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Step. W. E. B. Change. A. N. A. L. in. Y. T. I. C. S. Web Analytics. Jaisri Chety. Participate. This is a highly interactive session; request all of you to participate with questions, challenges & solutions…. Web Analytics 1.0. Click Stream data Visits Visitors

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  1. Step W E B Change A N A L in Y T I C S Web Analytics Jaisri Chety

  2. Participate • This is a highly interactive session; request all of you to participate with questions, challenges & solutions…

  3. Web Analytics 1.0 • Click Stream data • Visits • Visitors • Geo Targeting • Average time spent • Funnel conversion • Landing page optimization • Conversion rates…. In Brief we were looking at the What, When & where questions

  4. What did we miss?

  5. Advent of Web 2.0 • User generated content • Content distribution through Rss & Xml • Rich internet applications • Non traditional browsers like iPhone, BlackBerry. KPIs sans insight • Demand for more insights rather than aesthetically presented numbers/ Ratios. • Achieving marketing ROI with onsite optimisation & behavior targeting

  6. Change in how Web Analytics is perceived by SEM

  7. Large gap in off-site and on-site spending… $ $ $ $ $ $ $ $ $ $ $ $ Off-site Resources Off-site Resources Email marketing Affiliate programs Behavioral Targeting Paid search management Banner advertising Call center referrals Search Engine Optimization Offline marketing to web In-store Web promotion Registration optimization Site testing Web analytics Usability testing On-site Resources On-site Resources On-site Resources Large Investment Gap

  8. On-site engagement determines conversion success Campaign Traffic Referred Traffic Direct Traffic Email marketing Affiliate programs Behavioral Targeting Paid search management Banner advertising Call center referrals Search Engine Optimization Offline marketing to web In-store Web promotion Campaign Landing Pages Home Page Product Category Pages Attrition losses Attrition losses Conversion Process Successful conversion $$$ Off-site Marketing Spending Critical Engagement Layer On-site Experience Determines Conversion Rate

  9. How automated 1 to 1 targeting works: CMS (Serves content) Call goes out toVisitor Profile Repository Optimalcontent decision sent to CMS Content library First-time visitor build profile retrieve profile Repeat visitor Visitor Profile Repository Self-learning Predictive Modeling Engine Visitor arrives at your website

  10. Highly predictive anonymous visitor profile If we could answer a few questions, we could determine what page to serve to each customer What is this visitor doing now? What have they done before? Where is this visitor Located? What is their online experience like? Offline Customer Variables How did this visitor arrive here? Have they already expressed what they want? When is this visit occurring? How frequently & recently have they visited?

  11. Site Behaviour Variables Environment Variables IP address Country of origin Time zone Operating system Browser type Screen resolution Customer/prospect New/return visitor Previous Visit pattern Tools usage Previous Product interests Searches Previous online purchases Previous Campaign exposure Previous Campaign responses Referrer Variables Referring domain Campaign ID Affiliate PPC Natural search Search keywords Direct/bookmark Temporal Variables Time of day Day of week Recency Frequency What data is used to select the relevant offer? Offline Customer Variables

  12. Lloyds TSB Initial Page

  13. Profile A

  14. Profile B

  15. Profile C

  16. Profile D

  17. Targeting on the secure logoff page

  18. Temporal targeting 3:15 PM

  19. 1:45 AM

  20. Why Web Analytics 2.0 is the inevitable response to the changing Internet A reflection that: • Page views are becoming less relevant as a fundamental measure on some sites • Quantitative data alone doesn’t tell us enough about visitor engagement • The browser wars are starting over again, this time on mobile devices • Available reporting mechanisms are increasingly inadequate • The nature of measurement is changing rapidly

  21. Web Analytics 2.0 is • the analysis of qualitative and quantitative data from your website and the competition, • to drive a continual improvement of the online experience that your customers, and potential customers have, • which translates into your desired outcomes (online and offline).

  22. Arrive at Insight • Clickstream — Typical web analytics. • Multiple Outcomes Analysis — All those objective outcomes need to be measured to see if the site is really driving the desired outcomes. • Experimentation & Testing — In it’s simplest form, this means A/B testing the design of your website, including text, graphics, buttons, banner ads, everything.  • Voice of the Customer — The results can be tied back to analytics data and may reveal customers’ true motivations. • Competitive Analysis — Your competitors may be running campaigns or launching products/features that are impacting your site’s performance (could be either up or down).

  23. Customer Experience Management • The core value of CEM systems is the ability to capture and report on every interaction a visitor has with a site. • It is highly diagnostic as it helps to determine whether the abandonment was audience or application related. • Pinpoints the true source of the problem

  24. Customer is still the king • Hence understanding the customer/ visitor behaviour through both quantitative & qualitative ways are critical. • Tools such as CEM, VOC & Click Stream give us a complete view of our customer behaviour.

  25. Web 3.0 The real problem we would all eventually face is • Web 3.0 will be about mobile computing • All the same problems … • On smaller screens … • With different usability challenges … • Potentially without JavaScript and cookies … • But Web 3.0 will create unique opportunities • Every request for information could be tied to a good unique ID • Every request for information could be coupled with a geographic location

  26. Web Analytics 3.0 Some new questions we’ll be able to ask with Web Analytics 3.0! • Which of our stores was the visitor in or near when they came to our site? • What offers do we have in the visitor’s neighborhood at work or at home? • Can the visitors location or demographic profile be used to disambiguate search? • Which ads work best based on the visitors phone browsing platform and time of day? • What message would be most appropriate given time of day, geographic location, and observed visitor behavior? • Web 3.0 will bring advertisers and marketers closer than ever to their customers • And how will we help them take advantage of these new opportunities

  27. Source • Improving Customer Acquisition through Analytics - Brent Hieggelke • CUSTOMER EXPERIENCE MANAGEMENT ND WEB ANALYTICS From KPIs to Customer Transactions - Eric Peterson • Multiplicity: Succeed Awesomely At Web Analytics 2.0! - Avinash Kaushik

  28. Questions • I would also cover the 3 step changes in detail in my blog - web-scapes.blogspot.com • If you want any clarification or want to post questions on the same please feel free to post it as comments in the blog as above or mail me at jaisrichety@gmail.com Thank You

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