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CHI-WEB  September 2004, Week 1

CHI-WEB September 2004, Week 1

Subject:

SUMMARY: Using log and site data for design

From:

Hagan Rivers <[log in to unmask]>

Reply-To:

Hagan Rivers <[log in to unmask]>

Date:

Tue, 7 Sep 2004 12:09:13 -0400

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (324 lines)

In June I asked about examples where designers had made use of
log data and/or web-site/application related data to make design
decisions. My original (long) posting is last in this message.

I received five replies, all quite detailed. I'd like to thank everyone
for taking the time to write me and apologize for NOT taking the
time to share what I have learned until now (bad Hagan!).

--
   Hagan Rivers
   Two Rivers Consulting Corporation
   [log in to unmask]
   www.tworivers.com


----------------------------------------------------------------------
RESPONSE #1:
Stewart Walker took the time to share this lengthy response:
----------------------------------------------------------------------

This is a subject dear to my heart, as I believe there are huge
unexplored
possibilities out there.

I'll share some of our recent experiences with implementing a new search
engine on an intranet...

By analysing log files, we know the top 100 search terms our staff use.
For
the top 50 (I think), we have decided which page a user wants when
searching for each of these terms. We then seeded the relevant intranet
pages with metadata so that the search engine returns the intended page
as
the top result.

Analysing popular search terms and trying to find matching content also
tells us whether staff are searching for some content that we might not
have on the site and therefore need to add. We do a similar analysis of
search terms that do not return any results.

Also, our search engine doesn't have a thesaurus, but from we can
identify
synonyms by analysing search terms and then add the synonyms as
metadata so
user can still search successfully even if they don't know exactly the
right term to use.

We also know that the number of search result pages viewed per "search
visit" has decreased fairly substantially. We see this as a measure of
increased relevance of search results. We have worked out this as a time
saving, and then apply average staff costs to calculate it as a cost
saving
too. It's a pretty big number.

At a previous employer I also started doing something similar with
online
help for a web app before the project to build the web app was canned.
The
traditional approach to doco is to document an app to death but much of
the
doco goes unused because it's documenting obvious things. My approach
was
to document as little as I dared, focusing on the areas where I thought
the
users might have trouble. I then used server log analysis and analysis
of
support requests to look at issues such as:

- which screens and fields users most often requested help for
- search terms used in help (similar analysis to what's discussed above)
- relevance of search results (which results do they click through on,
and
what page of the results do they click through from?).

The approach was to document minimally to begin with and then add more
content or more depth to existing content based on issues identified
from
the analysis of actual usage. My belief was that over time you'd end up
with a more useful body of help content than with the traditional
approach,
and at a lower cost (most of the cost of creating help systems is in
content creation). Unfortunately the app I was documenting never made it
into production, but the results during testing were promising.

Two final points:

- Our intranet team has a site stats specialist, who is invaluable in
helping get value out of the data.
- Site stats tell you what's happening but not *why*. Having said that,
though, they're a great starting point in identifying opportunities to
improve the design.

Anyway, that's it from me. I'll be very interested in the responses you
get.

Cheers

Stewart

----------------------------------------------------------------------
RESPONSE #2:
Johnvey Hwang at the State Bar of California shared this story:
----------------------------------------------------------------------
Here at the State Bar of California, we have a specialized attorney
search (http://members.calbar.ca.gov/search/member.aspx) in addition
to the general site search (http://calbar.ca.gov/).  Although the
attorney search is heavily promoted, analysis of the search term
report showed that people were still typing in attorney names into the
site search, unaware that there was a specialized tool for it.  This
often produced undesirable search results because many attorneys share
the same name, and typing in one name would often return a discipline
summary for another -- obviously to the dismay of the innocent lawyer.

I decided to replace our existing search engine with a Google Search
Appliance, which produces XML output for easy integration (not to
mention fantastic search relevancy), and created an integrated site
search that returned both attorney search results and general site
search results on one page.  Now, users who type attorney names into
the general site search still still see proper search results from the
attorney search app.  In addition to having a more usable search, the
new features have also reduced the number of libel threats sent by
miffed attorneys with a case of mistaken identity.


Johnvey Hwang
Webmaster
The State Bar of California

----------------------------------------------------------------------
RESPONSE #3:
And Megan Ellinger wrote to say:
----------------------------------------------------------------------
Saw your post on the CHI list.  Here is just one example of how we have
used log files to identify design problems and/or evaluate the
popularity of a new feature:

For our on-line bookstore, we created a "QuickPick" feature that would
ideally allow our catalog buyers to enter the ISBN number into an input
box and retrieve the title.  We felt fairly confident that the proximity
of the search box and the QuickPick box were far enough apart that we
wouldn't find customers entering search terms into the QuickPick box on
accident.  After lauch, we were quickly able to see that yes, people
were using the new QuickPick box and most of them were using it
correctly.  But we also saw that many errors were occurring.  We have
since made improvements and there are still others that we can make.

While the log file analysis is useful in determining some design
problems, the results can be misleading.  And as I'm sure you'll hear
from others, log file analysis is in no way a substitute for user
testing.

Best,
Megan Ellinger

The National Academies
Web Communications Specialist
500 Fifth Street, NW
Washington, DC  20001

202.253.5440

(I asked a follow-up question and Megan replied...)

A good concrete illustration of misleading log analysis.  That's a fair
question.

Here's an example--one that you actually started to mention in your
original post.

We launched a web site that has a publication page with several sorting
options.  The default sort is set to date.  Users see a heading
displaying the year, followed by book titles.  The book titles within
the year are not sorted alphabetically.  There's a second column that
follows the book title that shows an exact date (month, day, year) and
that's how the information is actually sorted. The other sort options
are "Topic", "Board", and "Title".

When we ran the usability test for the site, the testers didn't comment
about the publications page even though they encountered it several
times.  However, I was cautiously optimistic that "Date" was the right
default and suggested that we watch the Web stats to see if visitors
were changing to another view on a regular basis.  After a month, the
stats showed that of the available views, visitors were most frequently
changing to the "Topic" view.  (I can't remember the ratio of number of
visits to the page and the number who changed the view.)  This made
sense to me though because the site is organized around the topics.  But
does the statistic mean we should change the default sort?  I'm not sure
and here's why.

 From the statistics you cannot learn things like:
- What task(s) the user was trying to complete (finding a specific book
vs. browsing the titles vs. verifying the year the book was published
vs. something else all together)
- How familiar the users are with the site--first time or repeat
(especially if you're analyzing by IP and Browser only); Was the
switching isolated to a certain type of user (e.g. first time visitors).
- Whether the page met their expectation and/or whether they were
successful completing their task.
- Did the user switch to "topic" only to switch back to "date" because
date was actually a better view for them?

So while the data is useful, it can be misleading and I probably won't
get answers to the questions above unless I run more testing.  Hope this
helps,
Megan




----------------------------------------------------------------------
RESPONSE #4:
Eric Martin at Future Studios Digital Design Center wrote:
----------------------------------------------------------------------
I am the developer of SushiLinks http://www.sushilinks.com - a resource
site
for sushi lovers.

I used the logs and the search terms used onsite to discover that many
women
were looking for information about sushi and pregnancy. From this I
decided
a page dedicated to the subject was in order. I launched the page a few
weeks later and it is now one of the most popular on the site.

I have another site for Photoshop users http://www.photoshopsupport.com
and
also used the search terms from the online search to see what the
website
was "missing" for the users. I discovered that many were looking for
free
video tutorials. I then created a page with free video tutorials that I
was
able to acquire from Total Training, and it is now one of the more
popular
pages on the site.

Hope that helps you.

    Eric

:: Eric Martin
:: Director
:: Futura Studios Digital Design Center
:: http://www.futurastudios.com
:: [01] 514-933-7431


----------------------------------------------------------------------
RESPONSE #5:
And Jim Jansen wrote:
----------------------------------------------------------------------

I have use transaction logs for the study of searching on Web search
engines.

A couple of examples linked below.

Jim

http://ist.psu.edu/faculty_pages/jjansen/academic/pubs/pages_viewed.pdf

http://ist.psu.edu/faculty_pages/jjansen/academic/pubs/ipm98/ipm98.pdf

**************************************
Jim Jansen
Email: [log in to unmask]
URL: http://ist.psu.edu/faculty_pages/jjansen/
Phone: 814-865-6459 Fax: 814-865-6426
School of Information Sciences and Technology
The Pennsylvania State University
392F Information Sciences and Technology Building
University Park, PA, 16802, USA


------- original request for information

Years ago I worked at Sun and one of our projects was to "instrument"
the software so that we could find out what users were clicking on and
learn more about which features were used, which ones were ignored, and
where problems were encountered. Our instrumented software generated an
enormous amount of data which we sifted through, with some success.

A new era has dawned and now we have web applications with logs, which
make tons of information about user behavior available. For sites with
personalization features, we can learn even more; matching demographic
data to features, and even watching usage evolve.

I am looking for some specific examples of turning use or log
statistics into meaningful design change. Some examples might be:

- We saw from our statistics that more women were coming to the site
and making more purchases, so we redesigned the whole appearance to be
subtly more feminine and more appealing to women.

- We rolled out a new design for a specific feature and saw that use
fell by 80% within a week. Our revenue on that area plummeted. We
decided to send a survey to users who used to use the old feature but
stopped using it since the redesign and find out why.

- We put in a ton of different features for viewing the tables; you
could sort, rearrange columns, etc. We went over the logs for a few
month's use and we found that there were really only 3 main ways people
looked at the information. So we took out all the controls and just had
3 views.

- We studied the search terms that came into our site over a one month
period and completely redesigned our search engine. Now instead of just
doing page contents, our in-site search engine has a list of "target
words" and knows just what pages to bring up in response to a search
for those terms. We also took the top 10 terms and added links
referring to them on the home page.

If you can say a little about what the problem was, how you used the
statistics to help you, and how it improved design, that would be
super. I'm collecting this information as part of a class I will be
teaching, so please take care to only share public information.

Please respond to me and I will post a summary. Thanks.
--
    Hagan Rivers
    Two Rivers Consulting Corporation
    [log in to unmask]
    www.tworivers.com

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