User Engagement – A Scientific Challenge

Mounia Lalmas

Yahoo! Research Barcelona

mounia@acm.org

Ioannis ArapakisRicardo Baeza-YatesGeorges DupretJanette LehmannLori McCay-PeetVidhya NavalpakkamElad Yom-Tov

Collaborators

Outline

Motivation and DefinitionCharacteristics of User EngagementCurrent measurements

Vision and focus

Two “opposite” studies1. Models of user engagement

2. Saliency and user engagement

Motivation

User engagement is a quality of user experience that emphasizes the positive aspects of interaction – in particular the fact of being captivated by the technology.

Successful technologies are not just used, they are engaged with.

A web interface that is boring, a multimedia presentation that does not captivate users’ attention or an online forum that fails to engender a sense of community are quickly dismissed with a simple mouse click.

(O’Brian and Toms, 2008)

User engagement is how we nurture and build a community. (John Byrne, Business Weeks’ online editor 2009)

Would a user engage with this web site?

http://www.nhm.ac.uk/

Would a user engage with this web site?

http://www.amazingthings.org/ (art event calendar)

Would a user engage with this web site?

http://www.lowpriceskates.com/ (e-commerce – skating)

Would a user engage with this web site?

http://chiptune.com/ (music repository)

Would a user engage with this web site?

http://www.theosbrinkagency.com/ (photographer)

What is user engagement?“The emotional, cognitive and/or behavioural connection that exists, at any point in time and over time, between a user and a technological resource”

“Newish” field of research stemming from the increasing interaction between users and web services and technologies

Current work focuses on two aspects What are the characteristics of user engagement? How can we measure user engagement?

-Subjective measures-Objective measures

Characteristics of user engagement (I)

Characteristics of user engagement (II)

Forrester Research – The four I’s

Measuring Engagement, Forrester Research, June 2008

Peterson etal Engagement measure – 8 indicesClick Depth Index: page viewsDuration Index: time spentRecency Index: rate at which users return over timeLoyalty Index: level of long-term interaction the user has with the site or product (frequency)Brand Index: apparent awareness of the user of the brand, site, or product (search terms)Feedback Index: qualitative information including propensity to solicit additional information or supply direct feedbackInteraction Index: user interaction with site or product (click, upload, transaction)

Peterson etal. Measuring the immeasurable: visitor engagement, WebAnalyticsDemystified, September 2008

Subjective measuresRecord a user’s perception (generally self-reported) of the technology

Two-part processes:

Increased use of crowd-sourcing based studies

Objective measuresTo overcome the subjectivity of post-experience questionnairesTo measure engagement over large populations and over time

Objective measures – Online activities Proxy of user engagement

bounce rates, CTR, days since last visit, exit rate, entrance rate, frequency, number of new visitors, number of visitors, number of returning visitors, number of bookmarks (e.g. on Delicious), number of comments posted, number of content syndication (RSS), content contribution (adding a comment, adding a review, rating, uploading an image or video), number of repeated visitors, page views, time spent (dwell time), visits per visitors, number of tags (e.g. on Flickr), number of emailed and printed stories, number of Facebook likes, number of re-tweets, number of messages sent (instant, email), number of conversions (e.g. subscribing, buying), number of Facebook fan pages, number of search queries, common paths (e.g. from front page to mail tool then exit), external comments and reviews (for products), etc …

Characteristics and measures

S. Attfield, G. Kazai, M. Lalmas and B. Piwowarski. Towards a science of user engagement (Position Paper), WSDM Workshop on User Modelling for Web Applications, 2011.

Diagnostic and what we can doDiagnostic: work exists, but fragmented. In particular: What and how to measure depend on services and goals Lack of understanding of how to relate subjective and

objective measures

What I (we) have done:1.“Towards” Models of user engagement2.Stylistics and “engagement”

Saliency, interest, attention and positive affect Front page styles and downstream engagement Automatic linking and reading experience

Page stylistics + layout +links + saliency

user engagement within and across site

Measurements and methodologies + online analytics metrics (dwell time, …) + questionnaires + crowd-sourcing - biometrics (eye tracking, mouse tracking, …)

Goals + Models of user engagement - Metrics of user engagement

Engagement types + attention + emotion

+ activity + popularity + loyalty - functionality +/- intent & interest

Focus – Two “opposite” studies

activity metricsloyalty metrics

popularity metrics

hobbiesnavigation

social media

e-commerce

magazinesport

news

search

weathermail

weekly news

…

focused attentionaffect (emotion)

saliencymodels

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1. Models of user engagement 2. On saliency, attention and positive effect

1. Models of user engagement

User engagement characteristics depend on the web application: e.g. reading mail or browsing a news portal

results in different types of engagement

Diversity of engagementModels of engagement through clustering

using various criteriae.g. user types, temporal aspects

Data and Metrics

Interaction data, 2M users, July 2011, 73 US sites

Popularity #Users Number of distinct users

#Visits Number of visits

#Clicks Number of clicks

Activity ClickDepth Average number of page views per visit.

DwellTimeA Average time per visit

Loyalty ActiveDays Number of days a user visited the site

ReturnRate Number of times a user visited the site

DwellTimeL Average time a user spend on the site.

Diversity in user engagement

Users and Loyalty Sites have different user groups Proportion of user groups is site-

dependent

Time and Popularity Site engagement can be periodic

or contains peaks

Engagement of a site depends on users and time

mail, social media

shopping, entertainment

media(special events)

daily activity,navigation

media,entertainment

Methodology

General models User-based models Time-based modelsDimensions

8 metrics5 user groups8 metrics per user group

weekdays, weekend8 metrics per time span

#Dimensions 8 40 16

Kernel k-means with Kendall tau rank correlation kernel

Nb of clusters based on eigenvalue distribution of kernel matrixSignificant metric values with Kruskal-Wallis/Bonferonni

#Clusters (Models) 6 7 5

Analysing cluster centroids = models

Models of user engagement

• 6 general models

• Popularity, activity and loyalty are independent from each other

• Popularity and loyalty are influenced by external and internal factors e.g. frequency of publishing new

information, events, personal interests

• Activity depends on the structure of the site

Models based on engagement metrics

interest-specific

e-commerce,configuration

periodicmedia

Models of user engagement

User-based [7 models] Models based on engagement per

user group

Time-based [5 models] Models based on engagement

over weekdays and weekend

Models based on engagement metrics, user and time

navigation game, sporthobbies,interest-specific

daily news

Sites of the same type (e.g. mainstream media) do not necessarily belong to the same model

The groups of models describe different aspects of engagement, i.e. they are independent from each other

Relationships between models

General

User Time

General

0.00 3.50 4.23

User 3.50 0.00 4.25

Time 4.23 4.25 0.00

Groups of models are independent from each other

Example: Model mu2

[high popularity and activity in all user groups, increasing loyalty]

50% to model mt2 [high popularity on weekends and high loyalty on weekdays]

50% to model mt3 [high activity and loyalty on weekends]

Variance of Information [0,5.61]

Models of user engagement – Recap & NextUser engagement is complex and standard

metrics capture only a part of itUser engagement depends on users and timeFirst step towards a taxonomy of models of user

engagement … and associated metrics

NextInteraction between modelsInteraction between sites (complex networks)User demographics, time of the day, geo-location, etc

Focus – Two “opposite” studies

activity metricsloyalty metrics

popularity metrics

hobbiesnavigation

social media

e-commerce

magazinesport

news

search

weathermail

weekly news

…

focused attentionaffect (emotion)

saliencymodels

US

ER

EN

GA

GE

ME

NT

1. Models of user engagement 2. On saliency, attention and positive effect

2. Saliency, attention and positive affect

How the visual catchiness (saliency) of “relevant” information impacts user engagement metrics such as focused attention and emotion (affect)focused attention refers to the exclusion of

other thingsaffect relates to the emotions experienced

during the interaction

Saliency model of visual attention developed by Itti and Koch L. Itti and C. Koch. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40, 10-12 (2000).

Manipulating saliency

Web page screenshot Saliency maps

salie

nt c

ondi

tion

non-

salie

nt c

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tion

Study design8 tasks = finding latest news or headline on celebrity or

entertainment topicAffect measured pre- and post- task using the Positive

e.g. “determined”, “attentive” and Negative e.g. “hostile”, “afraid” Affect Schedule (PANAS)

Focused attention measured with 7-item focused attention subscale e.g. “I was so involved in my news tasks that I lost track of time”, “I blocked things out around me when I was

completing the news tasks” and perceived time Interest level in topics (pre-task) and questionnaire

(post-task) e.g. “I was interested in the content of the web pages”, “I wanted to find out more about the topics that I encountered on the web pages”

189 (90+99) participants from Amazon Mechanical Turk

PANAS (10 positive items and 10 negative items)

You feel this way right now, that is, at the present moment [1 = very slightly or not at all; 2 = a little; 3 = moderately;

4 = quite a bit; 5 = extremely]

[randomize items]

distressed, upset, guilty, scared, hostile, irritable, ashamed, nervous, jittery, afraid

interested, excited, strong, enthusiastic, proud, alert, inspired, determined, attentive, active

D. Watson, L.A. Clark & A. Tellegen. Development and validation of brief measures of positive and negative affect: The PANAS Scales. Journal of Personality and Social Psychology, 47 (1988).

7-item focused attention subscale (part of the 31-item user engagement scale)

5-point scale (strong disagree to strong agree)

1. I lost myself in this news tasks experience

2. I was so involved in my news tasks that I lost track of time

3. I blocked things out around me when I was completing the news tasks

4. When I was performing these news tasks, I lost track of the world around me

5. The time I spent performing these news tasks just slipped away

6. I was absorbed in my news tasks

7. During the news tasks experience I let myself go

H.L. O'Brien. Defining and Measuring Engagement in User Experiences with Technology. PhD Thesis, 2008.

Study protocolMechanical Turk: Introduction/Consent, HIT acceptance

Link to external survey; entry of MTurk worker ID

Pre-task PANAS

Pre-task topic interest questions

8 Non-Salient tasks 8 Salient tasks

Self-report of time spent finding headlines

Post-task PANAS

Self-report of affect

Focused attention scale

Self-report of focused attention

Interest and task ease questions

Demographics questionnaire

Comments

Return to MTurk to submit HIT

Saliency and positive affect

When headlines are visually non-salientusers are slow at finding them, report more

distraction due to web page features, and show a drop in affect

When headlines are visually catchy or salientuser find them faster, report that it is easy to focus,

and maintain positive affect

Saliency is helpful in task performance, focusing/avoiding distraction and in maintaining positive affect

Saliency and focused attentionAdapted focused attention subscale from the online

shopping domain to entertainment news domain

Users reported “easier to focus in the salient condition” BUT no significant improvement in the focused attention subscale or differences in perceived time spent on tasks

User interest in web page content is a good predictor of focused attention, which in turn is a good predictor of positive affect

Saliency and user engagement – Recap & Nextinteraction of saliency, focused attention, and

affect, together with user interest, is complexNext:

include web page content as a quality of user engagement in focused attention scale

more “realistic” user (interactive) reading experiencebio-metrics (mouse-tracking, eye-tracking, facial

expression, etc)

L. McCay-Peet, M. Lalmas, V. Navalpakkam. On saliency, affecr and focused attention, CHI 2012

The big picture ……….… my vision

Page stylistics + layout +links + saliency

user engagement within and across site

Measurements and methodologies + online analytics metrics (dwell time, …) + questionnaires + crowd-sourcing - biometrics (eye tracking, mouse tracking, …)

Goals + Models of user engagement - Metrics of user engagement

Engagement types + attention + emotion

+ activity + popularity + loyalty - functionality +/- intent & interest

Gracias

mounia@acm.org

www.dcs.gla.ac.uk/~mounia