What Lies Beneath Impressions and
Clicks: Mining Foursquare to
Improve Day parting for LocationBased Mobile Advertisers
Sy Banerjee,
Vijay Viswanathan,
Kalyan Raman
Hao Ying
Location Based Mobile
Advertising
• According to e Marketer, LBA is a rising star
The Problem
“However, as I looked at Sense’s list of
the “top 50 brands with the biggest retail
retargeting opportunity in mobile,” I
noticed a problem — although I’m
almost always within the presence of
one of them, I only frequent a few of
them. While I always seem to find
myself nearby a Subway (ranked highly
on Sense’s list because of its omnipresent
nature, presumably), I can’t imagine the
company could place an ad on Angry
Birds good enough to lure me inside.”
LBA
LBA is more effective than standard mobile
advertisements due to the added relevance by
geographical proximity (Jagoe 2003; Unni and
Harmon, 2007).
But context affects the effectiveness of LBA.
Specifically
Location –Public/Private (Banerjee & Dholakia, 2008)
Task Situation-Work/Leisure(Banerjee & Dholakia, 2008)
Audience Gender (Banerjee & Dholakia, 2012)
Can we time/schedule ads to reach consumers when
engaged in different activities? How do we find out
what who is doing, and when?
Why Day part?
Right Audience + Right Time = AD RELEVANCE
•
+
Why Day part?
Day parting Goals
by Media
• TV:
DV: Viewer
Engagement
• Internet:
DV:
Clicks,
Purchases,
Click through
rates
How to Make LBA more
Relevant?
Goal of LBA : To bring people physically to the store
In a place like Times Square,
where there are so many
things to do, (work, exercise,
tourism, shop, eat,) a
location of 2 mile radius is
not sufficient to determine
relevance. The activity
patterns of the people must
be known to make the ads
congruent and relevant.
Foursquare : Insight into
activity patterns
Methodology
• We mined data from the API of Four Square, a
SoLoMo application, and retrieved 87,000 check-ins
from 2 miles radius around Times Square, New York,
during a summer month.
• The data related to individuals checking in to
various businesses, including bars, restaurants,
shopping malls, movie theaters, workplaces, fitness
centers, etc.
• Gender and residence location of the user was
used to analyze the day of the week, time of the
day and location of checkin to reveal individual
patterns of activities over time.
Arts & Ent. Top Choices
MADISON SQ
GARDEN 13790 (24%)
MOMA 5295 (9%)
Event
apocalypse
5278 (9%)
Webster Hall
2843 (5%)
Regal Union Square
Stadium 14 - 3882 (7%)
Arts & Ent. Check-ins
Subcategory
12am to
12pm
12pm to
5pm
5pm to
12am
Predicted
Probabilities
2034
555
852
495
471
2238
2118
1953
669
1047
3327
6051
890
6291
7213
0.21
0.24
0.10
0.21
0.24
General Entertainment
Movie Theater
Museum
Performing Arts Venue
Stadium
No. of Category Check-ins by Hour
12000
10000
8000
6000
4000
2000
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Top Food Brands
•
2703
(10%)
1196
(4.4%)
1245
(4.6%)
1019
(3.7%)
991
(3.6%)
Food Check-Ins
Subcategory
11pm to 11am
11am to 2pm
2pm to 5pm
5pm to 11pm
327
325
1942
119
73
2228
203
4662
384
1020
798
115
1327
98
161
3596
765
711
330
744
American
Asian
Quick Bite
European
Mexican
No. of Category Check-ins by Hour
6000
5000
4000
3000
2000
1000
0
Predicted
Probabilities
0.35
0.07
0.43
0.05
0.10
22
20
18
16
14
12
10
8
6
4
2
0
Shopping & Service
- Top Picks
EATLALY
3300 (13%)
3178 (12%)
Shopping Check-ins
Subcategory
Department Store
Electronics Store
Food & Drink Shop
Gym or Fitness Center
Other Stores
12pm to 11am
11am to 5pm
5pm to 12pm
Predicted
Probabilities
358
179
1038
1341
77
1721
401
3545
705
689
760
157
3508
3316
526
0.15
0.04
0.44
0.29
0.07
No. of Category Check-ins by Hour
4500
4000
3500
3000
2500
2000
1500
1000
500
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Night Life Top Check-ins
732 (4%)
909 (5%)
STOUT - 680
(3.5%)
230 Fifth Rooftop
Lounge - 882 (5%)
Lillie’s Victorian Bar 605 (3%)
Night Life Check-ins
Subcategory
Beer Garden
Cocktail Bar
Lounge
Other Bars
Pub
Sports Bar
3am to 6pm
6pm to 9pm
9pm to 3am
223
111
259
89
1177
824
215
374
375
159
2074
1552
54
375
1106
211
2752
988
No. of Category Check-ins by Hour
2500
2000
1500
1000
500
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Predicted
Probabilities
0.04
0.07
0.13
0.04
0.46
0.26
Analysis
• Divided each category into suitable number of
subcategories
o Combine subcategories that could be perfect substitutes
o Ensure sufficient observations to estimate parameters
• Used a Multinomial Logit Model for the estimation
o Evaluated addition of various 2-way and 3-way interactions in the model
o Report results for models that had the best fit based on Log-Likelihood
scores and BIC
• Given the large number of coefficients estimated
for each subcategory, we report only the net
average marginal effect
Average Marginal Effects
Gender, residence location, time, day of the week
Gender
& Residents/tourists
• Men are more likely to go to the stadium for
entertainment, electronic stores for shopping and
sports bars for nightlife
• Women are more likely to go to museums, movies,
performing arts, Department stores for shopping
and Lounges for nightlife.
• Locals are more likely to go for general events,
Asian food/quick bites, fitness centers and pubs for
nightlife.
Interaction Effects – A&E
Interaction Effects- Food
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What Lies Beneath Impressions and Clicks