=Paper=
{{Paper
|id=Vol-3171/paper99
|storemode=property
|title=Analytical Method for Social Network User Profile Textual Content Monitoring Based on the Key Performance Indicators of the Web Page and Posts Analysis
|pdfUrl=https://ceur-ws.org/Vol-3171/paper99.pdf
|volume=Vol-3171
|authors=Victoria Vysotska
|dblpUrl=https://dblp.org/rec/conf/colins/Vysotska22
}}
==Analytical Method for Social Network User Profile Textual Content Monitoring Based on the Key Performance Indicators of the Web Page and Posts Analysis==
https://ceur-ws.org/Vol-3171/paper99.pdf
Analytical Method for Social Network User Profile Textual
Content Monitoring Based on the Key Performance Indicators of
the Web Page and Posts Analysis
Victoria Vysotska 1,2
1
Lviv Polytechnic National University, S. Bandera Street, 12, Lviv, 79013, Ukraine
2
Osnabrück University, Friedrich-Janssen-Str. 1, Osnabrück, 49076, Germany
Abstract
The article considers the development of methods and software for processing web pages and
user messages in social networks, blogs, or forums. A method of textual content support based
on analysing information about the comments from web pages of social network users is
proposed. This method is built on the principles of web analytics and uses analytical data to
evaluate web resources. The analytical method of text content support uses the analysis of key
performance indicators to form many keywords to increase the potential audience of the blog
or e-commerce sites. Software tools for technical support of textual content have been
developed. A method of designing and implementing systems for monitoring textual content
of Internet blogs and Internet forums, which reflect theoretical research results was proposed.
From the standpoint of a systemic approach, propose the application of the principles of web
resources processing for the implementation of the life cycle of textual content, which allowed
to develop of a method of content support. The main problems of functional services for
managing a web page or profile of users of social networks, blogs, and forums for their further
promotion in search engines and attracting a potential/permanent audience are analyzed.
Keywords 1
Content, text content, Web resource, business process, content management system, content
life cycle, Internet blog, Internet forum, social network, page conversion, e-commerce
1. Introduction
The analysis of visitor’s business content is important, but few people use it as a guide to action in
the management of Web resources or Web pages [1]. However, people are beginning to realize that you
can significantly increase the revenue – double or even triple – if you just find out what traffic is more
likely to drive conversions, what visitors do (and what they do not) on a particular Web site, and how
to measure the effectiveness of the changes they make on the site to increase traffic conversion.
Objectives and Key Results (OKR) are about understanding business goals [4]. You need to first
understand these goals, and only then deal with specific performance indicators of the Web site. This
should be agreed upon, and should begin with the setting of the OKR [5-10]:
1. Make a list of stakeholders;
2. Conduct a "brainstorming" with stakeholders;
3. Define an OKR list (include anything that can be considered a success for Web site):
a. Generate more conversions that lead to sales;
b. Download more directories in PDF format;
c. Encourage the customer to purchase several products / services at the same time
(thereby increasing the average cost of the order);
COLINS-2022: 6th International Conference on Computational Linguistics and Intelligent Systems, May 12–13, 2022, Gliwice, Poland
EMAIL: victoria.a.vysotska@lpnu.ua (V. Vysotska)
ORCID: 0000-0001-6417-3689 (V. Vysotska)
©️ 2022 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)
d. Contribute to the creation of a more recognizable brand or product;
e. Increase traffic;
f. Provide customer service (reduce the number of calls to the call center);
g. Build relationships with visitors (for example, to increase blog comments, forum posts,
etc.);
4. Highlight and articulate OKR (focus on the most important 5-10 OKR).
2. Related works
Google Analytics is a free data collection and reporting tool [11]. However, it is not able to improve
the site. To analyze and interpret the data of the reports, and then take action, you need a clear algorithm
of actions and a coordinated team [12].
Most business organizations around the world use Key Performance Indicators (KPIs) to measure
performance. These are sometimes referred to as Key Success Indicators (KSIs) or Balanced Score
Cards (BCS) [13]. KPIs are used in the analytical departments of companies to assess the situation in
business. Once an organization has established an OKR, it needs a way to evaluate the success of its
activities. This assessment allows obtaining key performance indicators [4].
Similarly, in Web analytics, KPI is a Web metric that is essential to the success of an online
organization. KPI requirements are [11-14]:
• In most cases, a KPI is a ratio, a percentage, or an average, rather than a processed number.
This makes it possible to present data in context. Examples of raw data:
a. The website lost 15 orders yesterday because the server processing the online store
order did not respond within M minutes;
b. N potential earnings were lost last week because the ordering system does not work
for visitors who use Firefox;
c. Last month, L monetary units were spent on PPC keywords, which did not bring
any conversion.
• The KPI must be time-bound. This emphasizes the changes and their speed.
• KPIs provide an incentive for action that is important to the business. Most parameters can be
measured and evaluated, but this does not make them key to an organization's success.
Most of the complex work of preparing a KPI consists of defining the OKR. The key results used to
set goals are KPIs. You just need to turn them into real Web-indicators that are available for social
network, e-commerce, the circular economic etc. [15-18].
KPI preparation algorithm
1. Determine the OKR. The KPI must meet the business objectives of the organization.
2. Convert OKR to KPI, i.e., determine specific Web-indicators that meet the business objectives
of OKR (Table 1), such as server uptime, server response speed, notes on any offline companies
that may affect the numbers, changes made to the site, information about the launch of new
products or user reviews, etc. All this will help to better understand the data of the reports and,
accordingly, increase their value.
3. Verify that KPIs are measurable and motivating indicators.
4. Create hierarchical KPI reports. You need to make sure that each recipient of the KPI report
receives the data he needs. The more relevant the proposed information, the more attention and
interest will be given to it.
5. Determine the partial KPIs. One of the most popular is to increase the conversion rate of the
site. Usually, this indicator is easy to estimate, but at the same time, it is too contradictory - the
visitor either carries out conversion, or not. For example, if the conversion is to upload a file,
then the transition to the page may be a partial KPI. Similar partial KPIs include:
a. Go to the contacts page;
b. For a multi-page request form, fill in the first page;
c. Achieve the defining moment in the process of filling out the form;
d. Go to the promotion page;
e. Fill in the search box on the site.
6. Combine KPIs. After creating a list of required KPIs for each department representative,
combine them and eliminate duplicates. The purpose of the KPI is to focus on important
indicators for the business. If the KPI report presents all the key factors needed to assess success,
then each KPI must be at least 10% of the total. If the importance of one KPI is much less than
10%, it must be removed or combined with another to get a more significant KPI.
Table 1
Example of converting OKR to KPI for social network, e-commerce, the circular economic etc. [19-27]
OKR department representatives Proposed KPIs
Increase the number of visitors who come Increase the number of visitors who come to the
to the site from search engines; site from search engines;
Sell more goods / services; The percentage of visits during which visitors add
items to the cart;
Percentage of visits during which visitors add items
to the cart and place an order;
The percentage of visits during which visitors
interrupt the order after adding goods to the cart;
Increase the number of visitors who The percentage of visits during which visitors leave a
participate in the work of the site; comment in the block or upload a document;
The percentage of visits during which visitors fill out
a feedback form or click on a mail to link;
Average time spent on the site per visit;
The average number of pageviews per visit;
Sell more related products to customers; Average cost of orders;
The average number of goods per transaction;
Increase the positive customer experience Percentage of visits during which only one page was
of the site. viewed * bounce rate);
Percentage of internal site searches that resulted in
zero results;
The percentage of visits that resulted in an
application being submitted to support.
KPI reports are not something that does not change over time. They can and should change and
evolve as departmental representatives learn to understand the performance of the Web site and take
action to make changes. It is recommended that you review the KPI list at least quarterly. For example,
an online marketer will obviously be interested in the difference between search engine visitors (SE)
and non-search traffic, and how likely it is to convert that traffic (for example, a tour order may be a
conversion). Example of KPI report for social network, e-commerce, web resources etc. [28-39]:
1. In month i + 1 the profit of online orders decreased by x% compared to month i.
2. Approximately y% of all Web site visitors come from search engines.
3. For visitors from search engines the probability of entering the ordering system is almost k times
higher than for visitors who did not come from search engines.
4. For visitors from PPC-systems the probability of entering the ordering system is a1-a2% higher
than for visitors who found the site in the natural results of search results.
5. Since the ordering mechanism of the Web site does not support browsers other than Name, The
website loses z1-z2 currency per month.
Actions to be taken by departmental representatives based on this KPI report:
• Check whether the reason for the decline in online profits is a seasonal factor that is
characteristic of the entire industry or only for the online channel.
• At first glance, y% of visitors who come to the site from search engines – can be a remarkably
high figure. But is it the result of an effective search engine marketing strategy or are other channels
just not working very effectively?
• At high a1-a2% most of the budget of PPC-companies they work perfectly. However, perhaps
good results here are caused by shortcomings in search engine optimization. Therefore, this question
needs to be investigated in more detail. Although in the short term, it makes sense to increase the
budget of PPC-companies.
• Create a better ordering mechanism that other browsers will support.
3. Methods and materials
As a result of segmentation of KPI-reports usually receive too much detailed information which
cannot be used to give instructions to workers. For the developer of marketing strategy such
information, on the contrary, is simply necessary. For example, of KPI-reports are built using the
principle of hierarchy:
1. The retail director's marketing director needs hierarchical KPIs, for example:
a. Average conversion rate;
b. The average cost of the order;
c. The cost of attracting visitors.
2. The developer of the marketing strategy needs the same information, segmented by means (paid
or in-kind search results, email marketing or banner display, etc.).
Segmentation using the in-depth data method is a great way to quickly understand the behavior of
different segments of visitors. By identifying key segments of visitors who come to the site, you can
create special profiles for them to make individual reports. Such separate segmented reports allow more
detailed, fast and effective research of visitor behavior. Segmentation in most cases includes the type
of visitor, the source of the transition or the geographical location of the visitor, for example:
• Examples of segments by visitor type;
• New and returning visitors;
• Customers and non-customers;
• New customers who are customers (or returning visitors who are customers);
• Examples of segments by source of visitors;
• Visitors who came as a result of the search (or not as a result of the search);
• Visitors who came for affiliate programs (or not for affiliate programs);
• Visitors who came only for paid search results;
• Visitors who came only for natural search results;
• Visitors who came only by e-mail;
• Examples of segments by geographical location;
• Only visitors from Lviv, only from Ukraine, Europe, etc.;
• Only regional visitors (Europe, Asia, Africa, Oceania, etc.).
• Ukrainian-speaking visitors (or visitors who use all other languages of the world).
When performing segmentation, it is necessary to find a balance between the clarity of information
about visitor behavior and large amounts of data.
KPIs vary significantly depend on the e-business sector, such as retail, tourism, technology, B2B,
finance, and so on. Even within the sectors, there are large differences, such as the sale of vouchers,
tours or airline tickets, and the retail sale of souvenirs or clothing. Even when compared to competitors
with the same goals, estimates can only be very approximate. The exact path that visitors must take to
reach the goal, and their impressions of the process, will be different for each Web site. The biggest
changes in these areas can have a significant impact on conversion rates. For example, retail managers
want to distinguish between visiting existing customers and visiting non-customers. Therefore, the use
of a standard industry-wide conversion rate can be misleading.
Conversion rates for e-commerce can be calculated in different ways.
𝑁 𝑁 𝑁 𝑁 (1)
𝐾𝑐𝑣 = 𝑁𝑐𝑣 ; 𝐾𝑐𝑣 = 𝑁𝑐𝑣 ; 𝐾𝑐𝑣 = 𝑁 𝑐𝑣 ; 𝐾𝑐𝑣 = 𝑁 𝑐𝑣 ;
𝑣𝑡 𝑣𝑟 𝑣𝑡𝑏 𝑣𝑟𝑏
where 𝐾𝑐𝑣 is the conversion factor, 𝑁𝑐𝑣 is number of conversions, 𝑁𝑣𝑡 is the total number of visits to
the Web-site, 𝑁𝑣𝑟 is the total number of visitors to the Web-site, 𝑁𝑣𝑡𝑏 is the total number of visits to the
Web-site, during which the product was added to the cart, 𝑁𝑣𝑟𝑏 is the total number of visitors to the
Web-site, during which the product was added to the cart.
In the list below, you can also replace the conversion word with a transaction word. In other words,
the visitor can make a purchase and, if he really liked the site and the organization of the purchase
process, he returns to make an additional purchase during the same session. Depending on which Web
analytics tool is used and what consents are accepted in the organization, this can be defined as one
conversion with two transactions or two conversions with two transactions. For example, Google
Analytics will show one conversion and two transactions since the visitor became a buyer, and this can
only happen once during a visit. Other intra-site factors that significantly affect, and therefore may
complicate, comparison is the following:
• Visibility of the Web site in search engines (natural and paid search results);
• Usability (convenience and ease of use) of the site (ease of navigation on the site, intuitive
navigation system);
• Adequacy of displaying the Web site in all major browsers;
• The need for pre-registration / authorization for the purchase;
• Response time and page load;
• Quality of text and graphic content of the page;
• Use of trust factors such as shopping security logos, privacy policy, warranty, use of encryption
for payment pages, customer recommendations, etc.;
• The presence of broken links or the absence of some images;
• Fast and accurate search on the site.
Examples of KPIs by roles in the organization
To illustrate the example, the online tourism website or online blogs/forum about tourism was
chosen. Its business goals are twofold: to sell tours and try to get applications for professional services.
To do this, the Web site has several main sections:
1. Online store section. Purpose is to sell tourist tours, the price of which is relatively high
compared to the prices in most online stores in this area.
2. Section for generating requests for services. Purpose: to try to get visitors to apply for
professional services (excursions, trainings, tips for exclusive tours with guides, consultations
for individual tours). These are also expensive services.
3. Brand promotion section. It includes writing articles for blogs, which give positive practical
advice on organizing recreation based on services and tours provided on the company's website.
In terms of roles on the Web site 𝑆𝑡𝑚 , there is an online store manager 𝑀𝑖𝑠 , marketing manager 𝑀𝑚𝑟 ,
copywriter 𝑀𝑐𝑝 (employee who writes quality and effective unique content, i.e. author, journalist,
copywriter of the content Web-site) and Web-master 𝑀𝑣𝑚 . For each calculate their own KRI:
𝑆𝑡𝑚 =< 𝑀𝑖𝑠 , 𝑀𝑚𝑟 , 𝑀𝑐𝑝 , 𝑀𝑣𝑚 >. (2)
The site with the online store probably has the most KPIs to choose from, as the main purpose
(purchase) is easy to assess. The purpose of the site (to encourage visitors to add the product to the cart)
is defined quite clearly. Google Analytics has a whole section dedicated to e-commerce reports. But
most KPIs are better taken from other sections. The online store manager, includes additionally the
number of visitors 𝑁𝑣𝑟 offered by the KPI:
𝑀𝑖𝑠 =< 𝑁𝑣𝑟 , 𝑆𝑐𝑐 , 𝑆𝑐𝑜 , 𝑆𝑐𝑣 , 𝑆𝑟𝑜 , 𝑃𝑛𝑣 , 𝐼𝑛𝑣 >, (3)
where 𝑆𝑐𝑐 is average conversion rate, 𝑆𝑐𝑜 is average cost of orders, 𝑆𝑐𝑣 is the average cost per visit or
the average usefulness of the visit, 𝑆𝑟𝑜 is average ROI or average return on investment, 𝑃𝑖𝑣 is percentage
of profits from new visitors, 𝐼𝑛𝑣 is index of new customers at the first visit - a new defined KPI.
𝑆𝑐𝑐 and 𝑆𝑐𝑜 Google Analytics calculates in the e-commerce section. By default, it also calculates
two types of values 𝑆𝑐𝑣 – the usefulness of the purpose of the visit (based on the usefulness of the
goals) and the usefulness of the visit (based on the data of e-commerce transactions).
The formula for calculating the return on investment in Google Analytics:
𝐼𝑛𝑐𝑜𝑚𝑒−𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠 (4)
ROI = 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠
,
where𝐼𝑛𝑐𝑜𝑚𝑒 is profit, 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠 is costs.
A negative ROI indicates that you are losing money: the cost of attracting visitors is greater than the
cost. Of course, when launching a new company, the ROI is likely to be negative until the number of
returning visitors increases and the brand becomes recognizable, leading to an increase in conversions.
Of course, ROI is an indicator of efficiency for total gross profit. It does not consider what profit
you get from the sale. It also does not take into account the number of transactions or visitors. For
example, the ROI for a company (too centralized) may be high and the profit may be small. And with
a lower ROI of a less specialized company, the profit can be quite large due to the large number of
visitors. The formula for the rate of return is as follows:
𝐼𝑛𝑐𝑜𝑚𝑒−𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠 (5)
RR = .
𝐼𝑛𝑐𝑜𝑚𝑒
Buying expensive goods, including tours, usually takes more time to think than buying cheap goods,
such as souvenirs. This is usually equivalent to the number of visits required to persuade to make a
purchase. Such KPI as 𝑃𝑖𝑣 , will allow to learn, whether it is characteristic of a site from Internet tourism.
The value of the probability that new visitors will become new customers on the first visit will help to
calculate 𝐼𝑛𝑣 .
𝑃 (6)
𝐼𝑛𝑣 = 𝑃𝑡𝑣 ,
𝑛𝑣
where 𝑃𝑡𝑣 is the percentage of transactions from new visitors, 𝑃𝑛𝑣 is percentage of new site visitors.
Value 𝐼𝑛𝑣 =1 suggests that new and returned visitors will become customers with equal probability.
A value less than 1 means that a new visitor is less likely to become a customer than a returned one.
And a value greater than 1 means that a new visitor will be more likely than a returning visitor.
Attracting good visitors to the Web site (those who generate sales or requests for services) is one of
the main tasks of marketing. Online marketing includes the following sources: search engine
optimization (free search engine rankings), PPC advertising (paid search results), banner advertising,
affiliate networks, blog marketing, links from other sites, and email marketing.
To determine the best traffic, you need to analyze the conversion rate 𝐾𝑐𝑣 , company costs, earnings,
and ROI. Therefore, the KPI for the marketer significantly intersects with the KPI for the online tourism
manager. An important difference is that marketers pay attention not only to the conversion rate for
purchases, but also to the conversion of goals, as this speaks to building relationships with visitors who
are likely to eventually go to the purchase. If you omit such indicators as the total number of site visitors,
the KPI for the marketer are as follows:
𝑀𝑚𝑟 =< 𝑃𝑣𝑧 , 𝐾𝑐𝑧 , 𝑃𝑣𝑘 , 𝑃𝑐𝑘 , 𝐼𝑐𝑘 , 𝑆𝑟𝑘 , 𝑃𝑣𝑘 , 𝑃𝑜𝑏 , 𝐾𝑣𝑏 , 𝐼𝑦𝑘 >, (7)
where 𝑃𝑣𝑧 is the percentage of visits by type of tool through AdWords; 𝐾𝑐𝑧 is target conversion rate (as
a percentage) by AdWords asset type; 𝑃𝑣𝑘 is the percentage of visits by business type through AdWords;
𝑃𝑐𝑘 is the percentage of conversion of business-type goals through AdWords; 𝐼𝑐𝑘 is goal conversion
index by company type; 𝑆𝑟𝑘 is average ROI by company type; 𝑃𝑣𝑘 is the percentage of new and
returning visitors; 𝑃𝑎𝑝 is percentage of new and returned buyers; 𝐾𝑣𝑏 is brand recognition ratio; 𝐼𝑦𝑘 is
company quality index.
Company quality index 𝐼𝑦𝑘 . The new KPI is about assessing how well-targeted a company is, that
is, how effective they are at attracting targeted traffic to an online tourism site.
𝑃 (𝑥) (7)
𝐼𝑦𝑘 (𝑥) = 𝑐𝑣 ,
𝑃𝑣𝑘 (𝑥)
where 𝐼𝑦𝑘 (𝑥) is company quality index function (for company x), 𝑃𝑐𝑣 (𝑥) is the function of determining
the selection of conversion prices for evaluation from the company х, 𝑃𝑣𝑘 (𝑥) is the function of
determining the percentage of evaluation from the company х.
Let us say, for example, that 𝑃𝑣𝑘 = 50% of AdWords visitor ratings, but this company source is only
responsible for 𝑃𝑐𝑣 =20% of conversions. This company works inefficiently, because if two companies
are equally targeted and each generates 50% of traffic, then two should give 50% of conversions. If one
time will be effective for another, generating more of its own conversion rate, then, by definition, this
company is the best targeted.
An index value of 𝐼𝑦𝑘 = 1.0 means that a visitor from this company will convert with the same
probability as a visitor from any other company. A value of 𝐼𝑦𝑘 <1.0 means, respectively, that a visitor
from this company is less likely to convert than a visitor from any other company. And the value 𝐼𝑦𝑘 >
1.0, respectively - the visitor will convert more likely than the seller from any other company (Table
2). The company Forum is a very well targeted company. Company Yahoo! Organic is also well
targeted, but the number of conversions is quite low, so you do not need to pay attention to it until more
data is collected. You can waste a lot of time and effort finding the reason why visiting with Yahoo!
Organic is almost three times more efficient than Google organic. Although, the statistical sample is
too small.
Table 2
Convert for different company
Company 𝑃𝑣𝑘 , % visits 𝑃𝑐𝑣 , % conversions 𝑃𝑎𝑘 , % all conversions 𝐼𝑦𝑘 (𝑃𝑎𝑘 /𝑃𝑣𝑘 )
(𝑃𝑐𝑣 /𝑁𝑐𝑣 )
Forum 2,02 51,00 19,32 9,56
Google cpc 4,90 11,00 4,17 0,85
Google organic 40,84 83,00 31,44 0,77
YSM ppc 3,62 3,00 1,14 0,31
Yahoo! Organic 0,56 3,00 1,14 2,04
Referral 29,59 73,00 27,65 0,93
Direct 16,22 39,00 14,77 0,91
Other 2,25 1,00 0,38 0,17
Total N 𝑁𝑣𝑘 =100,00 𝑁𝑐𝑣 =264,00
Brand recognition ratio:
𝑁 +𝑁 (8)
𝐾𝑣𝑏 = 𝑁𝑏𝑞+𝑁𝑡𝑣,
𝑎𝑞 𝑡𝑣
where 𝑁𝑏𝑞 is the number of search queries with the brand name; 𝑁𝑡𝑣 is number of direct visits to the
Website; 𝑁𝑎𝑞 is total number of search queries.
Note that in the formula, search queries are keywords that are entered into search engines. Direct
visits are included because they are made by people who know the address of the Web site, which means
that employees exclude the brand from the reports of visits to the Web site. For site content developers,
the main goal is to maximize audience engagement. How much time people spend reading the content
of the site and how much of it they read - these are the key indicators of assessing audience engagement.
There are three categories of content in Web sites:
• Product and organization information from corporate information sites; product reviews sites,
blogs, technical support sites, online training sites, etc.
• Advertising content from sites with free content that earn revenue from the sale of advertising
(banners or text ads), which are placed alongside other content on the site.
• Subscribed content coming along with advertising revenue can offer subscription, i.e. the user
pays a subscription to receive materials (possibly more complete versions of articles).
Regardless of the business model of the content site, increasing the interest of visitors is a key factor
for success. That is why content managers are always looking for ways to include additional topics in
each article or page to increase that interest. Accordingly, for content sites, the number of visits per day,
week or month is an important KPI.
𝑀𝑐𝑝 =< 𝑆𝑡𝑝 , 𝑆𝑝𝑣 , 𝑃𝑣𝑣 , 𝑆𝑛𝑐 , 𝑃𝑧𝑣 , 𝑃𝑎𝑣 , 𝐾𝑣𝑏 , 𝑃ℎ𝑡 , 𝑃𝑠𝑡 , 𝑃𝑙𝑡 >, (9)
where𝑆𝑡𝑝 is average length of stay on the site through AdWords; 𝑆𝑝𝑣 is the average number of
pageviews per visit through Google Analytics; 𝑃𝑣𝑣 is bounce rate (as a percentage), for example for one
page as; 𝑆𝑛𝑐 is average number of ad clicks per 𝑁𝑣𝑟 visit; 𝑃𝑧𝑣 is the percentage of interest of visitors;
𝑃𝑎𝑣 is the percentage of new visitors and those who returned through Google Analytics; 𝐾𝑣𝑏 is brand
recognition ratio; 𝑃ℎ𝑡 is the percentage of repeat user visits that occurred since the previous visit in less
than days 𝑡1 (calculated according to Google Analytics); 𝑃𝑠𝑡 is the percentage of repeat user visits that
occurred since the previous visit between 𝑡1 and 𝑡2 days at 𝑡1 <𝑡2 (calculated according to Google
Analytics); 𝑃𝑙𝑡 is the percentage of repeat user visits that occurred from a previous visit of more than 𝑡2
days (calculated according to Google Analytics).
The average bounce rate (as a percentage) 𝑃𝑣𝑣 can be found through Google Analytics. From the
point of view of the author of the content, high value 𝑃𝑣𝑣 means low interest of visitors, i.e. weak interest
in the site. Segmentation in this case is the most important condition for making information decisions.
One-page bounce rate:
𝑁 (10)
𝑃𝑣𝑣𝑝 = 𝑣𝑛𝑝 ,
𝑁𝑖𝑛𝑝
where 𝑁𝑣𝑛𝑝 is the number of one-page visits for this page via Google Analytics; 𝑁𝑖𝑛𝑝 is the number of
times users visit this page directly through Google Analytics.
Average ad clicks per 𝑁𝑣𝑟 visit:
𝑁 (11)
𝑆𝑛𝑐 = 𝑁𝑐𝑟 ⋅ 𝑁𝑣𝑟 ,
𝑎𝑣
where 𝑁𝑐𝑟 is the average number of clicks on AdWords advertising; 𝑁𝑎𝑣 is total number of visits via
Google Analytics; 𝑁𝑣𝑟 is number of visits for analysis.
Visitor interest rate:
𝑁 (12)
𝐾𝑧𝑣 = 𝑁𝑎𝑑,
𝑎𝑣
where 𝑁𝑎𝑑 is the total number of actions on the site through AdWords; 𝑁𝑎𝑣 is total visits through Google
Analytics.
Percentage of visitors interested:
𝑁 (13)
𝑃𝑧𝑣 = 𝑧𝑣 ,
𝑁𝑣𝑘
where 𝑁𝑧𝑣 is the total number of interested visitors through AdWords; 𝑁𝑣𝑘 is total number of visitors
through Google Analytics.
To analyze revisits related to repeat visits, you need to choose the ideal time intervals for a particular
e-business model 𝑡1 <𝑡2 . With a successful e-business:
𝑃ℎ𝑡 >> 𝑃𝑠𝑡 >> 𝑃𝑙𝑡 . (14)
This is usually impossible to achieve. But in the periodic study of these indicators, can deduce
patterns for adjusting the content, which in turn improves the ratio at least as 𝑃ℎ𝑡 𝑃𝑠𝑡 𝑃𝑙𝑡 .
Webmasters are responsible for the efficient and continuous operation of the website. Therefore,
they need to know what the load on the servers will be, i.e. how many visitors can expect to see the
server. You also need to know which browsers and language settings users use most often.
𝑀𝑣𝑚 =< 𝐾𝑑𝑢 , 𝑃𝑢𝑙 , 𝑃𝑢𝑏 , 𝑃𝑢𝑠 , 𝑃𝑢𝑟 , 𝑃𝑢𝑝 , 𝑃𝑢𝑚 , 𝑃𝑒𝑝 , 𝐾𝑖𝑠 >, (15)
where 𝐾𝑑𝑢 is indicator of the number of visitors, visits and page views; 𝑃𝑢𝑙 is the percentage of visitors
who support English / Ukrainian; 𝑃𝑢𝑏 is the percentage of visitors who use a particular browser through
Google Analytics; 𝑃𝑢𝑠 is an proportion of visitors who use a specific operating system; 𝑃𝑢𝑟 is the
percentage of visitors who have a high, medium / low screen resolution; 𝑃𝑢𝑝 is the percentage of visitors
who have a high-speed Internet connection; 𝑃𝑢𝑚 is the percentage of visitors who have a high-speed
Internet connection; 𝑃𝑒𝑝 is the percentage of pages published with an error; 𝐾𝑖𝑠 is internal search
indicator.
The metric 𝑁𝑑𝑢 is basic for Webmasters and is determined through Google Analytics:
𝑁𝑑𝑢 =< 𝑆𝑛𝑣𝑡 , 𝑆𝑛𝑢𝑡 , 𝑆𝑛𝑝𝑡 , 𝑆𝑛𝑝𝑣 >, (16)
where 𝑆𝑛𝑣𝑡 is average number of visits for a certain period of time; 𝑆𝑛𝑢𝑡 is the average number of unique
visitors over a period of time; 𝑆𝑛𝑝𝑡 is the average number of page views for a given period of time; 𝑆𝑛𝑝𝑣
is the average number of pageviews per visit.
Percentage of error pages that should be minimized:
𝑁𝑒𝑝 (17)
𝑃𝑒𝑝 = 𝑁 ,
𝑝𝑝
where 𝑁𝑒𝑝 is the total number of pages published with an error; 𝑁𝑝𝑝 is the total number of pages viewed.
Internal search metrics are also determined through Google Analytics:
𝐾𝑖𝑠 =< 𝑃𝑢𝑖𝑠 , 𝑆𝑣𝑟𝑠 , 𝑃𝑢𝑜𝑠 , 𝑃𝑢𝑛𝑠 , 𝑇𝑠𝑣𝑠 , 𝑆𝑛𝑢𝑝 , 𝑃𝑢𝑢𝑟 , 𝑃𝑢𝑛𝑟 , (18)
𝑃𝑢𝑐𝑠 , 𝑃𝑝𝑜𝑝 , 𝑃𝑐𝑢𝑠 , 𝑃𝑏𝑢𝑠 , 𝑃𝑘𝑠𝑝 , 𝑃𝑢𝑡𝑠 , 𝑁𝑛𝑛𝑠 , 𝑃𝑛𝑟𝑝 >,
where 𝑃𝑢𝑖𝑠 is the percentage of visits that use site search; 𝑆𝑣𝑟𝑠 is the average number of search results
viewed per search; 𝑃𝑢𝑜𝑠 is the percentage of visitors who left the site after viewing the search results;
𝑃𝑢𝑛𝑠 is the percentage of visitors who conduct multiple searches on the site during the visit (excluding
multiple searches for the same keyword); 𝑇𝑠𝑣𝑠 is the average time spent on the site to visit after the
search; 𝑆𝑛𝑢𝑝 is the average number of pages viewed by visitors after the search; 𝑃𝑢𝑢𝑟 is the percentage
of visitors who use the site search; 𝑃𝑢𝑛𝑟 is the percentage of visitors who do not use site search; 𝑃𝑢𝑐𝑠 is
the percentage of conversions from visitors using site search; 𝑃𝑝𝑜𝑝 is the percentage of failures after
visiting one page as a search result; 𝑃𝑐𝑢𝑠 is the percentage of buyers among visitors who use site search;
𝑃𝑏𝑢𝑠 is the percentage of purchases made among visitors using site search; 𝑃𝑘𝑠𝑝 is the percentage of
visitors that view more than k pages after a search; 𝑃𝑢𝑡𝑠 is the percentage of visitors who spent more
than t time on the site after the search; 𝑁𝑛𝑛𝑠 is the number of zero search results on the site; 𝑃𝑛𝑟𝑝 is the
percentage of zero search results on the site pages, in particular,
𝑁𝑛𝑝𝑠 (19)
𝑃𝑛𝑟𝑝 = ,
𝑁𝑣𝑝𝑠
where 𝑁𝑛𝑝𝑠 is the total number of zero-page search results; 𝑁𝑣𝑝𝑠 is the total number of search pages
viewed.
To calculate for further analysis 𝐾𝑝𝑠 - the rate of use of site search:
𝑁 (20)
𝐾𝑝𝑠 = 𝑠𝑣 ,
𝑁𝑛𝑠
where 𝑁𝑠𝑣 is the number of visits with site search; 𝑁𝑛𝑠 is number of visits without site search.
As the number of e-commerce sites that support RIA technology (Rich Internet application)
increases, so does the need to define KPIs for them.
Rich web application (rich Internet application RIA or installable Internet application) is a Web
application that has many features of traditional software. The concept is closely related to a one-page
program and can allow the user to interact with features such as dragging, background menu,
WYSIWYG editing, and more. HTML5 is the modern standard for developing advanced web
applications that are supported by all major browsers. Typically, a RIA system:
• Runs locally in a security environment - "sandbox" - a mechanism for safe execution of
programs
• Runs in the browser and does not require additional software installation;
• Passes the required part of the user interface to the Web client, leaving most (program
resources, data, etc.) on the server.
Analysts should not think in terms of 𝐾𝑑𝑢 pageviews, but in terms of 𝐾𝑎𝑠 actions and events that
indicate user / visitor / customer interactions with the site. That is, according to the results of the analysis
of indicators, it is necessary to redefine 𝐾𝑎𝑠 the set of actions to be performed by visitors, so that it is
considered an interaction with the site, in particular,
𝐾𝑎𝑠 =< 𝑃𝑛𝑣 , 𝑃𝑢𝑣 , 𝑆𝑛𝑣 , 𝑆𝑡𝑣 >, (21)
where 𝑃𝑛𝑣 is the percentage of new site visitors; 𝑃𝑢𝑣 is the percentage of unique visitors; 𝑆𝑛𝑣 is average
number of views per visit; 𝑆𝑡𝑣 is average duration of visit; 𝑆𝑐𝑐 is the average conversion rate.
Combining the KPIs of interaction with the 𝐾𝑑𝑢 site and 𝐾𝑎𝑠 event tracking, you can define the
following KPIs:
𝐾𝑢𝑠𝑎 = 𝛼(𝐾𝑑𝑢 , 𝐾𝑎𝑠 ) =< 𝑃𝑣𝑐𝑢 , 𝑃𝑠𝑎𝑢 , 𝑃𝑠𝑖𝑢 >, (22)
where 𝑃𝑣𝑐𝑢 is the percentage of visitors interacting with different types of content presentation, such as
zooming, panning, viewing the next communication; 𝑃𝑠𝑎𝑢 is the percentage of visitors who run various
kinds of the events, such as lose, pause, next, rating, click on the ad; 𝑃𝑠𝑖𝑢 is the percentage of interaction
with the site, i.e. the execution of certain actions, such as subscribe, register, comment, determine the
rating, add to favorites.
Defining different KPIs allows to focus on those elements of the online strategy that are most
effective for attracting visitors, generating conversions, conversions, and e-commerce profits. It will
also help determine the optimal structure of the website to improve the efficiency of its use and increase
the volume of regular visitors and customers. Thus, you can identify many ineffective web pages. When
analyzing data about visitors, it is necessary to optimize the pages of the site for the effectiveness of
users on it. In many cases, you can improve your site, for example, by fixing broken links, changing
login URLs to effectively visit the pages you want, or adjusting the content of the page to deliver the
required advertising message.
The algorithm for identifying problem areas of the site structure for further optimization is as
follows:
1. Identify the set of ineffective web pages through the analysis of their usefulness.
2. Determine the set of popular login pages through the analysis of failure rates.
3. Analyse the entry sources (search engines, paid advertising, links in e-mails, links to other sites,
direct access to the address, for example, from the history of previous visits of user or the first
direct visit).
4. Analyse the login keywords.
5. Visualize the site conversions by the user to achieve the goal.
6. Evaluate the success of the search on the site
To identify many ineffective pages with web analytics tools by analyzing the list of indicators:
• The value of the measure of usefulness of the page $𝐼𝑑𝑥 ;
• Many of the Top Landing and Exit Pages;
• Funnel Visualization tree.
The usefulness of the page is calculated as
𝑅𝑐𝑣 + 𝑅𝑒𝑐 (23)
$𝐼𝑑𝑥 = ,
𝑁𝑢𝑝𝑣
where 𝑅𝑐𝑣 is usefulness of the purpose of the visit (based on the usefulness of the goals) and usefulness
of the visit (based on the data of e-commerce transactions); 𝑅𝑒𝑐 is income from e-commerce; 𝑁𝑢𝑝𝑣 is
unique page views.
That is, if page 𝑥𝑖 is viewed by visitors who reach the goal, then the usefulness of this goal increases
the usefulness of page 𝑥𝑖 . The more often the 𝑥𝑖 page is viewed by visitors who reach the goal, and the
higher the usefulness of the goal, the greater the value of $𝐼𝑑𝑥 . This method of evaluating the usefulness
of pages has nothing to do with goals and conversions. Ranking pages by the value of $𝐼𝑑𝑥 sets the
order of their optimization. Unexpected pages in this set (which are not related to the goals) indicate a
problem with the structure and content of the website.
In parallel, you need to analyse the most popular pages. The main value in the analysis of many
popular pages is the failure rate; if visitors get to the login page 𝑥𝑖 and immediately leave the site, it is
a sign of low user involvement in the site. If the 𝑥𝑖 login page has a high bounce rate, then the content
of the 𝑥𝑖 page does not meet user expectations. It is necessary to investigate the source of conversions
both in the middle of the site, and conversions from other sources. The statistics of low indicators of
the last causes to intensify work depending on transitions in the following directions:
• Search engine optimization (SEO);
• Campaigns with paid search results;
• Offline / Online marketing activities;
• Advertising and maintaining pages on social networks.
Keyword analysis is a real market research, i.e., visitors report what content they expected to receive
when visiting the site. Visualization of conversions on the site by the user to achieve the goal will assess
the problem areas of the site structure, where the potential visitor / buyer faces problems, such as
incorrect or unclear or difficult stages of payment / ordering. Site search is an internal search engine
that visitors often replace with a site or menu navigation system. For large websites with hundreds or
thousands of pages of content, the internal search engine is an important component for visitors to
quickly find the content they need. Internal search engines typically use the same architecture and
mechanisms as external search engines like Google. Evaluation of the success of the search on the site
is to analyse the failure rate, as well as a few other indicators, in particular:
• Conversion rate achieved 𝑃𝑐𝑣 :
𝑁𝑐𝑣 (24)
𝑃𝑐𝑣 = ∙ 100%,
𝑁𝑣𝑡
where 𝑁𝑐𝑣 is number of conversions, 𝑁𝑣𝑡 is number of visits.
• Income indicator 𝑃𝑖𝑝 :
𝑃𝑖𝑝 = 𝑅𝑐𝑣 + 𝑅𝑒𝑐 , (25)
where 𝑅𝑐𝑣 is usefulness of the purpose of the visit, 𝑅𝑒𝑐 is usefulness of e-commerce.
• Indicator of average utility 𝑆𝑐𝑣 :
𝑅𝑐𝑣 + 𝑅𝑒𝑐 (26)
𝑆𝑐𝑣 =
𝑁𝑐𝑣 + 𝑁𝑡𝑟
where 𝑅𝑐𝑣 is the usefulness of the goal, 𝑅𝑒𝑐 is usefulness of e-commerce, 𝑁𝑐𝑣 is number of conversions,
𝑁𝑡𝑟 is number of transactions.
• Conversion rate in e-commerce𝑅𝑒𝑐𝑐 :
𝑁𝑡𝑟 (27)
𝑅𝑒𝑐𝑐 = ∙ 100%
𝑁𝑣𝑡
where 𝑁𝑡𝑟 is number of transactions, 𝑁𝑣𝑡 is number of visits.
• An indicator of the usefulness of the visit𝑃𝑢𝑣 :
𝑅𝑐𝑣 + 𝑅𝑒𝑐 (28)
𝑃𝑢𝑣 = ,
𝑁𝑣𝑡
where 𝑅𝑐𝑣 is the usefulness of the goal, 𝑅𝑒𝑐 is usefulness of e-commerce, 𝑁𝑣𝑡 is number of visits.
A visitor who uses site search is several times more valuable than a visitor who does not use the site
search. Therefore, the development and development of site search service effectively affects the
performance of site visits by increasing the volume of a regular audience.
To do this, use the calculation of the impact on income of the search function on the site 𝐼𝑠𝑠𝑝 :
𝐼𝑠𝑠𝑝 = (𝑅𝑠𝑠𝑣 − 𝑅𝑠𝑛𝑣 ) ∙ 𝑁𝑠𝑠𝑣 , (29)
where 𝑅𝑠𝑠𝑣 is usefulness of visiting with site search, 𝑅𝑠𝑛𝑣 is usefulness of visiting without searching
the site, 𝑁𝑠𝑠𝑣 is number of visits with site search.
This indicator regulates plans and strategies for further investment in the development of site search
service. This figure should be 80% of the monthly revenue for the website.
Search Engine Marketing (SEM) Optimization Algorithm is as follows:
1. Keyword research (for paid / unpaid search results).
a. Visitors who came according to natural search results.
b. Visitors who use internal site search.
2. Campaign optimization (paid search results).
3. Login page optimization and SEO (search engine optimization) (for paid / unpaid search results).
4. Optimize your ad positions for your AdWords campaign (paid search results):
a. Optimization of positions per visit (pages / visits, average length of stay on the site);
b. Position optimization by percentage of new visits (bounce rate, conversion rate achieved
for goal 1 [for goals 2-4], conversion rate achieved, [profit, transactions, average utility,
e-commerce conversion rate, visit utility]);
c. Optimization of positions for the usefulness of the visit;
d. Daytime optimization in AdWords;
5. Optimize ad versions for your AdWords campaign (paid search results).
For keywords that drive conversions, you need to optimize your investment by setting a maximum
cost-per-click (CPC) in AdWords. The amount of return on investment (ROI) must be positive, ie the
income received must exceed the costs, i.e.:
𝐼𝑛𝑐𝑜𝑚𝑒 − 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠 (30)
𝑅𝑂𝐼 = ∙ 100% > 0,
𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠
where Income – profit, Expenses – costs.
The ROI for gross profit is
(𝐼𝑛𝑐𝑜𝑚𝑒 ∙ 𝐴𝑝 )/100 − 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠 (31)
𝑅𝑂𝐼𝑣𝑝 = ∙ 100%,
𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠
where 𝐴𝑝 is the amount of profit.
That is, you can calculate how many percent (q%) more money should be allowed to spend on a
particular keyword in AdWords, without the risk of getting a negative ROI. At the start of a campaign,
ROI may be negative as long as the brand and website are unknown. Visitors typically need several
visits to a new website before they can convert. But such a situation (with a negative ROI) can only be
acceptable for a short period of time - about a few weeks, depending on the situation.
To calculate the maximum amount that can be spent on attracting visitors - the maximum cost of
attracting (С𝑎𝑚𝑎𝑥 ), you must use the formula:
𝐼𝑛𝑐𝑜𝑚𝑒 ∙ 𝐴𝑝 (32)
С𝑎𝑚𝑎𝑥 = 100
𝑅𝑂𝐼𝑣𝑝
+1
100
Knowing the conversion rate for each keyword, you can now calculate the maximum cost-per-click
(С𝑐𝑚𝑎𝑥 ) for that keyword.
𝑅𝑒𝑐𝑐 (31)
С𝑐𝑚𝑎𝑥 = С𝑎𝑚𝑎𝑥 ∙
100
The result of this system is that you do not have to overpay for AdWords keywords.
Keyword topics are a term used in search engine marketing to describe a set of keywords that
accurately describe the content of a page. Properly defined keyword topics for search engines
significantly improve the effectiveness of user visits because of search.
Typically, topics usually contain 5-10 phrases per page, in which keywords intersect. More than ten
common phrases weaken the impact and effectiveness of the page - in terms of user experience and
search engine rankings. If you already have a page with more than 10 keyword phrases, it is best to
create a separate page dedicated to additional keywords. Basic tips:
1. It is always necessary to put the interests of visitors and customers first.
2. For campaigns, you need to use special login pages - for visitors who came for both paid and
unpaid search results.
3. Login pages should be next to the call to action.
4. Website content should be built around the topic of keywords with 5-10 keywords and
intersecting phrases.
5. You should place keyword-rich content closer to the top of the page.
6. You should use keywords in HTML tags .
7. You need to use keywords in anchors, i.e., in HTML-tags .
8. Avoid placing text in images, Flash, or other embedded content.
9. You must use a robots file. t x t to control which pages should be indexed by search engines.
10. Do not abuse keywords and do not spam search engines.
An algorithm for site promotion and calculation of its efficiency is:
1. Assign utility to goals.
2. Activate e-commerce reports.
a. Define an unlimited number of goals (standard number - 4 goals for each profile).
b. Determine the amount of time and visits a user needs to convert.
c. Investigate the contribution of each goal (product) to the overall revenue of the website.
d. Group goals by category.
e. Generate lists of individual transactions as individual targets.
3. Track non-commercial content of the site as elements of e-commerce (downloading pdf-files,
images, etc.).
4. Track offline marketing activities or offline visitors.
a. Prestigious URLs - in the case of a well-known brand, all web content should be hosted
on one central domain.
b. Coded URLs - in the case of a well-known brand or if the products already have separate
websites.
c. Combination with search - brand awareness is less than product or service awareness,
or the target audience is more price-oriented than brand-oriented.
4. Experiments, results, and discussion
For the detailed textual content analysis and monitoring in social network user profile or the Web
site, such as the online blog or forum and online forum, eight different systems were developed and
implemented, respectively, each supporting different number of stages of the content life cycle. That is,
not all components have been developed for different implemented systems or subsystems of Web
resources processing as content creation; management and maintenance have not been developed at all.
Table 3 presents a list of implemented Web sites with indication of the availability of implemented
subsystems of Web resources processing with the textual content life cycle support. The table 4 presents
the results of the developed systems according to Google Analytics. The analysis of the results of text
content support allows to determine the reasons for the formation of the target audience using a set of
characteristics of the operation of Web resource. By regulating the thematic set of text content, its
uniqueness, efficiency of its formation and adequate management according to the individual needs of
the regular user, you can model the boundaries of the target social audience and the number of unique
visitors from search engines. Fig. 1-3 presents the results of the developed systems in the form of graphs,
which show that in the presence of all stages of the content life cycle significantly increases the number
of visits and unique users.
Table 3
Implementation of stages of Web resources processing in the developed systems
No Resource address Type formation management support
1 fotoghalereja-vysocjkykh.com forum +/– + +/–
2 vgolos.com.ua forum + + +
3 victana.lviv.ua blog +/– + +
4 tatjana.in.ua blog – +/– +/–
5 www.autochip.vn.ua blog – + +/–
6 kursyvalyut.com blog + + -
7 dobryjranok.com blog +/– +/–
8 goodmorningua.com blog +/– +/– –
9 зсш3львів.in.ua blog – – –
10 presstime.com.ua forum +/– +/– +/–
Table 4
The results of the systems
Name 1 2 3 4 5 6 7 8 9 10
Visiting 20132 5997052 3654456 1381 8724 4865 9606 25 7 3138
Formation 90% 100% 40% 10% 70% 30% 20% 60% 0% 50%
Management 40% 100% 80% 50% 60% 90% 30% 20% 0% 70%
Support 50% 100% 40% 10% 80% 30% 20% 60% 0% 70%
Content 20% 80% 70% 100% 50% 100% 30% 40% 100% 60%
Uniqueness
Average 1:02 2:14 2:04 3:56 2:27 4:41 1:51 8:12 0:46 4:15
duration of
visit
Unique 16586 2501402 1501202 728 4996 3215 7105 7 5 1345
visitors
Pages / visits 1,59 1,93 1,67 3,96 2,17 4,54 2,59 3,24 1,67 5,78
% new visits 82,39 41,68 39,88 52,57 57,23 65,45 73,88 28,0 97,32 42,86
% repeated 17,61 58,32 60,12 47,43 42,77 34,55 26,12 72,0 2,68 57,14
visits
Page views 31982 11588861 769923 5464 18892 22071 24908 81 12 18132
Failure rate 82,92 71,90 83,08 53,15 68,15 56,14 55,67 48,0 97,02 32,92
(%)
Social 0,06 0,02 0 4,56 0,01 0,06 0,02 0 0,01 7,81
network (%)
Organic 34,83 36,10 31,22 22,23 24,34 4,67 8,91 0 0,13 26,04
search (%)
Not set/non- 58,22 52,48 42,46 62,49 26,984 91,14 88,13 23 6,72 31,90
organic
search
results (%)
Direct traffic 5,34 11,20 26,12 7,53 48,73 2,14 2,35 77 93,12 27,88
(%)
Other sites 1,55 0,20 0 3,19 0,04 1,99 0,58 0 0,02 6,37
(%)
Conversion 0 0 0 0 0 7,83 12,51 0 0 0
rate
achieved
Dependence of sales of commercial content on visiting the information
resource fotoghalereja-vysocjkykh.com
160
Number Visiting the information resource
Visiting regular users
140
Implementation of commercial content
120
100
80 Time
60
40
20
0
The ratio of visits to the information resource fotoghalereja-
vysocjkykh.com and the implementation of commercial content,
depending on the application of methods of processing commercial
1000 content
The number of all visits to the information resource
Number of visits of regular users
900
The number of sales of content to regular users
800
700
600
500
400
300
200
100
0
<>
Management, Support> Management>
Application of methods for processing the information resource fotoghalereja-
vysocjkykh.com at different time intervals
Figure 1: Statistical analysis of the functioning of the "Photo Gallery Vysotsky"
Statistical distribution of visits to the information resource victana.lviv.ua and
250 sales of commercial content
Visiting
Number
Regular users
Content implementation
200
150
100
50
0
Time
-50
Dependence of visiting the information resource victana.lviv.ua and the
700 implementation of commercial content on the connection of software tools for
processing information content
Visiting all users
600
Visiting regular users of information
resources
Implementation of commercial content
among regular users
500
400
300
200
100
0
Management> Support>
Connected software for processing commercial content
Figure 2: Statistical analysis of the functioning of "Victana" site
The service of keeping visits statistics to the Web resource allows to estimate the increase in sales
of textual content in direct proportion to the increase in the number of visits to the information resource,
the number of regular users, the prospects of marketing activities (Fig. 3). The presence of subsystems
for the formation, management, and maintenance of textual content in Web resource processing systems
increases the sales of textual content to the regular user by 9%, actively attracting unique visitors,
potential users and expanding the target and regional audience by 11%, viewed pages by 12%, time of
visiting information resources by 7%.
The number of visits to the information resource kursyvalyut.com
200
Regression analysis
180
Implementation of commercial content
160
140
120
100
80
60
40
20 Without the use of methods
Using the methods
0
1 101 201 301 401 501 601 701 801 901
Mathematical expectation of the implementation of commercial content through
<>
the information resource
5
4,5
4
Expected value
3,5
3
2,5
2
1,5
1
0,5
Time
0
2011 2012 2013 2014
Figure 3: Regression analysis of increased sales of textual content
Results from further analysis of Victana blog (Fig 4-5):
• Detection of time series trends by smoothing methods (Fig 6-12).
• Constructing the correlation field and determining the value of the correlation coefficient.
• Calculating the correlation ratio and building graphs of autocorrelation functions.
• Dividing one of the sequences into three equal parts.
• Constructing a correlation matrix for them.
• Finding the coefficients of multiple correlation (Fig. 13).
• Obtaining the result of cluster data analysis (Fig 14-15).
It is necessary to divide a given set of objects, each of which is characterized by the same set of
specific features, into separate groups, using hierarchical agglomerative cluster analysis.
17.07.2012
07.08.2015
26.07.2015
14.07.2015 29.07.2012
10.08.2012
22.08.2012
02.07.2015
20.06.2015 80 03.09.2012
15.09.2012
08.06.2015
27.05.2015 27.09.2012
15.05.2015 09.10.2012
03.05.2015 70 21.10.2012
02.11.2012
21.04.2015 14.11.2012
09.04.2015 26.11.2012
28.03.2015 60 08.12.2012
16.03.2015 20.12.2012
04.03.2015 50 01.01.2013
20.02.2015 13.01.2013
08.02.2015 40 25.01.2013
27.01.2015 06.02.2013
15.01.2015 30 18.02.2013
03.01.2015 02.03.2013
22.12.2014 20 14.03.2013
10.12.2014 26.03.2013
28.11.2014 10 07.04.2013
16.11.2014 19.04.2013
04.11.2014 0
01.05.2013
23.10.2014 13.05.2013
11.10.2014 25.05.2013
29.09.2014 06.06.2013
17.09.2014 18.06.2013
05.09.2014 30.06.2013
24.08.2014 12.07.2013
12.08.2014 24.07.2013
31.07.2014 05.08.2013
19.07.2014 17.08.2013
07.07.2014 29.08.2013
25.06.2014 10.09.2013
13.06.2014 22.09.2013
01.06.2014 04.10.2013
20.05.2014 16.10.2013
08.05.2014 28.10.2013
26.04.2014
14.04.2014 09.11.2013
02.04.2014 21.11.2013
03.12.2013
21.03.2014
09.03.2014
25.02.2014 15.12.2013
27.12.2013
08.01.2014
13.02.2014
01.02.2014 20.01.2014
Figure 4: Graph in the Polar coordinate system of the functioning of "Victana" site
80
70
60
50
40
30
20
10
0
01.04.2012 18.10.2012 06.05.2013 22.11.2013 10.06.2014 27.12.2014 15.07.2015 31.01.2016
-10
Figure 5: Graph in the Cartesian coordinate system of the functioning of "Victana" site
Figure 6: Cumulative results according to histogram data
Figure 7: Statistical value for visits of Victana resource
Figure 8: Analysis of Web site monitoring by days based on SAM method
Figure 9: Analysis of Web site monitoring by days based on Medians method
Figure 10: Analysis of Web site monitoring by months based on SAM method
Figure 11: Analysis of Web site monitoring by months based on Medians method
Figure 12: Detection of the trend of the time series by smoothing methods
Figure 13: Correlation matrix
Figure 14: Visiting forecast (highlighted in gray)
Figure 15: Dendrogram of attendance as a result of cluster analysis
Accuracy of model is 22.033898305084744%.
5. Conclusion
General recommendations for the design of information resource processing systems have been
developed, different from the existing by providing more detailed stages and the availability of
information resource processing subsystems that allow for effectively implement information resource
processing at the system developer level (reducing resources and development time and improving
information processing systems resources). Structures of modules of the system of information
resources processing for the realisation of stages of the life cycle of text content were proposed. The
application software for the formation, management, and maintenance of textual content to achieve the
effect at the level of the owner (increasing the profitability, user interest) and user (clarity, simplification
of the interface, unification, expansion of choice) of information processing systems are developed and
implemented. A method of text content support was developed based on the analysis of statistics about
the functioning of the information resources processing system to change the values of management
parameters and requirements for the formation of text content. It allowed increasing sales of text content
to the regular user by 9%. The structure of the information resources processing system was improved
based on the analysis of information resources processing processes, different from the existing ones
by the subsystems of text content formation, management, and maintenance, which made it possible to
implement the stages of text content life cycle and develop recommendations for designing standard
systems. Recommendations for designing the structure of the information resources processing system,
different from the existing stages by the detail and the availability of subsystems of information
resources processing, which allow maintaining the life cycle of textual content at the system developer
level (reducing resources and development time and improving system quality). Software for creating,
managing, and maintaining textual content to increase the active involvement of potential users and
expand the target audience by 11% to improve the functioning of information resources at the level of
the owner (increasing profitability, user interest) and user (intelligibility) are developed and
implemented, provided simplification of the interface, automation of information resources processing
and expanding the choice of functionality).
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