online multitasking and user engagement
TRANSCRIPT
ONLINE MULTITASKING AND USER ENGAGEMENT
CIKM 2013
In collabora*on with: Mounia Lalmas,
Ricardo Baeza-‐Yates, George Dupret
Jane%e Lehmann
outline
1. Mo%va%on How do users browse the web today?
2. Characteris%cs of online mul%tasking Ac2vity during and between visits
3. Measuring online mul%tasking Defini2on of new metrics, case study
Lights on by JC*+A!
ONLINE MULTITASKING
4 JaneGe Lehmann Mo2va2on
Browsing the “old way”
facebook news news news news mail
1min 2min 1min 3min
Dwell 2me during a visit on a news site: 7min on average
news site
ONLINE MULTITASKING
5 JaneGe Lehmann Mo2va2on
Nowadays
news facebook mail news news news
1min 2min 1min 3min
Dwell 2me during a visit on a news site: 2.33min on average (1min | 3min | 3min)
ONLINE MULTITASKING
6 JaneGe Lehmann Mo2va2on
• Users switch between sites, to do related or totally unrelated tasks • E. Herder [1]:
» 75% of sites are visited more than once » 74% of revisits are performed within a session
Measuring browsing behavior can lead to incorrect conclusions.
[1] E. Herder. Characteriza*ons of user web revisit behavior. In LWA, 2005.
DATA SET
Interac%on data • July 2012 • 2.5M users • 785M page views
• We defined a new naviga2on model (see paper for detail)
• Categoriza2on of the most frequent accessed sites (e.g. mail, news, shopping) » 11 categories (news), 33 subcategories (e.g. news
finance, news society) » 760 sites from 70 countries/regions
8 JaneGe Lehmann Characteris2cs
Visit activity
Visit frequency
9 JaneGe Lehmann Characteris2cs
Mul%tasking depends on the site under considera%on • Social media sites are revisited the
most
• News (tech) sites are the least revisited sites
news (finance)
news (tech)
social media
2.09
1.76
2.28
2.09
4.65
1.59
4.78
4.61
#Visits (avg sd)
Visit activity
Ac%vity between visits
10 JaneGe Lehmann Characteris2cs
Differences in the absence %me • 50% of sites are revisited aCer less
than 1min -‐ Interrup*on of a task
• There are revisits aCer a long break -‐ Returning to a site to perform a new task
0.00
0.25
0.50
0.75
1.00
10ï2 10ï1 100 101 102
mailsocial media
news (finance)news (tech)
Cum
ula
tive
pro
bab
ility
Absence time [min]
* v2 v1 * v3 * -‐ absence 2me
Visit activity
Ac%vity paLern
11 JaneGe Lehmann Characteris2cs
• Four types of "aGen2on shiCs”
• Complex cases refer to no specific paGern or repeated paGern
• Successive visits can belong together (i.e., to the same task)
0.23
0.28
0.33
mail sites
news (finance) sites news (tech) sites
social media sites
decreasing attention increasing attention
constant attention complex attention
Pro
por
tion
of to
tal
dw
ell tim
e on
site
p-value = 0.09m = -0.01
p-value = 0.07m = -0.02
p-value = 0.79m = 0.00
0.23
0.28
0.33
Pro
por
tion
of to
tal
dw
ell tim
e on
site
Cumulative activity
Cumula%ve ac%vity
vi Browsing ac2vity during the ith visit ivi Browsing ac2vity between the (i-‐1)th and ith visit k=3 Rescaling factor for ivi m Browsing ac2vity (e.g. dwell 2me, page views)
Assump%on: If users return aCer short 2me, they return to con2nue with same task. If users return aCer longer 2me, they return to perform a new task -‐ an indica2on of loyalty to the site.
13 JaneGe Lehmann Metrics
CumActm,k = log10 (v1 + ivik •vi
i=2
n
∑ )
iv2 v2 v1 iv3 v3
Cumulative activity
Cumula%ve ac%vity
vi Browsing ac2vity during the ith visit ivi Browsing ac2vity between the (i-‐1)th and ith visit k=3 Rescaling factor for ivi m Browsing ac2vity (e.g. dwell 2me, page views)
Assump%on: If users return aCer short 2me, they return to con2nue with same task. If users return aCer longer 2me, they return to perform a new task -‐ an indica2on of loyalty to the site.
14 JaneGe Lehmann Metrics
CumActm,k = log10 (v1 + ivik •vi
i=2
n
∑ )
iv2 v2 v1 iv3 v3
Cumulative activity
Cumula%ve ac%vity
vi Browsing ac2vity during the ith visit ivi Browsing ac2vity between the (i-‐1)th and ith visit k=3 Rescaling factor for ivi m Browsing ac2vity (e.g. dwell 2me, page views)
Assump%on: If users return aCer short 2me, they return to con2nue with same task. If users return aCer longer 2me, they return to perform a new task -‐ an indica2on of loyalty to the site.
15 JaneGe Lehmann Metrics
CumActm,k = log10 (v1 + ivik •vi
i=2
n
∑ )
iv2 v2 v1 iv3 v3
v1 + v2 + v3
Cumulative activity
Cumula%ve ac%vity
vi Browsing ac2vity during the ith visit ivi Browsing ac2vity between the (i-‐1)th and ith visit k=3 Rescaling factor for ivi m Browsing ac2vity (e.g. dwell 2me, page views)
Assump%on: If users return aCer short 2me, they return to con2nue with same task. If users return aCer longer 2me, they return to perform a new task -‐ an indica2on of loyalty to the site.
16 JaneGe Lehmann Metrics
CumActm,k = log10 (v1 + ivik •vi
i=2
n
∑ )
iv2 v2 v1 iv3 v3
Cumulative activity
Cumula%ve ac%vity
vi Browsing ac2vity during the ith visit ivi Browsing ac2vity between the (i-‐1)th and ith visit k=3 Rescaling factor for ivi m Browsing ac2vity (e.g. dwell 2me, page views)
Assump%on: If users return aCer short 2me, they return to con2nue with same task. If users return aCer longer 2me, they return to perform a new task -‐ an indica2on of loyalty to the site.
17 JaneGe Lehmann Metrics
CumActm,k = log10 (v1 + ivik •vi
i=2
n
∑ )
iv2 v2 v1 iv3 v3
v1 + (iv2)3� v2 + (iv3)3� v3
Activity pattern
ALen%on shiN and range
n=4 Number of visits in session σ Variance in the visit ac2vity μ Average of the visit ac2vity inv Modifica2on of the “Inversion number”
Descrip%on: AGShiC models the shiC of aGen2on in the browsing ac2vity AGRange describes fluctua2ons in the browsing ac2vity
18 JaneGe Lehmann Metrics
AttShiftm,n =invm,n −min Invm,n
| max Invm,n |− | min Invm,n |AttRangem,n =
σ (Vm,n )µ(Vm,n )
Activity pattern
ALen%on shiN and range
19 JaneGe Lehmann Metrics
-‐1 0 1
0
constant constant constant
> 0
decreasing complex increasing
AUen*on shiV
AUen*o
n rang
e
Comparing the ranking of the sites • Visitdt – Dwell 2me during a visit • Sessiondt – Dwell 2me during a session Ø Visitdt and Sessiondt correlate Ø Otherwise no correla2on à the other metrics capture different aspects of
browsing behavior
Comparing metrics
20 JaneGe Lehmann Metrics
Visitdt Sessiondt CumActdt ALShiNdt
Sessiondt 0.57
CumActdt -‐0.04 0.24
ALShiNdt 0.09 0.22 0.02
ALRangedt -‐0.01 -‐0.01 -‐0.26 0.19
“Models” of browsing behavior
• Clustering of sites using mul2tasking and standard engagement metrics: • CumActdt, AGShiCdt, AGRangedt • Visitdt, Sessiondt
• We iden2fied five cluster:
Models of browsing behavior
21 JaneGe Lehmann Metrics
C4: 74 sites
0.25
-0.25
0.75
-0.75
C5: 166 sites
0.25
-0.25
0.75
-0.75
C3: 156 sites
0.25
-0.25
0.75
-0.75
C2: 108 sites
0.25
-0.25
0.75
-0.75
C1: 172 sites
0.25
-0.25
0.75
-0.75
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
Models of browsing behavior
22 JaneGe Lehmann Metrics
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
C2: 108 sitesauctions, front page,
shopping, dating
0.25
-0.25
0.75
-0.75
C1: 172 sitesmail, maps, news,
news (soc.)
0.25
-0.25
0.75
-0.75
One task during a session § High dwell 2me per visit and during
the whole session § Users return to con2nue a task (short
absence 2me) § C1: aGen2on is shiCing to another site § C2: aGen2on is shiCing slowly towards
the site
C4: 74 sitesfront page, search,
download
C3: 156 sitesauctions, search,
front page, shopping
0.25
-0.25
0.75
-0.75
0.25
-0.25
0.75
-0.75
Models of browsing behavior
23 JaneGe Lehmann Metrics
Several tasks during a session § Users perform several tasks on these
sites during a session
§ No simple ac2vity paGern
§ C3: Dwell 2me per visit is low, but the dwell 2me per session is high
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
C5: 166 sitesservice, download,
blogging, news (soc.)
0.25
-0.25
0.75
-0.75
Models of browsing behavior
24 JaneGe Lehmann Metrics
Sites with low ac%vity § Users do not spend a lot of 2me on
these sites § Time between visits is short § AGen2on is shiCing towards the site
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
C2: 108 sitesauctions, front page,
shopping, dating
0.25
-0.25
0.75
-0.75
C3: 156 sitesauctions, search,
front page, shopping
0.25
-0.25
0.75
-0.75
Models of browsing behavior
25 JaneGe Lehmann Metrics
Browsing behavior can differ between sites of the same category § C2: users visit site once to perform
their task
§ C3: users visit site several 2mes to perform task(s)
Visitdt
[min] CumActdt,3
AttShiftdt,4
AttRangedt,4
Sessiondt
[min]
SUMMARY and Future Work
JaneGe Lehmann 26
• Online mul2tasking affects the way users access sites – Standard metrics do not capture this!!!
• We defined metrics that describe different aspects of mul2tasking • CumAct accounts for the 2me between visits • AGShiC, AGRange describe aGen2on shiCs
• We showed that mul2tasking depends on the site under considera2on
Future work: • Can we improve the defini2on of a task? • How does mul2tasking affect other metrics, such as bounce rate and click-‐
through rate? • Does mul2tasking differ in different countries?
Summary
Janette Lehmann Universitat Pompeu Fabra, Spain [email protected] Mounia Lalmas Yahoo Labs London [email protected] George Dupret Yahoo Labs Sunnyvale [email protected] Ricardo Baeza-Yates Yahoo Labs Barcelona [email protected]
Online Multitasking
+ User
Engagement