What Lies Beneath Impressions and
Clicks: Mining Foursquare to
Improve Day parting for Location-
Based 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
General Entertainment
Movie Theater
Museum
Performing Arts Venue
Stadium
12am to
12pm
2034
555
852
495
471
12pm to
5pm
2238
2118
1953
669
1047
5pm to
12am
3327
6051
890
6291
7213
Predicted
Probabilities
0.21
0.24
0.10
0.21
0.24
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%)
1245
(4.6%)
1196
(4.4%)
1019
(3.7%)
991
(3.6%)

Subcategory
American
Asian
Quick Bite
European
Mexican
Food Check-Ins
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
Predicted
Probabilities
0.35
0.07
0.43
0.05
0.10
No. of Category Check-ins by Hour
6000
5000
4000
3000
2000
1000
0

EATLALY
3300 (13%)
Shopping & Service
- Top Picks
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
358
179
1038
1341
77
1721
401
3545
705
689
760
157
3508
3316
526
Predicted
Probabilities
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
230 Fifth Rooftop
Lounge - 882 (5%)
732 (4%)
909 (5%)
STOUT - 680
(3.5%)
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
Predicted
Probabilities
0.04
0.07
0.13
0.04
0.46
0.26
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

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