Proposal for an Efficient Mobile Advertising Model Based on Foursquare Data
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Proposal for an Efficient Mobile Advertising Model Based on Foursquare Data

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In this presentation, I and 3 fellow Carnegie Mellon graduate students present our findings on Foursquare mayorships, and their diversity in location and venue type....

In this presentation, I and 3 fellow Carnegie Mellon graduate students present our findings on Foursquare mayorships, and their diversity in location and venue type.

Using this data, we develop an efficient market model for mobile advertising on Foursquare and perhaps other platforms.

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  • Vishal
  • Alan
  • Matt drives to his friends homes before he goes with them to dive bars and diners. He doesn’t stay at these dive bars for long and hops from one to another.
  • Mayor of two hotels and one bar within a hotel. Rides the bus to work and buys coffee. Enjoys going to the parks and landmarks as well as the Strip district.
  • Bi Modal distributions? Median of distance from centroid?
  • Note: 1.875 chosen because it is the maximum average distance from the centroid

Proposal for an Efficient Mobile Advertising Model Based on Foursquare Data Proposal for an Efficient Mobile Advertising Model Based on Foursquare Data Presentation Transcript

  • Diversity of Foursquare Mayorships: Venue Types and Venue Locations Troy Effner Haris Krijestorac Alan Pan Vishal Patel
  • What is Foursquare ?
    • Location-based social networking platform for mobile devices
    • Users ‘check in’ to venues
    • User with most checkins to a venue becomes its ‘mayor’
  • Motivation for Research
    • Location-based advertising
      • How much value equity is there in targeting local venues or venues of similar type?
  • Objective
    • Based on Foursquare user data, create an efficient model for mobile advertising
  • Gathering Data
    • Location
      • API returns city, state, zip, and longitude and latitude coordinates
      • Limited to mayors of Pittsburgh locations
    • Venue Type
      • API returns ‘categories’ and ‘subcategories’
  • Data Overview
    • 557 users with > 1 mayorship
    • 1897 mayorships Total
    • Average of 3.4 mayorships per person
    • 2536 Venues
  • Measuring Location Diversity
  • Location Diversity Metrics
    • Given a set of locations for which a user is mayor, find the centroid
    Centroid : Geometric center of shape formed by all points of mayorship
  • Location Diversity Metrics
    • Average distance from centroid
    • Largest and smallest distance from centroid
    • Variance in distances from centroid
  • Location Diversity Data Users hold mayorships over pretty diverse locations! Metric Value (in miles) Mean Distance from centroid 0.58 St. Dev. Distance from centroid 0.57 Range of distances from centroid [0.002, 1.875]
  • Measuring Venue Type Diversity
  • Venue Type Diversity Metrics Note that there are venue categories and venue subcategories … We just use categories Venue Category Arts Food Nightlife Venue Subcategory Museum Ice Cream Lounge
  • Measuring Venue Type Diversity
    • # Unique venue categories where user is mayor
    • ÷
    • Total # venues where user is mayor
    • Metric value is proportional to diversity
  • Venue Type Diversity Example
    • Low Diversity User
      • Mayor of 10 venues of 2 unique types (Arts, Food)
    • 2 ÷ 10 = 0.2
    • High Diversity User
      • Mayor of 10 venues, 8 unique types
    • 8 ÷ 10 = 0.8
  • Venue Type Diversity Data Users hold mayorships in diverse types of venues! But users are pretty similar in their level of diversity. Metric Value Avg. Venue Diversity 0.72 St. Dev. Venue Diversity 0.24
  • Interesting Findings
  • Popularity of Venue Types
    • Most popular
      • Home (4.7%)
      • Office (4.3%)
      • Bar (2.1%)
    • Least popular
      • Dorm (.007%)
      • Bus Station (.008%)
      • Bank (.009%)
  • Power User : Matt A.
    • 35 Pittsburgh Mayorships
      • 2 Dive bars, 6 homes, 2 tattoo parlors, mayor of Mt. Washington
    • Matt drives to his friends homes before he goes with them to dive bars and diners. He doesn’t stay at these dive bars for long and hops from one to another.
  • Power User : Derek
    • 20 Pittsburgh Mayorships
    • Mayor of two hotels and one bar within a hotel. Rides the bus to work and buys coffee. Enjoys going to the parks and landmarks as well as the Strip district.
  • Acknowledging our Challenges
  • Do Mayors have Different Habits in Different Cities ?
    • Solution :
    • Do same research across various cities.
    • If patterns are different, localize.
    • If not, generalize.
  • Null Values in our Data
    • About 25% venue types are null
    • Is there anything special about the nulls?
      • Privacy conscious people?
      • Venues that did not fall into a category?
      • Self-created venues?
  • Multiple Centroids ?
  • Applying our research
    • How relatively likely is a specific user to pursue mayorship of a specific venue
      • In a specific location
      • Of a specific venue type
  • Targeting Function
    • Function that calculates how targeted an ad for a specific venue would be to a specific user ?
  • Targeting Function Inputs
    • User Inputs:
      • Mayorship Centroid (U M )
      • Location Diversity (U LD )
      • Venue Type Diversity (V VD )
    • Venue Inputs
      • Venue Type (V T )
      • Venue Location (V L )
    • User - Venue Inputs
      • % of user’s mayorships of the same type as that of the venue (UV V )
      • Distance between U M & V L (UV L )
  • Targeting Function
    • Value of an ad to User U from Venue V
    • = UV V (1-U VD ) + UV L (1.875-U LD )
    Proportional to % mayorships of same type As venue Inversely proportional to user’s venue diversity Proportional to distance between venue location and mayorship centroid Inversely proportional to user’s location diversity
  • Advertiser’s Perspective
    • Advertisers can take different approaches…
    • Broad Targeting
      • Serve ads to many low-relevance foursquare users
      • Good for promoting awareness
    • Specific Targeting
      • Serve ads to few highly relevant foursquare users
      • Good for bringing in customers
    • Advertisers can specify a level of relevancy
  • Foursquare’s Perspective
    • Foursquare can charge advertisers according to their specified level of relevancy
    • Advertisers will reach out to the right audience
    • Foursquare can charge in accordance with advertiser’s goals
    • Creates efficient market for location-based advertising
  • Q&A