Webanalytics congress the Netherlands, Amsterdam June 2nd 2010, Judah Phillips

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  • 1. The Practice and Evolution of Web Analytics: A Manager’s Perspective * By Judah Phillips Senior Director, Global Site Analytics Monster Worldwide *opinions are mine, not Monster’s
  • 2. The Little Engine that Could…   An American children’s story that teaches the value of optimism and hard work.   After many larger engines reject helping move cargo over a mountain, a smaller engine succeeds by thinking “I know I can” Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 3. Web Analytics is the Engine that Can…   Amidst a sea of other, maybe larger, engines, that can’t including: >  Ad Servers (internal and external) >  Customer Relationship Management Systems >  Business Intelligence Systems >  Financial Systems >  Enterprise Data Warehouses >  Silo’ed datamarts >  Spreadmarts >  Third-party data providers   YOU are the engine that can – and you have to believe it! Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 4. The Engine that Can Do Many Things PAST PRESENT FUTURE INFORMATION What Happened? What is Happening What will Happen? Now? (Data Mining and (Trending, Reporting) (Alerts) extrapolation) INSIGHT How and Why did it What’s the Next What’s the Best Happen? Best Action? and Worst that can happen? (Data Modeling and (Recommendation) Experimental Design) (Prediction, Simulation) ACTION How do we How do we How can we Apply Leverage what we Dynamically Modify the Data to the Already Now? the Site in Real- Future? time? (Dynamic Interaction/ (Ongoing Profiling) (Detection) Optimization) First two rows from Tom Davenport. “Analytics at Work.” Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 5. The Engine that Serves Many Functions Marketing Product Sales Executive IT Landing Page Behavioral Customer Value Dashboarding Performance Optimization Analysis Monitoring Life Time Value / Search Engine Sales Readiness Scorecarding Site Usage for RFM Models / Optimization Capacity Planning Customer Segmentation Search Engine Demo/Geo/Firma Sales Collateral Custom Research Disaster Recovery Marketing Analysis Ad and Media Funnel and Flow RFP’s and RFI’s Financial Infrastructure Plan Optimization Optimization Performance Enhancements Social Media Application and Customer Usage Competitive Tagging and QA Optimization Product Information Intelligence Performance Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 6. The Engine That Has Many Challenges…   Challenges:   Solutions: >  Never enough time! >  Ask the right questions! >  No precedent. >  Verify assumptions. >  Decision makers are >  Guide with the facts. experienced. >  Understand the data. >  Some things can’t be >  Suggest other ideas. measured. >  Prove your data is “right.” >  Errors and Mistakes. >  Generously listen. >  Measuring too late or takes >  Set data expectations. too long. >  Don’t over-commit >  Mistrust and dissatisfaction. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 7. What Does it Take to Make the Engine Work?   Data that is easily available, self-service, integrated, appropriately granular, standardized/defined, and of high- quality.   Company that believes in data, not the just numbers, and is framed around want to use data for decisions.   Managers and executives who can form leadership and governance around the data.   Goals, goals, and more goals. They provide context for performance measurement.   People, people, people   Technology is the least important. All the tools do mostly the same thing – and all “suck” in different ways. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 8. The Engine needs a Conductor   Leaders, and I’m not talking about the C*.   People-oriented and skilled.   Leads by example.   Focuses on delivery and results and prove it.   Hires smart people, trust them, and give them credit.   Advocates for data and analysis.   Teaches people what they are talking about.   Forms business relationships for leverage.   Doggedly persistent and tenacious.   Works cross-functionally.   Builds out an analytics “ecosystem.”   Knows when to say “NO.” Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 9. The Engine needs Stewards, Ticketmasters, Mechanics, and Railworkers   These are the analysts, engineers, developers, and QA people: > Numeracy (i.e. quantitatively focused) > Sufficiently Technical. > Business Focused. > Visually-oriented and pattern recognizers. > Consultative. > Thoughtful. > Inquisitive. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 10. How could you structure the Engine?   Centralized   Decentralized   Consultative   Stovepiped by function   Hub and Spoke (i.e. Center of Excellence)   Federated
  • 11. When is it the Right Time to Use the Engine?   When you have data!   Need information in the data.   Need consistency and control.   “Many cooks” who have input into the decisions.   When the company is very cross-functional.   When it is possible to improve the situation with data.   Revenue is at risk. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 12. When is it the Wrong Time to Use the Engine?   When you have no data or the data is bad.   Have not defined why your site exists.   Have no agreement on business goals.   Have ill-defined “business questions.”   Unanswerable questions that aren’t data focused.   Takes too long to get the data.   No support from teams on which you are dependent.   No ability to take action on the data. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 13. What Can You Achieve?   Improve revenue.   Reduce costs.   Maximize efficiency.   Enhance the customer experience.   Optimize the web site. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 14. Web Analytics Evolving 1990’s (wild west) | 2000-5 (rough country) | 2005-present (sheriff in town) JavaScript page Log file parsing on Hybrid forms of data collection: page tags, log tagging on- files, db logging, 3rd party and internal data premise demand or on- feeds. Basic metrics: premise •  Visitors Detailed metrics highly-specific to the overall •  Visits Detailed metrics: business. Everything in earlier generations, plus •  Page Views •  Time-derivatives custom metrics and event models (AJAX and •  Campaigns Flash). Improved segmentation. •  Referrers •  Exit/Entry pages •  Keywords •  Conversion Managed by a centralized business function with •  Hits little IT support beyond tagging. •  Pathing Managed by an IT Business could use the data to understand department. Managed by a performance across various systems and distributed channels whether online, offline, in-store, third Business could use business function party, or partner AND to optimize the site. the data to with heavy IT understand popular support. Data can be fed out of analytics systems and pages and traffic integrated with other tools, such as BI, Ad patterns. Business could Servers, Surveys, Bid Management (SEM), SEO, use the data to Site Optimization, Predictive, Social, Mobile, Reporting ANALYZE site Video, CRM, ERP. performance. Reporting > Analysis > Data Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 15. What does a Web Analytics Organization Look Like?   People are key. Followed by the technology.   At Monster we split the Site Analytics function: 1.  Architecture Team 2.  Reporting Team 3.  Analysis Team   The end result is the creation, distribution, and operationalization of analysis, reporting, and data that guides the business on both tactical and strategic decisions. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 16. Audience Measurement (AM) and Web Analytics (WA): The Yin and Yang   Too much complaining about how these two systems don’t match or conflict with each other – waste of time – expecting them to match is unrealistic. Get over it!   Determine how these systems can complement each other according to your business questions. Pick one tool as the “gold standard” for different purposes.   Sometimesyou use different data from both systems for the same audiences. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 17. I wonder what the future holds…   New Data Collection Models >  Universal tagging >  Application logging   Automated Tagging >  Flash events directly into Analytics without manual tagging   Deeper Capabilities for Integration >  Roundtrips from EDW, Ad Servers, CRM, Targeting technologies, Finance   Enhanced Targeting and Closed-loop, Self-optimizing, Customer Behavioral Feedback Systems >  Support more targeting attributes and direct feeds into technologies >  Event and rules based detection modifying the user experience   Improved attribution modeling >  First, last, indirect, direct, appropriate – can be confusing and insufficient. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 18. Still wondering…   Global consensus-based, Practiced (not preached) industry standards adhered to by vendors and enforced via demand by customers >  Global, not regional, country, association/organization, or company specific for key measures/metrics. Let’s go beyond the Big 4! >  Verticalized KPI’s for specific industries.   Increased importance of auditing >  Requires deeper standards. Global companies don’t have the time, resources, or often need to participate in all standards bodies.   It’s about Multichannel >  It’s not just web – it’s mobile, social, and nonline sources (call center, kiosks)   Site Optimization based on Performance >  Not just conversion, but the macro and micro events on the site that drive revenue. Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 19. All this will continue lead to…   FurtherCultural Change and Quicker Shifts in Organizational Dynamics: > To support data-driven decision making •  Not the HIPPO. •  Not based in Feelings. •  Not based on Intuition. •  Based on improved data analysis.   Tothe point where companies are truly competing on (Web) Analytics! Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 20. So What? What does this mean to you? A Business   You need to define what you are solving for. >  Why are you doing analytics? >  What are your business goals and drivers? >  What are your site goals in the context of business goals? >  Don’t get bogged down in the details. Think of the larger themes.   You need to staff appropriately to compete on Web Analytics: >  One part timer is not enough. You need staff.   You need to invest in technology >  Technology without people is useless. You need staff.   You need to create cross-functional teams and processes >  How do you define goals? Tag? QA? Distribute? Analyze? Optimize? Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 21. So What? What Does This Mean to a Consultant or Vendor?   Help get the data in order!   Figure out the tagging and QA problems.   Define key targets and segments.   Build dashboards and scorecards.   Recommend how to take action on the data.   Build predictive models.   Create organizational processes.   Define what optimization means and do it! Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 22. The Engine That Could Said…   Answer why your site exists.   Figure out the business goals.   Structure your team correctly for your goals.   Be relentless and ruthless about data quality definitions.   Determine your key performance indicators and the drivers for those indicators.   Report the data against goals and historic benchmarks to give context.   Analyze the data in words and use visualizations.   Define what it means to optimize the site.   Remember “You can do it!” Proprietary and Confidential. Do not use without Permission from Judah Phillips.
  • 23. BEDANKT! Questions?   judah.phillips@monster.com judahphillips@gmail.com @judahp Works Cited: Tom Davenport “Analytics at Work,” Eric T Peterson “Web Analytics Demystified.” Proprietary and Confidential. Do not use without Permission from Judah Phillips.