what lies beneath impressions and clicks: mining foursquare to improve day parting for...
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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. - PowerPoint PPT PresentationTRANSCRIPT
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. SpecificallyLocation –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%)
Regal Union Square Stadium 14 - 3882 (7%)
Webster Hall 2843 (5%)
Arts & Ent. Check-ins Subcategory 12am to
12pm12pm to
5pm5pm to 12am
Predicted Probabilities
General Entertainment 2034 2238 3327 0.21Movie Theater 555 2118 6051 0.24Museum 852 1953 890 0.10Performing Arts Venue 495 669 6291 0.21Stadium 471 1047 7213 0.24
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
2000
4000
6000
8000
10000
12000No. of Category Check-ins by Hour
Top Food Brands•
2703 (10%)
1245 (4.6%)
1196 (4.4%)
1019(3.7%)
991 (3.6%)
Food Check-InsSubcategory 11pm to 11am 11am to 2pm 2pm to 5pm 5pm to 11pm
Predicted Probabilitie
sAmerican 327 2228 798 3596 0.35Asian 325 203 115 765 0.07Quick Bite 1942 4662 1327 711 0.43European 119 384 98 330 0.05Mexican 73 1020 161 744 0.10
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
0
1000
2000
3000
4000
5000
6000No. of Category Check-ins by Hour
Shopping & Service - Top Picks
EATLALY 3300 (13%) 3178
(12%)
Shopping Check-insSubcategory 12pm to 11am 11am to 5pm 5pm to 12pm Predicted
ProbabilitiesDepartment Store 358 1721 760 0.15Electronics Store 179 401 157 0.04Food & Drink Shop 1038 3545 3508 0.44Gym or Fitness Center 1341 705 3316 0.29Other Stores 77 689 526 0.07
1 2 3 4 5 6 7 8 9 10 1112 1314 1516 1718 1920 2122 23240
50010001500200025003000350040004500
No. of Category Check-ins by Hour
Night Life Top Check-ins
909 (5%)230 Fifth Rooftop Lounge - 882 (5%)
732 (4%)
STOUT - 680 (3.5%) Lillie’s Victorian
Bar -605 (3%)
Night Life Check-insSubcategory 3am to 6pm 6pm to 9pm 9pm to 3am Predicted
ProbabilitiesBeer Garden 223 215 54 0.04Cocktail Bar 111 374 375 0.07Lounge 259 375 1106 0.13Other Bars 89 159 211 0.04Pub 1177 2074 2752 0.46Sports Bar 824 1552 988 0.26
1 2 3 4 5 6 7 8 9 1011121314151617181920212223240
500
1000
1500
2000
2500No. of Category Check-ins by Hour
Analysis• Divided each category into suitable number of
subcategorieso Combine subcategories that could be perfect substituteso Ensure sufficient observations to estimate parameters
• Used a Multinomial Logit Model for the estimationo Evaluated addition of various 2-way and 3-way interactions in the
modelo 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