1 Copyright © 2010 by ASME

Proceedings of the ASME 2010 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference

IDETC/CIE 2010 August 15-18, 2010, Montreal, Quebec, Canada

DETC2010-28197

ASSESSMENT OF ADVERTISING EFFECTIVENESS THROUGH AUDIENCE’S EYE MOVEMENTS

Shize Jin, Yong Zeng, Chun Wang Institution for Information System Engineering Faculty of Engineering and Computer Science

Concordia University {shi_jin, zeng, cwang}@ciise.concordia.ca

ABSTRACT The evaluation of advertisement effectiveness during the

advertisement design phase and pre-launch phase is critical for

the advertisement’s success in the targeted market. This

evaluation should predict advertisement’s final performance as

accurately as possible. In today’s advertisement business,

questionnaire-based evaluation methods, such as attitude and

opinion rating are widely used. To obtain good survey results,

high quality questionnaires and proper interviewing

procedures have to be developed with the support of the

competent execution and supervision. These activities are

usually costly even though some of them can be conducted

online. This paper proposes a novel method for assessing the

advertisement effectiveness through the automated capturing

and analyzing of audiences’ eye movements. This method is

based on the assumption that some attributes of audiences’ eye

movements are correlated to their visual attention defined in

the context of advertisement effectiveness. To validate our

research hypotheses, experiments were conducted. In the

experiments, subjects were required to watch several

advertisements in sequence and the subjects’ eye movement

data were collected simultaneously. By analyzing the data

patterns and comparing them with the effectiveness evaluation

obtained from questionnaire-based method, we found that the

proposed method produces similar evaluations to those

resulted from the traditional attitude and opinion rating

method.

1. INTRODUCTION Advertising effectiveness is concerned with making a

tangible contribution to a company or the brand by impacting

customers’ buying decisions through advertisements [1]. Based

on the objectives that an advertisement or advertising

campaign strives to achieve in markets, advertising

effectiveness can often be assessed by the effect on customer’s

short term and long term reactions [1]. While long term effects

can be gauged by the impact on customers’ structure of

decisions, attitude, preferences, beliefs and intentions as well

as the sales, short term effect is mainly represented by

customers’ attention to the advertising.

The effectiveness of advertising has significant financial

implications to the advertisers. Based on the advertising

objectives, the advertisers usually apply a variety of measures

to evaluate advertisement effectiveness before the final launch.

Commonly used measures include aided or unaided recall of

the brand name or advertisement and persuasion (beliefs,

attitude change, purchase intentions) [1]. However, obtaining

numerical values of these measures is not a trivial business.

Among many methods, such as opinion and attitude ratings,

recognition tests, objective methods, and laboratory testing and

analyses of content, opinion and attitude rating was the first

method widely applied in evaluating the effectiveness of

general consumer advertisements [2]. In the opinion and

attitude rating test, people are first provided with a scale and a

set of nouns or adjectives describing the advertisement; they

are asked to apply a scale or indicate their attitudes in relation

to the advertisement base on their feelings. The attitude rating

thus helps assess the advertisement effectiveness by examining

whether people are interested in, pay attention to, understand

and remember the information delivered by the advertisement.

Opinion and attitude rating test is usually conducted

through questionnaires after an advertisement is presented to

audiences. Many issues have to be addressed to guarantee a

good survey result. For example, only with a proper

development of questionnaires and interviewing procedure,

supported by the competent execution and survey supervision,

quality data can be collected and a good survey can be

achieved [2]. For a large number of customers (usually needed

to guarantee a quality survey result), opinion and attitude

rating test is a labor intensive, time consuming and costly

process. Furthermore, the results of this method sometimes are

inaccurate due to its subjectivity. People do not always say

2 Copyright © 2010 by ASME

what they really think and do; people may also forget and

change minds or make things up when they fill the

questionnaire.

While methods with the subjective nature, such as opinion

and attitude rating test and other questionnaire based methods,

are vulnerable to small influences coming from subjects’ inter

awareness and outer circumstances in different situations,

objective methods can be an effective alternative, which

provides experimental data for advertisement effectiveness

analysis. Among other effectiveness factors, customers’ visual

attention can be objectively assessed through their eye

movements. Customer’s attention is one of the significant

forms to represent the customer's short-term reaction [3],

which has considerable impacts on customers’ long term

reactions. Research in advertising effectiveness has concluded

that advertisements attracting the audience’s attention could

develop the potential preferences for the products or the

service in the future [4, 5, 6]. For example, Rossiter and Percy

pointed out that customers’ attention is capable of increasing

the customer’s product attitude and preference, which could

lead to the ultimate sales [4]. They believe that an

advertisement could guarantee a high memorability if it can

hold the customer’s attention for at least two seconds. The

primary concept of evaluating advertisement effectiveness

through customer’s attention is established by Miniard [5, 6].

They pointed out that it is important to know how and when

the final consumer would pay attention to the commercial

stimuli and to identify the critical factors that affect the

patterns and strategies related to customer’s attention. In

Advertising Response Modeling (ARM), which provides a

framework to assess advertising performance by means of

integrating several measures, it is clearly stated that gaining

the customer’s attention is the most important characteristic

that enables advertising to break through [1].

Although it is clear in the literature that customers’

attention has considerable impact on advertising effectiveness,

to our knowledge, it still remains to be an open question to

model the impact quantitatively. In this paper, we attempt to

quantify the relationship between customers’ visual attention

and advertising effectiveness through a set of controlled

experiments on audience’s eye movements. By analyzing the

experiment results, we conclude that the intensity of

customers’ visual attention correctly reflects the level of the

advertising effectiveness. This conclusion implies that eye-

tracking tools can be used for developing automated systems

to asses advertising effectiveness. This type of systems have

the potential of significantly reducing the labor and time costs

needed to evaluate advertising effectiveness. The rest of the

paper is organized as follows. Section 2 briefly describes the

eye-tracking method used to assess customers’ attention to the

advertisements. Section 3 introduces 2 experiments conducted

in this research. Finally, conclusions and future research

directions are presented in Section 4.

2. EYE-TRACKING METHOD AND EYE-DATA

Although there is not a complete one-to-one

correspondence between eye movement and attention, human

intention and interests can be revealed automatically by

tracking their eye movements [3].

Eye tracking can be described as a process to measure

where people look at or how eyes move relative to the head.

Different eye-trackers are invented to capture the eye

movements. Buswell built the first non-intrusive eye trackers

by recording on the film the beams of light that were reflected

on the eye [7]. Using the trackers he did systematic studies

into reading and picture viewing [8, 9]. In the 1950’s, Yarbus

conducted systematic research on the relationship between

human eye movements and thought processes [10]. It was

shown that there is a strong relationship between an observer’s

fixations and interest, which is reflected by the fact that the

observer's attention was usually focused on certain elements of

a picture. Since its invention, the eye tracking technologies

have been greatly improved. Many eye-tracking studies and

eye trackers are now used in cognitive, psychology, and

human-machine interface design [11]. Many publications have

revealed the close relationship between human eye movements

and psychological processes through tracking some critical

factors among the eye parameters. Experiments have

suggested that the attention played an important role in

voluntary eye movements [12].

Throughout the history of eye tracking research, several key

variables have emerged as significant indicators of ocular

behaviors, including fixation, saccade, pupil diameter, and

blinking frequency. By exploring the relationship between

attention and eye movement, researchers have found that

attention may affect saccade programming in different ways

[13]. Braun and Breitmeyer suggested that saccadic latencies

depend on the disengagement of attention from any location in

the visual field [14, 15]. O'Craven and his team found that eye

blink frequency becomes low during high attention conditions

[16]. Similar results were also obtained by Collins and Seeto

[17]. Furthermore, for eye blinking, there are 3 different types

of it. Two of which are regarded as blinking without external

stimuli (voluntary blinking and involuntary blinking). The

third type is reflex blinking, which is a rapid closure

movement of the eyelids. It is defined as a short duration

which responses to a variety of external stimuli, usually

auditory, cognitive, trigeminal or visual, including a

component of other motor behaviors. [18]

3. EXPERIMENT The objective of this study is to evaluate the advertisement

effectiveness through the assessment of audience’s attention

when they watch the advertisement. We focus on analyzing

one attribute of audience’s eye movements, blinking

frequency, because of two reasons. One is blinking frequency

is one of the most important indicators for visual attention, the

3 Copyright © 2010 by ASME

other is viewing advertising is a kind of external stimuli to eye

which causes eye blinking.

The hypothesis underlying our study is that the audience’s

attention can be assessed by studying audience’s eye

movements. The following two specific hypotheses were

tested in our study:

[H1]. Audience’s eye movement attributes are correlated

to their attention while watching TV advertisements.

[H2]. The audience’s eye data captured during TV

advertisement watching could quantitatively reflect the level of

audience’s attention, which is in line with the result obtained

by the traditional questionnaire-based method.

To verify the above hypotheses, two experiments were

devised and conducted. Note that, the acceptance of H1is the

precondition of conducting the H2 verification experiments.

In our experiments, we have used faceLAB4.5 as our eye-

tracking system. The faceLAB4.5, developed by “Seeing

Machines”, is a high accuracy vision-based eye-tracking

system. The system continuously monitors the head pose, gaze

direction and eyelid closure information in real-time manner.

No equipment needs to be worn during the testing. Figure 1a)

shows the hardware system of faceLab 4.5; Figure 1 b) shows

the systems’ real time eye-tracking user interface.

a) Hardware of faceLAB Eye-Tracking System

b) GUI of faceLAB Eye-Tracking System

Figure 1 faceLAB Eye-Tracking System

3.1. Experiment 1 This experiment was conducted to exam hypothesis H1.

We want to know that whether the audience’s eye movement

attributes are correlated to their visual attention while

watching TV advertisements. Here the main objective is to

obtain a qualitative conclusion.

3.1.1. Method Eight 30-second television commercials were selected and

divided into two groups as stimuli with each group containing

four TV commercials. In the first group, the four TV

commercials were chosen from a number of high-ranking TV

advertisements on www.youtube.com. In contrast, in the other

group, four TV advertisements with low-ranking were

selected. This experiment is designed as within-subjects,

where each subject was asked to watch two groups of TV

commercials and the subjects’ eye data were recorded

simultaneously by an eye-tracker (faceLAB system). The

collected eye data is analyzed to find out whether the eye

movements would change with the audience’s attention when

they watched the TV advertisements.

3.1.2. Subjects Five graduate students from Concordia University

voluntarily participated in the research. They were regular TV

audiences. English is their native or working language. Each

experiment for one subject lasted for approximately fifteen

minutes.

3.1.3. Procedure Subjects were invited respectively to come to the lab

where the experiment took place. After signing the consent

form, the subject was asked to watch the two groups of TV

commercials in sequence and their eye-data was recorded.

Subjects were asked to sit in front of the LCD at ease, and the

eye-tracker was placed lower than the LCD directly facing to

the subject. Experiment setting was shown in Figure 2. After

an explanation of the eye-tracking system, calibration of the

subject’s eye took place. Hence, when a subject was watching

the advertisements, his/her eye movements were observed

simultaneously by the eye-tracker (faceLAB system).

Figure 2 Experiment setting

4 Copyright © 2010 by ASME

3.1.4. Result As shown in Figure 3, the subject’s blinking frequency

(BF) was lower when they watched ads with the higher

ranking (blue bars) than when they watched ads with the lower

ranking (red bars). Hence, two groups of BF data were

captured while audiences watching randomly selected high

ranking and low ranking TV ads. Sample BF data were scatter

plotted in Figure 4. Mean values of BF associated with high

ranking TV ads and low ranking TV ads were presented in

Table 1.

Figure 3 Pattern of audience’s blinking frequency

when they watch low and high ranking TV ads

0.180.160.140.120.100.080.060.040.02

99

95

90

80

70

60

50

40

30

20

10

5

1

C1

Percent

Mean 0.1045

StDev 0.02185

N 14

AD 1.075

P-Value 0.005

Normal - 95% CI

Probability Plot of High Ranking ADs

a) Probability plot of high ranking ADs

0.300.250.200.150.100.050.00

99

95

90

80

70

60

50

40

30

20

10

5

1

C1

Percent

Mean 0.1432

StDev 0.04252

N 21

AD 0.734

P-Value 0.047

Normal - 95% CI

Probability Plot of Low Ranking Ads

b) Probability plot of low ranking Ads

Figure 4 Probability plot of high and low ranking Ads

In addition, we employed hypothesis T-testing to verify

whether the audience’ BF were lower when watching high

ranking TV ads. The T-testing construction is shown in the

following equations.

H0:μ1 − μ2 = 0 (1)

H1:μ1 − μ2 < 0 (2)

Sp2 = n1 − 1n1 + n2 − 2 S12 + n2 − 1

n1 + n2 − 2 S22

(3)

t0 = x�1 − x�2Sp� 1n1 + 1n2

(4)

In equations (1) and (2), μ� and μ� are the mean of the

audience’s BF when they watched high ranking TV ads and the

when they watched low ranking TV ads respectively.

Table 1 T-test: Subject’s blinking frequency while watching TV ads with high and low rankings

T-test

TV ADs with High Ranking TV ADs with Low Ranking

Subject Mean SD Sample1 size

Mean SD Sample2 size

t0

S1 0.32379 0.008 6844 0.36201 0.011 6843 (232.46)

S2 0.34276 0.017 6203 0.57997 0.03 6934 (548.88)

S3 0.20789 0.004 6782 0.24539 0.01 6964 (287.25)

S4 0.09711 0.003 6684 0.15567 0.011 6864 (420.28)

S5 0.08093 0.013 6963 0.22749 0.008 6984 (802.07)

0

0.5

1

S1 S2 S3 S4 S5

Blin

kin

g F

req

ue

ncy

Blinking Frequency

(High Ranking ADs VS Low Ranking ADs)

TV ADs with

high Ranking

TV Ads with

low Ranking

5 Copyright © 2010 by ASME

If t� < −t∝,���� 2 , we can reject H� (1) and accept

H� (2), which means the audience’ BF was lower for watching

high ranking TV ads than for watching low ranking TV ads.

The subject’s BF while watching high and low ranking TV ads

is represented by as sample 1 and sample 2, respectively, as

shown in Table 1. It was derived that subject’s BF were lower

while watching high ranking TV ads than while watching low

ranking TV ads. (α = 0.005, −t∝,���� − 2 = −4.576,t� < −4.576).

Experiment 1 provides convincing evidence in favor of our

Hypothesis H1. It suggests that the subjects blinked more

frequently when they watched unattractive TV ads. The main

finding of this experiment supports H1.

3.2. Experiment 2 Our first experiment supports H1. Experiment 2 goes one

step further and aims to verify the correlation pattern between

audiences’ eye movements and attention paid to the

advertisements.

3.2.1. Method Six 30-second TV commercials were selected as stimuli.

Subjects’ brand attitude and brand preference, as well as the

advertisements playing sequence which would affect the result.

Considering above facts, the stimuli contained several different

brands and different types of fast moving consumer goods

(FMCG). Furthermore, the stimuli were pre-edited in six

sequences. Each subject was asked to watch one sequence. The

intervals between each advertisement was inserted a 10–second

MTV, which enable the subject indentify each separate ad

clearly. The subjects were users of the advertising products.

Subjects were asked to watch TV advertisements in

sequence and the subjects’ eye-movements were recorded

simultaneously by an eye-tracker (faceLab system). After the

subjects finish watching the TV ads, the subjects were asked to

finish an attitude rating survey pre-designed to evaluate the

different advertisement’s attraction, which is the traditional

advertisement evaluation method. Finally, the results from two

approaches were analyzed to detect the correlation pattern.

As a traditional method, attitude rating survey is used to

evaluate the advertisement effectiveness through questionnaire.

In our attitude rating survey experiment, several items were

used to measure the advertisement effectiveness with an

emphasis on the visual attention aspect. Each item was

measured on a seven-point scale. Table 2 shows a sample

questionnaire. Relevant keywords, such as “appealing”, “eye-

catching”, “favorable” and “memorable” were selected to elicit

the level of attention that audiences pay to an advertisement.

Once the data collection from questionnaires was

completed, mean, standard deviation, min and max were

calculated for each item. A total score was generated by adding

the scores for all the items previously defined in the

questionnaire. The item-to-total result shows the attraction level

of each tested TV advertisement.

Furthermore, the data collected from eye-tracking device is

analyzed and compared to those obtained from the

questionnaire. Based on the analysis of the effectiveness results

from two methods, we attempt to verify that, for the same set of

advertisements, the attention patterns obtained through

questionnaire and eye-tracking are similar or identical.

Table 2 Sample questionnaire for attitude rating survey in evaluating the advertisement’s attraction

On each of the scale below, please check the space that you feel best describe the advertisement you have just watched

Unappealing 1 2 3 4 5 6 7 Appealing Not Eye Catching 1 2 3 4 5 6 7 Eye Catching

What is your overall reaction to the above advertisement?

Unfavorable 1 2 3 4 5 6 7 Favorable

How memorable did you find this ad

Unmemorable 1 2 3 4 5 6 7 Memorable *Seven-point scales used; “7” represents the most favorable (for the greatest amount of association) and “1”the least favorable (or the

smallest amount of association)

6 Copyright © 2010 by ASME

3.2.2. Subjects Six graduate students from Concordia University

voluntarily participated in the research. They were regular TV

audiences. English is their native or working language. Each

experiment of one subject lasted for approximately fifteen

minutes.

3.2.3. Procedure Subjects were invited respectively to come to the LAB

where the experiment took place. We conducted the Experiment

2 with the exactly same facility setting and facility calibration

procedure used in Experiment 1. However, different stimuli

materials were used.

Before the experiment, the potential subject was given the

following statement and instruction: “We are interested in

obtaining your opinions concerning particular test

advertisement. You will be shown 6 TV commercials

uninterrupted, and then you will be asked several questions

concerned with your attitude towards these advertisements.

Furthermore, we’ll record your eye movements simultaneously

when you watch the advertisements.”

After reading the statement above, if the potential subjects

agreed to participate in the experiment, they will sign the

“consent form to participate in research” before the experiment.

The subject was asked to sit in front of the LCD at ease for the

eye-tracking facility calibration. Generally, it takes the subject

around 4 minutes. Afterwards, the experiment starts, the stimuli

(6 TV advertisements) are shown to a subject in a monitor with

a comfortable size and distance.

After watching 6 TV commercials, the subject will be shown a

card with the seven semantic differential scales, which was first

applied by Mindak in the advertising research problems [1].

The card requires the subject to give the most negative ad a

score of 1 and the most positive ad a score of 7. This scale will

be used to answer questions such as those listed in Table 2.

3.2.4. Result All the questionnaire items in our analysis used a 7-point

scale to examine the TV ad’s performance in order to find out

which TV ad was the most attractive. Means and standard

deviations reflect the level of affection and attractiveness. The

final result through the attitude rating survey is reported in

Table 3. Furthermore, Figure 5 illustrates the ranking of stimuli.

It shows that, among 6 test TV ads, AD2 gained the highest

score (Mean Weight =5.25), which means that it was the most

attractive one. Accordingly, AD6 gained the lowest score (Mean

Weight = 2.84), which means that it was the most unattractive

one to the audiences.

Figure 5 Ranking list of attitude rating survey

Table 3 Response of Attitude Rating Survey

Attitude Rating SurveyAttitude Rating SurveyAttitude Rating SurveyAttitude Rating Survey

AD1AD1AD1AD1 AD2AD2AD2AD2 AD2AD2AD2AD2 AD4AD4AD4AD4 AD5AD5AD5AD5 AD6AD6AD6AD6

Weight Weight Weight Weight Mean Mean Mean Mean SDSDSDSD Mean Mean Mean Mean SDSDSDSD Mean Mean Mean Mean SDSDSDSD Mean Mean Mean Mean SDSDSDSD Mean Mean Mean Mean SDSDSDSD Mean Mean Mean Mean SDSDSDSD

Appealing Appealing Appealing Appealing 25% 4.33 1.03 5.00 0.63 4.67 0.52 4.17 0.41 3.83 0.41 3.00 0.89 Eye Eye Eye Eye catching catching catching catching

25% 4.17 0.75 5.33 0.82 4.33 0.82 3.67 0.52 3.50 0.55 2.50 0.55

Favorable Favorable Favorable Favorable 25% 4.33 0.82 5.50 0.55 4.00 0.89 4.17 0.41 4.00 0.63 2.67 0.82

Memorable Memorable Memorable Memorable 25% 4.17 0.75 5.17 0.75 3.83 0.75 3.83 0.41 3.67 0.52 3.17 0.98

Mean Mean Mean Mean Weight Weight Weight Weight

4.254.254.254.25 5.255.255.255.25 4.214.214.214.21 3.963.963.963.96 3.753.753.753.75 2.842.842.842.84

*Seven-point scales used; “7” represents the most favorable (for the greatest amount of association) and “1”the least favorable (or the

smallest amount of association)

0

2

4

6

AD1 AD2 AD3 AD4 AD5 AD6

Ranking List of Attitude Rating Survey

Attitude

Rate

Blinking Frequency (times/second)Blinking Frequency (times/second)Blinking Frequency (times/second)Blinking Frequency (times/second)

S1S1S1S1 S2S2S2S2 Mean SD Mean SD

AD1AD1AD1AD1 0.4084 0.006 0.1596 0.007

AD2AD2AD2AD2 0.3906 0.021 0.1524 2.776-16

AD3AD3AD3AD3 0.5902 0.013 0.1956 0.006

AD4AD4AD4AD4 0.4463 0.035 0.1573 0.014

AD5AD5AD5AD5 0.5282 0.019 0.1737 0.008

AD6AD6AD6AD6 0.6451 0.068 0.3034 0.080

The results of blinking frequency (BF) thro

tracking method are shown in Table 4, which includes the

means and standard deviations of each subject’s

watched 6 test TV ads. From Table 4, two main findings were

obtained: one was that all the subjects’ BF was the

they watched AD2 (data in frame) whereas the other was that

except S3, the subjects’ BF was the highest when they watched

AD6 (data in dashed frame). Base on the result of Experiment

1, that for the advertisements audiences, BF would

when they watched attractive ads than they watched

unattractive ads. Thus this indicates that, in Experiment 2, AD2

was the most attractive and AD6 was the most unattractive

among the 6 stimuli. This result complies with

the attitude rating survey. Table 5 shows the detailed

advertisement ranking results obtained from both eye tracking

method and the attitude rating survey. It is important to note

that the two methods give identical overall rankings

of 6 test TV ads.

ADADADAD Ranking result Ranking result Ranking result Ranking result (Attitude rating survey )(Attitude rating survey )(Attitude rating survey )(Attitude rating survey )

Ranking resultRanking resultRanking resultRanking result(Eye Tracking )(Eye Tracking )(Eye Tracking )(Eye Tracking )

AD1AD1AD1AD1 2222 2222 AD2AD2AD2AD2 1111 1111 AD3AD3AD3AD3 3333 3333 AD4AD4AD4AD4 4444 4444

AD5AD5AD5AD5 5555 5555 AD6AD6AD6AD6 6666 6666

Table 5 Advertisement ranking (Attitude rating survey vs BF)

7

Table 4 Blinking Frequency

Blinking Frequency (times/second)Blinking Frequency (times/second)Blinking Frequency (times/second)Blinking Frequency (times/second)

S3S3S3S3 S4S4S4S4 S5S5S5S5 mean SD mean SD mean SD

0.007 0.2559 0.008 0.2494 0.014 0.2685 0.013

16 0.2028 0.023 0.2188 0.008 0.2293 0.012

0.006 0.2180 0.016 0.2820 0.035 0.2384 0.009

0.014 0.4203 0.029 0.2681 0.006 0.2752 0.013

0.008 0.3148 0.018 0.3191 0.025 0.2853 0.011

0.080 0.380 0.032 0.3348 0.007 0.3457 0.055

through the eye-

which includes the

means and standard deviations of each subject’s BF when they

two main findings were

the lowest when

he other was that

when they watched

AD6 (data in dashed frame). Base on the result of Experiment

would be lower

n they watched

in Experiment 2, AD2

most unattractive

complies with the result from

shows the detailed

obtained from both eye tracking

method and the attitude rating survey. It is important to note

nkings for a group

Ranking resultRanking resultRanking resultRanking result (Eye Tracking )(Eye Tracking )(Eye Tracking )(Eye Tracking )

Attitude rating survey

a) Attitude rating survey vs BF (S1)

b) Attitude rating survey vs BF (S2)

Copyright © 2010 by ASME

S6S6S6S6 SD mean SD

0.013 0.2359 0.010

0.012 0.2058 0.026

0.009 0.2143 0.000

0.013 0.2147 0.011

0.011 0.2651 0.028

0.055 0.3438 0.050

survey vs BF (S1)

b) Attitude rating survey vs BF (S2)

c) Attitude rating survey vs BF (S3)

d) Attitude rating survey vs BF (S4)

e) Attitude rating survey vs BF (S5)

f) Attitude rating survey vs BF (S6)

Figure 6 Attitude rating survey vs BF

8

vs BF

The comparison of subjects’ eye movement

data and their attitude survey results shows

effectiveness ranking for subjects S1, S2, S5 are

Attitude survey results show that

regarded AD2 as the most attractive

unattractive among the stimuli. Also note that

plotted in Figure 6a,b,e in red)

highest BFs were generated respectively

watched AD2 and AD6. The eye movement data

subjects suggests that AD2 was

AD6 was the most unattractive.

discrepancies between the ranking results

both methods, the discrepancy is minor and

conclude that the results of eye

reflect the level of the advertisement effectiveness obtained

through traditional attitude rating surveys.

4. CONCLUSIONS AND FUTURE WORKExisting studies indicate that eye activities ha

relationship with attention and that eye

the human visual attention. In the present

objective approach to quantifying advertisement effectiveness

through capturing and analyzing audiences’ eye movement

attributes. We conducted two

hypotheses and to find a quantitative relationship between the

values of eye movement attributes and the levels of

advertisement effectiveness. Our experiments show that, while

watching TV advertisements, audiences’ eye

is correlated to the audience’s attention.

analyzing the patterns of audience’s

effectiveness ranking of a set of TV advertisements in terms of

audiences’ attention and the predicted ranking is identical to

those obtained from traditional attitude rating surveys.

results provide considerable evidence that

TV advertisement is able to be

patterns when they watch TV ad

raises the possibility of developing automated advertisement

effectiveness evaluation systems, which are significantly more

cost effective than traditional questionnaire

The work presented in this paper is still at

stage. Additional experiments are needed

insights of the relationship between eye movements and

advertisement attention. To this end

invited to participate in the future research. They will be of the

different ages and occupations. Furthermore,

systematic within-subjects and between

also plan to study the pattern of audiences’ attention changing

when they watch TV advertisements

ACKNOWLEDGEMENT This project is partially by an NSERC

(Grant number RGPIN 298255)

program.

Copyright © 2010 by ASME

eye movement experimental

and their attitude survey results shows that the

for subjects S1, S2, S5 are identical.

ttitude survey results show that all of these three subjects

most attractive and AD6 as the most

Also note that, the mean of BF (

) shows that the lowest and

were generated respectively while the audiences

eye movement data of these 3

2 was the most attractive and that

most unattractive. Although there were some

ranking results of S3, S4 and S6 in

the discrepancy is minor and it is reasonable to

of eye-tracking method correctly

advertisement effectiveness obtained

aditional attitude rating surveys.

AND FUTURE WORK that eye activities have a close

that eye-data would change with

In the present research, we study an

approach to quantifying advertisement effectiveness

through capturing and analyzing audiences’ eye movement

wo experiments to verify our

hypotheses and to find a quantitative relationship between the

nt attributes and the levels of

Our experiments show that, while

watching TV advertisements, audiences’ eye blinking frequency

the audience’s attention. In addition, by

patterns of audience’s BF, we can predict the

of a set of TV advertisements in terms of

audiences’ attention and the predicted ranking is identical to

those obtained from traditional attitude rating surveys. These

evidence that the effectiveness of

be measured by audience’s BF

TV advertisements. This, in turn,

raises the possibility of developing automated advertisement

effectiveness evaluation systems, which are significantly more

cost effective than traditional questionnaire-based methods.

sented in this paper is still at its preliminary

are needed to obtain more

insights of the relationship between eye movements and

attention. To this end, more subjects will be

invited to participate in the future research. They will be of the

Furthermore, we will conduct

subjects and between-subjects studies. We

pattern of audiences’ attention changing

when they watch TV advertisements.

This project is partially by an NSERC Discovery Grant

and Canada Research Chair

9 Copyright © 2010 by ASME

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