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6910 week 3 - web metircs and tools


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  • Decentralized decision making? (T-Mobile very much this way, all needed to see numbers before making a call)Cover your back culture? (Microsoft is very much this way, don’t want to make a bad call and need someone to blame)Risk adverse? (Microsoft is this way, why I have lots of work… agency makes the risky recommendations so the client has someone to blame) Distribution of knowledge? (T-Mobile was like this, knowledge was power people kept info for themselves, Analyst level had very little view into what changed)
  • Transcript

    • 1. Web Metrics & ToolsISM 6910 – Week 3
    • 2. Week 3 Topics• Tools Discussion• Site Metrics Continued
    • 3. Know your organization Are you looking for reporting or analysis? • Decentralized decision making? • Cover your back culture? • Risk adverse? • Distribution of knowledge?
    • 4. Factoring in price Free tools aren’t free! • Implementation can be a huge cost • Cost for custom tags and reports? • What about added cost for dashboards etc. (example: SQL, Tableau) • Incremental time and cost now added to making changes on the site (tag management)
    • 5. Implementation How strong is your IT? • We’ll cover implementation in more detail later. • There are consultant firms that pretty much just do implementation • Get it wrong and all of your metrics can be off! • Support for tagging, custom tags? Note: If you have any interest in learning or know JavaScript and get certified in one of the top analytics tools you’ll have multiple job offers the next day. There is a big shortage in the market place right now for people with these skills, Razorfish has really struggled to fill these roles.
    • 6. Clickstream vs. Web Analytics 2.0 How sophisticated are you really looking to be? • A/B and multivariate testing? • Heat mapping? • Advanced segmentation? • Most of the big tools do all of the above, but some better than others.
    • 7. Reporting How easy is it to pull out the data from your tool? Most of the time your data will finally land in Excel or PowerPoint. • Easy to use API feeds? • Data exports? • Automation and hooks directly through Excel? • Most of the big tools do all of the above, but some better than others.
    • 8. Razorfish POV Web Analytics Tools Comparison Platform Pros Cons • External data integration • Complex interface • Excel client • Learning curve • Retroactive meta-data integration through SAINT • Complex Tags • Strong segmentation • Cross-visit attribution modeling through Discover • Best in class alerts, dashboards, and target features Adobe • Focus on ad hoc reporting SiteCatalyst v15 • Flexible pathing analysis and Discover 3 • Custom reports (variables/events) • Multiple levels of persistence • Conversion deconstruction • Genesis integrations with email service providers, Salesforce, and others • $$ • Highly intuitive, refined and easy to navigate interface • Report building backend similar to V8.5 • Storyboard mode • Reports not very flexible • Well-organized profiles through Spaces • Difficult to perform ad hoc reporting • Meta-data integration – Translation tables • REST API inferior to Excel plugin Webtrends • REST API access through Excel • External data integrations mostly custom Analytics 10 and • Good export features • Segments tool inferior to Adobe Discover • Custom reports (variables/events) • Complex Tags Segments • Multiple levels of persistence • Unlimited variables • $$ Pricing (based on 30MM PVs) $ < $25K $$ $25 - $50K $$$ $50 - $100K $$$$ >$100K
    • 9. Razorfish POV Web Analytics Tools Comparison Platform Pros Cons • Comparative benchmarks and intelligence • Comparative benchmarks limited to content sites • Tabbed reporting (report toggling) • Limited segmentation capabilities • Social media monitoring • Clogged interface • Visitor profiling – LIVE • Sampled data • Powerful metric attribution features • Limited MS Office integration IBM CoreMetrics • Most complex tagging required • Unclear data integration capabilities • Difficult implementation (12 tag types) • $$$ • Easy to use interface – fast learning curve • No raw data feeds available • Rich dashboards and visualization • Very limited external data integration • Strong segmentation • No forecasting • Click-based attribution modeling • Segmentation is limited to the visit • Real-time reporting • No metadata upload capability Google Analytics • Custom reports (variables/events) • Very limited user provisioning Premium • Multiple levels of persistence • New tagging required • Social assist feature • Limited unsampled data (no dashboards/scheduled • Simplest tagging reports/interface) • Intelligence / automatic alerts • $$$$ Pricing (based on 30MM PVs) $ < $25K $$ $25 - $50K $$$ $50 - $100K $$$$ >$100K
    • 10. Switching gears back tolast week and metrics…
    • 11. Visitor Acquisition The big 3 (Omniture, Webtrends, and GA) break out visits by: • Direct • Search • Referring • Other
    • 12. Visitor Acquisition (cont.) Some things you should keep in mind with Direct traffic: • Direct traffic is not free! • If you don’t set up your campaigns right it will look like direct traffic Webtrends example:
    • 13. Watch out! Your Web Analytics tool ad traffic number will never match your ad serving (Atlas & DoubleClick) counts! • Web analytics tools won’t match either, they all have their own business rules. • Tags are in different locations on the page. • 3rd party servers have issues to.
    • 14. Simple segmentation You can learn a lot by segmenting on just the acquisition traffic source: • Bounce rates by source • Conversions by source • What are the top key words? • Top converting referring sites • Top performing media?
    • 15. Watch out for averages Averages like, average time on site, or average pages per visit can sometimes be a bit deceiving. It’s definitely worth taking the time to look at the distribution.
    • 16. Another example… The same website but looking at the number of page views.
    • 17. A page load FYI Be carful when using page loads as an engagement metric. Sometimes one page acts like multiple pages… A lot of new sites now use tabs and accordions to navigate and display content, if the url doesn’t change it’s not a new page.Tab example: example:
    • 18. Time to convert Another watch out! Most of the web analytics tools tend to focus most of their reports on a single visit. Number of purchasers 28% of total 10 min. 43% of total 45% of total 68% of total 62% of total 30 min. 1 hr. 6 hrs. 2 hrs. Time between first Visit & purchase (Min.)
    • 19. Tracking multiple visits Same as time to convert, here is another example…I recently needed a new Found the cartridge I Epson’s cart experienceprinter cartridge, so I needed, and then started sucks so I ended up buy theGoogled Epson price shopping. new cartridge on Amazon …but had Epson’s site been better I would have converted on my second visit.
    • 20. Another watch outIn the last example I used Google to find the Epson site, but on my secondvisit when I went to purchase the cartridge I just typed in the url… so am I inthe search segment or the direct traffic segment? which channel would havegotten credit for the conversion had I actually purchased the cartridge on theEpson site?
    • 21. What are converters doing? FPP Upgrade Conversion Rate By Page Load (FYQ2 2011)
    • 22. Click density reports It’s a real pain to put these reports together but site managers love them:
    • 23. Heat map reports Site manager love these reports!
    • 24. Internal Search Insights • Can shed some light on what content is important but hard to find or missing. • How successful are those internal searches?
    • 25. Focus on the critical few Its all about a handful of KPIs, don’t get overwhelmed by tracking and interpreting every possible metric. • Example – share of search, what can you do to improve that metric? We’ll talk more about this one next week.
    • 26. Macro and Micro Conversions Another great method taken from the book on how to decide what the critical few should be Traffic to Website Conversion Traffic to Website Support Research Careers $ Micro Micro Conversion Conversio Macro Conversion n
    • 27. Steps to purchase Another great approach is optimize the funnel, i.e. the different steps a customer typically takes before making a final purchase/conversion.
    • 28. One more approach Another great approach is focus on the customer’s lifecycle. Customers will have different needs and will use your site differently based on where they are in the customer lifecycle. This approach is great for service type products and sites.
    • 29. Lifecycle approach For example... KPIs: Other metrics:
    • 30. Lifecycle approach For example... KPIs: Other metrics:
    • 31. Lifecycle approach For example... KPIs: Other metrics:
    • 32. Lifecycle approach For example...KPIs:Other metrics:
    • 33. Lifecycle approach For example...KPIs:Other metrics: