=Paper=
{{Paper
|id=Vol-2870/paper121
|storemode=property
|title=Application of Online Marketing Methods and SEO Technologies for Web Resources Analysis within the Region
|pdfUrl=https://ceur-ws.org/Vol-2870/paper121.pdf
|volume=Vol-2870
|authors=Volodymyr Kuchkovskiy,Vasyl Andrunyk,Maksym Krylyshyn,Lyubomyr Chyrun,Anatolii Vysotskyi,Sofia Chyrun,Nataliia Sokulska,Ilona Brodovska
|dblpUrl=https://dblp.org/rec/conf/colins/KuchkovskiyAKCV21
}}
==Application of Online Marketing Methods and SEO Technologies for Web Resources Analysis within the Region==
https://ceur-ws.org/Vol-2870/paper121.pdf
Application of Online Marketing Methods and SEO Technologies
for Web Resources Analysis within the Region
Volodymyr Kuchkovskiya, Vasyl Andrunyka, Maksym Krylyshyna, Lyubomyr Chyrunb,
Anatolii Vysotskyic, Sofia Chyrunc, Nataliia Sokulskad, Ilona Brodovskae
a
Lviv Polytechnic National University, S. Bandera Street, 12, Lviv, 79013, Ukraine
b
Ivan Franko National University of Lviv, University Street, 1, Lviv, 79000, Ukraine
c
Anat Company, Chervona Kalyna Avenue, 104, Lviv, 79049, Ukraine
d
Hetman Petro Sahaidachnyi National Army Academy, Heroes of Maidan Street, 32, Lviv, 79012, Ukraine
e
Limited Liability Company Financial Company Absolute Finance, E. Konovaltsya Street, Kyiv, 01133, Ukraine
Abstract
This article presents the content analysis methods for information Web resources within a
particular region. The model describes content analysis for processing Web resources in online
marketing and simplifies the content automation management technology. The main problems
of semantic and syntactic content analysis and functional services for textual content
management are analysed. The rapid growth of Internet and e-business facilitates the publication
and moderation of articles, which offers an intelligent system of marketing decisions to distribute
content from a specific region to a particular target audience. The article describes developing
information technology for processing Web resources of e-commerce based on online marketing
and SEO methods. A new approach to the e-business processes application and implementation
to such intelligent systems building is formulated. Processing content and information resource
methods based on SEO technology and online marketing are described. Software for content and
information resource processing is developed.
Keywords 1
Content, business process, management system, SEO-technology, Google analytics, targeting,
information resource, content management, typical regional information resource, search engine,
internet marketing, search engine marketing, content analysis, content management system,
conversion path, conversion rate, attribution model, content management interoperability
service, direct marketing, target audience, web content management, web site, commerce content
system, processing information resource, e-commerce content, post-click analysis, formal
content management model, content creation
1. Introduction
Available content management technology is online marketing with Public relations, information
management, Internet integration, customer service and sales in various fields [1-4]. Online marketing
utilizes all essentials and aspects of traditional marketing in conjunction with new research methods (e.g.,
viral marketing) and information analysis using modern IT [5-9]. These relatively constant contact with
users are effective thanks to the automatic tracking of statistics, which uses the Return on Investment
(ROI), Rate of Return (ROR) and visitor efficiency to analyse or convert in e-commerce. The primary
COLINS-2021: 5th International Conference on Computational Linguistics and Intelligent Systems, April 22–23, 2021, Kharkiv, Ukraine
EMAIL: forgta.team@gmail.com (V. Kuchkovskiy); vasyl.a.andrunyk@lpnu.ua (V. Andrunyk); maksym.krylyshyn.mnsa.2020@lpnu.ua (M.
Krylyshyn); Lyubomyr.Chyrun@lnu.edu.ua (L. Chyrun), anat1957@gmail (A. Vysotskyi); chyrunsofia@gmail.com (S. Chyrun);
natalya.sokulska@gmail.com (N. Sokulska); Ilonabrodovska@gmail.com (I. Brodovska)
ORCID: 0000-0002-7941-6329 (V. Kuchkovskiy); 0000-0003-0697-7384 (V. Andrunyk); 0000-0002-2964-9546 (M. Krylyshyn); 0000-0002-
9448-1751 (L. Chyrun), 0000-0001-9190-7051 (A. Vysotskyi); 0000-0002-2829-0164 (S. Chyrun); 0000-0002-3425-5517 (N. Sokulska); 0000-
0001-8838-7788 (I. Brodovska)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
purpose of online marketing is to get the maximum effect from the potential audience of the site with the
ability to demand, sales, and visits statistics, etc. [10-12].
2. Related works
Online marketing involves using strategies and areas of traditional marketing and the particular regions
of research that apply to the Internet space e-business (Fig. 1, Table 1). Online marketing is not only
content trading but also e-business models, software, content spaces, etc. [1-4]. Google, Yahoo, and MSN
have taken it to the next level and segmented the online advertising market by offering e-commerce in-
house advertising services. Due to the automation of the audience research process, the return on
investment is increasing, and costs are decreasing. The online marketing advantages: the possibility of
the most accurate targeting (Table 2-4), interactivity, the opportunity of post-click analysis to maximize
website conversion rates and ROR/ROI of online advertising [1-4].
а) b)
c)
Figure 1: a) Viral marketing; b) basic directions of Internet marketing; c) areas of Internet marketing
Table 1
Areas of influence of Internet marketing
Name The result of the impact of Internet marketing
Musical In the music industry, many consumers have started buying and downloading MP3
industry music over the Internet instead of buying a CD
Banking Online banking is convenient for the customer because it eliminates the need for him
case to visit the bank or its branches each time
Auctions Online auctions have gained popularity. Unique things found in flea markets are now
on sale at online auctions such as eBay and more. Auction development has
dramatically influenced the prices of unique and antique items.
Advertising The impact on the advertising industry has been and still is enormous. Over the years,
market online advertising has grown steadily, reaching tens of billions of dollars a year.
Advertisers have begun to change their priorities actively, and today online
advertising already occupies a significant market niche.
Market It increases the volume and geography of portable electronic devices (mobile phones,
portable players, etc.) using modern marketing methods to promote the product to the
devices Internet market.
E- Increase the volume, shorten the cycle of production/sale of intangible goods, and
commerce provide relevant services.
content
Internet Online shopping sites have long ceased to be bulletin boards, some of which have
sites become large corporations that provide a range of marketing services. Prices for
participation in such venues (privileged membership) are also increasing, despite their
increasing number.
Site conversion is the ratio of the number of visitors to the site. It is who completed the targeted actions
(hidden / direct instructions from advertisers, sellers, content formation, visits a particular page of a site,
passing through an advertising link), to the total number of visitors site [1-9].
Table 2
Basic Elements of Internet Marketing
Name Definition
Product An object sold via the Internet through a Web site; has its price and decent quality,
is in demand, competes with similar things of other Web sites and traditional stores.
Price Controlled quantitative indicator of the cost of goods with the following properties:
much lower than in a regular store due to cost savings; regularly compared to prices
for similar competing products.
Promotion A set of methods of promoting the site on the Internet and goods, which includes a
vast arsenal of tools (search promotion, contextual advertising, banner advertising,
e-mail marketing, affiliate (affiliate, English. Affiliate) marketing, viral marketing,
hidden marketing, interactive advertising, work with blogs and more
Place Point of sale (Web site), where graphic design and usability play a significant role,
the usability of the site, quality of processing applications from the site, speed of
loading, work with payment systems, terms of delivery, work with customers
before, during and after the sale.
Marketing Processes aimed at creating/increasing demand and achieving e-business goals
Trends through Internet technologies to maximize customer/product needs.
Table 3
Areas of Internet Marketing
Name Characteristic
Display An effective way to promote (increase visits) and a powerful tool to improve the
advertising image of website owners. Static/animated image ads (often Web-banner size 468
Media × 60 pixels), linked via a hyperlink from the advertiser or a page with more
advertising information. Together with content advertising is one of the significant formats
or banner of advertising on the Internet. Classic Web Banner - A GIF, SWF or JPG image file
advertising with static/animated image. Web banners are also created using Flash or Java
technologies. Unlike traditional (bitmap) graphics, they use vector graphics to
implement animation and sound effects at a small size, increasing the banner's
effectiveness as an advertising medium.
Post-click An effective way to increase sales and marketing results by focusing on website
marketing visitors who are responsive to Internet marketing activities (pay per click
advertising, HTML email, search to increase conversions)
Target - The advertising mechanism (Table 4) isolates from the entire existing audience
goal only the part that meets the set criteria (for example, the target audience) and
shows the advertisement to it.
Content- The advertising principle focuses on the website content, either manually or
targeted automatically as a banner or text ad. The most robust contextual advertising -
advertising Geo-Targeting, to choose geographic display pages. Show time frame limits are
also applied.
Search A set of measures is aimed at increasing search engine traffic. Previously, the
engine search result meant only links to relevant pages (usually on the left side of the
marketing, page). Now they include advertisements on the right side of the page. Most
SEM search engine technologies do not allow you to achieve your advertising goals:
they do not always directly report the advertised product or service; do not
continuously pursue the purpose of sale; with their help, it is challenging to raise
brand awareness; it is not possible to bring a new product to the market. The
results of using any search engine technology can be two: attracting users to the
site, and for each case, the characteristics of that audience are different;
disseminating information about your site or business to search engines.
Social media Optimization for social media, a set of purely technical measures aimed at
optimization, transforming the site content so that it can be used as simply as possible in
SMO online communities (forums, blogs)
Social Media Promoting or promoting anything in social media (blogs, forums, online
Marketing, communities)
SMM
Direct A type of marketing communication is based on direct personal communication
marketing with the recipient (B2C consumer or B2B client) to build relationships and profit.
using the Direct marketing is based on the attitude towards the client as an individual,
e-mail, provides feedback and does not use information brokers for communication. In
RSS and more Western marketing, direct marketing is referred to as BTL events (below the line,
used instead of ATL - direct advertising), which combines promotions, Direct e-
mail, exhibitions, POS and more. Direct marketing often uses direct mailing to
the target audience via email, e-mail, SMS and more. Direct marketing is focused
not on the target groups but individual individuals. Direct marketing activities
require creating a database of clients - structured, constantly updated fresh
information about individuals or legal entities and their consumer preferences
and needs to further process this information and formulate a product offering
that meets their needs. Databases in direct marketing are closely linked to the
CRM concept of customer or customer relationship management, allowing you
to consolidate all customer information and streamline all stages of customer
relationships from marketing and sales to after-sales service.
Viral Guidelines are based on encouraging individuals to pass on a marketing message
marketing and to other individuals that create the potential for exponential growth in the
viral advertising impact of the news. Like viruses, such techniques use every opportunity to
increase the number of messages transmitted.
Guerrilla A marketing concept is that seeks to find a firm/entrepreneur in their marketing
marketing niche, refusing to compete openly with their powerful competitors,
concentrating efforts on segregated areas of the front, and using non-traditional
but effective ways to advertise and promote their products/services. Partisan
marketing is characterized by flexibility and mobility, sometimes called low-
budget/low-cost.
Aiming A method is for increasing Internet marketing success through the technology of
Precision retaining existing customers, cross-selling and up-selling. Precision analysis and
marketing ROI / ROR e-business and conversion rate allow you to get sales statistics,
demand, and more instantly. Emphasizes the relevance of commercial content
that is achieved directly through the personal preferences of site visitors, the
collection and analysis of users' behavioural and transactional data.
Table 4
Types of targeting
Name Definition
Selection of The most popular targeting type. It is done by selecting the advertising sites so
advertising that their visitors match the target audience.
playgrounds
Thematic Show content ads on websites that are relevant to the content.
targeting
Orientation Display advertising (contextual advertising) by the interests of visitors to the site.
by interests
Geo-targeting Shows ads to a target audience that is restricted to a specific geographic region
(geo-targeting) selected by the advertiser.
Orientation It allows you to limit your ad delivery by the time of day (morning/evening,
by Showtime weekdays/weekends), weeks, and years.
Socio- Display ads that focus on a specific class of target audience: split ads by the
demographic target audience by age, gender, revenue, position, etc.
targeting
Quantity limit It allows you to adjust the number of impressions of the advertising medium to
impressions to one unique user as he interacts with the advertising platform. Most commonly
one to the user used in banner ads with pay per 1000 impressions.
Behavioural The most promising direction to date is to introduce a mechanism for collecting
targeting user actions on the Internet through cookies.
Geo- AlterGeo first mentioned the concept. The bottom line is that knowing about the
dinky object's movement (the exact location up to the institution where the thing is
targeting located can determine modern geo-social services. For example, AlterGeo.ru). It
stops at some points, and you can represent the habits and passions of the
object. For instance, if a thing goes to a beerhouse, the beer is probably
interested.
Psycho- Also, one of the most promising areas is based on information on psychological
angry qualities (psycho-type, society), various advertising is published. Theoretical
targeting background - Socionics.
MSB The newest technology, the essence of which is: For each customer, based on the
(Matrix Shop specifics of communication of their managers with clients, a behavioural matrix is
Behaviors) created, which contains not only stereotypical models of thinking of customers in
this business model, but also weighting coefficients of their importance in
percentage. This detail allows you to evaluate the site's quality (structure,
usability, design, the text content), not at the level of good/bad, but the group of
specific figures.
Staying in touch with users is effective thanks to the automatic tracking of statistics, which uses
ROI/ROR rates and Web resources conversion to analyse. The modification or performance of Web
resource visit is the ratio of the number of information resource visitors who completed the targeted
activity to the total number of Web site visitors [3]. It is hidden/direct instructions from sellers,
advertisers, and content creators, i.e. subscribe, purchase, sign up, visit of a particular Web resource page,
and navigate to an advertising link (Table 5) [1-4].
Figure 2: Main directions of Internet representation
Table 5
The online marketing advantages, developed by [1, 3 - 4]
Name Definition
Interactivity The principle of organizing a system where the goal is achieved by exchanging
(Interaction ) content between the environment and system elements
Search Engine The increasing search engine traffic, generating search results lists and
Marketing advertisements (Table 6)
Targeted An advertising mechanism allows you to allocate an information resource from
advertising the entire audience to a target resource that meets the set criteria for its
advertising
Post-click A post-click marketing method that maximizes the performance and conversion
analysis of your information resource and Internet ROI
Usability user is total fertility convenience when using the object. The user interfaces development of
e is focused on the maximum visual/psychological comfort; coefficient of efficiency of execution of menu
design and system of navigation on the information resource; convenience and ease of use, user-
friendliness, and practicality of the user interface.
Table 6
Search engine-marketing (SEM) technology developed by [1]
Technology Purpose of search engine technology
Search advertising Distribute information to search by placing keyword-targeted ads
Search engine Actions to change the status of an Web resource and elements of the
optimization external environment to gain high positions in search results
Contextual Advertise on thematic Web resources
advertising
SEM technology (Fig. 3, a) has following characteristics [1-4]:
Context analysis (advertising site topics, etc.).
Dealing with queries by specific keywords.
Search Engagement (Website Search, Search Engines).
Increase of the Findability content of the Web site content (Fig. 3b).
a)
b)
Figure 3: Technologies: a) search engine marketing and b) Findability
Usability – user is a general convenience factor when using object; the user interfaces development
focused on the maximum visual/psychological convenience; menu design performance and site
navigation; convenience and ease of use, user-friendliness, and practicality of the software interface.
Search engine marketing does not reach advertising goals due to the following factors [3]: they do not
always directly report the advertised product; do not continuously pursue the purpose of selling the
product/service; with their help, it is challenging to increase brand recognition; it is impossible to market
a new service /product/content.
Using SEM result is [3]:
1. Attracting users to an information resource where the audience differs on a case-by-case basis,
therefore attracting a broad (increasing attendance rate of the Web resource) or an interested
audience;
2. Content dissemination about Web resources in SEO.
The criterion of a successful SEM strategy is the number of visitors to the Web resource and the quality
of the resulting audience. A simple measure for checking the popularity of Web resource is the dynamics
of the number of external links to the Web resource and the increase in references to the name of a
service/product/content or company trademark. The marginal case of SEO and contextual advertising is
the placement of advertisements in the content search results of the Web resource. The development of
the Internet has fostered new search engine marketing technologies for social networks (Video Marketing
Marketing). The separation of search engine marketing into a separate standalone strategy is associated
with [1]:
The continued growth of the Internet market;
The growth of the contextual / search advertising market;
Using SEO technology [1];
The need for optimal surfing/navigation in content;
Supporting the complex content lifecycle process it goes through while managing the various
stages of publishing.
The process of designing and creating an electronic content commerce systems (ECCS) based on the
results of online marketing is iterative and proceeds from analysis, design, plan development to prototype
creation and experimental testing, starting with the specification, layout, template creation, content
formation, and content according to the structure of the information resource (Fig. 4).
Figure 4: Internet marketing for e-commerce content systems
Concentrate on end-user business goals and needs. In the initial stages, users are connected to the
definition/development of functional requirements using survey letters, design alternatives, and
prototypes of varying degrees of readiness. They collect valuable information, giving users a sense of
direct involvement in the design process and gaining their trust.
A well-known method of analysing textual information is that content analysis is a standard research
method in the social sciences (Fig. 42, Table 7-9). The subject is the analysis of the content of text arrays
and communicative correspondence (comments, forums, electronic mail, articles, etc.). The concept of
content analysis is not uniquely defined [4], so systems built on different approaches are incompatible.
Applying the content analysis to e-commerce content systems offers several benefits for simplifying your
business and resolves some problems facing business process participants, namely:
1. Filtering user content on an information resource;
2. The ability to automatically create a portrait of a regular user based on the analysis of his
comments;
3. The ability to automatically create a "portrait" of the target audience based on the analysis of
"portraits" of regular users;
4. Reducing the number of moderators of the information resource in the ECS;
5. Reducing the time for posting user content on an information resource due to its automatic
processing, not by moderators;
6. Elimination of the language barrier due to the automatic formation of dictionaries of regular users
and automatic translation.
Table 7
Ambiguous definitions of content analysis
Author Definition
D. Jerry, JJ. The method of objective qualitative and systematic research of the content of
Jerry communication media
D. Systematic quantitative elaboration, evaluation, and interpretation of the form and
Mannheim, content of the information source
R. Rich
V. Ivanov The qualitative and quantitative method of document research (characterized by
the objectivity of conclusions and rigour of the procedure) and quantitative
processing of the text further interprets the results. The subject of the study - the
problems of social reality, which are expressed and hidden in the documents, and
the internal laws of the object of study
B. Krasnov It consists of searching in the text definite meaningful concepts (units of analysis),
identifying the frequency of their appearance and correlation with the content of
the whole document
E. Tarshish Research technique for obtaining results by analysing the content of the text about
the state and properties of social reality
Table 8
The main components of content analytic research
Name Properties of the main components of content analytic research
Observation Elaboration of the mass of texts, using the typical sociological procedures of
continuous/selective observation, with the observance of representativeness
requirements
Structuring The assumption of structuring, segmentation, dismemberment of texts or the
separation of meaningful invariants (repetition in all / several texts) in the studied
mass population
Formalization They ensure the uniformity of segmentation and isolation of invariants, application
of a high degree of formalization, strict operating rules and formal algorithms in
analytical procedures
Abstracting Formalized separation of whole texts or selection of individual elements for
subsequent collection using the analytical and synthetic procedures
Analysis Use of probability theory and mathematical statistics methods for text processing.
Table 9
Types of content analysis methods
Name Quantitative (meaningful) Quality (structural)
Definition A study of words, topics, and A study that examines not its content but its
messages that focuses on content. form and structure
Example As a first step, the researcher should Determining the period or amount of print
create a dictionary in which each space that is assigned to a topic in a given
observation will identified and source, or how many words or columns are
assigned to the appropriate allocated to each topic in the appropriate
category. category.
Feature Before analysing the selected The specific weights of P each topic and
linguistic units, predict their content category are calculated ( P R T , where R is the
(create a dictionary) and determine number of units of this category, T is the total
each possible observation result by number of units) and a comparative analysis of
the researcher's expectations. the relevant topics is carried out to further
predict events, processes.
Most definitions of content analysis are constructive, that is, procedural (Table 7). Due to different
initial approaches, they generate different algorithms, which are sometimes contradictory [1]. The most
questionable is the neglect of the role of context (Table 8). The practical importance of the method avoids
many contradictions. Rev ' union means, and methods of natural selection and repeated evaluation of the
results obtained provide the opportunity to release or confirm factual knowledge and power/utility toolkit
(tab. 9). Content analysis is a quantitative and qualitative analysis of text arrays for further meaningful
interpretation of the obtained quantitative and qualitative patterns. The method is to create from a variety
of text an abstract model of content. It is used to analyse sources invariant by structure/content in the form
of unsystematic, disorderly organized text [1].
The content analysis of commercial text content is used to determine the tone of the text, duplicate
content, and spam and identify new events to identify thematic plots of text content.
Content analysis is used to study sources invariant in structure/content and existing as non-systematic,
disorderly organized textual material [6]. The method of content analysis is to form from a variety of text
material abstract models of the text's content. There are two methods of content analysis: quantitative and
qualitative. In a study of the mechanisms of generating textual information, it is found in [6] that the
choice of a text description model depends on constructing a probabilistic linguistic test and organizing
the selection from the text of its units. Probabilistic modelling of textual information and its components
is an introductory, preparatory stage for describing the functions of linguistic units in the text. The study
of language and speech functioning through probabilistic modelling of content relies on quantitative
linguistics, probability theory, mathematical statistics, information theory, and combinatorics. In
linguistic studies, especially during the implementation of content search algorithms [6], there are
constant problems associated with predicting the occurrence of a certain number of word-forms/phrases
in specific classes in a segment. Probabilistic modelling of text and syllables, word combinations,
grammatical categories allows determining the sample required to provide the corresponding linguistic
unit [6]. Quantitative evaluations of content information in text, words, and phrases (Fig. 5, a) are obtained
based on the meaning of syntactic information and using the idea of contextual conditioning [6].
a) b)
Figure 5: Mechanisms a) research and b) content formation
During the experiment of guessing letters of unknown text in work [6], it was noticed that the
participants of the investigation make their hypotheses about the most probable continuation of the text,
based on two types of combinatorial constraints: combinatorics of figures (letters and syllables) and
combinatorics of signs (morphemes, words, words ). The experiment shows that even at the fourth or fifth
letter steps, the combinatorics of letters and syllables are suppressed by the constraints related to the
compatibility of morphemes and words. As the text is expanded, word combinations are compounded
with word combinations and sentences, and restrictions associated with the combinatorics of paragraphs,
chapters, book sections, or articles appear. Thus, when guessing letters located at a sufficient distance
from the beginning of the text, the experiment participant relies not on the statistical combinatorics of
letters and syllables but on the text's meaningful (lexical-grammatical) construction. Suppose the content
removed from the original text section acts as a quantitative assessment of the distribution (distribution)
and letter statistics. In that case, the syntactic information obtained from the outer areas of the text serves
as a reflection of meaningful (semantic-pragmatic) information. These considerations make it possible to
propose a content analysis method for quantifying meaningful information in text and its segments. In
identifying new events, stream content consistently to the input ECCS using scanning or content router
and thematically selected results (Fig. 5, b) identify new developments in the content description [2, 8].
For them, they form story chains of similar content [8-10]. Content that reflects new events is the basis
of interdependent content clusters (Table 10) [11-13]. Each set can be the basis for the formation of a
complete story chain.
Table 10
Processes for detecting new events [11-13]
Author Stages of discovering new events
G. Salton 1. The first content under review is the first cluster. Each cluster is represented by a
vector of terms (keywords) included in the content of that cluster. The term vector,
normalized in some way, is a centroid. Sometimes centroid is content that is closest in
some measure to the term vector of a given cluster, which does not change the essence
of this algorithm [11-12].
2. Each subsequent content is compared to the centroids of the existing clusters (some
degree of closeness is introduced).
3. If the content is closed enough for some cluster, it is attributed to that cluster, after
which the corresponding centroid is enumerated.
4. If the content is not close to the existing clusters, then a new set is formed to which
such content is attributed.
5. The time range of the content under consideration is commonly referred to as the
"observation window". Clusters whose entire content is outside the observation window
are taken out of the scope of review.
The algorithm, as a result, every new cluster that arises corresponds to a new event
reflected in the content of that cluster.
R. Papka 1. Requests for general topics are formed (using Text Mining techniques - identifying and
selecting concepts from content) [13].
2. New incoming content is compared to existing requests.
3. If the content does not respond, it is associated with a new event.
4. A new request corresponding to such content is included in the system (optional).
Determining the content tone based on text analysis is more complex than detecting spam. When
seeing spam, consider two hypotheses (spam, not spam) and select the vibrant colour style (positive,
negative, neutral) and their combinations. The Bayesian method uses spam databases to determine spam,
two content corpses, one of which is made up of spam and the other is not [8]. For each content, the
frequency of use of each word and the weight rating (0 to 1) are calculated, that is, the conditional
probability that the content with that word is spam [9]. Weight values close to ½ are not considered in the
integrated calculation, so talks with such weights are ignored and deleted. The hypothesis space contains
the Tonality H1 (negative), Tonality H 0 (neutral) and Tonality H1 (positive) key. In the case of
hypotheses H1 from the set with a positive tone, choose the terms specific to this content. They choose
words t with a probability calculated by Bayes formula and greater than ½. Decisions about the content
tonality are made considering the difference between the values of the weighted estimates of the
hypotheses H1 and H 1 [9]. According to the Paul Graham method, if the content n contains terms with
the weighted forecast w1 ,..., wn , then the conditional probability of spam occurrence [9-12] is based on
the data from the evaluation bodies and is calculated as
Spm wi / wi 1 wi .
If S is an event that is that list is spam, A is an event that a letter has contains the word t. According to
Bayes’ formula [9].
P A | S PS
P S | A .
P A | S PS P A | S P S
To detect such content ci and duplicate c j , the rule of reflectivity is valid, but the condition of
transitivity is not fulfilled ci c j,c j ck ci ck . The content is similar to the text in the mix that
includes it, but the combination is not like it. Alternatively, content identical to the other two is compiled,
but the originals are significantly different. For the duplication relation, symmetry and transitivity are
performed, that is, ci c j c j ci and ci c j , c j ck ci ck . The relation of reflexivity, symmetry,
and transitivity is related to equivalence [1-8], i.e. the relation of duplication. Each content ci according
to the above algorithm of coincidence of terms in signatures is assigned a vector with elements [9]:
1, ci c j , a11 a1n
aij , at U .............. .
0, інакше, B
an1 ann
In the analysis of similarity criteria, the conditions of symmetry i, j : aij a ji and transitivity are
used i, j, k : aij 1, a jk 1 aik 1 , changing the volume of comparison terms to find the
corresponding coefficients [110-111], i.e.
N N N N N N N N N
a ij a ji a ij 0 and a ij a jk a ik a ij max ,
i j
i j k
i j i j
where N is the amount of content. The asymmetry coefficient is associated with the definition of
duplicates approximately and the transitivity level with completeness.
When new events are detected from a content stream sequentially fed to an ECCS input from a scanner
or content router and selected on a thematic request, recent events are described in the content [9-15]. For
them, they form story chains of similar content. Content that reflects new possibilities is the basis of
interdependent content clusters [9-12]. The collection becomes the basis for the formation of the story
n
chain ut1 sim ci , Dictionary , ut sim ci , Dictionary , where n is the volume of content
2
j 2
flow; c1 is current content; cn is the latest content; ci is i the content; Dictionary is the dictionary;
sim ci , c j is a measure of the content's proximity i to j; sim ci , Dictionary is a measure of the closeness
of the content to the dictionary, and are empirically determined parameters [9]. If the content is a
plurality of terms ci wik w : w ci , then ci c j w : w ci | w c j it is the union of terms from
the content ci and c j into a vector Ei eik of dimension N, which is defined as
e11 e1n
1, wik ci ,
eik that is E .............. .
0, wik ci ,
en1 enn
The proximity measure is given
N
sim ci , c j eik e jk N .
k 1
In [9], the proximity apparatus uses a conditional probability apparatus (the entry of some term w into
the content ci , provided that it is included in the content c j ).
To develop ECCS, a Web Content management system, CMS [1-3], if it meets a specific set of
requirements (Fig. 6). CMSs do not support the entire life cycle of the content stream and do not solve
the main problems of information resource development is content creation and maintenance [4]. The
main disadvantage of CMS is the lack of communication between the input information, the content and
the output information (Fig. 7) [14-18].
Usually, such systems are used to store and publish a large amount of content (documents, images,
music, videos, etc.). Such CMSs allow you to manage text and graphics content by providing users with
convenient tools for storing and publishing information. CMS is a tool (Table 11) for modelling the
branched structures of information resources in the SEC and managing their content [1-13] without
special technical skills such as programming or HTML layout. CMS is developed to generate content
using dynamic collection and content caching, so its safety [2]. CMS provides control over access to the
information resource and changes and aims to simplify managing the information resource while
maintaining the flexibility of settings and management. The main components of the information resource
in CMS are presented in Table 12 [14-18].
Figure 6: Basic requirements for content management systems for the construction of e-commerce
content systems
Figure 7: The main components of content management systems for the construction of ECCS
Table 11
Characteristics of major content management systems for the development of e-commerce content
systems, developed by [4]
Name Software Requirements In
Eas
systems Learn Session User use XHT
y to Extensi Scalab
N manage Webserv Databa Langu ing manage manage of ML /
inst bility ility
ment er se age curve ment ment the CSS
all
content mes
1 Ruby on Apache, MySQL, Ruby +/– +/– +/– +/– + + +/– +
Rails FastCGI Postgre
SQL
2 Drupal Apache MySQL, PHP +/– +/– + + + + + +
IIS Postgre
SQL
3 Mambo Apache Apache PHP + +/– +/– + +/– + +/– +
IIS IIS
4 Typo3 Apache Apache PHP - - + + + + +/–
IIS IIS
5 Movable Apache Apache Perl + +/– - +/– +/– + +/– -
Type IIS, Jetty IIS,
Jetty
6 Word Apache, Apache PHP + - - +/– +/– - +/– +
Press mod_re
write
7 Text Apache Apache PHP + - - +/– - - +/– +
Pattern
8 Joomla! Apache MySQL PHP + + + + + + + +
Using CMS does not require software installation. They use a browser for editing and administration.
The intuitive interface and ease of use of the system make it easier to manage the information resource
and reduce its cost. [14-18]. CMS includes the following features: rapid updating and retrieval of content
in an information resource; collecting customer and lead data; forming and editing polls; analysis of
information resource visit. There are large streams and volumes of different content in ECCS. Most of
these streams of content drawn from easily formalized and automated procedures and commercial
content. However, there is no overall approach to formalizing, designing, developing and implementing
the ECS. Such systems do not describe or disclose the relationships and dependencies between input data,
commercial content, output data and processes of processing information resources (Table 12).
Table 12
The main components of the subsystem of processing of information resources, developed by [1-4]
Name Feature Content management system
Menu items Adding, editing, managing menu items of information resources of any
level
Articles Adding, editing, scheduling and publishing articles (information resource
pages)
News Adding, editing, and publishing news
Photo gallery The ability to do photo galleries with sub-galleries, automatic photo zoom
Board Adding ads with photos, descriptions and contact information
Settings Storage of all settings of the information resource and its management
system
Users Management of the rights of registered users
Catalogue of Adding, editing, publishing firms in subgroups of any nesting
companies
Poll Adding / editing polls results as graphs
The value of the content determines its attractiveness to the consumer. Integrating content makes an
attractive resource and the integration of applications - useful [14-18]. Using CMS does not require
software installation. They use a browser for editing and administration. The intuitive interface and ease
of use of the system make it easier to manage the information resource and reduce the further costs of
maintaining it. [14-18]. CMS includes the following features: rapid updating and retrieval of content in
an information resource; collecting customer and lead data; forming and editing polls; analysis of
information resource visit.
3. Materials and methods
3.1. Content management tools in e-commerce
Several content lifecycle models (Tables 13-14) are proposed and described by several authors with
properties supported set by different technologies and processes [19-29].
Table 13
Define the term content [1-18]
Appointment Define the term content
Algebra For example, the common determinant of the coefficients is the value among the
plurality of data (the largest, smallest, average computation time of a function).
Measurement An additive function that determines the value of a field (variable)
theory
Web content The information published on the Internet
Media content The published information by users, designers, or administrators of Web portals,
such as audio/video/graphics.
Quantitative Volume, sphere, space, size
determinant
Applied System of definitions or semantics of language; subject matter/question formed
Linguistics in the form of a book or document; part of the language being examined, for
example, content in a sentence - noun or verb; the information conveyed in a
conversation and easily perceived after reading and analysing.
Formatted Encoded format for displaying melons, such as password hash or private/public
content key for network communication.
Free content Published material that is legally protected by copyright through free licenses.
Open content Published material free use based on creating material for publications
Mathematical An object that contains contextual information (explaining an area or its
Linguistics meaning, explaining the difference between objects)
Content table The content of the document, for example, the contents of a scientific book or
administrative document
Information The object of an information resource of predefined form; text content on the
Technology Website
Table 14
Content life cycle classification [19-29]
Author Stages Features of the model
McKeever collection, Web Content Management has four levels of hierarchy: content,
Susan delivery / activity, output, and audience to illustrate WCM latitude [23]
publication
Bob collection, The build stage is the collection, creation, and editing,
Boiko management, management, workflows, reconciliation, versioning, archiving,
publication syndication, metadata management [19]
Gerry creation, Knowledge, content and map information are suitable for three
McGovern editing, key processes: creation, editing, publishing. Creation is an
publication approximate definition of knowledge for a particular idea or set of
ideas. Editing is the process of modifying professional content
with these ideas in mind. The publication is the submission of
content to the right person at the appropriate time. The creating,
editing and publishing processes are required to generate content
benefits. Thus, the benefits of content are described by the
formula: ContentBenefit Create Edit Publish. [22]
JoAnn creation, The module covers the concept of metadata. She is deeply
Hackos archiving, immersed in terms of metadata and how it should be used.
drafting/ Focuses is on data legalization, database searching, and
combination, information transformation into knowledge. The author gives an
publication idea of the correspondence of information accumulation to a
solid and stable information model. She describes a merger of
usability ideas, information architecture, and content
management in the model to focus on end-users [20].
Ann creation, The creation phase consists of planning, designing, authorization,
Rockley revision, and verification. A unified content strategy is a systematic
management, method of identifying all the major content requirements to
delivery create consistently structured content, reuse, manage recognised
content sources, and edit content based on user requirements
and needs [26].
Russell presentation, The author paid particular attention to the process of managing
Nakano comparison, Web content, including collecting versions and simultaneous
updates, changes [24].
mergers,
publication
The State development, The archiving stage consists of the sub-stages of accumulation,
government quality approval, archiving, recycling. In addition, this model reflects three
of Victoria publication, significant aspects of Web content flow: status, process, and role
(Australia) cancellation (author content, quality management content, quality
publications, management process, management records).
archiving
AIIM capture / The model's focus is on the ability to organize the collection,
absorption, management, storage, accumulation, and supply of the necessary
management, information to the right people at the right time.
accumulation,
delivery, storage
CMP planning, Each stage is divided into sub-stages. For example, the planning
org-tion development, stage consists of sub-stages of alignment, analysis, modelling,
management, design. Stage of development - creation, collection, collecting,
deployment, classification, editing [11-15].
storage,
evaluation
Bob organization, The model is based on the principle of seven +/- two. That is, it
Doyle creation, allows making changes (increase/decrease the number of stages)
accumulation, in the model itself, depending on the purpose of Internet
workflow, marketing.
version control,
publication,
archiving
Woods legalization, In the model, the focus is on the categories of problem-solving
Randy template, rather than the very stages from the initial (content creation) to
creation, final (content publishing). Most content management issues fall
modification, into one of the following categories: legal content migration,
version control, reasoning template, content creation and reuse, controlled
rotation, version and site rollback, content and end rotation, process
monitoring and monitoring, and success management (results) [25].
success
management
Halverson audit, analysis, This model focuses on content strategies, provides a high-level
strategy, overview of the benefits, roles, activities, and outcomes
classification, associated with strategy content. Model Purpose: Web
structuring, consulting, design, and industry development describes processes
create, view, and methodologies applied to all types of content: text, images,
re-viewing, video, and audio [21].
final viewing,
approval,
testing,
formatting,
publishing,
updating,
archiving.
In some content lifecycle models (Table 15), project management concepts, information management,
information architecture, content strategies, Web site management, and semantic printing are provided
30-38]. Different authors suggest different stages of the content lifecycle [49-54]. The main steps (content
creation, development, viewing, distribution and archiving) are present in almost all proposed models
[55-64]. The lifecycle of processes, actions, content status, and content management role differ in models
depending on organizational strategies, needs, requirements, and capabilities [11-29].
Table 15
Content life cycle classification [19-29]
Development of information resources
Author No
Formation Management Realization
McKeever S. 1 +/– - +/–
The State Victoria 2 +/– - +/–
Russell Nakano 3 +/– - +/–
Ann Rockley 4 +/– +/– +/–
Jo Ann Hackos 5 +/– - +/–
McGovern G. 6 +/– - +/–
Bob Boiko 7 +/– +/– +/–
Bob Doyle 8 +/– +/– +/–
CMP organization 9 +/– +/– -
AIIM 10 +/– +/– +/–
Woods Randy 11 +/– + +
Halverson 12 + +/– +/–
Web Content Lifecycle Models 1-7 do not solve the problems of content creation and implementation,
and they do not solve all of the issues of managing a convention, for example:
Submitting a plurality of content to an end-user according to their request, history or data portfolio;
Generation of digests and user or content portraits;
Automatic discovery of thematic subjects;
Construction of tables of the interrelation of concepts;
Calculation of ratings of terms;
Collection of information from different sources and its formatting;
Identification of keywords and concepts of content;
Content categorization, identification of duplicate content, selectively distributing content.
Models 8-10 successfully cope with content management issues and some content generation issues
but ignore content implementation issues. Models 11-12 solve content generation and content
management issues and partially address content implementation issues. Not all of these models support
Web 2.0-3.0 (Table 16) [66-75].
Table 16
Comparison of Web 1.0-2.0 parameters
Name Web 1.0 Web 2.0 New properties
User developer or content User as co-developer, reader as co- The right to
creator and reader author or partner participate;
Actor cancellation of third
party regulatory
side (moderation)
The software was created Create software for the Web; Web as a platform;
for the PC; Software - Software - service, application; removal and erosion
Software
goods; closed source open-source, API, open-source of barriers and
codes, ARI; licensed sale; software; The software may be restrictions (free
binding of software to free; software over the equipment; access, versatility,
equipment; focus on search for applications already simplification).
invention; scheduled invented; eternal beta; alternative
release; use a browser to means of perception.
view content.
Database replenishment: Database replenishment - having Network as a single
payment to the content one becomes immediately collective mind,
provider or hiring accessible to everyone; content
moderators; the data is organized in a volatile atomization,
taxonomic organization of manner; data usage tools - APIs; aggregation,
data; automatic two-way links syndication.
hierarchy of headings; submission form - blogs; dynamic
data storage facilities - site; the address has a trace
catalogue, library, storage; element of content; the source is
one-way links submission the collective mind; an interface for
Content
form - personal pages; handling data across the network;
static site; the site page "Free" GNU FDL license; content is
has an address; the source not required to visit the site - read
is the mind of the content RSS feeds.
creator; menu navigation
site to work with its data;
copyright; to view the
content, visit the site by
clicking on a link or
bookmark.
Software ordering and Collaboration through the Software Cooperation;
manufacturing; publication Support Department; interacting, amateur; single
of the content by the adding properties, values, creating mass relationships
authors and their readers' shared content with each
perception; appeal to a participant; self-service based on
Events
third party - a mediator to the partner architecture of the
use its resources; big, not service, which is merely an
many deals. intermediary between users using
their resources; small many
numerous transactions.
Value in the software - the Value in the database - the Working with the
software owner makes database owner and services to database; service,
Value/
money; The Internet is work with them make money; The not product; saving
cost
valuable as a source of Internet is valuable as a time and attention
information. communication tool.
A well-known content management tool is the Content Management System (CMS), software for
organizing Websites or other Web resources or individual computer networks [1-9]. CMS must meet
certain requirements set (Fig. 8). Usually, such systems are used to store and publish a large amount of
content (documents, images, music, videos, etc.) [10-13]. Such CMSs allow you to manage text and
graphics content by providing users with convenient tools for storing and publishing information. In Fig.
8, b is a CMS classification [14-18]. CMSs do not support the entire content lifecycle and do not solve
the main problems of Web resource development - content generation and implementation. The main
drawback of CMS is that there is no link between the input, content, and output. CMS is often used for
the construction of SEC and ECCS (Table 17-20), such as online newspapers (Fig. 9, a) [14-18]. Web
Content management system, WCMS developed to generate content within portals with the same
problems (dynamic collection, caching content, security, etc.) and other Web applications [76-83]. The
value of the content determines its attractiveness to the consumer [84-99]. Content integration makes
portals attractive and application helpful integration. As users are increasingly drawn to portal
applications, there are more and more applications, including ECM. The WCMS admin panel allows you
to change/add new information for different language versions of the Web site [100-112]. Changes to the
site are displayed immediately after making and saving.
a) b)
Figure 8: a) Requirements and b) CMS classification
Figure 9: Classification of Internet newspapers
Table 17
The main differences between ECS and ECCS [30-38]
Characteristic ECS ECCS
Type of goods Material Intangible (content).
Volume of goods Decreases Constantly.
Availability of a warehouse Present Missing.
Product DB Product description Content and its description
Promotion By keywords in the product By keywords in the content
goods description description and the content
itself
Product search By keywords in the product By keywords in the content
description description and the content
itself
Detection Manually Automatically
duplication (programmatically),
goods application of known methods
of detection and elimination of
duplication of information.
Definition of ageing Manually or on time Automatically
goods (programmatically),
application of known methods
of research of information on
ageing
Definition Manually or by the number of Automatically
relevance recent orders compared to (programmatically),
goods other charges or periods application of known methods
of information research for
relevance
Analysis By orders, visits, and On content orders and
the audience comments; a limited number content, page views and
of product attributes for comments
analysis; comments do not
capture the opinion of the
audience
Automatic Impossible; For example, the automatic
formation a short description is formed generation of descriptions
digest manually (digest, i.e. brief content) of
articles in an online newspaper
Automatic Impossible For example, it may be
formation possible to generate a new
goods article in an online newspaper
from a trusted news source.
Automatic Impossible Such as, for example,
formatting automatic formatting of
goods articles in an online newspaper
according to audience class
(age, gender, profession, etc.).
Experience It does not significantly affect Significantly affects the level of
user the increase in sales of goods subscription of goods.
Table 18
Classification of Internet newspapers by type of articles created [113-119]
Articles Characteristic
Actually Own journalists describe events, interview participants of events.
generated
Moderated Journalists/moderators independently search for material on the Internet, analyse and
organize the material received, write based on this material of the article.
Legalized Material for future articles looks for a module of the system by links or subscriptions,
moderators analyse and organize the material received, write based on this material
of the article.
Combined A combination of the first three types
Filtered Material for future articles searches for the module of the system by links or
subscriptions and filters according to the dictionaries contained in the module, and
moderators analyse and organize the material received, write based on this material
of the article.
Aimed at Filtered, but the formation of the article considers the level of user experience,
user formed pieces primarily for users with more experience.
Table 19
Formal Content Management Models [9, 39-48]
Name Formal Content Management Model
C A, B, T . Internet space is divided into stable and dynamic components with
different characters and joints regarding content flow management integration. The
regular Internet component contains "Long-lasting" content, and the active
ingredient has constantly updated resources. Some of this component then flows
into the stable, the other part and to disappear or fall into the hidden Web-segment
space is not available for additional users of my public information retrieval systems.
The most dynamic segment of the content is news, which is also the highest level of
upgrades, and it generated and distributed large amounts of data. Therefore, it is
Barton- the best object of research. The processes of content ageing, the loss of its
Kebler relevance, are described by an equation of two components m t 1 aeT be2T ,
where m t is part of the helpful information in the total flow over time T , the first
denial corresponds to the stem and flax resources, and the second - dynamic (news).
Content dynamics in the network is conditioned with many factors that cannot be
accurately analysed. Within modelling, a reasonable assumption is the general
nature of the temporal dependence of the number of thematic publications
determined by simple laws. For more adequately support should refer to the
complex of about actually.
C TF , IDF . The model described by the equation C TF * IDF where TF is the
local frequency Terms (Term Frequency, level of significance of terms within the
content), and IDF is the reciprocal frequency of the message throughout the stream
of content that E s tit this term (Inverse Document Frequency, level of uniqueness
Term throughout the content stream). The product of these values - a criterion for
determining the significance of what was the (weight). Content (messages, news) is
ageing, losing its relevance with an intensity determined by some empirical law. For
illustration, let's assume that it's exponential law. One of the suggested approaches
to that pare of generalization, such as message ranking, is the use of dependent
Classic parametric factors. For example, it is determined the weight of the message as the
space-vector
product multiplication TF * IDF * et . Value is some constant, t is the length of
time that has elapsed since the notification in the stream of content. Value is a
factor n and the half-life relevance of content. If the expected use exponential
model is et 1 2 determined with expert route by which the message through
ageing loses its significance in half. Accounting for content ageing (loss of some
relevance) is of great importance in analytical studies, the creation of information
products (information portraits), the central storylines of events, and the ranking of
information retrieval systems. An approximate estimation of the rate of content
ageing is of practical value in determining the set of topical content.
C Y , T , T0 ,V . Management Dynamics thematic content (relevance/ageing) is done
linearly. The number of messages in the time t described by the formula
y t y t0 v t t0 where y t is some notes at a time t , v is the average speed of
increase/decrease of content intensively in time (e.g., due to ageing). Content
component content quantitatively assessed as fluctuation (deviation from but he mi)
Linear stream content. It is change the default critically departed
i
1
y t y t v t t . With change t process of changing the subject
2
ti k 0 i 0
i
k 0
content is a process independent of magnification (do not include links to the
previous content).In case of standard deviation by time such as t t , the bigger
(degree of connection between random events 1 2 ;1 ) the higher is the
correlation between the current and previous content.
C N , T0 , T , . The process of increasing the relevance/ageing of content is
described by dependency N t N t0 e t t0 , where is the average relative change
in the intensity of the content stream. With a shift in power at a specific time
ti N ti N ti 1 / N ti 1 . Change the value fluctuations ti about the mean
i
Exponential
options ti 1
i t . When changing t as a square root of time, the
k 0
k
2
process is independent increases (correlation between individual and m content is
insignificant). A significant amount of dependent content is valid t t within the
range 1 2 ;1 , which indicates the presence of long-term system memory. Such
systems make class-similar processes taking into account correlations between
content at different times.
C N , T , M . Generators of content in most operating in a steady-state
characterized by maximum content space capacity N (the dimensionality of the
parameters and their measurement are not taken into account). Each organization
generator generates a stream of content on average constant for the number of
signs and messages of Laziness. Over time, only the volume of notes on a topic
Logistic changes. Increased number one topic content accompanied in decline in the range
T M
of another issue because T we
n t dt NT were the amount of content per
0 i 1
i
unit of time and the total number of all the possible problems. The part is ni t
always 0.
w
C W , D . Weight content is defined as WD wD where WD is weight content w is
D
weight keyword content (it is a monotonically increasing function of n ) D is the
Analytical
number of keywords in the content ( 1 D 12 ). Average weight University and
locally keyword are w n n / v n n1 / K where n is the total number of words
stream K is the number of unique words.
Table 20
CMS components [9, 39-48]
Model Advantage Drawback
R.E. Burton Describes the ageing process of content, the It isn't peculiar in terms of
and R.W. loss of its relevance. The equation of the interpretation of the results. It
Kebler model has an exact solution in an elementary grows monotonically and does not
and convenient function - exponents. It is describe the processes that must
determining the speed of development of have local extremes by their
individual thematic. nature.
Spatially- Defining a meaningful term across the entire Mandatory ' these patterns ranking
vector content stream. Ability to identify the most content using pairs of parametrical
up-to-date content from the many available multipliers that depend on the time
ones
Linear Determining the intensity of a thematic They are applied with linear
content stream over time (e.g., as a result of dynamics control of thematic
ageing) content.
Exponential Describes the ageing process of content, the There is no correlation between
loss of its relevance. unique content and content.
Logistic It combines the relative simplicity of problem Study the dynamics of only a single
formulation with the ability to vary the thematic flow. The dimensionality
solution using a set of parameters with more of the parameters and their
or less transparent physical content. measurement is not taken into
account.
Analytical Describes the ageing process of content, the Mandatory ' compulsory presence
loss of its relevance. dictionary of keywords
Using a CMS does not require installing special software in the workplace. They use a regular browser
(Google Chrome, Internet Explorer or Opera) for editing and administration. The intuitive interface and
ease of use of the system make it easier to manage the site and reduce the further costs of maintaining the
Web resource. To work with the design, you need only have basic Internet skills. The most popular CMS
is Drupal and Joomla! When considering the content item dynamics at current content in the county
limited models (Table 19-20) that paves the way for further research [1-18]. Content management models
are designed to determine the ageing/relevance of content flow. They do not solve the problems of content
creation, implementation, and they do not solve all content management problems:
Submission of a plurality of content to the end-user according to his / her request, history or
portfolio;
Formation of digests; automatic information portraits;
Automatic detection of thematic subjects;
Automatic construction of tables of the interrelation of concepts;
Calculation of concept ratings;
Automatic collection of information from various sources;
Automatic formatting of information;
Automatic detection of keywords;
Automatic content categorization;
Duplicate detection and selective dissemination of content;
Attract potential users.
Fig. 8 is a graphical diagram of a typical regional information resource developed by CMS Joomla!
Apply method and online marketing for the analysis of Web resources in the region. Fig. 10 shows IDF0
diagram of a typical regional information resource.
Figure 10: Context diagram of a typical regional information resource
3.2. The most effective methods of attracting potential customers
Forms on the information resources of some advertiser-friendly are due to both. They have the right
to send their contact details and additional information about the products/services of the advertiser.
Persons who have provided information through such forms on an advertiser's information resource are
potential customers. Analysing this data and using it effectively helps build a profile for your possible
audience. To do this, you must use the following methods.
1. Optimize your AdWords campaigns. After clicking on the ad, visitors expect to take some action
on the landing page. The visitor must have the right idea of what he can expect before clicking on an
advertisement. For this optimization campaign AdWords, will establish rates for the required keywords
if necessary using negative keywords (search terms reports analysis), uses UT accurate yet descriptive
and engaging ad text and set up conversion tracking. Tracking the landing page bounce rate and
conversion rate and comparing them to different variations of ad text that drives traffic to the page can
help you determine how well your ad was performing (Fig. 11).
Figure 11: IDF0 diagram of a typical regional information resource
2. Landing page optimization. When a visitor lands on a landing page, their expectations must be
met or exceeded. Goal pages will upload fast; transitions between them should be convenient and satisfy
you with Google's landing page. To expand the client base with the use of forms, landing pages have to
be done very simply and understandable. Visitors landing page may feel uncomfortable providing their
information if they think the site is trustworthy. Reducing the amount of information a visitor needs to
enter and solely requesting the information they need to continue communicating with the visitor will
increase the likelihood of completing the form. If a visitor starts filling in your paper but then decides to
leave before submitting (such as the form is too long), the program Web - analysts register it as a rejection.
3. Involvement of interested clients. Not every potential customer who fills in the form will become
a real customer. There may be several reasons: the landing page is not clear; the record does not confirm
the information provided; a unique program that scans sites and distributes spam and more automatically
fills the form. Google cannot control user behaviour on the site. In addition to optimizing your AdWords
campaign and landing page, there are several other ways to prevent bogus customers from registering on
your site.
1. Add word verification.
2. Enable auto-tagging in AdWords. To URL-address of the target page linked the GCLID parameter
for defined spare revision of the page because of clicking on an ad.
3. Registration of applications. Each time a visitor submits a form, you can log their IP address,
GCLID parameter (if available), referrer URL, and user agent. Based on this data, you can determine if
there is traffic from ad clicks and whether there is suspicious activity. When questionable first asset of art
and visitors coming to your site via AdWords can resend to Google GCLID options or the visitor's IP
address. Google's Advertising Traffic Quality team will check your account.
3.3. View your Google Analytics data in AdWords reports
By looking at Google Analytics site engagement statistics along with AdWords performance data, you
can find out what users do after they click on your ads and land on a landing page. These statistics
submitted the following information.
% New Sessions is the approximate percentage of first-time user visits.
Pages / Session is the average number of pages viewed per session.
Average Session Duration is the average amount of time a user stayed on the site.
Bounce Rate. If a site visitor has viewed only one page or triggered only one event, Analytics
counts it as an opt-out. Bounce Rate is the percentage of sessions that are interrupted.
This content shows how effective the content is on the content resource and helps you decide on
optimising your budget, bids, landing pages, and ad text. For example, by comparing the bounce rate and
CTR for the content group (Fig. 12), you can get an idea of how well the site meets the users' expectations
involved in the ads.
Figure 12: IDF0 diagram of Content Search Ads
For example, in Table 21 that the Topic 1 ad group has not only a higher CTR (8%) compared to the
similar Topic 2 ad group (6%) but also a higher bounce rate (60%).
Table 21
An example of analyzing content ad groups on an information resource
Ad group topics CTR Impressions Bounce Rate Ad clicks Users who remain on the site
Theme 1 8% 1000 60% 80 32
Theme 2 6% 1000 30% 60 42
The means that more than half of the users who visited the site after clicking ads in topic group 1 did
not stay there to view the offers or make a purchase in more detail. Even though ad group topic 2 receives
fewer clicks, the return on investment in them is higher because users involved in this advertising are
more likely to linger on a typical regional information resource (Fig. 13).
Figure 13: IDF3 diagram of a typical regional information resource
3.4. Analysis of indicators of refusal to visit the information resource
Bounce Rate is the percentage of visitors who view only one page when they visit your site. There are
reasons number why there may be a high failure rate (Fig. 14) [120-124].
Figure 14: Examples of bounce rates
For example, visitors may leave the site on the sign-in page because of the site design or usability
issues. Alternatively, it may be that only certain pages on the site have a high bounce rate for excellent
reasons. Here are some of the problems that can cause a high bounce rate: To understand the difference
between an Exit Rate and a Bounce Rate for a specific site page, you should consider these three aspects
[120-124].
For all page-views, the exit metric is the percentage of recent views per session.
For all sessions initiated from a page, the bounce rate is the percentage of visits per session.
The bounce rate for the page is calculated based only on the hits that started from that page
Consider this last aspect in a simple example. The site has pages A through C. There is only one session
per day with the page view below [120-124].
The Content Report for Page A will show you three page views and a bounce rate of 50%. The bounce
rate will not be 33% because Thursday's page-view A is not considered in the bounce rate calculation. A
speech is a session in which there is only one interaction with the visitor. The bounce rate for a page is
only relevant when a session starts from this page. Now let's examine the performance and failure rates
for a group of days with one session on the site [120-124].
Below are the calculated percentages of outputs and failures [120-124]. Output metric:
Bounce Rate:
3.5. Goal Conversion Rate
For chosen objective i , where i 1,20 , this figure represents the percentage of visits that resulted in a
conversion (this goal). Transformation occurs when a visitor reaches a destination. There are three types
of plans [120-124]:
The destination URL is the page that visitors see immediately after the action is completed. To
sign up for an account, thank you for registering or thank you for your purchase page or a receipt
page. This goal initiates a conversion when a visitor views the specified page.
Site Time is the time limit you specify for a site moderator. When a visitor spends more or less
time on a site than a specific time limit, a conversion is made.
The number of pages viewed per visit allows you to determine the page-view threshold. When a
visitor views more or fewer pages than the limit, the conversion is completed.
Goals and Funnels is a versatile way to determine the success of an information resource or program
based on your goals. The funnel can be used to specify the expected traffic path to reach a goal. The
combination of goals and funnels allows you to analyse how effectively a site or program is driving users
toward the goal. Each time a user action meets a goal, a conversion is recorded in Google Analytics. If
you set a goal value in monetary terms, your conversion data will also contain the corresponding values.
Goals with Goal Completions (i.e. Goal Completions Levels) can be viewed in Goal reports. You can
also analyse goal completion conversions with other messages, including visitor reports, traffic, Site
Search reports, and event reports [120-124].
There are four types of targets to choose. When a visitor completes a selected action, a conversion is
triggered and recorded in goal reports. You can choose the type of tracking goal you want from the list
when setting up goals in your account [120-124].
Destination URL: The address of a specific placement, such as a web page (or virtual page) or
application screen that has been downloaded. Thus, a webpage or screen of a sign-up application
can be a destination for an e-commerce campaign to identify potential customers. This goal goes
well with the sequences.
Visit Duration: Visits that last for a fixed time or longer. You can use this goal to determine the
number of visitors who stay on the page or purchase screen for more than 2 minutes.
Pages / Visits (For Web Pages) Screens / Visits (For Applications): A visitor views a certain
number of pages or screens during a session. Uses UT this type of goal when you want to capture
visitors who see, for example, less than three pages.
Event: The visitor triggers an action that the moderator defines as an event (such as a social
recommendation or an ad click). You must first set up Event Tracking before setting up a Goal of
this type.
Goals are automatically grouped in sets, but the moderator determines which plans should belong to
each location. Uses UT sets to categorize the different types of goals for the site. You can track downloads,
signups, and receipt pages in separate goal sets. Goals are set at the profile level (up to 20 in a single
profile). Each profile can create four groups with a maximum of five goals. To track more than 20 goals
for a website or program, create a new profile for that property [120-124].
When setting goals conversion value of a dollar, each time the goal is reached, that number will be
logged. Then, all cases of the registered number will be added and displayed as Goal Cost. Any action
taken by a user on a website or program can be carried over in dollars. One way to determine the value
of a goal is to calculate how often customers who have reached a goal become customers. If the sales
team is addressed by 10% of subscribers to newsletters, and the average transaction amount is 500, then
the cost of a subscription to newsletters can be set at 50 (i.e. 10% of 500). Visitors reach their goals when
they go to the last sign-up page. However, if only 1% of all subscribers make a purchase, then the cost of
the Newsletter subscription can only be assigned a value of five. Although it is unnecessary to set prices
when setting goals, they use this option to estimate weight and profit from any visitor interaction with the
site. System Google Analytics also uses the Goal Value data to calculate other indicators such as return
on investment (ROI) and average. You can use the funnel to specify the expected traffic path to reach
your destination. Focusing on the steps of the funnel fixed by the moderator, Analytics tracks the times
when visitors come and leave the goal. This WMS to obtain valuable information about the site. For
example, you could identify a page in a funnel that captures many exits on the way to a goal, indicating
problems with that page. If there are many missed steps, this will display difficult navigation or too many
conversion paths with many unnecessary steps. Funnels can only use in conjunction with a destination.
The last page in the funnel is a goal page (entered as a goal), while the previous pages form a funnel. If
the goal of the funnel is to attract leads, then the first page of the funnel can be assigned the URL of the
contact request form, and the landing page is the URL for the thank you page for the request that is
displayed after the user requests the contact [120-124].
3.6. Calculation of indicators
1. "Compared to homepage" metric. This metric compares the conversion rates of the experiment
page and the original page [120-124].
The following formula makes the comparison:
рейтинг конверсій експериментального варіанта
1 100 .
рейтинг конверсій початкового варіанта
For example, if case Variation effective than the original:
Experiment Conversion Rate = 35%
Initial conversion rate = 25%
35
Then, value index n is compared to your original 1 100 40 %.
25
If the experimental variant is less effective than the original variant:
Experiment Conversion Rate = 20%
Initial conversion rate = 40%
20
Metric value Compared to % original page 1 100 50 .
40
The assist-to-recent conversions ratio results are from dividing the number of conversions. If a channel
has repeatedly played an ancillary role on the path to one conversion, only one conversion is counted as
an associate conversion for the metric. Associated conversions for different channels do not exclude each
other. Associated conversion is taken into account for all channels that have an ancillary role in one
conversion path [120-124].
To view a subset of conversion funnels, you need to apply conversion segments instead of filters with
filters. Using a filter profile can adversely affect the accuracy of Multi-Channel Funnels reports. Using
means the creation of conditions to specify the conversion paths to be included in the segment. For
example, to set up a conversion segment that contains only conversion courses that begin with
example.com, you must specify the following [120-124].
An Attribution Model is a rule or set of rules that defines how credits for sales and conversions are
assigned to touchpoints with audiences in the path to conversions. For example, the Last Engagement
attribute gives 100% credit to endpoints with audiences (i.e., clicks) that precede sales or conversions.
The First Engagement attribute assigns 100% credit to the audience touchpoints that begin the path to
conversions. Below is an example of attribution models. The calculated conversion value (and the number
of modifications) for each marketing channel will vary according to the attribution model. A track that is
mostly starting to drive conversions will have a higher conversion value than the Last Interaction
attribution model, thanks to its attributive First Interaction model. A customer found the site by clicking
on one of the paid ads. He returns in a week via a social network. On the same day, he produced a third
time to get the goods - this time for one campaign through email. In the Attribution Model:
Last Interaction last touchpoint - in this case, channel email - receive 100% credit for the sale.
First Engagement, The first point of contact with your audience, is the paid ad channel - will get
100% credit for the sale.
Linear every touchpoint on the path to conversion - in this case, the Paid Advertising channel,
social network feeds and email - share equal credit (33.3% each) credit for the sale.
Over time, audience touchpoints that are closest in time to sales or conversions get the most credit.
In this case, email and social network channels will receive most of the credit because the customer
interacted with them on the conversion day. Since engagement with a paid ad occurred a week
earlier, this channel will receive much less recognition.
Based on the position, 40% of the credit is assigned to the first interaction, 20% to intermediate
interactions, and 40% to the last interaction. Feeds paid ad and email receives 40% credit, and
social network - 20%.
Attribution Modelling lets you compare the impact of different attribution models on evaluating
marketing channels. In the tool, select an attribution model (such as Last Interaction). Then the table will
show the number of conversions (or, depending on your choice, the cost) for each channel calculated by
the model you selected. You can select up to three attribution models at a time and compare the results of
each one in the table. After importing cost data into AdWords or other similar information, columns will
appear to help you analyse and compare the metrics. In addition to basic models, you can use attribution
modelling to create, save, and apply a custom model that uses the rules specified by the moderator. It
allows you to tailor the models specifically to the set of assumptions that need to estimate in the
conversion path data. In the "Applying Special Credit Rules" section, you can specify conditions that
determine touch points on the conversion path based on characteristics such as position (first, last,
average, assist), touchpoint type (click, impression, direct visit), and also the type of campaign or traffic
source (campaign, keyword, or other dimensions). After defining the touchpoints you want, you can
specify how you would allocate the conversion credit for those points to other points [120-124].
All the rules is determined by the relative distribution of loans. For example, a linear model splits the
conversion credit equally between touchpoints. Therefore, on the way to a four-conversion conversion,
each touchpoint will receive 25% credit. However, if a paid ad channel is credited with 2 points and the
third point on the path is "Paid Ads" [120-124], the credit will be applied as follows, as shown in Fig. 15.
Figure 15: Paid Ads Example
After applying multiple rules to a single point of contact, the weight of the credit rules overlap will
multiply. The Last Interaction Model transfers 100% of the conversion price to the last channel that the
user interacted with before purchasing or completing the conversion. Google Analytics uses this default
model to pass conversion costs into reports that are not multi-channel funnels. Because the Last
Interaction model is the default model for messages that do not relate to multi-channel funnels, it provides
valuable results compared to other models. Also, suppose ads and campaigns are designed to attract
people at the time of purchase or are primarily engaged in transactions and have sales cycles that do not
reflect phase. In that case, the Last Interaction model may be acceptable [120-124].
The First Interaction Model transfers 100% of the conversion cost to the first channel, with the user
interacted. This model is acceptable if you are running ads or campaigns to create initial awareness. For
example, if a brand is poorly known, you can focus on the keywords or channels that are the first to
represent the brand to the customer [120-124].
Model Line distributes equal credit to each channel interaction on the conversion path. The model is
helpful if campaigns are designed to keep in touch with the customer and keep him informed throughout
the sales cycle. In this case, each point is vital for the reflection process. If the sales cycle involves a brief
reflection phase, you can use the Impairment Model over time. This model transfers most of the credit to
the touchpoints that occurred in time closest to the conversion. If you run one- or two-day advertising
campaigns, you can give more credibility to your interactions during the advertising days. In this case,
the interactions that occurred a week before the promotion would receive only a tiny fraction of the credit
compared to the touchpoints near the conversion. Over time, the Attenuation model appropriately credits
touch points during the one or two days leading up to a conversion [120-124].
4. Experiments, results and discussion
Fig. 16 shows a contextual diagram of data flows of a typical regional information resource, and Fig.
17 shows a detailed flow chart of a specific regional information resource. Model Position-based allows
you to combine the Last Interaction and First Interaction models. Instead of giving all the credit to the
first or last interaction, it can split between them. Typically, 40% of the credit is assigned to first and last
interactions, and 20% is to intermediate interactions. If they value more points of contact, which
introduced a user's brand and points that led to the sale, uses a model based on the position. Fig. 18 shows
an ERD of a typical regional information resource.
Figure 16: Contextual DFD is a typical regional information resource
Figure 17: A detailed DFD of a typical regional information resource
Figure 18: An ERD of a typical regional information resource
Mathematical relation for deterministic systems in the general case [125-136]:
y1
dy
f y, t , y t0 y0 , y .
dt y
n
For the site of the Lviv city of consider the model administrator-visitor [125-136]:
where y1 is the number of administrators, y2 is the number of visitors, Cavg is the amount of content, Call
is the total amount of content, ALL is useful information determined scale from 0 to 1.
The managed system based on Lotka-Volterra equations, also known as the predator-prey equations,
looks like [125-136]:
Linearization of function:
Discrete-deterministic model of information system. To implement this system, you can use 1 of 2
machines: the Mealy or Moore machine [125-136].
z t 1 z t , x t , t 0,1, 2,...;
Type 1: Mealy Machine (Fig. 19-20, Table 22-23):
y t z t , x t , t 0,1, 2,...;
z t 1 z t , x t , t 0,1, 2,...;
Type 2: Moore machine:
y t z t 1 , x t , t 0,1, 2,...;
y t z t , t 0,1, 2,...;
Figure 19: Mealy Machine Example, where Z 0 is request to the server, Z1 is the processing of the
request, Z 2 is running of the script, Z 3 is the connection to the database, Z 4 is generation and delivery
of the page to the visitor.
Figure 20: Discrete-stochastic model of information system example
Table 22
Mealy Machine Example
Xi Zk
Z0 Z1 Z2 Z3 Z4
Transitions between system states
X1 Z1 - - - -
X2 - Z2 - - -
X3 - - Z2 - -
X4 - - - Z3 -
X5 - - - - Z4
Exits
X1 Y1 - - - -
X2 - Y2 - - -
X3 - - Y2 - -
X4 - - - Y3 –
X5 - - - - Y4
Table 23
Discrete-stochastic model of information system
Z0 Z1 Z2 Z3 Z4
1 0 0 0 0
0 0.8 1 0 0
0 0 0.4 0 0
0 0 0 0.5 1
0 0 0 0 0
Continuous-stochastic model of information system. Every day new articles and new information
are added to the database. Therefore, there is nothing in the drive at the beginning of the day. Thus, the
states of the subsystem will be described by the system of equations [125-136]:
Pn t t Pn t 1 t Pn1 t t Pn1 t t , n 1,
P0 t t P0 t 1 t P1 t t ,
where Pn t is the probability of the system being in a state, zn t Z at time t, that is, n applications,
we differentiate a system if it has n applications [125-136]:
dPn t
Pn t Pn1 t Pn1 t
dt
dP0 t P t P t
dt 0 1
When equating time to zero [125-135]:
pn pn1 pn1, n 1, 1 pn pn1 pn1, n 1,
p0 p0 , p1 p0 .
when P1 1 : pn n 1
The mathematical expectation of the amount of content in the system [125-136]:
ln npn 1 n n 1
n 0 n 0
Average stay:
2
l
lH n
1
Network model. With the help of the network model, I will build the principle of CMS work for the
project (Fig. 21). To begin with, a user has requested a server [125-136]:
Figure 21: Network model example
Then the server transmits data to the script handler, the script itself to routing, and routing to
modularity (Fig. 22). There is the module search, GET query template.
Figure 22: Network model example
After routing, the script selects the module type (content, directory, search) shown in Fig. 23.
Figure 23: Network model example
The content type, module template, system module options, content and module itself are downloaded
(Fig. 24). The server outputs the generated page to the user in the browser.
Figure 24: Network model example
Combined model. Fig. 25 shows Aggregation system [125-136], where А1 is server, А2 is CMS of
our project, А3 is DB and
1 is a server request.
2 is the connection to the database.
3 is errors.
4 is giving the client a generated page, shutting down.
5 is data transmission via POST, GET, HEAD method.
6 is data acquisition by POST, GET, HEAD.
7 is delivery of content by criteria.
Figure 25: Aggregation system Example
5. Conclusions
1. The analysis of ways of forming commercial content is carried out. The known models of a life
cycle of content and the standardized services of management of content give the chance to define
requirements for creating an optimum life cycle of commercial content.
2. Internet technologies for the construction of service-oriented e-commerce systems are studied,
making it possible to classify e-commerce systems and e-content commerce systems.
3. Information resources and production processes of e-commerce systems are considered in detail,
making it possible to develop an optimal content life cycle and a typical architecture of e-
commerce systems.
4. The technology of content management in e-commerce is analyzed, allowing the development of
formal models, unified methods and software tools for processing information resources in e-
content commerce systems.
5. From the standpoint of a systematic approach, an analysis of modern methods and tools for
designing, modelling, and implementing electronic content commerce systems and substantiated
the need and feasibility of creating unified procedures and software for processing information
resources.
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