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.

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
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
What web analytics is often about

4. 4
Web analytics is often about:
“So What?”

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
What web analytics is really about:
Furthering Program Goals
Reuters: Toru Hanai

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
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. 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. 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
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
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
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
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
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
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. 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
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
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
Case Studies: Interpreting the Dashboard

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
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. Smithsonian Archives (SIA)
High Depth visits of all content average is 1.21%

24. Smithsonian Archives (SIA) - High Depth visits of history
content average is 2.35% - 94% higher!

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. Visit Frequency, All Visits (2012)

27. Visit Frequency from Wikipedia (2012)

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
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. Conversion Rate (Ask Us) from
Wikipedia

31. 31
Hands-On Practice

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
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. 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
Details, details: working with GADG

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
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
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
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
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. GA Best Practices / Tips and Tricks
41

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
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
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
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
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
Custom segment: engaged visits
These visits:
 Were
deeper
than three
pages.
 Were
longer than
three
minutes.

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
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
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
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
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. Thanks!
53
Brian Alpert
Smithsonian Institution
alpertb@si.edu
202-633-3955

54. Backup
54

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. 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. 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. 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. 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