Real Problem Overly complex and expensive segmentation tools Expensive software Web analytics and audience research solutions give different numbers No real web analytics community association Data integration nightmares No standard definitions of engagement and influence No information about visitor and customer intent Crummy data export tools No standard qualitative inputs Poorly defined key performance indicators Poorly defined processes for using web analytics Lousy vendor documentation WEB ANALYTICS IS HARD Smarmy web analytics sales people Not enough great books on web analytics Crappy implementations Too few relevant case studies No data about statistical relevance Software is too complex Too many reports, not enough information Expensive consultants No help from Information Technology (I.T.) No real tools for modeling and predictive analytics Few formal web analytics training courses No standard definitions in the industry Too many HiPPOs Contradictory explanations of what the data means Too few people who understand web analytics Cookie deletion, cookie blocking, and cookie‐less mobile browsers 26

There is a Solution! 28

How to Compete on Web Analytics 1. Architect Your Web Analytics Technology 2. Manage Your Web Analytics Talent 3. Focus Your Analytical Efforts 29

Architect Your Technology • Web analytics absolutely depends on technology • You can put too much emphasis on software • Key challenges: – Right data – Right systems – Right output 31

What is the Right Data? • The “right” data Visitor comes from Voice of Customer multiple systems Scope of Insight Session • Integration of Integrated View of Visitor Behavior these systems is the new Web Analytics opportunity Customer Experience Management Quantitative Type of Data Qualitative 32

What Are the Right Systems? Visitor Scope of Insight Session Integrated View of Visitor Behavior Quantitative Type of Data Qualitative 33

What is the Right Output? Analytics Decision Optimization What’s the best that can happen? Competitive Advantage Predictive Analytics What will happen next? Forecasting What if these trends continue? Statistical models Why is this happening? Alerts What actions are needed? Query/drill down Where exactly is the problem? Ad hoc reports How many, how often, where? Standard reports What happened? Reporting Degree of Intelligence 34

Unfortunately … Analytics Decision Optimization What’s the best that can happen? Competitive Advantage Predictive Analytics What will happen next? Forecasting What if these trends continue? Statistical models Why is this happening? Alerts What actions are needed? Query/drill down Where exactly is the problem? Ad hoc reports How many, how often, where? Standard reports What happened? Reporting Degree of Intelligence 35

How Are You Doing with Technology? • Four signs of the “right” technology: 1. Nobody says “if only we had Solution X” 2. Nobody questions the money you’re spending 3. Nobody uses Excel because they have to 4. Nobody is forced to whine on Twitter for help! 36

How Are You Doing with Technology? • Four signs of the “right” technology: 1. Nobody says “if only we had Solution X” 2. Nobody questions the money you’re spending 3. Nobody uses Excel because they have to 4. Nobody is forced to whine on Twitter for help! 37

Architect Your Technology Visitor Scope of Insight Session Integrated View of Visitor Behavior Quantitative Type of Data Qualitative 38

Manage Your Talent • People are critical to analytics • But analysts alone cannot get the job done • Analytics is a team effort 40

Analytical Executives 41

Analytics Amateurs 42

Analytical Professionals 43

The Description Has Not Changed 44

How Much Should You Spend? 45

The 50/50 Rule for Analytics Investment • For every dollar you invest in technology • Spend one dollar for dedicated resources – Using free tools? Estimate costs – Hiring freeze? Consider consultants – No budget? You get what you pay for • This is how the analytical competitors are getting it done 46

The Analytical Organization 47

How Are You Doing With Staffing? • Five signs of the “right” staffing model: 1. You have a senior person who “owns” analytics 2. You have dedicated resources for web analytics 3. You know who your “analytics amateurs” are 4. Your analytics hub supports the whole company 5. Your analytics hub produces insights and recommendations, not just reports 48

Manage Your Analytical Talent 49

Focus Your Analytical Efforts • Fewer than one-in-five companies have a company-wide strategy for web analytics * • Lacking strategy, chaos reigns • Developing a strategy requires understanding the Hierarchy of Analytical Needs * Econsultancy 2008 51

The Hierarchy of Analytical Needs Recommendations Insights Information Data 52

What Most Companies Get Today … Recommendations Insights Information Data 53

What Companies Really Need Recommendations Insights Information Data 54

What Management Really Wants! Recommendations Insights Information Data 55

Recommendations Require Maturity Stage 0 Stage 1 Stage 2 Stage 3 Stage 4 Relative Number of Staffing Chasm Companies Investment Chasm Process Chasm Maturity of Analytics Use Source: JupiterResearch (8/05) 56

Cross the “Process” Chasm! • To provide insights and recommendations you must first define analytics processes • There is simply no way around this 57

Focus on Internal Analytics Process • Ask yourself? – Does the business understand what you do? – Do you present data or generate recommendations? – Do you have clearly defined workflow? – Do you have a governance model? – Do you know where our internal processes break-down? 58

How to “Kick-Butt” with Analytics • Six hallmarks of “Analytical Champions”: 1. Clearly defined analytics governance model 2. Appropriate technology investment 3. Appropriate resource allocation 4. Intense process-awareness 5. Ability to generate recommendations 6. Ability to measure impact of recommended actions taken 59

Focus Your Analytical Efforts Recommendations Insights Information Data 60

At the End of the Day it Takes … 62

People … 63

Process … 64

… and Technology 65

… in Roughly Equal Measures 66

Success is Awesome! • “Our success is split evenly between building authentic brands and using analytics to leverage and innovate pull demand marketing online.” – Dustin Robertson, CMO Backcountry.com 67

“If it’s worth doing, it’s worth doing analytically.” 69

“If it’s worth doing, it’s worth doing web analytically.” 70

Want to Know More? 71

Eric T. Peterson eric.peterson@webanalyticsdemystified.com www.webanalyticsdemystified.com Twitter: @erictpeterson 72

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Competing On Web Analytics by Eric T Peterson, Web Analytics Demystified

by Creuna Denmark on Oct 09, 2009

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Competing On Web Analytics by Eric T Peterson, Web Analytics Demystified — Presentation Transcript