Intelligent Techniques for Web Personalization and Recommender Systems
AAAI Technical Report WS-12-09
Variable Categorization and Modelling: A Novel Adversarial
Approach to Mobile Location-Based Advertising
En-Shiun Annie Lee, Franky Kin-Wai Yeung, and Tzu-Yang (Ben) Yu
University of Waterloo
200 University Avenue West,
Waterloo, Ontario, N2L 3G1
Abstract
models to account for our findings, including the Personal
Profile Matching Model and the Donut Model.
Adversarial location-based advertising is a new problem
domain that has not been explored previously. Advertisers
can promote their own products based on their location, as
well as compete for customers who are close to their location. Therefore, this technology takes advantage of not-yetregulated property rights of wireless location. In order to
fully consider this new advertising avenue for targeting customers, the Personal Profile Matching Model must be considered. This paper proposes a basis for adversarial locationbased mobile advertising.
Location-based advertising has gained a lot of attention from
advertisers in recent years because of the rapid increase the
number of in smart phone users. Adversarial location-based
advertising is a novel concept that stretches beyond conventional mobile advertising. Advertisers can promote their own
products based on location and also compete for customers
who are close-by. We determined a set of variables that influences the customer’s purchasing decisions, as well as categorized these variables and ranked the important variables
within each category. Our generic model matches the individual’s profile of distance to the competitor’s store and of
coupon discount preference, which is then used to implement
discount discrimination strategy. We conclude that customers
will accept adversarial advertising and change their purchasing decisions. It is a powerful advertising tool to know that,
given a predefined condition for the purchase, a customer will
make the extra effort to accept a better offer.
Background
Mobile Advertising
The new smart phone technology is revolutionizing the advertising industry by reaching its potential customer based
on the location-specific and time-specific information (Ranganathan and Campbell 2002). Location-based advertising
(LBA) uses marketer-controlled information that is specially
tailored to the user’s location when accessing an advertisement (Gordon C. Bruner II and Kumar 2007). For example, the improvement of the GPS and WAP can return
precise geographic location of the user. However, Kolmel
proposes that LBA are unpopular due to (1) the limitation
of screen size, (2) the expense to building LBA, and (3)
the low accuracy for finding location (Kolmel and Alexakis 2002). However, the acceptance mobile advertising
are contributed by the following seven-key factors: Choice
(Leppaniemi and Karjaluoto 2005), Control (Leppaniemi
and Karjaluoto 2005), Customization (Leppaniemi and Karjaluoto 2005), Mutual Benefit (Leppaniemi and Karjaluoto 2005), Brand-Establishment (Merisavo and Leppniemi
2007), Product Price (Unni and Harmon. 2007), Personalization Factors (Xu D. J. and Li 2008).
Many location-based advertising frameworks have been
proposed in the past. Randell and Muller (Randell and
Muller 2000) created a Shopping Jacket system which used
GPS and pinger in stores for positioning. Aalto et al. (Aalto
2004) introduced a Bluetooth Mobile Advertising system to
deliver permission-based location-aware mobile advertisement. Rashid et al. (Ranganathan and Campbell 2002) presented a system that can be used with any current mobile
Introduction
Today, of the three billion mobile subscribers worldwide,
over one billion are smart phone users; the smart phone
market in 2010 increased at a rate of nearly 50% (J. Mller
and Michelis 2011; Cozza 2008). This new emerging technology is growing at a rapid rate with added device functionalities that are available for new advertising approaches.
New smart phone functionalities include Assisted Global
Positioning System to determine the user’s precise indoor
positioning and accelerometer to detect the user’s motion.
Mobile coupons applications, such as Foursquare or mobile Groupon, have been effective at attracting customers;
however, they do not use the full functionality of the smart
phone. Existing research on location-based advertising proposes frameworks with influencing factors. Adversarial advertising has been applied to traditional advertising and
search optimization; however, adversarial advertising is a
novel concept in the domain of location-based advertising
delivered by the smart phone.
We identify and categorize the conditions that influence
the customer’s decision to leave the line-up at the current
store in order to accept the competitor’s coupon to make
their purchase at the competitor’s store. We present several
c 2012, Association for the Advancement of Artificial
Copyright Intelligence (www.aaai.org). All rights reserved.
43
phone system to provide location-based information and/or
advertisements to any mobile phone. Bulander et al. (Bulander R and Kolmel 2005) studied the privacy issues of personalize and context sensitive advertising. Bruner & Kumar
(Gordon C. Bruner II and Kumar 2007) considers the optin type of permission marketing. These research found that
LBA offers a richly viable avenue of context-specific advertising by following users’ preferences.
Location-based smart phone technology has studied influencing factors as well as proposed implementation frameworks. The component of adversarial advertising has not
been incorporated into location-based smart-phone advertising. Adversarial Location-based Advertising (ALBA) is
a new avenue of advertising that has not be previously explored and this paper makes contribution into this problem
space.
(a) Category of Variables
(b) Personal Variables
Figure 1: (a) Categories of Variables. We divide the variables found into three categories: Environmental Variables,
Store Variables, and Personal Variables (a). (b) Taxonomy
of Personal Variables Classifications. Personal Variables are
the variables that are controlled by the individual customer
who is being served the coupon. This category of variables is
further broken down into Psychological Variables and NonPsychological Variables. Of the Psychological Variables,
they can either be a Consistent Variables or a Dynamic Variables. Furthermore, within the Non-Psychological Variables,
there can be Purchasing-Context Variables (b).
Method
We observe whether participants are willing to receive an
adversarial advertisement from a competitor while they are
already lined up to purchase their item at a store. We also
observe the variables that can change a person’s purchasing behaviour. Our preliminary observations are made from
discussion with colleagues and informal talks with people
lining-up for coffee. We started with conversational interviews with customers lining up at Tim Horton’s Coffee Store
in the Davis Centre and Student Life Centre at the University of Waterloo by asking their coffee consumption habitual
questions.
The University of Waterloo is located in Waterloo, Ontario with approximately 26,000 undergraduate and over
4,000 post-graduate students. The university is famous in engineering, computer science, and applied mathematics. The
Davis Centre is the building designed for the Department
of Computer Science and the Student Life Centre is for all
students. These two locations are where the interviews took
place.
Variables or Non-Psychological Variables. First, the Psychological Variables are further specified as Consistent Psychological Variables, which are variables that do not change often over time, and Dynamic Psychological Variables, which
are variables that changes frequently even in a day. Alternatively, within the Non-Psychological Variables, there is a set
of variables that are related to the specific purchasing context. These are specific variables that are directly related to
the current purchasing situation of the customer at that specific time and location.
Environment Variables
The Environment Variables are list in Table 1 by the order of
importance. These Environment Variables are independent
from the variables in the other two categorizes.
Environment
Variables
Time of day
Categories of Variables
To establish the relationship of variables that were discovered in our observations, we categorized the variables into
three major categories: Environmental Variables, Store Variables, and Personal Variables (Figure 1(a)). We observed
that, as their names suggest, each of the variables are controlled by their named entities. For example, the Store Variables are directly controllable by the store itself; these variables are dependent on the store. The Store Variables is replicated into two sets of variables in adversarial location-based
advertising: the Current Store Variables, and the Competitor’s Store Variables. The Personal Variables are directly
controllable by the customer, thus are dependent on the customer. Lastly, the Environmental Variables are those external factors that cannot be controlled by either the store or the
customer.
To better understand of how an individual makes a purchasing decision and is influenced by a coupon, we refined
the Personal Variables into a more detailed taxonomy (Figure 1(b)). Personal Variables are divided into Psychological
Day of week
Temperature
Weather
Descriptions
The time of day at the moment the coupon ad
sent to potential customers
The day of week on which the coupon ad sent
to potential customers
The temperature at the moment the coupon ad
sent to potential customers
The weather at the moment the coupon ad
sent to potential customers : sunny, cloudy,
light/heavy rain, light/heavy snow
Table 1: Environment variables and the descriptions
Store Variables for both Current Store and
Competitor’s Store
The Store Variables are controlled by an individual store,
except for some partially controlled variables such as length
of line. Thus, this partial adversarial control indicates that
the Store Variables are crucial to adversarial location based
mobile advertising. The Store Variables are list in Table 2 by
the order of importance.
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Store Variable
Distance
Length of Line
Type of Coupon
Location
Speed of Service
Brand
Quality
Product Diversification
Surrounding Neighbourhood
Ambience
Accessibility
Payment Method
Descriptions
Distance between the competitor’s and the current coffee shop
The length of the line-up at the coffee shop. The line-up information is presented by position number
to the cashier
The discount coupon from coffee shop: Buy one get one free, Time-extended coupon, Incentive on
future purchase, Uncertainty of the incentive of coupon, Amount of coupon discount.
The store location
Average serving time, in minutes, per each customer
Brand name of the store and the product, including expectation from customers, and perceived status
Quality of the product
Higher degree of product diversification capturing variety of customer under different conditions (i.e.
temperature)
The location of the store and the surrounding area: {Business district/Industrial area/Residential area},
{High/Medium/Low traffic flow}, {Urban/Sub-urban/Rural area}
The internal atmosphere of the store, including dcor, lighting, music
The ease of accessing the store
Cash/Debit/StudentCard
Table 2: Store variables and the descriptions
Personal Variables
Many of the Personal Variables are dependent variables
which are closely related to the Environment Variables, such
as Dynamic Psychological Dependent Variables and Purchasing Contest Variables. In adversarial location-based mobile advertising, the availability of smart phone and data
plan are the most important factor as the competitor’s store
coupons are deliver via this channel. The Personal Variables
are list in Table 3 by the order of importance.
Figure 2: Individuals personal preferences profile categorized by Environmental Variables, Store Variables, and Personal Variables
Personal Profile Matching Model
The personal profile, called profile in short, is defined as personal preference of an individual. The profile is the combination of various profile variables, where each individual
has their own set of profile variables and these variables may
change over time. For example, in Figure 2, Person A care
more about the length of the line and the projected trajectory, whereas Person B care more about the type of coupon.
We also found that customer’s profile requires more precise
match in order to leave the line to the competitor’s store as
they move toward the cashier. For instance, when person
A just enters the line, we just require the Minimal Profile
Match to sway him/her to leave the line. The Minimal Profile
Match indicates each customer requires some basic satisfactoriness for leaving the line to the competitor’s store despite
the line-up position. On the other hand, If person B is on the
position zero, which is the next person to be served, then we
require almost 100% profile matching effort to get him/her
to leave the line.
Discount Discrimination: Value-Customers and
Selective-Customers
We categorized a set of variables that affects the customer’s
decision to accept the competitor’s coupon. Of the variables
discovered, we consistently observed some customers who
were price-sensitive. In fact, as long as a satisfactory discount amount was offered, the customer was willing to leave
the current line-up. Alternatively, we also encountered customers who made their decisions based on other conditions.
Thus, we separated our customers into two groups: ValueCustomers and Selective-Customers. The Value-Customers
are value sensitive and are willing to leave their current
line-up immediately because of a coupon. The SelectiveCustomers have alternative conditions that are important to
their decision to leave their current line-up. The most common reason Selective-Customers change their purchasing
decision is time sensitivity; the others reasons are, for example, brand and quality.
We observed that customers during weekdays were extremely busy and stressed. Thus variables such as length
of line, speed of service, and location of the store being in their projected trajectory were important in influencing their purchasing decisions. When customers were
not pressed by their regular weekday deadline, other variables took priority in their decision making process to pur-
Discussion
The previous section identifies variables that are important
in affecting customer behaviour in the design of adversarial location-based advertising. The next section proposes a
model which limits the number of variables to just the competitor store’s distance in the Store Variable category.
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Psychological Dependent Variables - Consistent
Valuation
Habit
Psychological Dependent Variables - Dynamic
Time sensitivity
Alertness
Social Interest
Intergroup Bonding
Appetite
Non-Psychological Dependent Variables
Smart Phone + Data Plan Availability
Demographic
Occupation
Purchasing Context Variables
Projected Trajectory
Prev. / Post Activity
Description
Subjective appreciation of worth
Habit of particular store location, brand name, and daily activity (Scheduling)
Description
Affected by tension and stress
Mentally responsive and perceptive
Currently being with friend?
The strength of the pairwise relationships within a social group
As described by the variable
Description
Customer possesses smart phone and data plan? This is the channel for ad delivery
to customers
Currently account for age and gender, open for other factors to accommodate future work
Customer type: {faculty, undergraduate / graduated student, professor},
{employer, student, retired}
Description
Planned route to the next destination after coffee purchase
What did the customers do before purchase and what will the customers do after
purchase?
Table 3: Personal variables and the descriptions
chase the competitor’s product. To account for the difference
between these Value-Customers and Selective-Customers,
we offered the Immediate-Use coupons in order to acquire
the Value-Customers and Time-Extended coupons to acquire the Selective-Customers. In this manner, we separated
our coupons into two redemption time periods: ImmediateUse coupons and Time-Extended coupons. The ImmediateUse coupons need to be redeemed at the competitor’s store
immediately after being received, and the Time-Extended
coupons are valid for an extended time and, thus, can be
redeemed at the competitor’s store at a later date. The key
is that Value-Customers will immediately change their purchasing decision because of the Immediate-Use coupons,
and Selective-Customers who, typically, are textbftime sensitive, will take the Time-Extended coupons for future purchases.
Figure 3: The Donut Model with Value-Customers and
Selective-Customer
change their projected trajectory. The Time-Extended
coupon allows the Selective-Customers to use the competitor’s coupons for a purchase at a later date.
Next, the Donut Model for the current store and the competitor’s store is combined to form an adversarial locationbased Donut Model (Figure 4). Both stores have the opportunity to compete for the Value-Customers in their outer circle
by offering their own Immediate-Use coupons. However, the
Selective-Customers will still go to their preferred store, regardless of the amount of the discount coupon offered. Thus,
at the inner circle, the Time-Extended coupon is offered to
Selective-Customers to provide incentive for them to return
later when they are more flexible.
However, two potential concerns implementers can consider are malicious use of the coupons and multiple adversarial competitors. Several solutions can be proposed to prevent malicious use of the coupons by measuring the customer’s trajectory to determine how long they have been in
the line-up (i.e., stationary time) and identifying the customer’s purchasing pattern (i.e., loyalty). Secondly, the value
of competitor coupons can be predicted once adversarial
location-based advertising is more widely used and sufficient data is collected.
The Donut Model
In adversarial location-based advertising, the smart phone is
able to determine the precise location of the customer. Thus,
we limited the two types of customers mentioned in the previous section to the location of the store. We propose the
Donut Model (Figure 3), which offers discount discrimination based on the location of the store. The centre of the
Donut Model is the location of the current store; then, two
circles, a smaller circle followed by a larger circle, represent equal distances from the centre of the current store. Initially, the Value-Customers are offered the Immediate-Use
coupons at the outer circle of the Donut Model, which is
further away from the store.
The reason for the Donut Model is that Value-Customers
are price sensitive and will change their projected trajectory to the competitor’s store after receiving the ImmediateUse coupon. On the other hand, the Selective-Customers
are offered the Time-Extended coupons at the inner circle
that is closer to the store because, typically, the SelectiveCustomers have time constraints and are not flexible to
46
ing. paper presented at the First International Conference
on Mobile Business, Athens, Greece.
Leppaniemi, M., and Karjaluoto, H. 2005. Factors influencing consumers’ willingness to accept mobile advertising: A
conceptual model. International Journal of Mobile Communications 3(3):197–213.
Merisavo, M., K. S. K. H. V. V. S. S. R. M., and Leppniemi,
M. 2007. An empirical study of the drivers of consumer
acceptance of mobile advertising. Journal of Interactive Advertising 7(2).
Randell, C., and Muller, H. 2000. The shopping jacket:
Wearable computing for the consumer. Personal and Ubiquitous Computing. 4(4):241–244.
Ranganathan, A., and Campbell, R. 2002. Advertising in
a pervasive computing environment. International Conference on Mobile Computing and Networking. 2nd International Workshop on Mobile Commerce 10–14.
Unni, R., and Harmon., R. 2007. Perceived effectiveness of
push vs. pull mobile location-based advertising. Journal of
Interactive Advertising 7(2).
Xu D. J., L. S. S., and Li, Q. 2008. Combining empirical experimentation and modeling techniques: A design research
approach for personalized mobile advertising applications.
Decision Support Systems 44 710724.
Figure 4: Two competitor’s stores applied the Donut Model
with Value-Customers and Selective-Customers
Future Work
For future work, the adversarial location-based mobile advertising can be expanded to include more discounts with
each coupon consisting of a more precise increment in percentage to improve the discount discrimination strategy.
Specifically, with fine-tuned categorizations between the
Value-Customers and the Selective-Customers, the Donut
Model can be developed into the Bulls-eyes Model to improve the discount discrimination strategy. An alternative
research direction is to characterize the adversarial variables
into multi-dimensional space, such as the impact on different
types of coupons in response to brand, distance, and variable
length of line span across different days of week. We would
be interested in studying group purchasing behaviour, such
as the strength of intergroup bonding and the social influence
of individuals within the group, and their responses to the
competitor store’s coupon. Lastly, the Action-graph games
(AGGs) from game theory can provide fine-grained resolution to the adversarial coupon problem for multiple adjacent
stores.
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