This paper is devoted to problem of the
most optimal allocation of website`s advertising
budget among teaser networks. We consider the
possibility of improving the efficiency of distribution
by means of an automated system. The main steps of
genetic algorithm and integral estimation adaptation
to the multiple-criteria problem solution are shown

Mishchenko, Pavlo Mykolaiovych

Shapovalov, Serhii Pavlovych

Мищенко, Павел Николаевич

Міщенко, Павло Миколайович

Шаповалов, Сергей Павлович

Шаповалов, Сергій Павлович

SocioEconomic Challenges Journal (Sumy State University)

  
 
MINISTRY OF EDUCATION AND SCIENCE OF UKRAINE 
SUMY STATE UNIVERSITY 
UKRAINIAN FEDERATION OF INFORMATICS 
 
 
 
 
 
 
 
 
PROCEEDINGS  
OF THE V INTERNATIONAL SCIENTIFIC 
CONFERENCE 
ADVANCED INFORMATION 
SYSTEMS AND TECHNOLOGIES 
 
AIST-2017 
(Sumy, May 17–19, 2017) 
 
 
 
 
 
 
 
SUMY 
SUMY STATE UNIVERSITY 
2017 
The Vth International Conference «Advanced Information Systems and Technologies, AIST 2017» 
17-19 May 2017, Sumy, Ukraine 
21 
 
Optimization of Websites Advertising Budget 
Distribution 
Pavel Mishchenko, Serhey Shapovalov 
 
Sumy State University, Ukraine, shap@id.sumdu.edu.ua 
 
Abstract – This paper is devoted to problem of the 
most optimal allocation of website`s advertising 
budget among teaser networks. We consider the 
possibility of improving the efficiency of distribution 
by means of an automated system. The main steps of 
genetic algorithm and integral estimation adaptation 
to the multiple-criteria problem solution are shown.  
Keywords – Teaser Network, Genetic Algorithms, 
Integral Estimation, Optimal Distribution. 
I. INTRODUCTION  
A significant share of commercial website budget 
(e.g. online stores etc.) refers to advertising and customer 
acquisition (Table I).  
TABLE I.  BUDGET DISTRIBUTION BY ADVERTISING DIRECTIONS 
Direction 
Annual results, million UAH 
2015 2016 
Search (paid issue in search 
engines) 
880 990 
Banner advertising 680 710 
Mobile advertising 150 200 
Digital video 325 455 
Total market 2 035 2 355 
Another digital 200 230 
Total 2 235 2 585 
 
Vast expansion of services provision to Internet users 
forces to look for new ways of overcoming their 
irritations associated with obsessive advertising or 
various referral links to follow. Teasers – the next 
generation of banner and contextual advertising – is one 
of the possible solutions. A teaser is an advertising 
message, which contains an attention-grabbing picture 
and a short description, like an intriguing title [3]. To 
place teaser advertisement on different platforms, 
websites apply to services of teaser networks (TN) – 
intermediaries, which are responsible for advertisements 
allocation and perform a quality control of targeted 
platforms.  
II. PROBLEM STATEMENT AND DESCRIPTION OF THE 
MATHEMATICAL MODEL 
The Internet advertising market is very widespread 
(Table II). Thus, a choice of TN is a key mission of 
promotion specialists. Commonly, they place advertising 
on several teaser networks. Then they analyze the results 
of their decision for a certain period, and try to find the 
optimal option of the budget distribution [1]. Such 
method is extremely time-consuming, it involves a large 
number of routine and monotonous tasks, that can finally 
lead to analytical errors and influence of personal 
preferences in context of human factor. Altogether, this 
approach negatively affects the number of new website 
visitors and the total cost of advertising. Therefore, to 
increase the number of visitors in terms of limited budget 
we need another tool for analyzing and allocating the 
budget between the TNs. 
TABLE II.  TEASER NETWORKS RATING 
Position Teaser network 
Year of 
foundation 
Traffic, mln 
clicks / day 
1 AdHub 2010 90 
2 Visit Web 2009 60 
3 Trafmag 2012 60 
4 Recreativ 2009 50 
5 TeaserNet 2008 7 
 
The purpose of this study is to optimize the 
distribution of the limited advertising budget of 
commercial website Bsum by means of automated system. 
The effectiveness of each TN will be defined as: 
,10 
v
c
k
k
E  
where kc – amount of unique visitors, provided by a 
teaser network, kv – amount of paid displays of website 
advertisement. Accordingly, the number of users attracted 
per day from the particular TN can be calculated by the 
following formula: 
,* i
i
di
di E
p
b
k 
 
where d – is the sequence number of the day of 
research, i – is the sequence number of ТN, bdi – is a 
share of daily budget, allocated to certain TN, pi – is a 
price of 1 displaying of advertisement inside the TN. 
Hence, a model of the optimal budget distribution is 
formulated: 
,
max
1 1
max
d
k
K
d
d
n
i
di
avg

 
 
where d – is the sequence number of the day of 
research, i – is the sequence number of ТN, dmax – is a 
research duration. 
The Vth International Conference «Advanced Information Systems and Technologies, AIST 2017» 
17-19 May 2017, Sumy, Ukraine 
22 
 
III. SOLUTION OF THE PROBLEM 
In terms of optimizing the budget distribution, it is 
necessary to take into account the cost of advertisement 
allocation in various TN and its efficiency. This problem 
can be considered as a multiple-criteria search problem. 
To solve it, genetic algorithms can be adapted – a modern 
and fast tool for finding solutions to multiple-criteria 
problems.  
The first step in applying the genetic algorithm is the 
choice of chromosomes. It is necessary to distribute the 
budget among teaser networks t1, t2, ..., tn, therefore the 
values of chromosome genes will be bdj – starting from 
Bsum/dmax to ti. An additional constraint is bdj > 0. 
Due to the fact that genes will not be formed 
classically (usually they are represented by sequence of 0 
and 1), it is necessary to change mutation and crossover 
operations. In order to save the distribution balance, 
mutation will be performed 2k times (even amount) for 
each chromosome. The crossover process will be 
converted into the average value calculation between two 
corresponding genes of the parent chromosomes 
,
2
,...,
2
,
2
),...,,( 212212211121 




 
 mm
bbbbbb
cmcc
bbbbbb
bbb  
where bc1,bc2,…,bcm – are genes of the daughter 
chromosome; 
 bb11,bb12,…,bb1m and bb21,bb22,…,bb2m – are genes of the 
parent chromosomes.
  
The next significant step is to assess the chromosomes 
fitness. It is based on calculating the fitness function of 
each chromosome in a given population. The higher the 
value of this function - the higher the quality of the 
chromosome [4]. 
One of the task`s criteria is the efficiency of the teaser 
network. The number of times the ad is shown kv is 
determined by the share of the budget allocated to the 
TN, while the number of visitors kc is estimated based on 
the website statistics. The effectiveness of the TN is 
unstable and its dependence on the share of the allocated 
budget is nonlinear, so the statistics of visits is measured 
for the last 7 days (Figure 1). Due to the condition bi> 0, 
we have the following two-dimensional function f(x,t) for 
each of the teaser networks used. 
 
Figure 1.  Statistics of conversions from the teaser network 
However, the fitness assessment based on a two-
dimensional random function in practice is quite complex 
and time-consuming. It is necessary to use the transition 
from a two-dimensional random function to a one-
dimensional function, by definition of the integral 
evaluation. Such evaluation Ix(t) for the distribution 
density f(x,t) of a random variable x was developed in 
[2]: 
,
),(
ln
1
)(
),(
dx
txf
x
tI
x
txf
x
x
x







 
where f(x,t) – the distribution density of a random 
variable x at time t; 
εx – a constant parameter having the same dimension 
as f(x, t). In fact, εx determines an unobserved part of 
generally infinite graph of the distribution density f(x, t); 
|δx| – a modulus of the threshold value for x, which 
was introduced to avoid division by zero. The values εx 
and |δx| are selected by the researcher depending on the 
problem to solve. 
The use of the evaluation makes it possible to 
quantify the changes in the graph of random variable 
distribution density or the graph obtained from the 
histogram observations. Using the integral evaluation, we 
set the one-dimensional function in accordance with each 
teaser network. The result is shown at Figure 2. 
 
Figure 2.  Integral estimation of transitions from a teaser network 
As a consequence, the integral evaluation is used as 
one of the fitness function criteria, since it displays all 
information about the effectiveness of the TN, but has a 
simpler way of processing the results. The final fitness 
function has the following form:  
max)(*
1
7
1
 
 
n
i j
iji tIb  
Chromosomes with the greatest fitness function value 
will be the most adapted and will participate in breeding. 
The reason for the break would be the absence of 
improvement possibility in the solution of the problem. 
So the best solution is the chromosome with the highest 
fitness function value. This chromosome is the desired 
solution of the problem, that is, the vector of 
characteristics of Bsum optimal distribution among the 
teaser networks. 
The Vth International Conference «Advanced Information Systems and Technologies, AIST 2017» 
17-19 May 2017, Sumy, Ukraine 
23 
 
IV. RESULTS 
The study was conducted on the 10 most popular TNs 
and was limited to 1 calendar month (30 days). Every day 
the equal part of the budget was distributed among all 
TNs. With the aim of comparison, the human-expert 
carried out manual distribution on the similar project with 
the same budget. The results of manual and automated 
distribution are available on Figure 3 and Figure 4, 
respectively. They represent the share in the total budget 
Bsum (in %), allocated to each of the TNs on average for 
the research period. 
 
Figure 3.  Manual budget allocation 
 
Figure 4.  Automated budget allocation 
The efficiency of distributions, expressed in the 
number of unique website visitors, is demonstrated at 
Figure 5. Horizontal axis represents the sequence 
numbers of research days (ascendingly). The vertical axis 
records the statistics of website visits by unique users on 
the relevant day. 
 
 
Figure 5.  Statistics of site visits by unique users 
The results of the automated distribution confirmed 
the assumption of its advantage in speed of the 
algorithm's reaction over the expert`s as a whole. 
Nevertheless, on the chart you can observe 3 days, when 
the results of manual distribution significantly (by 25-
45%) exceeded the automated one. This is because of a 
problem of incomplete algorithms knowledge in 
comparison with the expert. 
CONCLUSIONS 
Optimization of budget distribution with the help of a 
genetic algorithm allowing to maximize the number of 
transitions to the advertised website in conditions of 
limited budget. The integral evaluation allowed 
simplifying and formalizing of the effectiveness 
estimation for each teaser network used.  
The use of automated distribution increased the 
number of unique visitors in average by 45%, that means, 
it was 45% more efficient than manual. It is also possible 
to use automated distribution as a supplementary tool to 
human decisions. This approach will combine the 
advantages of an expert and automated methods: 
implement the expert's knowledge of the outside world, 
reduce the negative impact of the human factor and 
increase the speed up the reaction to changes in TN`s 
effectiveness. 
REFERENCES 
[1] Avinash Koshik, Web 2.0 analytics in practice. Subtleties and best 
practices. Dialectics Williams, 2014, p. 218. (in Russian) 
[2] V.V. Avramenko, A.P. Karpenko, “Integral estimation of the 
distribution density of a random variable” in Modern engineering 
technologies, vol. 1. Kiev: ISMO, 1997, p. 263. (in Ukrainian) 
[3] Rose Robert., Content management. A practical guide to creating 
a loyal audience for your business. Mann, 2014, p. 57. (in 
Russian) 
[4] D. Rutkovskaya, Neural networks, genetic algorithms and fuzzy 
systems. Moscow: Hot line-Telecom, 2006, p. 452. (in Russian)


English

Optimization of Websites Advertising Budget Distribution

Electronic Sumy State University Institutional Repository

   MINISTRY OF EDUCATION AND SCIENCE OF UKRAINE SUMY STATE UNIVERSITY UKRAINIAN FEDERATION OF INFORMATICS         PROCEEDINGS  OF THE V INTERNATIONAL SCIENTIFIC CONFERENCE ADVANCED INFORMATION SYSTEMS AND TECHNOLOGIES  AIST-2017 (Sumy, May 17–19, 2017)        SUMY SUMY STATE UNIVERSITY 2017 The Vth International Conference «Advanced Information Systems and Technologies, AIST 2017» 17-19 May 2017, Sumy, Ukraine 21  Optimization of Websites Advertising Budget Distribution Pavel Mishchenko, Serhey Shapovalov  Sumy State University, Ukraine, shap@id.sumdu.edu.ua  Abstract – This paper is devoted to problem of the most optimal allocation of website`s advertising budget among teaser networks. We consider the possibility of improving the efficiency of distribution by means of an automated system. The main steps of genetic algorithm and integral estimation adaptation to the multiple-criteria problem solution are shown.  Keywords – Teaser Network, Genetic Algorithms, Integral Estimation, Optimal Distribution. I. INTRODUCTION  A significant share of commercial website budget (e.g. online stores etc.) refers to advertising and customer acquisition (Table I).  TABLE I.  BUDGET DISTRIBUTION BY ADVERTISING DIRECTIONS Direction Annual results, million UAH 2015 2016 Search (paid issue in search engines) 880 990 Banner advertising 680 710 Mobile advertising 150 200 Digital video 325 455 Total market 2 035 2 355 Another digital 200 230 Total 2 235 2 585  Vast expansion of services provision to Internet users forces to look for new ways of overcoming their irritations associated with obsessive advertising or various referral links to follow. Teasers – the next generation of banner and contextual advertising – is one of the possible solutions. A teaser is an advertising message, which contains an attention-grabbing picture and a short description, like an intriguing title [3]. To place teaser advertisement on different platforms, websites apply to services of teaser networks (TN) – intermediaries, which are responsible for advertisements allocation and perform a quality control of targeted platforms.  II. PROBLEM STATEMENT AND DESCRIPTION OF THE MATHEMATICAL MODEL The Internet advertising market is very widespread (Table II). Thus, a choice of TN is a key mission of promotion specialists. Commonly, they place advertising on several teaser networks. Then they analyze the results of their decision for a certain period, and try to find the optimal option of the budget distribution [1]. Such method is extremely time-consuming, it involves a large number of routine and monotonous tasks, that can finally lead to analytical errors and influence of personal preferences in context of human factor. Altogether, this approach negatively affects the number of new website visitors and the total cost of advertising. Therefore, to increase the number of visitors in terms of limited budget we need another tool for analyzing and allocating the budget between the TNs. TABLE II.  TEASER NETWORKS RATING Position Teaser network Year of foundation Traffic, mln clicks / day 1 AdHub 2010 90 2 Visit Web 2009 60 3 Trafmag 2012 60 4 Recreativ 2009 50 5 TeaserNet 2008 7  The purpose of this study is to optimize the distribution of the limited advertising budget of commercial website Bsum by means of automated system. The effectiveness of each TN will be defined as: ,10 vckkE  where kc – amount of unique visitors, provided by a teaser network, kv – amount of paid displays of website advertisement. Accordingly, the number of users attracted per day from the particular TN can be calculated by the following formula: ,* iididi Epbk  where d – is the sequence number of the day of research, i – is the sequence number of ТN, bdi – is a share of daily budget, allocated to certain TN, pi – is a price of 1 displaying of advertisement inside the TN. Hence, a model of the optimal budget distribution is formulated: ,max1 1maxdkKddnidiavg  where d – is the sequence number of the day of research, i – is the sequence number of ТN, dmax – is a research duration. The Vth International Conference «Advanced Information Systems and Technologies, AIST 2017» 17-19 May 2017, Sumy, Ukraine 22  III. SOLUTION OF THE PROBLEM In terms of optimizing the budget distribution, it is necessary to take into account the cost of advertisement allocation in various TN and its efficiency. This problem can be considered as a multiple-criteria search problem. To solve it, genetic algorithms can be adapted – a modern and fast tool for finding solutions to multiple-criteria problems.  The first step in applying the genetic algorithm is the choice of chromosomes. It is necessary to distribute the budget among teaser networks t1, t2, ..., tn, therefore the values of chromosome genes will be bdj – starting from Bsum/dmax to ti. An additional constraint is bdj > 0. Due to the fact that genes will not be formed classically (usually they are represented by sequence of 0 and 1), it is necessary to change mutation and crossover operations. In order to save the distribution balance, mutation will be performed 2k times (even amount) for each chromosome. The crossover process will be converted into the average value calculation between two corresponding genes of the parent chromosomes ,2,...,2,2),...,,( 212212211121   mmbbbbbbcmccbbbbbbbbb  where bc1,bc2,…,bcm – are genes of the daughter chromosome;  bb11,bb12,…,bb1m and bb21,bb22,…,bb2m – are genes of the parent chromosomes.  The next significant step is to assess the chromosomes fitness. It is based on calculating the fitness function of each chromosome in a given population. The higher the value of this function - the higher the quality of the chromosome [4]. One of the task`s criteria is the efficiency of the teaser network. The number of times the ad is shown kv is determined by the share of the budget allocated to the TN, while the number of visitors kc is estimated based on the website statistics. The effectiveness of the TN is unstable and its dependence on the share of the allocated budget is nonlinear, so the statistics of visits is measured for the last 7 days (Figure 1). Due to the condition bi> 0, we have the following two-dimensional function f(x,t) for each of the teaser networks used.  Figure 1.  Statistics of conversions from the teaser network However, the fitness assessment based on a two-dimensional random function in practice is quite complex and time-consuming. It is necessary to use the transition from a two-dimensional random function to a one-dimensional function, by definition of the integral evaluation. Such evaluation Ix(t) for the distribution density f(x,t) of a random variable x was developed in [2]: ,),(ln1)(),(dxtxfxtIxtxfxxx where f(x,t) – the distribution density of a random variable x at time t; εx – a constant parameter having the same dimension as f(x, t). In fact, εx determines an unobserved part of generally infinite graph of the distribution density f(x, t); |δx| – a modulus of the threshold value for x, which was introduced to avoid division by zero. The values εx and |δx| are selected by the researcher depending on the problem to solve. The use of the evaluation makes it possible to quantify the changes in the graph of random variable distribution density or the graph obtained from the histogram observations. Using the integral evaluation, we set the one-dimensional function in accordance with each teaser network. The result is shown at Figure 2.  Figure 2.  Integral estimation of transitions from a teaser network As a consequence, the integral evaluation is used as one of the fitness function criteria, since it displays all information about the effectiveness of the TN, but has a simpler way of processing the results. The final fitness function has the following form:  max)(*171  ni jiji tIb  Chromosomes with the greatest fitness function value will be the most adapted and will participate in breeding. The reason for the break would be the absence of improvement possibility in the solution of the problem. So the best solution is the chromosome with the highest fitness function value. This chromosome is the desired solution of the problem, that is, the vector of characteristics of Bsum optimal distribution among the teaser networks. The Vth International Conference «Advanced Information Systems and Technologies, AIST 2017» 17-19 May 2017, Sumy, Ukraine 23  IV. RESULTS The study was conducted on the 10 most popular TNs and was limited to 1 calendar month (30 days). Every day the equal part of the budget was distributed among all TNs. With the aim of comparison, the human-expert carried out manual distribution on the similar project with the same budget. The results of manual and automated distribution are available on Figure 3 and Figure 4, respectively. They represent the share in the total budget Bsum (in %), allocated to each of the TNs on average for the research period.  Figure 3.  Manual budget allocation  Figure 4.  Automated budget allocation The efficiency of distributions, expressed in the number of unique website visitors, is demonstrated at Figure 5. Horizontal axis represents the sequence numbers of research days (ascendingly). The vertical axis records the statistics of website visits by unique users on the relevant day.   Figure 5.  Statistics of site visits by unique users The results of the automated distribution confirmed the assumption of its advantage in speed of the algorithm's reaction over the expert`s as a whole. Nevertheless, on the chart you can observe 3 days, when the results of manual distribution significantly (by 25-45%) exceeded the automated one. This is because of a problem of incomplete algorithms knowledge in comparison with the expert. CONCLUSIONS Optimization of budget distribution with the help of a genetic algorithm allowing to maximize the number of transitions to the advertised website in conditions of limited budget. The integral evaluation allowed simplifying and formalizing of the effectiveness estimation for each teaser network used.  The use of automated distribution increased the number of unique visitors in average by 45%, that means, it was 45% more efficient than manual. It is also possible to use automated distribution as a supplementary tool to human decisions. This approach will combine the advantages of an expert and automated methods: implement the expert's knowledge of the outside world, reduce the negative impact of the human factor and increase the speed up the reaction to changes in TN`s effectiveness. REFERENCES [1] Avinash Koshik, Web 2.0 analytics in practice. Subtleties and best practices. Dialectics Williams, 2014, p. 218. (in Russian) [2] V.V. Avramenko, A.P. Karpenko, “Integral estimation of the distribution density of a random variable” in Modern engineering technologies, vol. 1. Kiev: ISMO, 1997, p. 263. (in Ukrainian) [3] Rose Robert., Content management. A practical guide to creating a loyal audience for your business. Mann, 2014, p. 57. (in Russian) [4] D. Rutkovskaya, Neural networks, genetic algorithms and fuzzy systems. Moscow: Hot line-Telecom, 2006, p. 452. (in Russian)

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Optimization of Websites Advertising Budget Distribution

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