ADMSP Introduction to Social Media Measurement

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This tutorial was prepared for our Social Media Measurement Specialist, Yaritza Velez who was then replaced by Rinita Sen, who is handling our social media measurement. She is doing great! …

This tutorial was prepared for our Social Media Measurement Specialist, Yaritza Velez who was then replaced by Rinita Sen, who is handling our social media measurement. She is doing great!

You can find Rinita at:

www.linkingpublic.org

To learn about our community project, join us at www.admsp.org

Visit us at: www.altosdelmarsculpturepark.com

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  • 1. Interactive Measurement ++++++++++-------xxxxx%%%%%--++++====
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  • 7. Tracking vs. Analytics & Analysis • Website tracking refers to the act of archiving existing websites and tracking changes to the website over time. –Wikipedia.com • Web analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage. There are two categories of web analytics; off-site and on-site web analytics. – Wikipedia.com
  • 8. Tracking vs. Analytics & Analysis – Web analytics suites do the tracking and allow you to report on the data you’re tracking. Web analytics suites give you: • Ways to slice and dice the data • Charts and graphs • Reports - aggregate your log file data • Examples of some popular ones: – Google Analytics (It’s FREE!) ! – Omniture – Coremetrics – Webtrends – Etc.
  • 9. Tracking vs. Analytics & Analysis • Knowing what to track and selecting the right tracking tools are NOT accomplishing ANALYSIS!
  • 10. Tracking vs. Analytics & Analysis • Breaking it down – Tracking gives you data points such as: • Hits • Visitors to site – unique and repeat • Average time on site • Referring sites • Bounce rate • Keywords • Pages visited within a site • Clickstream data
  • 11. Tracking vs. Analytics & Analysis – Bounce Rate • Represents the percentage of initial visitors to a site who "bounce" away to a different site, rather than continue on to other pages within the same site. • Anything over 50% should scare you • Anything over 35% should be testing page elements to lower it • Anything between 25-35% is OK • Anything under 25% is worth doing a back-flip over!
  • 12. Tracking vs. Analytics & Analysis • Goals - Before you start analysis, you need to understand your goals. • Actionable Insights – If what you’re tracking, and what your web analytics suite is reporting on does not help you form actionable insights, you’re tracking the wrong thing OR you don’t understand how to use the data.
  • 13. Tracking vs. Analytics & Analysis • Inclusive – Being inclusive when it comes to analytics means two things: – Not analyzing data that exists in silos – Not analyzing in silos – include your stakeholders! – Classic Example of the former:
  • 14. Tracking vs. Analytics & Analysis Click Data @999 7@7Z9 Analysis [@Z99 T..)2" _#;.. What can we learn !0` YZ9 from this data? 9 B * B 1; #& #& #& &% (% ! 5 ]# ^"
  • 15. Tracking vs. Analytics & Analysis Click Data Conversion Data @999 b9 7@7Z9 aZ [@Z99 T..)2" 9 T..)2" _#;.. _#;.. YZ9 !0` [Z !0` 9 9 B * B B * B 1; 1; #& #& #& #& #& &% (% #& &% (% ! 5 ! ]# 5 ]# ^" ^"
  • 16. What it Means to Be Average • Why Averages Are Like Dessert – Like a good dessert buffet, it’s very tempting to use averages when talking about your web metrics. – But averages are not always the answer. – Dive deeper, look beyond the surface.
  • 17. What it Means to Be Average • A quantity, rating, or the like that represents or approximates an arithmetic mean; a typical amount, rate, degree, etc.; norm. – Dictionary.com • Aggregate averages are more or less, useless, especially for high volume trafficked sites. • It’s lazy.
  • 18. What it Means to Be Average • So, what is one to do? Check out what every user is doing and give them each a separate path through the site to accommodate them? • No. Segmentation is the answer. • Take all that rolled-up aggregate data that you your web analytics tool spits out in it’s invariably pre-programmed, pre-defined report and segment it. • Dig into your analytics suite and figure out its segmentation capabilities.
  • 19. What it Means to Be Average • Segmentation Example by Channel:
  • 20. What it Means to Be Average
  • 21. What it Means to Be Average Hey! Now we can gain some real insights about visitors!
  • 22. Dashboards • Why Use Dashboards – Understand business performance – Track critical business data in an easy to understand manner – Can vary by many factors: data available, seniority in institution, etc. – Excel 21
  • 23. Dashboards • General concepts that should be considered when creating your dashboard: – Benchmark & Segment • Provide context for dashboard readers – For example: previous sales, industry benchmarks, goals, etc. – Isolate Critical Few Metrics • 10 metrics or LESS • Each has to have context • Each has to be further segmented 22
  • 24. Dashboards – Don’t Stop at Metrics—Include Insights • Summarize • Recommended Next Steps • Opportunities/Missed Opportunities • Don’t make your executives think! – The Power of a!Single Page – Evolution (and stay relevant) 23
  • 25. Dashboards • Google Analytics Dashboard Segmentation by Channel Segmentation by Metric Needs Context and A Summary! 24
  • 26. Dashboards Benchmark by Date 25