How Just a Little Data Analysis Can Improve your Content

How just a little data analysis can improve your
content
Joe Pairman

Listening for movement in the mine. National Institute for Occupational Safety
and Health (NIOSH). www.flickr.com/photos/25069384@N03/2492849690/

Background

• DITA XML implementation at HTC: eﬀective web content a primary driver

• From “How do we design for the web?” to “What can we learn from the
web?”

• Co-ordinated analytics and user feedback plan

• Main focus is improving content

• This presentation covers methods, tips, and lessons learned from that

• Exploration of ideas rather than a technical guide

Introduction How just a little data analysis can improve your content — Joe Pairman

Slide types

Ideas and overviews

Cautionary notes

How to

Tips and insights


Examples in this presentation

Online knowledge base of support articles for a ﬁctitious e-reader device

http://commons.wikimedia.org/wiki/File%3AEbook_reader_icon.png By netalloy
(Open Clip Art Library image's page) [see page for license], via Wikimedia Commons


The predominant ﬂavor of web analytics

“This is a fast-growing category that's generated tremendous interest in recent
years due to the advertising and marketing value derived from tracking and
understanding user behavior.”

Morville & Rosenfeld, Information Architecture for the World Wide Web, 3rd
Edition (emphasis added)

• Much web analytics aims to directly improve sales

• In contrast, content-based sites focus on delivering eﬀective information

• Of course (for a commercial site), the goal is still sales, but indirectly


What can web data tell us about content?

• What people are searching for, and the language they use to search for it

• What they’re viewing and how long they’re staying there

• (With a ratings system) How much they like what they’re seeing

• (With a combination of metrics) What we can focus on for improvement

• What's the eﬀect of particular qualities (graphics, word count, links, etc)


You need…

• Access to analytics data

• Signiﬁcant body of homogeneous content, such as knowledge base,
established blog

• Signiﬁcant views of that content

• Data such as searches, page views, ratings


What can’t web data tell us?

• How to design our content (it can suggest which things work better but in the
end we still need a coherent design)

• Why the patterns exist (interpretation is up to us)

• What the full context is


Ultimately

The data provides focus and pointers, not answers


What can search query data tell us?

• Top searches (so crucial content)

• The vocabulary that customers use

• The way that customers classify things

• And much more

Search terms How just a little data analysis can improve your content — Joe Pairman

External search v.s. site search 1

Site search External search

• Users more likely to know what • Potentially many more queries
they’re looking for?
Pros
• A much wider range of data • A much wider range of search
terms
available

• Increasingly, Google is where • Still only those who made it to
people search ﬁrst your site
Cons
• Poorer range and quality of • Google encrypted search: now
results may drive people away up to a third of queries may not
from your site search have associated terms


External search v.s. site search 2

• It’s possible that site search is used more for technical or specialized info

• But some argue against this

• Best way would be to actually compare external (referral) to site (local) terms

Rosenfeld, Louis. 2011. Search Analytics for Your Site. New York: Rosenfeld Media. www.rosenfeldmedia.com/books/searchanaly tics/

• External is probably still the best way to get started


Processing search terms 1: Data collection

• Even if your content is only one section of the site, it’s best to get the whole
site’s search queries

• If a lot, try using a phrase to ﬁlter, such as "how to". Also ﬁlter out the obvious
irrelevant terms

• But if you do this, compare with other sources to make sure not too skewed


Processing search terms 2: Common phrases

• Filter out small words: and, the, a

• Consider getting 2- and 3-word phrases too:

back up ≠ back + up

• Even at this stage the results may be very interesting


Processing search terms 3: Categorizing

• Based on the frequent keywords, draft out categories. Not too granular; the
idea is to make big baskets to categorize quickly.

• Categorize the original search terms, based on these categories (automate
this!) Anything uncategorized goes in “Other”.

• Spot check your categorized terms so far.

• Look at “Other”, and think up new categories.

• Iterate a couple of times. Probably some manual categorization at the end.


Using search data 1: Prioritization

• Do you have gaps? Are you putting energy into the right places?


Using search data 2: Language

• Based on your categories, look into the language that people actually search for
most:

display or screen?

storage, memory, or just space?

• Best place for frequent terms is page title; next is intro paragraph

• After that, try to get terms into body of the page.

• Last resort is index or other non-visible keywords (but that’s mostly for internal site
search, not external searches)

• Strike a balance between using a range of terms and “stuﬃng”


Using search data 3: Classification

• How do your site users classify subject areas?

For example, a UI-driven category of “Sharing” might not match users’
distinct searches for recommend a book and sync notes

• If designing from scratch (or big revamp) this work should probably come first

• Search terms seem particularly amenable to a flat, “tagging” approach, but
can be informative no matter the approach


Other avenues for exploration

• Segmentation by screen size / geography / language

• Social media monitoring

• Further site search data such as audience and searches with no results


Food for thought

(simulated data)
Page views

Pages

Page views and time on page How just a little data analysis can improve your content — Joe Pairman

High (unique) page views

• Some indication of what's popular

• Compare with search keyword categories, to identify gaps

• Doesn’t identify whether the pages are doing a good job, or even if they’re
actually the things users were looking for


Low (unique) page views

• Generally could indicate candidate for removal, but...

• Could be not eﬀective information on a “niche” topic

• Could be useful but not ﬁndable


Time on page

• Seems appealing at ﬁrst — longer means better (up to a point)?

• But people can just leave a page open

• Some pages might be harder to read than others, so take longer?

• Some topics just deeper than others

• However, low time on page could be useful...


Time on page correlates with related keywords

• When people land on a page that wasn’t what they wanted, they don’t tend to
stay long:

• Pages with average time of less than a minute could be ﬂagged.

• Though tip-style pages may have short time on page but still be popular.


What can ratings tell us?

• Do people like the page or not? (For whatever reason.)

• Can be a good metric, when combined with other data. A simple example:

High page views Low page views
High positive Could be helpful info on a niche
Good
ratings ratio subject, or perhaps is hard to ﬁnd
Low positive
Needs improved Possible candidate for removal?
ratings ratio

Page ratings How just a little data analysis can improve your content — Joe Pairman

Cautions about ratings

• Avoid assumptions. “Not helpful” doesn’t always mean the page content is
unsuitable for its purpose.

• Don’t use in isolation.

• Combine with qualitative data if at all possible. Comments, usability studies,
social media monitoring, etc.


What you need…

• A rating per page

• Should have at least ability to rate positively and negatively (not just "like",
which is dubious - people don't even remember what they liked and why)

• Not really about lengthy surveys — they are a separate thing and require a lot
more preparation


Getting a better response rate

• Keep the ratings system as simple as possible

• If there’s the chance to provide a comment, make sure this shows up after a
rating is selected

Kohavi, R; Henne, R; and Sommerﬁeld, D: Practical Guide to Controlled Experiments on the Web: Listen to Your Customers not to the HiPPO (Slides from talk on Controlled Experiments).
www.exp-platform.com/Documents/controlledExperimentsHippoEbay.pdf


How to prepare ratings data

• Make sure it's comparable — i.e. don't compare product section to support
section

• If it's binary — helpful or not — divide positive by negative:

756 helpful divided by 230 not helpful gives a helpfulness ratio of 3.29

• Even if have multiple negative options, sum them and do the same, though
hang on to source data — it could be useful

• You end up with a list of pages, ranked by their helpfulness ratio


If few rate a page, do the ratings count?

• Response rate may correlate with helpfulness ratio (so don’t ignore pages
with low response rate)

(simulated data)
Response rate

Helpfulness

• Response rate is a useful metric in itself


Making a dashboard with
synthetic metrics

A combination of metrics could indicate …

• Which pages need to tackle their subject more effectively

• Which pages need to be more findable (similar to above but not the same)

• Which pages need to discourage wrong searches (different again)

• Which pages are candidates for removal

• Which pages work well (so are examples to follow)

Synthetic metrics dashboard How just a little data analysis can improve your content — Joe Pairman

Dashboard — overview


Relative measures are ﬁne

• What’s a good helpfulness ratio? How many page views do we need?

• Very hard to answer these kinds of questions (especially at ﬁrst)

• Rather, focus on relative measures: which pages are comparatively weak or
strong


Calculating low, medium, & high rankings

• For each metric, create a column to show whether the page is in the bottom third,
middle third, or top third

• In Excel, use something like this:

=IF(RANK(AC2,AC:AC)>ROUND(COUNT(AC:AC)*2/3,0),"Low",IF(RANK(AC2,AC:A
C)>ROUND(COUNT(AC:AC)*1/3,0),"Medium","High"))


Synthesizing metrics

• Indicators for Improve searchability: High helpfulness ratio, low page views,
and response rate at least medium.


Ratings with other metrics

• Improve content? — Low helpfulness, and page views are at least medium

• Improve searchability? Low page views, high helpfulness, and response rate at
least medium



• Unrelated searches — may be indicated by low time on page > check
keywords for these (remember tip-type pages may have low time on page too)

• Consider getting rid of — low page views, and low or medium helpfulness



• Good topics — high helpfulness, and at least medium response rate and page
views


Further research into a (potential) problem page

• Does it really have a problem? For example the time on page may be low, but
ratings very good. Is it a short, tip-style page?

• How do people get there? Where do they go when they leave? (Search terms,
navpaths, exits.)

• Is there anything the good pages have in common that the problem ones
don’t? (See next section … )


Investigating speciﬁc attributes

Ratings ratios for answering specific questions

• Are pages with graphics more helpful?

• Is it better to have more subtopics on a page?

• Does the number of links on a page affect bounce rate?

Specific content attributes How just a little data analysis can improve your content — Joe Pairman

Looking at relationships

• Excel CORREL function (0.3 or above is respectable)

• Scatter chart, with optional trend line

• But remember that correlation is not causation!


Correlating with XHTML / XML structure

• For example, pages with more graphics:

<img> or perhaps <ﬁg>

• More subtopics on a page:

<h2> or perhaps use information from DITA maps

• Several ways to automate this: Python with LXML library is powerful and not
too intimidating


Bounce rate

• Why should we try to keep people on the site? Don't we want to give them
the answer and then have them leave satisﬁed?

• However, bounce rate can indicate things like whether links are being used —
(correlate links on page to bounce rate)


Combining ratings with non-web data

• Assign human-judged ratings and see how they match up. (Is a particular
word usage important? Friendly style?)

• (For support content) Matching to support call issues. What types of pages
are used more on the web v.s. called about?


Web data in the whole organisation

• Content teams should have access to the data

• Can not only improve content but provide valuable feedback for other groups
in the organization

• Resourcing may require persuasion

• Potential legal issues may need to be addressed

• Once we have the data, we need to treat it responsibly

Next steps How just a little data analysis can improve your content — Joe Pairman

Schedule

• Search terms — every six months

• Synthetic metrics dashboard — every month or two

• Speciﬁc questions — as necessary


General principles

• Always present data in terms of the question it’s aiming to answer (though it’s
good to explore the data ﬁrst)

• Surprises are good. They indicate that you're not just conﬁrming your
prejudices.

• Don't assume that your data answers the question. Be very suspicious. Use
all other sources possible. And use common sense.

• Watch your resources.

• Analytics is not going to write your content or guarantee its success. And it's
reactive — only measures what's there, not what could be there.


Useful resources

• Search Analytics for Your Site, by Louis Rosenfeld — a thorough and thought-
provoking investigation of applications for internal site search data (Also see
slide deck with some key points at the same link.)
www.rosenfeldmedia.com/books/searchanalytics/

• Best Practices for “Was this helpful?” — a discussion about the design of
page ratings systems:
www.ixda.org/node/24101

• For “Was this page helpful” data, should I take response rate into account? —
a question with some useful comments and answers:
stats.stackexchange.com/questions/46428/for-was-this-page-helpful-data-
should-i-take-response-rate-into-account

Further information How just a little data analysis can improve your content — Joe Pairman

A simple synthetic metrics dashboard — steps

In Excel:

1. Get data from each source such as your analytics tool and your ratings database. Get the
data in any format that Excel can open.

2. Combine the data from diﬀerent sources. Use VLOOKUP formula if the value you’re matching
on is to the left of other values; INDEX and MATCH if not. If matching on page title, remember to
allow for any underscores / percent encoded characters / garbled characters.

3. Calculate rankings for key metrics. See slide 38. An example formula:
=IF(RANK(AC2,AC:AC)>ROUND(COUNT(AC:AC)*2/3,0),"Low",IF(RANK(AC2,AC:AC)>ROUND(CO
UNT(AC:AC)*1/3,0),"Medium","High"))

4. Set synthetic metric indicators. See slide 39. An example formula:
=IF(AND(AC2="Low", OR(L2="High", L2="Medium"), OR(N2="High", N2="Medium")), "1","")

Or, get your data as CSV/TSV, do steps 2-4 with a Python script, write to a CSV ﬁle, and then
open the result in any spreadsheet package.

Further information How just a little data analysis can improve your content — Joe Pairman

Find me on Google+ via:
joepairman.com

How Just a Little Data Analysis Can Improve your Content

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