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Site Search Analytics in a Nutshell Louis Rosenfeld [email protected] @louisrosenfeld Webdagane 10 September 2013

Site Search Analytics in a Nutshell

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Originally presented at SXSW March 13, 2011, on panel with Fred Beecher and Austin Govella. Modified and updated for Web 2.0 Expo talk, October 12, 2011, UX Web Summit September 26, 2012; Webdagene September 10, 2013.

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Site Search Analytics in a Nutshell

Louis Rosenfeld

[email protected] • @louisrosenfeld

Webdagane • 10 September 2013

Hello, my name is Lou

www.louisrosenfeld.com | www.rosenfeldmedia.com

Let’s look at the data

No, let’s look at the real dataCritical 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 /searchaccess=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

No, let’s look at the real dataCritical 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 /searchaccess=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

What are users searching?

No, let’s look at the real dataCritical 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 /searchaccess=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

What are users searching?

How often are users failing?

SSA is semantically rich data, and...

SSA is semantically rich data, and...

Queries sorted by frequency

...what users want--in their own words

A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences

A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences

Not all queries are distributed equally

A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences

A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences

Nor do they diminish gradually

A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences

A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences

80/20 rule isn’t quite accurate

(and the tail is quite long)

(and the tail is quite long)

(and the tail is quite long)

(and the tail is quite long)

(and the tail is quite long)The Long Tail is

much longer than you’d suspect

The Zipf Distribution, textually

Some things you can do with SSA

1.Make it harder to get lost in deep content2.Make search smarter3.Reduce jargon4.Learn how your audiences differ5.Know when to publish what6.Own and enjoy your failures7.Avoid disaster8.Predict the future

#1Make it harder to get lost

Start with basic SSA data: queries and query frequency

Percent: volume of search activity for a unique query during a particular time period

Cumulative Percent: running sum of percentages

Tease out common content types

Tease out common content types

Tease out common content types

Took an hour to...• Analyze top 50 queries (20% of all search activity)

• Ask and iterate: “what kind of content would users be looking for when they searched these terms?”

• Add cumulative percentages

Result: prioritized list of potential content types#1) application: 11.77%

#2) reference: 10.5% #3) instructions: 8.6%

#4) main/navigation pages: 5.91%

#5) contact info: 5.79%

#6) news/announcements: 4.27%

Clear content types lead to better contextual navigation

artist descriptions

album reviews

album pages

artist biosdiscography

TV listings

#2Make search smarter

Clear content types improve search performance

Clear content types improve search performance

Clear content types improve search performance

Content objects related to products

Clear content types improve search performance

Content objects related to products

Raw search results

Contextualizing “advanced” features

Session data suggest progression and context

Session data suggest progression and context

search session patterns1. solar energy2. how solar energy works

Session data suggest progression and context

search session patterns1. solar energy2. how solar energy works

search session patterns1. solar energy2. energy

Session data suggest progression and context

search session patterns1. solar energy2. how solar energy works

search session patterns1. solar energy2. energy

search session patterns1. solar energy2. solar energy charts

Session data suggest progression and context

search session patterns1. solar energy2. how solar energy works

search session patterns1. solar energy2. energy

search session patterns1. solar energy2. solar energy charts

search session patterns1. solar energy2. explain solar energy

Session data suggest progression and context

search session patterns1. solar energy2. how solar energy works

search session patterns1. solar energy2. energy

search session patterns1. solar energy2. solar energy charts

search session patterns1. solar energy2. explain solar energy

search session patterns1. solar energy2. solar energy news

Recognizing proper nouns, dates, and unique ID#s

#3Reduce jargon

Saving the brand by killing jargon at a community collegeJargon related to online education: FlexEd, COD,

College on Demand

Marketing’s solution: expensive campaign to educate public (via posters, brochures)

The Numbers (from SSA):

Result: content relabeled, money saved

query rank query#22 online*#101 COD#259 College on Demand#389 FlexTrack

* “online” part of 213 queries

#4Learn how your audiences differ

Who cares about what?

Who cares about what?

Who cares about what?

Who cares about what?

Why analyze queries by audience?

Fortify your personas with dataLearn about differences between audiences

• Open University “Enquirers”: 16 of 25 queries are for subjects not taught at OU

• Open University Students: search for course codes, topics dealing with completing program

Determine what’s commonly important to all audiences (these queries better work well)

#5Know when to publish what

Interest in the football team:

going...

Interest in the football team:

going...

...going...

Interest in the football team:

going...

...going...

gone

Interest in the football team:

going...

...going...

gone

Time to study!

Before Tax Day

After Tax Day

#6Own and enjoy your failures

Failed navigation?Examining unexpected searching

Look for places searches happen beyond main page

What’s going on?

• Navigational failure?

• Content failure?

• Something else?

Where navigation is failing (“Professional Resources” page)

Do users and AIGA mean different things by “Professional Resources”?

Comparing what users findand what they want

Comparing what users findand what they want

Failed business goals?Developing custom metrics

Netflix asks

1. Which movies most frequently searched? (query count)

2. Which of them most frequently clicked through? (MDP views)

3. Which of them least frequently added to queue? (queue adds)

Failed business goals?Developing custom metrics

Netflix asks

1. Which movies most frequently searched? (query count)

2. Which of them most frequently clicked through? (MDP views)

3. Which of them least frequently added to queue? (queue adds)

Failed business goals?Developing custom metrics

Netflix asks

1. Which movies most frequently searched? (query count)

2. Which of them most frequently clicked through? (MDP views)

3. Which of them least frequently added to queue? (queue adds)

#7Avoid disasters

The new and improved search engine that wasn’t

Vanguard used SSA to help benchmark existing search engine’s performance and help select new engine

New search engine “performed” poorlyBut IT needed

convincing to delay launch

Information Architect &

Dev Team Meeting

Search seems to have a few

problems… Nah

.

Where’s the

proof?

You can’t tell

for sure.

What to do? Test performance of common queries

“Before and after” testing using two sets of metrics1.Relevance: how reliably the search engine

returns the best matches first2.Precision: proportion of relevant results

clustered at the top of the list

Old engine (target) and new compared

Note: low relevance and high precision scores are optimal

More on Vanguard case study: http://bit.ly/D3B8c

Old engine (target) and new compared

Note: low relevance and high precision scores are optimal

More on Vanguard case study: http://bit.ly/D3B8c

uh-oh

Old engine (target) and new compared

Note: low relevance and high precision scores are optimal

More on Vanguard case study: http://bit.ly/D3B8c

uh-oh better

#8Predict the future

Shaping the Financial Times’ editorial agendaFT compares these

• Spiking queries for proper nouns (i.e., people and companies)

• Recent editorial coverage of people and companies

Discrepancy? • Breaking story?!

• Let the editors know!Seed your

Can SSA bring us together?

Lou’s TABLE OF OVERGENERALIZED

DICHOTOMIESWeb Analytics User Experience

What they analyze Users' behaviors (what's happening)

Users' intentions and motives (why those things happen)

What methods they employ

Quantitative methods to determine what's happening

Qualitative methods for explaining why things happen

What they're trying to achieve

Helps the organization meet goals (expressed as KPI)

Helps users achieve goals (expressed as tasks or topics of interest)

How they use data Measure performance (goal-driven analysis)

Uncover patterns and surprises (emergent analysis)

What kind of data they use

Statistical data ("real" data in large volumes, full of errors)

Descriptive data (in small volumes, generated in lab environment, full of errors)

Lands End and SKUs

Lands End and SKUs

SKU: # 39072-2AH1

Use SSA to start work on a site report card

Use SSA to start work on a site report card

SSA helps determine common information needs

Read this

Search Analytics for Your Site: Conversations with Your Customers by Louis Rosenfeld (Rosenfeld Media, 2011)

www.rosenfeldmedia.com

Use code WEBDAGENE2013

for 20% off allRosenfeld Media books