7/11/2007icmb 2007, toronto, canada1 estimating the capacity of the location–based advertising...

7/11/2007 ICMB 2007, Toronto, Canada 1

Estimating the Capacity of the Location–Based Advertising Channel

Győző Gidófalvi

Hans Ravnkjær Larsen

Geomatic ApS

Center for Geoinformatik

Torben Bach Pedersen

Aalborg University

27/11/2007 ICMB 2007, Toronto, Canada

Outline

Objectives

Real world data sources conzoom©: demographic data GallupPC®: consumer survey data bizmark™: products, services, and businesses ST-ACTS: simulated mobile users

LBA database and framework LBA relational database Proximity requirements on mobile ads Interest based on demography LBA – implicit vs. explicit interest case Uniqueness and user–defined quantitative constraints on mobile ads User–defined ST constraints on mobile ads

Experiments and results


Objectives

7% of the mobile consumers would be willing to receive promotional text messages “if they were relevant”

Relevance depends on at least two factors: Proximity of the mobile user to the product or service being advertised Match between the interest of the mobile user and the product / service

being advertisedInterest can be explicit (expressed by the mobile user), or implicit

(inferred from user’s demography / historical behavior)

Build a framework for Location-Based Advertising (LBA) both the explicit and implicit case.

Provide realistic estimates on the number of mobile ads that can be delivered.


Real World Data Sources

conzoom©: demographic data

GallupPC®: consumer survey data

bizmark™: products, services, and businesses

ST-ACTS: simulated mobile users


conzoom©: Demographic Data

Grid-based population statistics: 100-meter grid cells are grouped into as clusters such that:

the clusters have a minimum number of persons and/or households in them to protect privacy

grid cells in a cluster are as homogeneous as possible in terms of a number of publicly available 1-to-1 information about properties

grid cells in a cluster are close geographically

Information (counts) are projected down to the cell-level


conzoom©: Types and Profile

Based on the statistics the population is segmented into 29 conzoom© types

For example Cosmopolitans are more likely:

to be middle aged (30–59 years old), couples with children, who have a medium to long higher education, and hold higher level or top management positions in the financial or public sector

to live in larger cities in larger, multi–family houses that are either owned by them or are private rentals, and to have a better household economy than the average Dane (not shown)

Each grid cell is associated with one conzoom© type


GallupPC®: Consumer Survey Data

Answers of approximately 10,000 subjects to questions about: demographics; interests in culture, hobbies, and sports; purchasing habits, and more…

Yes/no question is re–phrased as categorical questions: Are you interested in fashion? Possible answer choices: very, rather, somewhat, not very, or not

interested

Questionnaires are related to conzoom© types using the demographic parts of the questionnaires.


bizmark™: Products, Services, and Businesses

1-to-1 information about businesses: location, business area size, number of employees, business branch code

Identified 40 product and service categories: Classical concert; pop/rock concert; discotheque; Art exhibition; museum; cinema; theater; Pharmacy; Bicycle / moped; car; Stereo / HI-FI; CDs / DVDs; computer/internet; new technologies/telecommunication; Do–it–yourself; Fashion; cosmetics / skincare; glasses / contacts; hairdresser; jeweler / watches; Interior design; travel; pets, fast-food; and 14 brand specific supermarkets

Businesses are related to product and service categories through international business branch codes


ST-ACTS: Important Principles of Social Mobility

First Principle: People move from a given location to another location with an objective of performing some activity at the latter location.Second Principle: Not all people are equally likely to perform a given activity. The likelihood of performing an activity depends on the interest of a given person, which in turn depends on a number of demographic variables.Third Principle: The activities performed by a given person are highly context dependent:

current location of the person set of possible locations where a given activity can be performed the current time recent history of activities that the person has performed

Fourth Principle: The locations of facilities, where a givenactivity can be performed, are not randomly distributed.


ST-ACTS: Activity Simulation with Spatio–Temporal Constraints

Mandatory activities: workers (students) go to “appropriate” working places (schools) on weekdays at appropriate times.

Temporal activity constraint: certain activities are more likely to be performed during some periods than others.

Activity duration constraint: not all activities take the same amount of time.

Activity repetition constraint: certain time has to pass before a person likely to perform the same activity.

Maximum distance constraint: for most activities there is a maximum distance a person is willing to travel.

Physical mobility constraints: it takes time to move from one location to another.


LBA Database and Framework

LBA relational database

Proximity requirements on mobile ads

Interest based on demography

LBA – implicit vs. explicit interest case

Uniqueness and user–defined quantitative constraints on mobile ads

User–defined ST constraints on mobile ads


LBA Relational Database

Implementation using Oracle RDBMS + Oracle Spatial Extension

Moving Object Component

User Profile Component

Relevance Component

Business Component

Product / Service Component

LBA Component


Proximity Requirements on Mobile Ads

A mobile add is likely to be relevant only if the current (or near future) location of the user is within a maximum distance, maxdist, to the location of the mobile ad.

Buffer the locations of businesses (mobile ads) and test for spatial intersection (join).

Index geometries of the trajectory segments and the buffered mobile ads using R-trees to make the spatial join fast.


Interest Based on Demography

Relevance of a mobile ad depends on the interest of the user.

Interest scores for conzoom© type and product/service combinations are estimated as the scaled, average survey responses using the GallupPC® consumer surveys.


LBA – Implicit vs. Explicit Interest Case

Two interest models: Implicit

Interest is inferred from demographic characteristics or historical user behavior (f.ex., reaction to previously received ads)

Push marketingCompany-specific interest score functions (using DM and ML)Represented as a many-to-many relation: interest_score = <conzoom_type, prodid, score>

ExplicitUsers can state their interest in certain products / services

explicitlyPull marketingRepresented as a binary relation: interest_score = <pid, prodid>


Uniqueness and User–Defined Quantitative Constraints on Mobile Ads

Mobile ad delivery is managed through a relation: mobile_ad_delivery = <pid, bid, prodid, delivery_time>

Receiving the same ad multiple times decreases its relevance Primary key constraints on the first 3 columns guarantee that a mobile ad is

delivered at most once to a user. Optional, delayed redelivery of messages can be controlled by recording

the delivery time of mobile ads.

As the number of delivered mobile ads increases, the likelihood of users getting annoyed by them increases also. Consequently, relevant ads might be perceived non-relevant by user.

Important for users to be able to control the maximum number of mobile ads received

Top-k queries are an efficient mechanism to provide this user-control


User–Defined ST Constraints on Mobile Ads

Time and location are important aspects of the context of mobile ads.

Need to provide user-control for spatio-temporal constraints on the delivery of mobile ads

Spatio-temporal Mobile Ad ProfilesSpatial + temporal region Multiple applicable profiles -> select most restrictive as the

active profileManagement of profiles can be server- or client-side

Spatio-temporal join between mobile ad profiles and mobile ads can provided the control needed.


Experimental Setup

4314 businesses offering products and services in 40 product and service categories (6532 mobile ads)

Movements of 1000 randomly selected simulated mobile users during the course of a day (3826 trips)

Estimate the number of deliverable ads for Implicit interest case: varying mindist and minscore parameters Explicit interest case:

Users are probabilistically assigned 1 product / interest category

Varying mindist parameter


Experimental Results: Implicit Case

Extremely large number of deliverable mobile ads even for high interest scores.

Average number of deliverable ads per user per day is high even for small values of mindist. The need for user-control is eminent.


Experimental Results: Explicit Case

Large number of deliverable ads.

Lower than expected LBA penetration.

Large average number of deliverable mobile ads.


Conclusions

Developed a framework for LBA. Relevance based on proximity and interest (implicit vs. explicit)

Presented a LBA database for the management / delivery of mobile ads.

Using a realistically simulated moving user population and real world data sources for mobile ads, estimated the capacity of the LBA channel.

The LBA channel is rather large, which is evidence for a strong business case.

This also indicates the need for adequate user–control on the delivery of mobile ads.

Maximum number of received adsSpatio-temporal Mobile Ad Profiles


Acknowledgements

Thanks for the help from co–workers, Esben Taudorf, Kasper Rϋgge, and Lau Kingo Marcussen.


Thank you for your attention!

Questions?

7/11/2007icmb 2007, toronto, canada1 estimating the capacity of the location–based advertising...

Documents

canada conzoom

number of mobile ads

mobile consumers

conzoom type slide

demographic data galluppc

mobile ads experiments

canada galluppc

conzoom types