Cut Through the Fog: How to Act on Your
Museum's Website Data
MCN 2012
11/7/2012
Brian Alpert
Web Analytics and SEM Analys...
2
Table of Contents, Part 1
• The Web Analytics Process
• GA Data Grabber
• GADG Custom Dashboard
• Case Studies
• Hands-o...
3
What web analytics is often about
4
Web analytics is often about:
“So What?”
5
What do you mean, “So what”?
• The typical proxy for website success is quantity of stuff.
– Aggregated “big numbers”
– ...
6
What web analytics is really about:
Furthering Program Goals
Reuters: Toru Hanai
7
Articulating your goals is the hard part
 Sometimes your museum's goals:
 Aren’t precisely articulated.
 Aren’t artic...
8
Your goal: storyteller
 Use data to tell a story.
 Management loves stories.
 The “So what?” factor melts
away becaus...
A systematic, step-by-step process
 Articulate your program’s goals.
 Decide strategies to achieve those goals.
 Decide...
Start by articulating specific goals
 Not too many!
 Express what your museum is trying to accomplish.
 Distill high-le...
11
Determine strategies & tactics
 Strategies – the plans you make to achieve the goals.
 Marketing, social media are st...
12
Decide how to measure your tactics
 Choose a few measurements.
 Trend them over time.
 Per the example:
 Measure: s...
13
You can’t set targets w/o benchmarks
 Set targets and timeframes based on benchmarks.
 You need at least six months o...
14
Keep it simple
 Don’t do too much.
 Once you’ve selected your strategies
and tactics, minimize the number of
measurem...
15
Let’s connect all that to Google Analytics
 You’ve made progress:
 Your goals/strategies/tactics are set.
 Your meas...
16
GADG Custom Dashboard
 ‘Engagement’ oriented metrics
 Visit Frequency
 Visit Length
 Visit Depth
 New vs. Returnin...
Dashboard pages are designed:
1) To help orient you toward action
2) To communicate with management
17
Summary defines
and...
18
GA Data Grabber (GADG)
 Populates the dashboard with data.
 Extracts data from the Google
Analytics API.
 Easy-to-us...
19
Customized GA Data Grabber
 Ten custom reports that
work with the Dashboard
 Do not rename
GADataGrabber.xlsm !
 ‘Qu...
20
Case Studies: Interpreting the Dashboard
All Visits data tells a nice story...
21
Minimal loyalty
group (purple)
downward trend
indicates
improving content
engagem...
22
This Impact of this Data on the Site or Program
• Organic search listings are driving poorly-targeted traffic
• Will re...
Smithsonian Archives (SIA)
High Depth visits of all content average is 1.21%
Smithsonian Archives (SIA) - High Depth visits of history
content average is 2.35% - 94% higher!
25
Wikipedia Case Study
 One Smithsonian unit worked closely with Wikipedia, incorporating a
range of their content withi...
Visit Frequency, All Visits (2012)
Visit Frequency from Wikipedia (2012)
Is the trend statistically significant?
• Control Limits Definition
• Avinash’s blog post
• ‘Instant Cognition’ (Clint
Ivy...
29
Wikipedia Case Study (cont’d)
• The next slide shows an additional datapoint which supports the
hypothesis.
• The group...
Conversion Rate (Ask Us) from
Wikipedia
31
Hands-On Practice
 The two files that work
together are:
 GaDataGrabber.xlsx (don’t
rename this one)
 GADG_Custom_Dashboard_
template.xls...
33
Let’s run your dashboards!
 Login to GA.
 Open and login to GaDataGrabber.xlsx
 Make sure macros are enabled.
 Cust...
2) Click at the top of the
spreadsheet to hand edit your
profile ID number.
34
Detail: customizing profile numbers
Alterin...
35
Details, details: working with GADG
36
Working with GADG
 Clicking the big,
green RUN THE
REPORT button
adds new
worksheet-reports to
your copy of GADG.
 Th...
37
Working with GADG
 On the customized
GADG, the all-important
‘querystorage’ tab is
already showing.
 If you’re workin...
38
Working with GADG
 Editing reports in querystorage:
 Advanced segments (rows 19,20)
 Custom segment #’s are
obtained...
39
Working with GADG
 To “save” a snapshot of
your work and continue
experimenting, rename
your GADG files and
Dashboard ...
40
Troubleshooting
 Never double-click the files from an email, always ‘Save-as.’ Opening from an email
breaks the spread...
GA Best Practices / Tips and Tricks
41
42
No-filters (raw) profile
 Create a profile that has no filtering of any kind, a so-
called “raw” profile
 Leave this ...
43
Filter-out internal-traffic
 If you want to exclude visitors surfing from within the SI network
 Admin >> Profiles >>...
44
Measure only traffic taking place on your site
 Scraping and re-publishing website content is a common practice.
 Tho...
45
Use annotations
 Super easy – a great way to know at-a-glance what
happened on your site, launches, promos, etc.
 You...
46
Custom segment: social media visitors
 Regular expression:
bit.ly|bitly|blogfaves.com|blogger|bloglines|blogspot|delic...
47
Custom segment: engaged visits
These visits:
 Were
deeper
than three
pages.
 Were
longer than
three
minutes.
48
Custom segment: highly-engaged visits
These visits:
 Were deeper than
four pages.
 Were in frequency
more than two
ti...
49
GA’s (relatively new) “Social” reports
 Make data-driven decisions for social media
programs:
 Identify the value of ...
50
Social conversions
 “Social performance at a glance and its impact on conversions.”
 “Which goals are being impacted ...
51
Social sources
 “Find out how visitors from different sources behave.”
 This is similar to the custom advanced segmen...
52
Resources
 GA Data Grabber
 http://www.gadatagrabbertool.com/
 Automate Analytics Google Group
 http://groups.googl...
Thanks!
53
Brian Alpert
Smithsonian Institution
alpertb@si.edu
202-633-3955
Backup
54
Frequency of Visits (“Loyalty”)
 Useful engagement metric for
content sites.
 Provides insight into how
compelling and/o...
Length of Visits
 Useful engagement metric for
content sites.
 Measures quality based on the
amount of time spent
consum...
Depth of Visits
 Useful engagement metric
for content sites.
 Number of pages per visit.
 Helps understand content
cons...
Segmented Bounce Rate
 Number of times a person
visits one site page and leaves
without clicking, divided by the
total nu...
Goal Conversions – Primary and Secondary
 Any high-value behavior that
supports the site's goals.
 PDF downloads
 Video...
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Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Analytics Data

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A common chorus from museum professionals is how challenging it is to make data-driven decisions with which to improve their programs. Popular tools such as Google Analytics are intuitive and seemingly easy-to-use, yet when the time comes to use data to measure a program's stated goals, too often the main question surrounding the data is "So what?" This workshop will focus on bringing clarity to this challenge. Presented at MCN2012, on 11/7/12.

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  • The files associated with this workshop are at the links below. When Dropbox asks you if you want to ‘Open and Save’ or ‘Save As,’ choose ‘Save As’ and save the files to the same folder. That will preserve the relationships between the spreadsheets.

    Customized GA DataGrabber files with ten custom reports https://www.dropbox.com/s/74zq26e8xsc54qz/GADataGrabber.xlsm

    Dashboard that incorporates data from customized GADG https://www.dropbox.com/s/bcx6v79kskne1ne/GADG_Custom_Dashboard_template.xlsx

    Demo version of earlier dashboard containing example verbiage https://www.dropbox.com/s/qzglg20ajznpci5/GADataGrabber_demo.xlsm
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  • One Smithsonian unit worked closely with Wikipedia, incorporating a range of their content within the online encyclopedia. The purpose was to make their content more accessible for younger students, those less sophisticated than the academics and professional researchers who comprise one of the site’s core audience segments. The hypothesis was that by doing so, this group would have their needs met more quickly and easily, without having to navigate the Smithsonian website’s more advanced, research-oriented structure. The data shows that the needs of the group referred from Wikipedia – a likely starting point for younger students – were largely being met by the content posted on Wikipedia. They were increasingly less likely to need to visit the Smithsonian site many times. This is in contrast to the relatively stable trend of the overall population of visitors shown on the previous slide.
  • Four of thirteen datapoints are outside of the upper and lower control limit ranges, 30% of the data. Is that enough to say yes, that’s a statistically significant trend? The answer is subjective, but arguably so.
  • An additional datapoint to support the previously stated hypothesis. The group referred by Wikipedia was increasingly less likely to need to ask the Smithsonian staff for help via the site’s contact form. In addition to indicating that the Wikipedia-referred audience was finding the content it needed on Wikipedia, the project resulted in a reduced burden on the Smithsonian staffers who attend to these requests. The same datapoint for two other referral segments are shown, returning visitors, and visitors from search engines, showing marked contrast with the Wikipedia segment.
  • Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Analytics Data

    1. 1. Cut Through the Fog: How to Act on Your Museum's Website Data MCN 2012 11/7/2012 Brian Alpert Web Analytics and SEM Analyst Smithsonian Institution Elena Villaespesa Web Analyst and Producer Tate
    2. 2. 2 Table of Contents, Part 1 • The Web Analytics Process • GA Data Grabber • GADG Custom Dashboard • Case Studies • Hands-on Practice • GA Best Practices / Tips and Tricks
    3. 3. 3 What web analytics is often about
    4. 4. 4 Web analytics is often about: “So What?”
    5. 5. 5 What do you mean, “So what”? • The typical proxy for website success is quantity of stuff. – Aggregated “big numbers” – Pageviews / visits / visitors • Aggregated data doesn’t indicate success. – It doesn't reflect a website’s efficiency or quality. – It doesn't reflect a website user’s experience. – It doesn't help us understand how to improve the website. • We can’t act on this data. • “All data in aggregate is crap.” – Google “Analytics Evangelist” Avinash Kaushik
    6. 6. 6 What web analytics is really about: Furthering Program Goals Reuters: Toru Hanai
    7. 7. 7 Articulating your goals is the hard part  Sometimes your museum's goals:  Aren’t precisely articulated.  Aren’t articulated at all (!)  Are too broad to meaningfully measure. “An institution for the increase and diffusion of knowledge." -- James Smithson Source: Smithsonian Institution Archives
    8. 8. 8 Your goal: storyteller  Use data to tell a story.  Management loves stories.  The “So what?” factor melts away because it makes sense:  What was happening.  What it meant.  What you did.  What’s happening now. Source: http://www.squidoo.com
    9. 9. A systematic, step-by-step process  Articulate your program’s goals.  Decide strategies to achieve those goals.  Decide tactics to pursue the strategies.  Decide what and how to measure.  Benchmark to get a sense of what’s normal.  The process isn’t “one size fits all”!  Interpretation and consensus-building are important . 9
    10. 10. Start by articulating specific goals  Not too many!  Express what your museum is trying to accomplish.  Distill high-level goals into more specific sub-goals:  “Increase influence” >> “Become the definitive source on Smithsonian history.”  Making the broad goal specific makes it easier to identify strategies and tactics.  By being specific, strategies can emerge.  Articulate goals & next steps on your own.  What do you think they are?  Work with management to redefine and finalize. 10
    11. 11. 11 Determine strategies & tactics  Strategies – the plans you make to achieve the goals.  Marketing, social media are strategic pursuits.  Tactics – the things you do to advance the strategy.  Advertising, search engine optimization (SEO) are tactics.  Per the example:  Goal: “Become the definitive source on Smithsonian history.”  Strategy: search engine performance.  Rationale: search engines have sophisticated algorithms that determine which websites are highly relevant, or, "authoritative.“  Tactic: SEO.  Search metrics become proxies for authority.
    12. 12. 12 Decide how to measure your tactics  Choose a few measurements.  Trend them over time.  Per the example:  Measure: segment history-specific content in GA  Directories (site.edu/history)  Dedicated content (site.edu/historyblog)  Google Analytics custom variables.  Apply SEO metrics to that content:  Number of keywords referred per month.  Number of history pages drawing visits from search engines.
    13. 13. 13 You can’t set targets w/o benchmarks  Set targets and timeframes based on benchmarks.  You need at least six months of data.  Data fluctuates; is often seasonal.  Six months is just an opinion.  It also depends on how much traffic your site gets.  Peer data is valuable, but hard to come by.  Balance your targets with factors beyond your control:  Are the improvements you’re seeking known to be difficult to achieve?  What is the current status of your program (i.e., brand new, mature)?  How much resources will you have to devote to implementing tactics?
    14. 14. 14 Keep it simple  Don’t do too much.  Once you’ve selected your strategies and tactics, minimize the number of measurements.  If they turn-out to be inconclusive, refine or change them!  It’s an ongoing process.
    15. 15. 15 Let’s connect all that to Google Analytics  You’ve made progress:  Your goals/strategies/tactics are set.  Your measurements are chosen.  You want to use GA data to understand:  What’s happening.  How it impacts your program.  What you can do.  Google Analytics Custom Dashboard  Enables segmentation and trending.  Datapoints mostly relate to ‘engagement.’  GA Data Grabber  Flexible, Excel-based GA automation tool.  Enables you to see trends better than in the GA U-I.
    16. 16. 16 GADG Custom Dashboard  ‘Engagement’ oriented metrics  Visit Frequency  Visit Length  Visit Depth  New vs. Returning Visits  Bounce Rate  Conversion Rate  Search Engines  A foundation to make data actionable  “Key Trends and Insights”  “Impact on Site/Museum”  “Steps Being Taken” The easily updated, trended data is what makes the dashboard powerful.
    17. 17. Dashboard pages are designed: 1) To help orient you toward action 2) To communicate with management 17 Summary defines and puts the metric in context Chart shows segmented data tracked and trended over time. Suggestions for Possible Additional Segments. Red/Yellow/Green status marker shows at-a-glance each metric’s status. ‘Action’ section answers the question “So what?” • Key Trends and Insights • Impact on Website / Unit • Steps Being Taken Profile data pulls automatically from GADG; shows metrics at-a-glance. GADG Instructions; show how to create the reports from scratch.
    18. 18. 18 GA Data Grabber (GADG)  Populates the dashboard with data.  Extracts data from the Google Analytics API.  Easy-to-use and customize.  Exceptional charting capabilities.  14 days free.  $300 per year.  Limited documentation and support.  Excel for Windows 2003/2007/2010/2011.  Excel 2011 for Mac (slow!) http://gadatagrabbertool.com
    19. 19. 19 Customized GA Data Grabber  Ten custom reports that work with the Dashboard  Do not rename GADataGrabber.xlsm !  ‘Querystorage’ is unhidden  Change date ranges  Change profile #'s  Change advanced segments  Make changes by hand  Do not change cell formatting. The ‘querystorage’ tab is the key to editing the dashboard’s GADG reports.
    20. 20. 20 Case Studies: Interpreting the Dashboard
    21. 21. All Visits data tells a nice story... 21 Minimal loyalty group (purple) downward trend indicates improving content engagement High loyalty group (blue) upward trend indicates same This Impact of this Data on the Site or Program • This good-looking chart may indicate high content engagement and/or perceived value • This data may correlate to increasing conversion behaviors Acting on this Data • Identify moderate and high loyalty pages as a means of duplicating, or improving others • Examining conversion behaviors of these segments may yield add'l insights • Correlating high bounce rate pages to one-time visits may yield add'l insights • Test different content types in an attempt to move 'minimal' visitors into 'moderate' group Key Trends and Insights
    22. 22. 22 This Impact of this Data on the Site or Program • Organic search listings are driving poorly-targeted traffic • Will result in decreased organic search performance over time Acting on this Data • Refocus title tags, meta-description tags and page content for important pages • Perform link analysis to see where other SEO improvements can be made Minimal frequency group upward trend indicates organic listings are not appropriately targeted Moderate frequency group downward trend indicates same High frequency group trending slightly downward, in contrast to previous chart’s upward slope Key Trends and Insights …But applying segmentation tells a different story
    23. 23. Smithsonian Archives (SIA) High Depth visits of all content average is 1.21%
    24. 24. Smithsonian Archives (SIA) - High Depth visits of history content average is 2.35% - 94% higher!
    25. 25. 25 Wikipedia Case Study  One Smithsonian unit worked closely with Wikipedia, incorporating a range of their content within the online encyclopedia.  The purpose was to make their content more accessible for younger students, those less sophisticated than the academics and professional researchers who comprise one of the site’s core audience segments.  The hypothesis was that by doing so, this group would have their needs met more quickly and easily, without having to navigate the Smithsonian website’s more advanced, research-oriented structure.  The data shows that the needs of the group referred from Wikipedia – a likely starting point for younger students – were largely being met by the content posted on Wikipedia.  They were increasingly less likely to need to visit the Smithsonian site many times.  This is in contrast to the relatively stable trend of the overall population of visitors shown on the “all visits” slide (26).
    26. 26. Visit Frequency, All Visits (2012)
    27. 27. Visit Frequency from Wikipedia (2012)
    28. 28. Is the trend statistically significant? • Control Limits Definition • Avinash’s blog post • ‘Instant Cognition’ (Clint Ivy) blog post Four of thirteen datapoints are outside of the upper and lower control limit ranges, 30% of the data. Is that enough to say yes, that’s a statistically significant trend? The answer is subjective, but arguably so.
    29. 29. 29 Wikipedia Case Study (cont’d) • The next slide shows an additional datapoint which supports the hypothesis. • The group referred by Wikipedia was increasingly less likely to need to ask the Smithsonian staff for help via the site’s contact form. • In addition to indicating that the Wikipedia-referred audience was finding the content it needed on Wikipedia, the project resulted in a reduced burden on the Smithsonian staffers who attend to these requests. • The same datapoint for two other referral segments are shown, returning visitors, and visitors from search engines, showing marked contrast with the Wikipedia segment.
    30. 30. Conversion Rate (Ask Us) from Wikipedia
    31. 31. 31 Hands-On Practice
    32. 32.  The two files that work together are:  GaDataGrabber.xlsx (don’t rename this one)  GADG_Custom_Dashboard_ template.xlsx  Save the files – don’t open them from the email!  Store both spreadsheets in the same directory.  Find and select your profile.  Note the Profile ID number on the right. 32 Getting Started Click here to synch with GA. New GADG Reports are programmed here. The ‘REFRESH ALL REPORTS’ button runs the custom dashboard reports. Clicking ‘RUN THE REPORT’ does not refresh the dashboard – it adds new reports to GADG. Profile ID Numbers. Your GA Profiles.
    33. 33. 33 Let’s run your dashboards!  Login to GA.  Open and login to GaDataGrabber.xlsx  Make sure macros are enabled.  Customize ‘querystorage’ with your profile number – row 67.  Refresh all reports.  Open GADG_Custom_Dashboard_template.xlsx  Data should be updated in the dashboard.  Let’s look at some examples. Select the first profile ID cell (C67), then click at the top of the spreadsheet. Edit-in your profile ID by hand. Don’t risk altering the cell formatting by selecting the cell and doing copy/paste. Filling to the right is OK.
    34. 34. 2) Click at the top of the spreadsheet to hand edit your profile ID number. 34 Detail: customizing profile numbers Altering the cell formatting in ‘querystorage’ breaks the macros. 1) Select cell C67. 3) Filling the rest of the row to the right is OK. Back
    35. 35. 35 Details, details: working with GADG
    36. 36. 36 Working with GADG  Clicking the big, green RUN THE REPORT button adds new worksheet-reports to your copy of GADG.  They are named “report1”, “report2”, etc.  They are easily removed by clicking the red “Remove sheet” button on the worksheet.
    37. 37. 37 Working with GADG  On the customized GADG, the all-important ‘querystorage’ tab is already showing.  If you’re working from a clean copy of GADG, unhide this tab by right- clicking on the tabs at the bottom  Select ‘Unhide’ and then ‘querystorage.’
    38. 38. 38 Working with GADG  Editing reports in querystorage:  Advanced segments (rows 19,20)  Custom segment #’s are obtained by creating a one-off report using that segment, and finding it in querystorage  Dates (rows 26,27,28)  Profile ID numbers (row 67)  ############ is normal  You can run reports from the ‘Analytics’ page OR querystorage  Keep track of important querystorage elements  Profile ID numbers  Segment names and numbers
    39. 39. 39 Working with GADG  To “save” a snapshot of your work and continue experimenting, rename your GADG files and Dashboard files.  To ensure the renamed Dashboard doesn’t automatically update, follow these steps:  Save as  Data  Edit Links  Select the dashboard you want to save  Execute “Break Links”
    40. 40. 40 Troubleshooting  Never double-click the files from an email, always ‘Save-as.’ Opening from an email breaks the spreadsheet relationships.  Be sure you’re logged into GA as yourself.  In querystorage, always edit by clicking at the top of the spreadsheet, and either editing by hand, or selecting and copying, then selecting and pasting in another cell. Never select the cell itself and copy/paste. Filling to the right is OK.  Do not change the cell formatting in querystorage; that will break the macros.  Peculiarities can sometimes be attributed to Google’s API, and not GADG.  Data labeled “(other | other)” sometimes appears – occasionally data has to be hand- manipulated to get it properly into the Dashboard charts.  Occasionally a blank worksheet remains after refreshing (“report1”) – it can be deleted.  If you change profiles and re-run a report, GADG occasionally leaves the previous profile name in the worksheet chart. I removed profile names from the charts, but they sometimes reappear.  I’m happy to answer questions, but the real expert is GADG creator Mikael Thuneberg. Post questions to his Google Group – automateanalytics. He’s pretty responsive.
    41. 41. GA Best Practices / Tips and Tricks 41
    42. 42. 42 No-filters (raw) profile  Create a profile that has no filtering of any kind, a so- called “raw” profile  Leave this profile alone – it serves as a backup  Protection against unintended consequence  Possible names:  Unit/profile name (backup)  Unit/profile name (unfiltered data)
    43. 43. 43 Filter-out internal-traffic  If you want to exclude visitors surfing from within the SI network  Admin >> Profiles >> Filters >> +New Filter >> External Traffic Only
    44. 44. 44 Measure only traffic taking place on your site  Scraping and re-publishing website content is a common practice.  Those sites exist to serve Google Adsense ads and make money.  Unfortunately they also scrape your GA “UA” account number.  Their traffic goes into GA as your traffic!  Include all domains, if you use others than si.edu.  Filter pattern:  si.edu  si.edu|example.com
    45. 45. 45 Use annotations  Super easy – a great way to know at-a-glance what happened on your site, launches, promos, etc.  You think you’re gonna remember – you’re not!
    46. 46. 46 Custom segment: social media visitors  Regular expression: bit.ly|bitly|blogfaves.com|blogger|bloglines|blogspot|delicious|digg|facebook|feedburner|flickr|f oursquare|goo.gl|groups.google|groups.yahoo.com|hootsuite|instagram|linkedin|m.facebook. com|newsgator|ow.ly|pinterest|plus.google|plus.url.google.com|reddit|stumbleupon|t.co|techn orati|tweetdeck|twitter|typepad|tumblr|wordpress|youtube The Regex can also be edited to include smaller groups, or types of social sites, i.e., facebook and twitter. Keeping it up to date is up to you!
    47. 47. 47 Custom segment: engaged visits These visits:  Were deeper than three pages.  Were longer than three minutes.
    48. 48. 48 Custom segment: highly-engaged visits These visits:  Were deeper than four pages.  Were in frequency more than two times in the measured period.  Were longer than two minutes. These values can be tweaked for your site, of course! A nice blog post on this topic is here.
    49. 49. 49 GA’s (relatively new) “Social” reports  Make data-driven decisions for social media programs:  Identify the value of traffic coming from social sites.  Measure how they lead to direct or “assisted” conversions.  Understand social activities happening on and off site.  Some of the reports require programming goals and assigning values  Understanding ‘likes’ and ‘shares’ involves tagging with the _trackSocial tag  Google’s ‘social analytics’ guide  Google’s ‘social reports’ launch blog post
    50. 50. 50 Social conversions  “Social performance at a glance and its impact on conversions.”  “Which goals are being impacted by social media.”  Requires adding chunks of code to all your pages.
    51. 51. 51 Social sources  “Find out how visitors from different sources behave.”  This is similar to the custom advanced segment. Other reports: • Social Plugins data • "Activity Stream" (lacks facebook & twitter)
    52. 52. 52 Resources  GA Data Grabber  http://www.gadatagrabbertool.com/  Automate Analytics Google Group  http://groups.google.com/group/automateanalytics/topics  Avinash Kaushik’s “Occam’s Razor”  http://kaushik.net/avinash  Lunametrics blog  http://www.lunametrics.com/blog  Google Analytics Blog  http://analytics.blogspot.com/  Slides and future dashboards will be made available.  Send me email (alpertb@si.edu)  Questions welcome!
    53. 53. Thanks! 53 Brian Alpert Smithsonian Institution alpertb@si.edu 202-633-3955
    54. 54. Backup 54
    55. 55. Frequency of Visits (“Loyalty”)  Useful engagement metric for content sites.  Provides insight into how compelling and/or valuable content is perceived to be.  Frequent visitors are:  More likely to be loyal visitors  Exhibit higher levels of engagement than infrequent, and especially one- time visitors 55
    56. 56. Length of Visits  Useful engagement metric for content sites.  Measures quality based on the amount of time spent consuming content.  Segmentation is critical.  Segments of time  Types of content consumed, or activities pursued.  For example, spending lots of time searching may indicate a poor website search experience. 56
    57. 57. Depth of Visits  Useful engagement metric for content sites.  Number of pages per visit.  Helps understand content consumption patterns, which can help paint the picture of the longer term relationships visitors have with the website. 57
    58. 58. Segmented Bounce Rate  Number of times a person visits one site page and leaves without clicking, divided by the total number of visits.  Easily misinterpreted as always negative.  Sometimes a high bounce rate is desirable or expected.  Visits to single-use informational pages (location/hours)  Blog visits 58
    59. 59. Goal Conversions – Primary and Secondary  Any high-value behavior that supports the site's goals.  PDF downloads  Videos watched  Donations  Completed orders  Conversions indicate higher engagement, deeper commitment than viewing pages.  "Conversion Rate" is the number of conversions divided by visitors. 59

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