Using web metrics to improve your site A presentation for Liberty Hill Foundation

1. Using web metrics to improve your site
A presentation for Liberty Hill Foundation
Dana Chinn
April 2010

2. • What’s noise vs. signals
• Questions to ask of your data
What data will give you answers
• Understanding web metrics
• Deciding which metrics to use,
what your site needs to have
www.slideshare.net/danachinn
@danachinn
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3. “After the disaster in Haiti, [our site] hit
168.6 million pageviews
in the month of January. A new record.”
--The ”famous metrics” term comes from web analytics guru Avinash Kaushik 3

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5. web analysis
usually starts
here...
...and ends here
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6. Internal vs. external metrics
Decision-making Advertising, marketing
• Census data • Panel data
100% of all visitors, visits, page Activity from a sample of self-
views for all sections selected people. Only total site
data for a limited number of sites.
• Analysis, decisions, • Marketing, trending,
actions, evaluation external comparisons
• Omniture
• comScore
Google Analytics Nielsen
WebTrends Compete
etc. etc.
• Web Analytics • Interactive
Association Advertising Bureau
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7. Recognizing the signals amid the noise
Noise:
Everything,
every one,
every second of
every day
Signals:
Metrics
you select -- Mark Smiciklas, IntersectionConsulting.com
for decision-making
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8. Only look at the signals you need 1
You need to know
• How your site traffic
changes due to external
2 events
so you can determine
whether your actions
made a difference (or not)
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• Whether your actions
led to the results
that you anticipated
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9. Two types of web metrics
What people do (behavioral)
Who they are, what they think (attitudinal)
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10. Unique visitors
visit websites,
generate page views.
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11. A “unique visitor” is actually a “unique computer”
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12. Unique visitors may be over- or undercounted
Work =33 unique visitors
= unique visitors
Hotel
Home
= 1 unique visitor
Work
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13. Questions to ask
your data
You
Your Data
Did people visit our site more than
once last week?
Was this more or less than previously?
If more, was it due to an outside event, OR
was it something we did?
Was the increase as much as expected?
If less, was there a holiday, OR
did we not do something?
Was the drop as much as anticipated?
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14. For every question...
Did people visit our site more than once last week?
...there’s an answer hiding in Google Analytics...
visits per unique visitor
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15. ...but you have to go
past the Dashboard...
visits per unique visitor
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16. ...change the time period,
and customize the reports for your decisions
default report shows a 5-week period
Use weekly
metrics, full-
week time
periods
so you can
identify unusual
movement
quickly
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17. On average, last week,
how many stories did
people see with each
visit?
Did most visits come from
returning visitors OR new ones?
Are we hooking in new visitors?
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18. On average, last week, how many stories did people
see with each visit? Are we hooking in new visitors?
page views per visit
percent of visits from new visitors
page views per new-visitor visit
percent of visits from returning visitors
page views per returning-visitor visit
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19. When someone came
to our site, what was
the first page they
saw?
Did they leave immediately when
they saw it?
Did new visitors leave more than
returning ones?
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20. When someone came to our site, what was the first
page they saw? Did they leave immediately?
bounce rate
of the page
where people
enter your site
most often
1. Overall
2. Visits from new unique visitors
3. Visits from returning unique visitors
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21. Example
Home page bounce rate
= over 50%
Over half of the visits to the CNN.com home page
left CNN.com without clicking into any other pages
Best (?) cases: Came only to get the headlines
Home page has dynamic content
not captured with page views
Worst cases: Couldn’t find what they wanted
Didn’t like what they saw
Source: “Can CNN, the Go-To Site, Get You to Stay?” by Brian Stetler, New York Times, Jan. 17, 2009
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22. Measure engagement...
No. of podcasts
-- put on iPod
-- played
-- listened to the end
...and construct your site to maximize success
Put “please donate by going to
ourspecialcampaignURL.com” at the beginning
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23. The perfect (measurable) Tweet
• A call to action to participate, engage with you
Look at this. Go here. What do you think?
• A link
To get news, information
Tweets are now a primary news source,
the new home page
To respond to the call to action
• A #hashtag and/or keywords
• Handle specific to person/topic
• A comment
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24. “Perfect” tweets are less than 120 characters
RT/via @handle + call to action/comment + link + #hashtag
100 characters 111 characters Watch handle,
hashtag sizes
Lost the link
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25. Two types of web analytics metrics
What people do (behavioral)
Who they are, what they think (attitudinal)
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26. Are you reaching the audiences
you meant to reach
and
in the numbers you want?
Is it your content?
Is it your design?
Is it you?
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27. Some questions that can’t be answered
from web traffic data
1. What was the purpose of your visit today?
2. Were you able to
complete your task today?
3. If not, why not?
4. If you did complete your task,
what did you enjoy most about our site?
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28. Evaluation assignment
What are the indicators that your web site
is - or isn’t - contributing to your project?
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29. How do we get
more [type of
people] to
register?
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30. Is our site selling
the number of
Upton Sinclair
Dinner tickets,
sponsorships
and ads that we
want?
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31. How do we get more [type of people] to register?
Look at trends
after flyer is
mailed to each
audience type
-- visits per unique visitor
by week
-- home page bounce rate
-- pop-up bounce rate
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32. How do we get more [type of people] to register?
Does our reg form capture
the info we need?
What’s our goal?
-- No. on print newsletter list [by zip code, business, name]
-- New vs. returning - “...register...even if you’ve been a supporter for decades”
--Percent of registered people [by type] who buy Upton Sinclair dinner tickets
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33. Is our site selling
the number of
Upton Sinclair
Dinner tickets,
sponsorships,
ads, eBay items
that we want?
-- Total sold last year from all sources, by
time period
-- Tickets (premier, standard);
sponsorships (by type); ads (by type)
--Percent of registered people [by type]
who buy tickets, sponsorships, ads
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34. Where are we losing people?
-- Percent, no. of visits that
started with dinner
overview page, ended with
completed payment
(ad option is below this screen fold)
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35. Web analytics is not easy...
• Have clearly defined, accountable goals, objectives
on which everyone agrees
each of which is someone’s direct responsibility
• Know the limitations of your data, metrics
Guess rather than rely on bad data, metrics
• Dedicate people, processes - budget - to analytics
Technology, software are just tools
• Let the hippos decide whether metrics
will really be used for decisions
Use only what you need
HIghest Paid Person’s Opinion
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36. Dana Chinn Blog
http://www.newsnumbers.com
Lecturer
chinn@usc.edu
213-821-6259
Analytics for news orgs bookmarks
http://www.delicious.com/
danachinn
Presentations
http://www.slideshare.net/
danachinn
Twitter: DanaChinn
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