Using Search Analytics to Diagnose What’s Ailing your Information Architecture

Using Search Analytics to Diagnose What’s Ailing your Information Architecture ASIS&T IA Summit Las Vegas, Nevada March 24, 2007 Rich Wiggins & Lou Rosenfeld www.rosenfeldmedia.com/books/searchanalytics

Trying to Fit into the IA Summit 2007 Theme… Rich Information Rich Interaction Rich Relationships Rich Wiggins

Thesis

By analyzing search logs, you engage in a conversation with your customers

At best, it’s a two way conversation:

Your users tell you what they seek

You tune your search engine (and your site) to give them what they seek the most

If you’re not analyzing your search logs, then you aren’t listening to your customers

Search is too important to leave in the hands of robots

The Wonderful Things Search Engines Do

Help harness massive amounts of content

Thousands, millions, billions of URLs

Cut across barriers

Document structure

Topical structure

Institutional structure, silos

The Horrible Things that Search Engines Do

Confuse low-value content with vital content

And point to obsolete content

And draft, internal, duplicative content

Rank leaf pages ahead of starting points

Rank popular or personal pages ahead of official content

MSU Keywords: Accidental Thesaurus

Circa 1999 MSU’s local AltaVista stopped scaling

Search for “human resources” and you get resume for a student in the HR program

We had to do something

We asked AltaVista for a way to goose the real HR site to the top of the hit list

They didn’t deliver

So we rolled our own Best Bets service, called it MSU Keywords

And it worked!

Methodology

Study the most popular unique searches

Map each to appropriate URL

“human resources” -> hr.msu.edu

“campus map” -> www.msu.edu/maps

Watch the results:

User complaints go down

So do content provider complaints

Continue to watch, learn, and act

Google Has Trained ’Em to Search First

Top 10 searches, www.msu.edu , Jan 2007

“ map” is a top search even with a map logo on the home page

MSU Usability Center, testing 2006 redesign, ordered testers to stay away from the search box

Nielsen 50% theory may underestimate

cata 3204 angel 3229 spartantrak 3575 bookstore 3584 schedule of courses 3690 study abroad 3745 library 4320 im west 5184 map 5859 campus map 7218 Unique Query Count

The Zipf Curve: Short Head, Torso, and Long Tail

Keep It In Proportion

7218 campus map

5859 map

5184 im west

4320 library

3745 study abroad

3690 schedule of courses

3584 bookstore

3575 spartantrak

3229 angel

3204 cata

Find the Sweet Spot; Avoid Diminishing Returns department of surgery 7 80.00 7877 hotels 124 50.02 500 msu union 295 40.05 221 computer center 650 30.01 98 webenroll 1351 20.18 42 housing 2464 10.53 14 campus map 7218 1.40 1 Query Count Cumulative Percent Rank

Look for Topical Patterns and Seasonal Changes

Does Best Bets Apply to Everyone?

Walter Underwood, former chief architect of Ultraseek:

Instead of Best Bets, Get a Better Search Engine

Best Bets requires human labor

Commitment of time and attention

… so do good search engine implementations

We Didn’t Start the Fire; credit to:

Vilfredo Pareto, circa 1890 – “the law of the vital few” (simplified as “80-20 rule”)

George Kingsley Zipf, Harvard, circa 1932 – counting the words used in Joyce’s Ulysses

“ the” is more common than “no” or “Dublin”

Bradford’s Law of Scattering, circa 1934 – a small number of journals accounts for a large percent of all important papers

Cited, most importantly, by the pricing model of Elsevier for leading scientific journals

Many other best bet pioneers: Microsoft, Raytheon, BBC, ESPN, AOL

Where will you Capture Search Queries

The search logs that your search engine naturally captures and maintains as searches take place

Search keywords or phrases that your users execute, that you capture into your own local database

Search keywords or phrases that your commercial search solution captures, records, and reports on (Mondosoft, Web Side Story, Ultraseek, Google Appliance, etc.)

Anatomy of a Search Log (from Google Search Appliance)

Critical elements in bold: IP address , time/date stamp , query , and # of results:

XXX.XXX.X.104 - - [ 10/Jul/2006:10:25:46 -0800] "GET /search?access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL%3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxystylesheet=www&q= lincense+plate &ip=XXX.XXX.X.104 HTTP/1.1" 200 971 0 0.02

XXX.XXX.X.104 - - [ 10/Jul/2006:10:25:48 -0800] "GET /search?access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL%3Ad1&ie=UTF-8&client=www&q= license+plate &ud=1&site=AllSites&spell=1&oe=UTF-8&proxystylesheet=www&ip=XXX.XXX.X.104 HTTP/1.1" 200 8283 146 0.16

XXX.XXX.XX.130 - - [ 10/Jul/2006:10:24:38 -0800] "GET /search?access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL%3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxystylesheet=www&q= regional+transportation+governance+commission &ip=XXX.XXX.X.130 HTTP/1.1" 200 9718 62 0.17

Full legend and more examples available from book site

Sample Query Analysis Report Excel template available from book site

Querying your Queries: Some basic questions 1/2

What are the most common unique queries?

Do any interesting patterns emerge from analyzing these common queries?

When common queries are searched, are the results the ones your users should be seeing?

Which common queries retrieve zero results?

Which common queries retrieve a large number of results, say 100 or more?

Querying your Queries: Some basic questions 2/2

Which common queries retrieve results that don’t get clicked through?

What page is the top source (referrer) per common query?

What is the number of click-throughs per common query?

Which result is most frequently clicked-through per common query?

What’s the average query length (number of terms, number of characters)?

Which URLs are users searching for?

Tune your Questions: Broad to specific

Netflix asks:

Which movies most frequently searched?

Which of them most frequently clicked through?

Which of them least frequently added to queue (and why)?

Examples:

“ OO7” versus “007”

Porn-related (not carried by Netflix)

“ yoga”: not stocking enough? or not indexing enough record content?

SA as Diagnostic Tool: What can you fix or improve?

User Research

Interface Design: search entry interface, search results

Retrieval Algorithm Modification

Navigation Design

Metadata Development

Content Development

User Research: What do they want?…

SA is a true expression of users’ information needs (often surprising: e.g., SKU numbers at LL Bean; URLs at IBM)

Provides context by displaying aspects of single search sessions

User Research: …who wants it?…

What can you learn from knowing these things?

What specific segments want; determined by:

Security clearance

IP address

Job function

Account information

Which pages they initiate searches from

User Research: …and when do they want it?

Time-based variation (and clustered queries)

By hour, by day, by season

Helps determine “best bets” and “guide” develop- ment

Search Entry Interface Design: “The Box” or something else?

SA identifies “dead end” points (e.g., 0 hits, 2000 hits) where assistance could be added (e.g., revise search, browsing alternative)

Syntax of queries informs selection of search features to expose (e.g., use of Boolean operators, fielded searching)

… OR…

Search Results Interface Design: Which results where?

#10 result is clicked through more often than #s 6, 7, 8, and 9 (ten results per page)

From SLI Systems (www.sli-systems.com)

Search Results Interface Design: How to sort results?

Financial Times has found that users often include dates in their queries

Obvious but effective improvement: allow users to sort by date

Search System: What to change?

Identify new functionality: Financial Times added spell checking

Retrieval algorithm modifications:

Deloitte, Barnes & Noble use SA to demonstrate that basic improvements (e.g., Best Bets) are insufficient

Financial Times weights company names higher

Navigation: Any improvements?

Michigan State University builds A-Z index automatically based on frequent queries

Navigation: Where does it fail?

Track and study pages (excluding main page) where search is initiated

Are there obvious issues that would cause a “dead end”?

Are there user studies that could test/validate problems on these pages?

Sandia Labs analyzes most requested documents to test content independent of site structure; results used to improve structure

Metadata Development: How do users express their needs?

SA provides a sense of tone: how users’ needs are expressed

Jargon (e.g., “cancer” vs. “oncology,” “lorry” vs. “truck,” acronyms)

Length (e.g., number of terms/query)

Syntax (e.g., Boolean, natural language, keyword)

Metadata Development: Which metadata values?

SA helps in the creation of controlled vocabularies

Terms are fodder for metadata values (e.g., “cell phone,” “JFK” vs. “John Kennedy,” “country music”), especially for determining preferred terms

Works with tools that cluster synonyms (example from www.behaviortracking.com), enabling concept searching and thesaurus development

Metadata Development: Which metadata attributes?

SA helps in the creation of vocabularies

Simple cluster analysis can detect metadata attributes (e.g., “product,” “person,” “topic”)

Look for variations between short head and long tail (Deloitte intranet: “known-item” queries are common; research topics are infrequent)

known-item queries research queries

Content Development: Do we have the right content?

SA identifies content that can’t be found (0 results)

Does the content exist? If so, there are wording, metadata, or spidering problems

If not, why not?

www.behaviortracking.com

Content Development: Are we featuring the right stuff?

Clickthrough tracking helps determine which results should rise to the top (example: SLI Systems)

Also suggests which “best bets” to develop to address common queries

Organizational Impact: Educational opportunities

SA is a way to “reverse engineer” how your site performs in order to:

Sensitize organization to analytics, specifically related to findability

Sensitize content owners/authors to benefits of good practices around content titling, tagging, and navigational placement

Organizational Impact: Rethinking how you do things

Financial Times learns about breaking stories from their logs by monitoring spikes in company names and individuals’ names and comparing with their current coverage