Presentation on theme: "What Lies Beneath Impressions and Clicks: Mining Foursquare to Improve Day parting for Location- Based Mobile Advertisers Sy Banerjee, Vijay Viswanathan,"— Presentation transcript:
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 (24%) MOMA 5295 (9%) Event apocalypse 5278 (9%) Regal Union Square Stadium (7%) Webster Hall 2843 (5%)
Arts & Ent. Check-ins Subcategory 12am to 12pm 12pm to 5pm 5pm to 12am Predicted Probabilities General Entertainment Movie Theater Museum Performing Arts Venue Stadium No. of Category Check-ins by Hour
Food Check-Ins Subcategory11pm to 11am11am to 2pm2pm to 5pm5pm to 11pm Predicted Probabilities American Asian Quick Bite European Mexican No. of Category Check-ins by Hour
Shopping & Service - Top Picks EATLALY 3300 (13%) 3178 (12%)
Shopping Check-ins Subcategory12pm to 11am11am to 5pm5pm to 12pm Predicted Probabilities Department Store Electronics Store Food & Drink Shop Gym or Fitness Center Other Stores No. of Category Check-ins by Hour
Night Life Top Check-ins 909 (5%) 230 Fifth Rooftop Lounge (5%) 732 (4%) STOUT (3.5%) Lillie’s Victorian Bar (3%)
Night Life Check-ins Subcategory3am to 6pm6pm to 9pm9pm to 3am Predicted Probabilities Beer Garden Cocktail Bar Lounge Other Bars Pub Sports Bar No. of Category Check-ins by Hour
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
Model Fit (s) A & E Food Shopping Nightlife
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.