proposal for an efficient mobile advertising model based on foursquare data

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 shapeformed 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

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]

Users hold mayorships over pretty diverse locations!

Measuring Venue Type Diversity

Venue Type Diversity Metrics

Venue Category

Arts Food Nightlife

Venue Subcategory

Museum Ice Cream Lounge

Note that there are venue categories and venue subcategories

…We just use categories

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

Metric Value

Avg. Venue Diversity

0.72

St. Dev. Venue Diversity

0.24

Users hold mayorships in diverse types of venues!But users are pretty similar in their level of diversity.

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 (UM)– Location Diversity (ULD)– Venue Type Diversity (VVD)

• Venue Inputs– Venue Type (VT)– Venue Location (VL)

• User - Venue Inputs– % of user’s mayorships of the same type as that of the

venue (UVV)– Distance between UM & VL (UVL)

Targeting Function

Value of an ad to User U from Venue V

= UVV (1-UVD) + UVL (1.875-ULD)

Proportional to % mayorships of same typeAs venue

Inversely proportional touser’s venue diversity

Proportional to distance betweenvenue location and mayorshipcentroid

Inversely proportional touser’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