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

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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 12pm

12pm to 5pm

5pm 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

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No. 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

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No. 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 Probabilities

Department 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

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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 Probabilities

Beer 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

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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