Web metrics
Web metrics A primer
1. Why measure?
2. Determining goals
3. What to measure?
4. How to measure?
5. Putting it all together (examples)
Web metrics
Web metrics - Caveats
• Web metrics is a huge (and growing) field, with new
strategies and businesses starting (and dying) every
day.
• This talk only covers a few, key, publicly available tools
and services (Google Analytics, YouTube, Facebook,
Twitter)
• Even for some public tools (e.g. Google Analytics),
there is no way to cover all the options available
• Many professional options available, but not covered or
evaluated here
• Counting on you to share info on additional tools,
services, tips, etc.
Web metrics
1. Why measure?
(determines what questions you want to ask)
• Curiosity
• Improve web-site visitor experience
• Increase visibility of institution
• Improve “reach” of news releases
• Important to decide on goals before
spending a lot of time, effort, and
possibly money on web monitoring
Web metrics - Why measure?
Benefits of measuring
• Find out what’s working (and what’s NOT
working) for web visitors
• Identify and improve key “channels” for
reaching media
• Impress management / justify programs and
staff efforts (cost/benefit analysis)
• Help with institutional goals for outreach,
marketing, fundraising – increasingly a part of
PIO’s role, especially for small organizations.
Web metrics - Why measure?
Cost-benefit analysis
• Increasingly important with tighter
budgets
• Costs are relatively easy to predict
compared with benefits
• Web metrics are a great way of
demonstrating benefits
• Don’t forget to include cost/time for data
analysis in web metrics
Web metrics
2. Defining goals
• Simple
• Easy to measure
• Realistic
• Relate to core mission of institution
(Strategic plan, etc.)
• Relate to core mission of your
department
Web metrics - Defining goals
Some general types of web goals
• Sharing information/inspiration
• Defining or improving institution’s public
image or “brand”
• Attracting new audiences /
increasing overall public awareness
• Facilitating 2-way communication
• Inspiring action (e.g. ocean conservation;
fund raising)
Web metrics - Defining goals
Examples of specific goals
• Make sure that all major wire services are aware of
our news releases
• Increase the size of our news release mailing list by
50%
• Increase number of times a month that our research
is mentioned in science blogs
• Increase # of Twitter followers by 50% over next six
months
• Increase number of monthly hits on “Open House”
web page by 20%
Web metrics
3. What to measure
(what CAN be measured)
• News coverage by organizations and by web
users (blogs, tweets, etc.)
• Number of views of a story, video, or tweet
• Number/type/demographics of users
• How interested users are in your information
• Paths that users follow to access your
information
• Whether users are going where you want
them do and doing what you want
• Trends over time
Web metrics - What to measure
Deciding what to measure
• There are so many different things you can
measure on the web that it is most efficient to:
– Do research (and periodic updates) on what metrics
are available for the web “channels” you use.
– Pick a limited set of metrics that are most useful for
you (customize if appropriate)
– Monitor those metrics consistently & regularly
– Keep your own records
– Periodically & regularly analyze the data and look for
trends over time (prepare reports for superiors)
Web metrics - What to measure
Measuring news coverage
• Traditional approach: How many publications wrote
articles on your release (and how big are the pubs?)
• How many blogs and news aggregator sites
reprinted your release verbatim (or with minor
variations)?
• How many blogs mentioned or linked back to your
release? (and how large is their readership?)
• Quality of coverage is subjective, but important
because web is an “echo chamber”
• How many times did it get tweeted/retweeted?
• How many people “Liked” it on Facebook?
Web metrics - What to measure
Measuring views of a
page/story/video, etc.
• Number, timing, and sources of page views
are easy to measure for internal web sites
using web monitoring tools
• Tracking links to external site tougher, but
tools available for some (e.g. YouTube)
• Highest page views often due to links from
mainstream-media sites and blogs with large
numbers of viewers (>5-10,000 viewers)
• Gee-whiz factor is often key for big views; but
most of these will be one-time viewers.
Web metrics - What to measure
Key terms used in web-page
monitoring (not standardized)
• “Hit”
– Occurs each time a FILE (any file) is supplied by the web
server (only available with “server-log tracking”; more on
this later).
– More representative of total server traffic than
popularity because many FILES may be downloaded as
part of a single page (and caching issues).
• “Page view”
– Occurs each time a particular type of file (e.g. html) is
supplied by the server (in “server logging”) or a
particular page script runs (in “page tagging”).
Web metrics - What to measure
Key terms used in web-page
monitoring (continued)
• “Visit”
– Occurs when a single client downloads a series of page
requests within a 30-minute period.
– A visit ends if no requests from a particular client come
over a 30 minute period.
• “Session”
– Like a visit, but ends either after 30 minutes without
accessing a local page OR if accesses a page from a
different site.
– “A session ends when someone goes to another site, or 30
minutes elapse between page views, whichever comes
first.”
Web metrics - What to measure
Measuring number /
demographics of users
• Number of visitors over a specific time period
• Number of regular visitors
(e.g. YouTube “subscribers,” Facebook
“likes,” Twitter “followers,” rss feed
subscribers?)
• Age, sex (for registered users)
• Geographic location (sometimes to zip code)
Web metrics - What to measure
Key terms used in user
monitoring (not standardized)
• “Unique visitor”
• A key term, based on identifying the computer (not the person) that
is accessing a particular web site over a specified time period of
record-keeping (typically a day, week, or month).
• Determined using IP address in server log or cookie/Flash script;
thus, a single person visiting from two different computers will
count as two Unique Visitors
• Note: If you add up the number unique visitors for each day in a
month, they will not equal the total number of unique visitors for
that month (because the same person visiting two days in a month is
counted twice in the daily counts of unique visitors)
Web metrics - What to measure
Key terms used in user
monitoring (continued)
• “New visitor”
– A new visitor is a visitor that has not made any previous visits
(over the entire period or record-keeping).
• “Repeat visitor”
– A repeat visitor that has made at least one previous visit
to the site in a specific period of time.
– Reliability limited by people whose browsers delete
cookies each time they exit (they look new each time).
– Note: The total number of unique visitors is not
necessarily the same as the new plus repeat visitors
because one person can be both new and repeat in a
single day.
Web metrics - What to measure
Importance of demographics
• Who is following you is at least as important is how
many.
• Demographics available from services where users
sign up (Facebook followers; registered YouTube
users)
• Demographics of registered users does not
necessarily represent demographics of all viewers
(especially on YouTube)
Web metrics - What to measure
Measuring how interested
users are in your information
• Time on site/page/session
• Repeat visitors/followers
• User interactions (comments, shares,
“likes,” etc.)
• Retweets
• Offsite links to content
Web metrics - What to measure
Key terms used in user-interest
monitoring (not standardized)
• “Bounce”
– A single page view without additional views in 30
minutes. The “bounce rate” is the percentage of visits in
this category over a particular period of time.
• “Time on page”
– Possible to measure using custom Javascript code. But
reliability questionable because user may have many
pages open at once.
• “Session duration”
• Possible to measure, but accuracy questionable
• “Average page views per session”
• Easier to measure (total page views/total number of
sessions)
Web metrics - What to measure
Determining paths users take
to reach your information
• Can help assess useability of site
• Find out what’s most (and least)
popular on your site
• For very busy sites, can be used in real
time to balance traffic loads and
prevent overwhelming servers.
• Used in “funnel analysis” (more on this
later)
Web metrics - What to measure
Key term used in user-path
monitoring
• “Click path”
– What pages a particular visitor follows during a
particular session.
– Related to “site overlay” view in Google
Analytics showing web pages with number of
clicks overlaid on each link (totals for a subset
of visitors)
Web metrics - What to measure
Determining whether users are
going where you want them go
and doing what you want them to do
• Very much a sales/marketing approach
• Definitely applicable to outreach and fundraising, and
possibly to media work(??)
• Require extra staff time & expertise
• “Event” analysis
– How many users successfully downloaded the video from
your last release?
• “Funnel” analysis
– What steps did each user have to take to find and download
this video? (details later)
Web metrics - What to measure
Measuring trends over time
• Comparing different web metrics is like
comparing apples, oranges, bananas,
and cumquats (there is no standard)
• Trends over time may be more reliable
and accurate than absolute numbers
Web metrics
4. How to measure
• News release exposure
• Internal web site traffic
• Facebook
• YouTube
• Twitter
Web metrics - How to measure
How to measure
news release exposure
• Traditional methods – clipping services (paper and online), Vocus multimedia monitoring, Eurekalert
• On-line searches (e.g. Google News)
• Advanced searches (unique words, blogs, Twitter
searches, etc.)
• Can measure number of original articles and
(increasingly) verbatim reprints of releases
• Can sometimes estimate “reach” of “publisher” (e.g.
number of blog readers)
• Can use various on-line tools for calculating “buzz”
Web metrics - How to measure
How to measure
internal web traffic
• Method 1: “Server log files”
– Software running on your web server counts every page
and file that is sent out to each IP address
– Data are stored locally in a format available to you and
your server administrators
– Not tied to a specific vendor
– Downside: Doesn’t count cached pages (pages sent once
to user’s site, but stored and re-used)
– Downside: May requires staff time, storage space
– Downside: Useful for server admins, but less so for
marketing/PIO types
Web metrics - How to measure
How to measure
internal web traffic
• Method 2: “Page tagging”
– Small Javascript code added to every web page on site
(easiest to do in a common header or footer).
– Sometimes combined with tracking cookies or persistent
code in Flash (not easily deleteable like cookies)
– Information from Javascript code is sent to outside
server (e.g. Google Analytics)
– Counts cached pages and allows customized scripts to
collect specific information about visitor behavior (e.g.
time on page)
– Downside: A few users disable Javascript; many more
delete cookies; only latest mobile phones support these.
– Has become de-facto standard
Web metrics - How to measure
Comparing server logging and
page tagging
• Example: MBARI web stats Jan-April 2011, based on
Google Analytics and freeware program Web Log
Expert:
Web log
Google Analytics
187,452 Visitors
55,123 Visits
303,665 Page views
122,934 Page views
1.62 pages/visitor avg
But trends are
nearly identical:
2.23 Pages/Visit
Web metrics - How to measure
Google Analytics
• The most widespread tool for web monitoring
(Google claims that well over 50% of the
largest web sites use Google analytics)
• Easy to use at basic level (and free if you have
a gmail account)
• Very customizable for the advanced user
• Becoming increasingly oriented toward
marketing and sales vs simple tracking
Web metrics - How to measure
Google Analytics –
How it works
• A “page tagging” system that uses both Javascript and cookies
• A bit of Javascript called the Google Analytics Tracking Code
(GATC) is added to every page of a web site.
• The code sends messages back to Google each time that page is
loaded into a browser.
• Google creates a single file about a user’s computer (based on
its IP address) that sends information to Google about when
they visited every page on that site, AS WELL AS any other sites
that use Google Analytics.
• The code also stored cookies on the user’s computer that show
whether the visitor has been to the site before, the time of the
visit, the web site that the user came from, as well as any
search terms used.
Web metrics - How to measure
Google Analytics –
How to use it (very briefly)
• (Open GA for MBARI’s web site)
• Dashboard – Overview of “big picture” site metrics
(customizable)
• Intelligence – Set custom “events” for which you want to
be notified (e.g. big rise or drop in traffic)
• Visitors – Demographics, “loyalty,” browsers used, etc.
• Traffic Sources – Find out who’s linking to you
• Content – Find out where people are going (and drill down
to see individual pages)
• Site search – Find out how people find you in searches
(search terms, etc.)
Web metrics - How to measure
Google Analytics –
How to use it (continued)
• Event tracking – Marketing/sales oriented options for
the advanced user
• Goals – Find out if people are doing things you want
them to do (e.g. successfully completing a form or
downloading a file)
• Custom Reporting – Allows advanced users to
graph/output combinations of stats listed above
Web metrics - How to measure
Google Analytics –
A few tips
• Add annotations of events such as news releases.
• Can track down sources of spikes to specific web
sites (select a SINGLE day)
• If you do see a source that drives traffic (and is
reputable), try contacting them to get them on
your email list, or as a Twitter follower
• Customize the dashboard to show key stats you
want to compare each time you log in.
• Others from the audience?
Web metrics - How to measure
Other free web tracking tools
• There’s a bazillion of them…
– Quantcast.com (standard page tagging)
– Compete.com (ranking w/other sites)
– Sharethis.com (counts people linking via a
variety of social networks)
Web metrics - How to measure
Metrics for Facebook
• Use Facebook “Insights” pages to track:
• Changes in the number of people who
“Like” your site over time
• Demographics (applies to Facebook
members only; not all viewers)
Web metrics - How to measure
Metrics for Facebook
• Example of “Likes” tracking
Web metrics - How to measure
Metrics for YouTube
• Can measure:
– Number of views
– Demographics (only covers YouTube
members who are logged in)
Web metrics - How to measure
Metrics for Twitter
• There are a bazillion services out there,
but I don’t have a specific one to
recommend.
• Does anyone have experience with
them? (audience comment - bit.ly
seems to be popular)
Web metrics
5. Putting it all together
(ideas and examples)
• MBARI experiences
• Experiences from other active users of
social media
• Tips and techniques from marketing types
• Caveats
Web metrics - Putting it all together
MBARI news releases –
effects on direct web traffic
• Visitors to news release page spikes within one or
two days of release, then tapers for a couple of
weeks:
• For example, Rappemonads release started at 165
unique visitors /day then tapered to 15-30/day over
next week; 5-10/day after that.
• Older 2010 releases get only 3-7 unique visitors/day
• Biggest hits are from releases featuring weird animal
photos: For example, the barreleye release averages
125 unique visitors / day; these pages are very spikey
depending on when random bloggers discover the
page.
Web metrics - Putting it all together
News release monitoring example:
rappemonads (a new type of algae)
• Release didn’t get much mainstream media coverage, but
it did get wide pickup in news aggregation sites and blogs
• Because the name of the algae had never appeared in the
literature before, a simple Google search (not in News)
turned up dozens of sites that used the release more or
less verbatim
• Based on slight differences in wording, I was able to track
the “flow” of information from my email release, our web
site (probably rss feed), and Eurekalert
• Some blogs show where they got the text, as well as
number of views, retweets, etc.
• The amount of secondary coverage is impressive
Web metrics - Putting it all together
News release monitoring example:
tracking a key term
Web metrics - Putting it all together
News release monitoring example:
tracking a key term (continued)
Web metrics - Putting it all together
MBARI Web-site monitoring example
• Goals:
– Increase general awareness of MBARI research
– Provide detailed information about MBARI
research for the general public and press.
• Implementation:
– New articles or photos posted about once every
week or sometimes 2 weeks
– One staff person spending 8+ hours/week
Web metrics - Putting it all together
MBARI Web-site monitoring example
• Use both server logging and GA (see previous
comparison chart)
• News site receives relatively low overall numbers of
viewers (a few hundred a day)
• Because of low overall visitor numbers to our news
site, any bump of 50-100 visitors/day can make a
big impact on our overall traffic.
• Bumps may be due to class assignments and outside
links from large aggregator or news sites
• Low retention because they just want to see a
particular image or video on the site
Web metrics - Putting it all together
MBARI Web-site monitoring example
Web metrics - Putting it all together
MBARI evaluation of potential
for Facebook exposure
• There is a relationship between frequency of postings and
number of “Likes.” However, it may not be cause and effect,
but covariance with other variables, such as general outreach
effort.
Facebook updates per month vs number
followers ("likes")
"Likes"
MBA
25
95,600
WHOI
14
3300
SIO
10
2200
MLML
6
180
Harbor Branch
6
680
VIMS
5
566
Duke marine lab
5
528
Long Lab
4
600
20
877
100,000
10,000
Followers
Institution
Avg
Updates/m
onth
1,000
100
10
1
0
10
20
Avg updates/month
MBARI (4/11)
30
Web metrics - Putting it all together
MBARI Facebook monitoring example
• Goals:
– Increase general awareness of MBARI
– Drive traffic to our main web site
– Share info about general marine topics (not just
MBARI)
• Implementation:
– Set up page Feb 8, 2011
– Posted about 20-25 times a month so far
– One staff person spending 1-2 hour/week*
Web metrics - Putting it all together
MBARI Facebook monitoring example
• Tracking “Likes”: Early exponential increase now
leveling off
• Very event-driven increase (e.g. push from Aquarium)
• Suggests we need to make more effort to get more
“Likes” if we want to get more “likes.”
MBARI Facebook "Likes"
2/
8/
20
2/ 1 1
15
/2
2/ 0 11
22
/2
01
1
3/
1/
20
3/ 1 1
8/
20
3/ 1 1
15
/2
3/ 0 11
22
/2
3/ 0 11
29
/2
01
1
4/
5/
2
4/ 01 1
12
/2
01
4/
19 1
/2
4/ 0 11
26
/2
01
1
1000
900
800
700
600
500
400
300
200
100
0
Date
Web metrics - Putting it all together
MBARI Facebook monitoring example
• Results from first four months:
• Exponential growth curve of “Likes” has flattened out
already (mostly driven by Aquarium publicity)
• But Number of impressions per posting is still increasing
(was 150-250 at end of 1st month; is now 2,000-2,500)
• This is a lot of exposure compared with number of
visitors to news articles on web site.
• We seem to have over 1,800 regular visitors who will
click on any new item (even ”dry” journal articles)
posted on our Facebook page
Web metrics - Putting it all together
MBARI Facebook monitoring example
• Results of MBA link Suggests possibilities for good
synergy between MBARI and Aquarium social
networking (we provide content, they provide
“eyeballs”)
• Our followers are relatively engaged, and comment
pretty frequently. (0.1 to 0.3 feedback rate)
• Very different demographics from YouTube:
– 2/3 of our facebook followers are female! (of these most
are 25-54, w/peak at 25-34)
– Of the males, same age range, but peak at 35-44)
– So far, mostly people in California (plus 5% non-US)
Web metrics - Putting it all together
MBARI Facebook monitoring example
Web metrics - Putting it all together
MBARI Facebook monitoring example:
Using GA to gauage effect on web visitors
Web metrics - Putting it all together
MBARI Facebook monitoring example:
Effect on web visitors
• Overall, the volume of traffic to main web site has
NOT changed significantly since last year (Jan-Feb
2010 vs Jan-Feb 2011), despite initiation of
Facebook and Twitter programs.
• We saw a 30% increase in NEWS web-site traffic, but
only due to short bumps due to blog listings – not
repeat or sustained visitors (see GA graph)
• We did see a tripling of the number of people who
were referred to our web site from Facebook, but
still only accounts for an average of 10 visitors/day
Web metrics - Putting it all together
MBARI YouTube channel
• Goals (not very measurable):
– Share cool videos with media and public
– Increase general awareness of MBARI research
– Share info about general marine topics (not just
MBARI)
– Drive traffic to our main web site (to find out more)
• Implementation:
– Set up account 3 years ago
– Updated about 1 to 2 times a month
– One staff person spending 10-20 hours a month
Web metrics - Putting it all together
MBARI YouTube monitoring
• One BIG hit: Barreleye video by far the most watched –
3.6 million views (2 years after first posting, this one
video still accounts for 70% of our YouTube channel
traffic)
• Over the past 2 months, our YouTube site saw an
average of about 1,250 unique views/day (1,521 unique
views/day over past 12 months)
(this is twice as many views as any section (not just
page) of our web site)
• Since 2009, we have acquired about 1,900 subscribers
(who seem to check out almost any new video we post).
• We are steadily increasing our subscriber base at an
average net rate of 1 to 2 a day (new subscribers minus
those dropping subscriptions).
Web metrics - Putting it all together
MBARI YouTube demographics
• Last year, almost ¾ of our YouTube viewers were
males (mostly 35-54 yr) Very few were under 18 or
over 55.
• Only about one quarter of viewers were females,
but they were spread throughout the age ranges
(there were more males than females in 13-17 yr
group).
• In the last six months, the proportion of female
viewers has increased to 35%, in response to Oct
2010 Halloween video and Jan 2011 Valentine
videos.
Web metrics - Putting it all together
MBARI YouTube engagement and effect
on web-site traffic
• Our weird animal videos get very high levels of
responses from viewers, at rates 2 to 10 times
higher than “normal” (1 to 2 per 100 views vs 0.1 to
0.3).
• Most of the comments are on the order of “What a
weird #$%^ fish!”
• We rarely see YouTube hit videos correlating with
bumps to our web site
• The exception are spikes in web-site visits following
posting of high-visibility news-release videos (spikes
may come from general release publicity)
Web metrics - Putting it all together
Web monitoring example:
Exploratorium and Google Analytics
• Provides Google Analytics info to NSF in support of NSFsponsored web projects (Ice stories).
• NSF also funded an outside consultant to survey users and
convene focus groups to review the effectiveness of the
website.
• Uses Google Adwords to advertise program for free
(nonprofits may apply through Google Grants)
• Likes using GA overlay to see where people are going.
• Suggests monitoring what words people are searching for
on the site; then create content about those terms if you
don’t already have it or make it easier to find these
terms.
Web metrics - Putting it all together
Web monitoring example:
KQED Quest (science program)
• TWITTER: Uses Hootsuite—a free tool to track Twitter analytics;
also has a built-in URL shortener. Dashboard shows who is
following QUEST and who QUEST is following. (QUEST has been
on Twitter since April 2009; currently has 3,600 followers, does
15-30 tweets a day, and gets up to 100 clicks per tweet.)
• FACEBOOK: QUEST launched their FB site in Jan. 2010;
currently has 2,400 fans. Used ad campaigns within FB to gain
fans at a cost of about $.57 per fan.
• FB “Insights” metrics shows demographics. It looks like women
between 35-45 years old in Pleasanton are the biggest group
following QUEST. (members only; like MBARI – mostly women)
• Consider what a FB follower is worth to you. According to Paul
Rogers, “The fact that people have an interest in you has an
intrinsic value, even if you don’t know what that is yet.”
Web metrics - Putting it all together
Example:
Using demographic information
• Demographic information is a blunt tool if you’re
trying to reach science writers, reporters, NGOs,
other PIOs.
• For groups such as this, a targeted approach to
cultivating an audience may be better:
• It pays to go through your follower (or email) lists
every now and then, and look for key individuals.
• Conversely, identify key individuals and invite them
to become followers (or get on your email list)
Web metrics - Putting it all together
Where demographics is useful
in targeting (and not)
• Members of media (target)
• Grade-school students (demographics)
• College students (demographics, demographics + .edu
domain tracking)
• Decision makers / resource managers (target)
• Local community members (demographics)
• Other researchers and institutions (target)
• NGOs and activists (target)
• Businesses / industry (target)
Web metrics - Putting it all together
Examples of programs
generating measurable user interaction
• “Ask a scientist” column
• Allow users to post comments, photos,
sightings, personal experiences, etc. on
moderated discussion board
• Conduct on-line surveys (more for interaction
than statistically useful data)
• Hold on-line contests (e.g. name a fish)
Web metrics - Putting it all together
Example of goal-driven user tracking:
“Funnel Analysis”
• Use user tracking to determine the steps that people take
in the process of doing something you want them to do (to
buy your product or click on your important web page).
• Try to figure out the percentage of people that are willing
to move through each step (the “conversion rate”)
Web metrics - Putting it all together
Example of steps in“Funnel Analysis”
• 1) Person Googles “dolphins”
• 2) Person clicks on the Google search result showing your
photo of a smiling dolphin, and lands on your “Dolphins
are our friends” blog entry.
• 3) Person clicks through to article on your web site on
dolphin research.
• 4) Person clicks on prominent button saying “Save our
dolphins”
• 5) Person signs up to receive email alerts (and funding
pleas) about saving dolphin research
• 6) After several emails, person writes check to your
institution to help fund dolphin research.
Web metrics - Putting it all together
Following up on a “Funnel Analysis”
• Determine where the bottlenecks are (no pun intended).
(Maybe your article on dolphin research is dry and boring,
or the funding button is tiny and hidden at the bottom of
the page.)
• Try to enhance that process at bottlenecks and maximize
the conversion rate at each step.
Web metrics
Thank you for listening!
• More questions?
• Discussion?