Mobile Analytics: Digital Strategies and Measurement Challenges
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Mobile Analytics: Digital Strategies and Measurement Challenges

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In this webcast Greg Dowling of Semphonic gives practical advice on…

In this webcast Greg Dowling of Semphonic gives practical advice on…

> Why Mobile Matters
> Mobile Measurement
> Mobile Strategy

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Mobile Analytics: Digital Strategies and Measurement Challenges Mobile Analytics: Digital Strategies and Measurement Challenges Presentation Transcript

  • Mobile Analytics: Digital Strategies and Measurement Challenges In this webcast Greg Dowling of Semphonic gives practical advice on… > Why Mobile Matters > Mobile Measurement > Mobile Strategy    
  • American Marketing Association April 2010 Mobile Analytics: Digital Strategies and Measurement Challenges
    • Semphonic is the world’s largest independent Web analytics consultancy. Founded in 1997, the company has helped leading corporations, government agencies and non-profits achieve measurable improvement in the performance of their web channel. Clients include American Express, Charles Schwab, National Cancer Institute, Nokia, Genentech and Intuit.
    About Semphonic Portland San Francisco Boston New York Washington, DC
  • AGENDA: - WHY MOBILE MATTERS - NOKIA MOBILE DATA STRATEGY - MOBILE MEASUREMENT - MOBILE STRATEGY
  • WHY MOBILE MATTERS
  • Why Mobile Matters Mobile is the most popular and rapidly adopted personal technology in the world.
  • Why Mobile Matters Mobile web browsing is experiencing a meteoric rise, but still a small percentage.
  • Why Mobile Matters Net Applications, February 2010
  • Why Mobile Matters Smartphones will make up nearly half of all U.S. handset sales by 2011.
  • Why Mobile Matters Mobile device operating systems vary widely depending on geographic location.
  • Why Mobile Matters Source: Gartner
  • Why Mobile Matters Source: Gartner Source: Gartner
  • Why Mobile Matters iPhone users have most applications installed, followed by Android Nielsen, Q4 2009
  • Why Mobile Matters Mobile application downloads will double in 2010 to 4.5B with $6.8B in revenue.
  • Why Mobile Matters Mobile commerce expected to triple by 2013 with 68B in global revenue.
  • Why Mobile Matters US mobile advertising spending to triple by 2013 with 70% currently “doing something”
  • NOKIA MOBILE DATA STRATEGY
  • A history of hi-tech and innovation
    • Founded in Tampere in 1865
    • Finnish Rubber Works Ltd. 1898
    • Finnish Cable Works Ltd. 1912
    • Nokia Corporation 1966
    • Electronics began in 1967
    © 2008 Nokia
  • A history of hi-tech and innovation
    • Founded in Tampere in 1865
    • Finnish Rubber Works Ltd. 1898
    • Finnish Cable Works Ltd. 1912
    • Nokia Corporation 1966
    • Electronics began in 1967
    © 2008 Nokia
  • A history of hi-tech and innovation
    • Founded in Tampere in 1865
    • Finnish Rubber Works Ltd. 1898
    • Finnish Cable Works Ltd. 1912
    • Nokia Corporation 1966
    • Electronics began in 1967
    © 2008 Nokia
  • A history of hi-tech and innovation
    • Founded in Tampere in 1865
    • Finnish Rubber Works Ltd. 1898
    • Finnish Cable Works Ltd. 1912
    • Nokia Corporation 1966
    • Electronics began in 1967
    © 2008 Nokia
  • A history of hi-tech and innovation
    • Founded in Tampere in 1865
    • Finnish Rubber Works Ltd. 1898
    • Finnish Cable Works Ltd. 1912
    • Nokia Corporation 1966
    • Electronics began in 1967
    © 2008 Nokia
  • A history of hi-tech and innovation
    • Founded in Tampere in 1865
    • Finnish Rubber Works Ltd. 1898
    • Finnish Cable Works Ltd. 1912
    • Nokia Corporation 1966
    • Electronics began in 1967
    © 2008 Nokia
  • A history of hi-tech and innovation
    • Founded in Tampere in 1865
    • Finnish Rubber Works Ltd. 1898
    • Finnish Cable Works Ltd. 1912
    • Nokia Corporation 1966
    • Electronics began in 1967
    © 2008 Nokia
  • A history of hi-tech and innovation
    • Founded in Tampere in 1865
    • Finnish Rubber Works Ltd. 1898
    • Finnish Cable Works Ltd. 1912
    • Nokia Corporation 1966
    • Electronics began in 1967
    © 2008 Nokia
  • Convergence “ Everything came to us in a device that could fit into a pocket”
  • Connecting
  •  
  • The Consumer Data Strategy Nokia´s future success as a direct to consumer business required efficient and innovative use of consumer data Nokia needed to develop Consumer Data as a strategic asset
      • Engaging consumers to foster a continuous relationship with Nokia
      • Developing targeted and relevant sales and marketing efforts
      • Developing more consumer-driven services and solutions
    Realizing synergies and building up common enablers was key for utilizing consumer data as a strategic asset
  • The Consumer Data Situation
  • The Consumer Data Situation
    • No common definitions for consumer data
    Common language was missing
  • The Consumer Data Situation
    • Consumer data in multiple fragmented databases, with limited capability to combine on service wide level
    • No common definitions for consumer data
    Consumer data was fragmented Common language was missing
  • The Consumer Data Situation
    • Consumer data in multiple fragmented databases, with limited capability to combine on service wide level
    • Lack of common data models and data management practices led to poor quality
    • No common definitions for consumer data
    Consumer data was fragmented Consumer data quality was poor Common language was missing
  • The Consumer Data Situation
    • Consumer data in multiple fragmented databases, with limited capability to combine on service wide level
    • Lack of common data models and data management practices led to poor quality
    • Need for competent data analyst resources at Nokia far exceeded current levels
    • No common definitions for consumer data
    Consumer data was fragmented Consumer data quality was poor Analytics competencies missing Common language was missing
  • The Consumer Data Situation
    • Consumer data in multiple fragmented databases, with limited capability to combine on service wide level
    • Lack of common data models and data management practices led to poor quality
    • Need for competent data analyst resources at Nokia far exceeded current levels
    • Consumer data not consciously in the scope of developing and operating a service or innovating new business models
    • No common definitions for consumer data
    Consumer data was fragmented Consumer data quality was poor Analytics competencies missing Consumer data & insights were not part of business processes Common language was missing
  • The Consumer Data Situation
    • Consumer data in multiple fragmented databases, with limited capability to combine on service wide level
    • Lack of common data models and data management practices led to poor quality
    • Need for competent data analyst resources at Nokia far exceeded current levels
    • Consumer data not consciously in the scope of developing and operating a service or innovating new business models
    • Lack of common marketing campaign tracking and optimization principles and practices
    • No common definitions for consumer data
    Consumer data was fragmented Consumer data quality was poor Analytics competencies missing Consumer data & insights were not part of business processes Marketing activities not guided by common principles Common language was missing
  • The Consumer Data Situation
    • Consumer data in multiple fragmented databases, with limited capability to combine on service wide level
    • Lack of common data models and data management practices led to poor quality
    • Need for competent data analyst resources at Nokia far exceeded current levels
    • Consumer data not consciously in the scope of developing and operating a service or innovating new business models
    • Lack of common marketing campaign tracking and optimization principles and practices
    • No common definitions for consumer data
    Consumer data NOT regarded as an asset Consumer data was fragmented Consumer data quality was poor Analytics competencies missing Consumer data & insights were not part of business processes Marketing activities not guided by common principles Common language was missing
  • MAKING THE HARD DECISIONS
      • People
      • Process
      • Tools
    Nokia Mobile Data Strategy
      • People
    Nokia Mobile Data Strategy
    • A special working group with the charter and the resources to address Nokia’s Consumer Data issues.
    Consumer Data & Interaction Program
    • NOKIA Vision: To Become a Consumer Driven Internet Company.
    • CDI Program Vision: Consumer Data Is An Integrated Part of Nokia Business.
    • CDI Mission:
      • Build Consumer Data Into a Strategic Asset
      • Communicate In-Depth Consumer Understanding
      • Utilize Consumer Data Effectively By:
        • Engaging consumers to have a continuous relationship with Nokia
        • Developing targeted sales and marketing efforts
        • Developing more consumer-driven solutions
        • Operating in direct-to-consumer businesses and uplifting mobile advertising business
    • Purpose :
    • Lead the Services wide consumer data initiative and enable fact based decision making
    • Improve the speed and quality of customer relationship management and product development
    • Accountability:
    • Consumer data strategy implementation
    • Analysis and cross promotion capability based on user data
    • Services level dashboards, Learning Agendas, and Optimization Plans
    Services Intelligence & Analytics Common definitions Results Analysis
      • People
      • Process
    Nokia Mobile Data Strategy
  • A cquire E ngage R etain C onvert What % of the target audience did the service reach? Where does the traffic come from? What is the cost ? Are we getting users to interact with the service? Are we building their trust? What is the cost ? Are visitors performing the actions that will lead to our success? Are these actions making our business successful? What is the cost ? Are we building loyalty? Are the users converting again over time? Are the users recommending our service? What is the cost ?
  • the user experience A cquire E ngage R etain C onvert What % of the target audience did the service reach? Where does the traffic come from? What is the cost ? Are we getting users to interact with the service? Are we building their trust? What is the cost ? Are visitors performing the actions that will lead to our success? Are these actions making our business successful? What is the cost ? Are we building loyalty? Are the users converting again over time? Are the users recommending our service? What is the cost ?
  • Unified Nokia Standard
  • Unified Nokia Standard How did we document KPIs? Business goals & resources used to achieve these goals Definitions of the AECR events specific to the service KPIs, Metrics & Funnels used to measure these goals List of all the data needed to populate the KPIs, Metrics & Funnels Campaigns specific KPIs & Metrics Goals KPIs, Metrics & Funnels AECR events Data list Campaigns
  • Unified Nokia Standard How did we document the Reporting Plan? Lists all the dashboards and reports to be delivered Describes the detailed specifications of each and every dashboard and report listed List all the users access rights for access to the online versions of the reports Dashboards Access rights Dashboards details
  • Unified Nokia Standard How did we document the Implementation Plan?
  • Unified Nokia Standard How did we document the Implementation Plan?
  • Unified Nokia Standard How did we document the Implementation Plan?
  • Unified Nokia Standard How did we document the Implementation Plan?
  • Unified Nokia Standard How did we account for cross platform visibility?
      • People
      • Process
      • Tools
    Nokia Mobile Data Strategy
  • Vendor Evaluation Process
    • Criteria for Evaluation
    • Ease of implementation
    • Traditional tracking
    • Mobile tracking
    • Reporting capacity
    • Analytics capacity
    • Business user usability
    • Analyst usability
    • Data export
    • Data import
    • Administration
    • Information security
    • Data storage
    • Legal
  • Vendor Evaluation Process
    • Criteria for Evaluation
    • Ease of implementation
    • Traditional tracking
    • Mobile tracking
    • Reporting capacity
    • Analytics capacity
    • Business user usability
    • Analyst usability
    • Data export
    • Data import
    • Administration
    • Information security
    • Data storage
    • Legal
  • Vendor Evaluation Process
    • Criteria for Evaluation
    • Ease of implementation
    • Traditional tracking
    • Mobile tracking
    • Reporting capacity
    • Analytics capacity
    • Business user usability
    • Analyst usability
    • Data export
    • Data import
    • Administration
    • Information security
    • Data storage
    • Legal
  • Vendor Evaluation Process
    • Criteria for Evaluation
    • Ease of implementation
    • Traditional tracking
    • Mobile tracking
    • Reporting capacity
    • Analytics capacity
    • Business user usability
    • Analyst usability
    • Data export
    • Data import
    • Administration
    • Information security
    • Data storage
    • Legal
  • Tool Selection Omniture Suite for Web Analytics Data Warehouse for Centralized Consumer Data Management
  • Consulting Partners Omniture Semphonic Mobile Standard Technical Support and Review Best Practices Implementation Support Fixed Web Standard AECR Framework Integration Implementation Design and Support
  • MOBILE MEASUREMENT
      • Platform Limitations
        • JavaScript is often not present or enabled
        • Cookies may not be allowed or may be very short-lived
        • Browsers are non-standard
      • Carrier Limitations
        • Some carriers aggressively strip HTTP headers so you don’t get everything you’d expect
        • Character limits on image requests limit the amount of information you can pass
      • Integrated Applications
        • Apps are significant part of mobile
    Measurement Challenges
  • Measurement Strategies
    • There are four common Mobile Measurement Strategies
      • JavaScript tagging as per the fixed web
      • Server-side image requests
      • Wire Line Capture
      • API Collection & Insertion
    • Each has some advantages and each has some significant disadvantages
  • JavaScript Tagging
  • Server Side Image Requests
  • Wire Line Capture
  • API Collection & Insertion
  • Potential Pitfalls
    • Unique Visitor Identification
      • Cookies are often problematic
      • SubscriberID sometimes stripped and unavailable.
      • Consider ‘waterfall’ or ‘hybrid’ methodology
    • Robots and Spider Detection
      • JavaScript solutions won’t work in most mobile scenarios
      • Mobile traffic DOES HAVE significant robotic presence
    • Mobile Applications
      • Early integration with development
      • Map data to fixed and mobile web
      • More rigorous testing required
  • So what did Nokia do?
    • Image Tags for Mobile
    • XML Data Insertion API for Mobile Apps
    • 1 st Party Cookie for fixed web, Subscriber ID for Mobile Web, application identity (UUID) for mobile apps.
    • Capture visitorID method (Subscriber ID, cookie, UA & IP) and Device Type as variables
    • Use of obfuscated Nokia Account ID to create a cross-channel view for fixed web, mobile web and mobile applications.
    • Separate report suites for each channel with different keys – insertion managed by automated rules.
  • MOBILE STRATEGY
    • Get S.M.A.R.T with mobile
    Mobile Strategy
    • Get S.M.A.R.T with mobile
    Mobile Strategy
    • Get S.M.A.R.T with mobile
    Mobile Strategy
    • S trategy
    • M easurement
    • A nalysis
    • R eporting
    • T actics
    Mobile Strategy
    • S trategy
      • Assess your ‘Mobile Readiness’
        • Determine product suitability
        • What are your objectives?
        • What is your commitment?
        • What is the ROI?
    Mobile Strategy
    • M easurement
      • Define a ‘Measurement Plan’
        • Establish KPIs
        • Tie KPIs to business objectives
        • Evaluate enablement options
        • Assess technical limitations
    Mobile Strategy
    • A nalysis
      • Establish intelligent ‘Correlations’
        • Appropriate dimensions
        • Fixed web vs. mobile web
        • High value tasks
        • Integration with offline data
    Mobile Strategy
    • R eporting
      • Make strategic information ‘Obvious’
        • Leverage data visualization
        • Allow for data interaction
        • Appropriate level of detail
        • Automate where possible
    Mobile Strategy
    • T actics
      • Generate ‘Actionable Insights’
        • Let data define actions
        • Enhance segmentation
        • Channel optimization
        • Leverage experimental design
    Mobile Strategy
  • Summing Up
  • Summing Up
    • Determine your ‘Mobile Readiness’
  • Summing Up
    • Determine your ‘Mobile Readiness’
    • Define a ‘Measurement Plan’
  • Summing Up
    • Determine your ‘Mobile Readiness’
    • Define a ‘Measurement Plan’
    • Establish intelligent ‘Correlations’
  • Summing Up
    • Determine your ‘Mobile Readiness’
    • Define a ‘Measurement Plan’
    • Establish intelligent ‘Correlations’
    • Make strategic information ‘Obvious’
  • Summing Up
    • Determine your ‘Mobile Readiness’
    • Define a ‘Measurement Plan’
    • Establish intelligent ‘Correlations’
    • Make strategic information ‘Obvious’
    • Generate ‘Actionable Insights’
  • Thank you! Email: [email_address] Twitter: @gregdowling
  • For More Information on our host, ReadyTalk , visit ReadyTalk.com/ama Questions for Today’s Speaker Email Greg: [email_address] Tweet: @gregdowling Questions for the AMA Email: [email_address] Continue the Conversation on Twitter #AMAAquent Thank you for your Participation!