Gemius audience methodology short description

1. methodology
for Moldova
Date: 2016-01-28
www.gemius.com

2. 2
Contents
Introduction to gemiusAudience methodology......................................................................................... 3
Definitions.............................................................................................................................................. 3
gemiusAudience Research Goals......................................................................................................... 4
Introduction: Research Modules........................................................................................................... 5
Basic Research Schematic ................................................................................................................... 5
Population Researched......................................................................................................................... 6
Results Estimated to Population.......................................................................................................... 6
Limitations in Estimating Results to the Population........................................................................... 7
The Cookie Panel Module .......................................................................................................................... 7
Measuring Online Activity ..................................................................................................................... 7
Preparing Demographics ...................................................................................................................... 8
Preparing the Set of Reporting Cookie Panellists................................................................................ 8
Structural Study ......................................................................................................................................... 8
Explanation of the Structural Study...................................................................................................... 8
Demographic Universes........................................................................................................................ 9
Site-centric Audit........................................................................................................................................ 9
Explanation of the Site-centric Audit.................................................................................................... 9
Procedures for Embedding Site-centric Scripts................................................................................... 9
Behavioural Universes..........................................................................................................................10
Algorithm for Calculating the Number of Visitors (Real Users) of Websites in the Behavioural
Universe.................................................................................................................................................10
Data Integration ........................................................................................................................................11
The Media Tree.....................................................................................................................................11
Data Quality Control .............................................................................................................................12
Panellist Demographic Data ................................................................................................................12
Structural & Behavioural Weighting.....................................................................................................12
Research Results......................................................................................................................................13
Appendix I - Weighting process of single panel........................................................................................14

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Introductionto gemiusAudience methodology
gemiusAudienceis a wide spread and popular internet audience measurement methodology in
Europe. gemiusAudienceis the currency for online media planning and buying in many European
and Middle East countries.
Gemius – the largest online research company in Europe – has developed the gemiusAudience
methodology using its thirteen years of experience in online research. As the first company in the
world, Gemius developedthe hybrid approach in the online audience measurement, joining the
advantages of user-centric and site-centric research. In fact, in the past thirteen years, Gemius has
conducted every possible type of online research – from log-file analysis to classic (CATI-
recruited) household panel monitoring – and on the basis of this experience has synthesized the
methodology resolving all currently known issues at the lowest possible costs.
This document describes the standard Gemius methodology applied in the gemiusAudience
measurement.
Definitions
Recruitment Survey – web survey used to gather demographic data and other essential
information about an Internet User. This survey is used for recruitment in Cookie Panel.
Research or Study – the research study described in this Methodology.
Site-Centric Audit or System (Site-Centric) – gemiusTraffic measurement system enabling the
monitoring of Website usage, using data collection scripts embedded into the web page source
code. The system measures (among others): Visitors (Cookies), Page views, Time Spent.
Throughout this document, wherever a site-centric system is mentioned, the assumption is that
this site-centric system is the gemiusPrism (gemiusTraffic) system.
Structural Study (or Establishment Survey)– minimum requirement: quantitative research study on
a representative sample of people at the age+ from urban areas whose results serve to determine
the size and structure of the Internet User universe.
Cookie – a unique indicator making it possible to identify the profile of a given web browser using
a file saved on the computer of that given profile’s user. Throughout this document, when a
„Cookie” is referred to, we are referring to a Cookie as generated by the Site-centric System.
Media Tree – a model for organizing the list of Websites reported by the Research.
Researcher – Gemius.

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Number of Estimated Cookies – estimated number of Cookies, which would have been on the
Website if cookie deletion introduced no bias.
Cookie Panellists Activity Logs – data on the activity of Cookie Panellists on Websites.
Site-Centric Logs – logs prepared on the basis of data about Panellists generated by the Site-
centric Audit.
Recruitment Location – Websites where respondents are recruited to participate in the Research.
Methodology – this document, describing the execution of the Research.
Analytical Software(or Media Planning Tool) – gemiusExplorer software, used to analyze the
Research Results.
Page View – downloading of page content from a URL in user’s browser.
Cookie Panellist – an internet user, represented by Cookie, who had filled out a Recruitment
Survey.
Reporting Cookie Panellist – a Cookie Panellist, in a certain age cohort (as determined by the
Structural Study, who successfully passed through applicable validation procedures.
Cookie Panel – the collective group of individuals recruited to the Research Study who have filled
out a Recruitment Survey
Reporting Cookie Panel – sub-set of the entire Cookie Panel, consisting of Reporting Cookie
Panellists.
Data Presentation Cut-off – minimum number of Cookie Reporting Panellists, who visited a given
Node in the given time period, that is necessary to credibly present results for that Node in the
given time period.
Internet User – an individual, who uses the Internet at least once a month (via web pages) –
collectively, „Internet Users”.
Computer User – a person using the given computer to access the Internet, regardless of Internet-
usage frequency.
Node – the lowest entity for which results are reported as presented in the Media Tree. May be a
Website, a sub-set of a given Website or a group of Websites.
Top-level Nodes – aggregated nodes in the Media Tree, which contain sub-nodes as necessary.
Website – a collection of explicitly associated web pages, belonging to one publisher.
gemiusAudience Research Goals
The basic research goals are:

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 determine the audience size for Websites,
 determine the number of visitors (real users) visiting said Websites,
 determine the usage frequency and intensity for Website(s) as expressed by usage
indicators (e.g. page views, time spent),
 determine the socio-demographic profile of Websites’ audience(s),
 developing a constant measurement of Website audiences and the population using
Internet applications, enabling for the following of trends and changes in the audience size
and structure over time.
Introduction: Research Modules
The Research process consists of four modules:
 The Cookie Panel
 The Site-centric Audit,
 The Structural Study,
 The Data Integration module, which combines data collected & analyzed in the four other
modules.
Each of these modules includes different data integral to the research process. An explanation of
the data collected and procedures followed in data processing for each of these modules can be
found below.
Basic Research Schematic
The Research is a user-centric study. Recruitment Surveys are issued to randomly selected
Cookies displayed at Recruitment Locations. Recruitment Surveys are issued to the individual
using a given web browser whose profile has a given Cookie assigned to it.
The Research makes use of additional data from external sources (notably the Structural Study
and the Site-centric Audit). To ensure accuracy of demographic estimation, Websites which take
part in the Site-centric Audit have site-centric weighting to ensure behavioural representativeness
Conceptual Architecture
The conceptual architecture of the gemiusAudience Cookie Panel system is as presented in the
diagram below:

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Figure 1 - Conceptual architecture
Population Researched
The population researched using this Study is:
 Internet Users from the measured country that use Websites when connected to the Internet from
their home or workplace, and that;
 over the minimum age provided in the definition for Reporting Cookie Panel.
The Researcher will determine the viability of conducting measurement of usage in other locations
(e.g. schools).
Results Estimated to Population
In the case of estimation of data from the Site-centric Audit, weighting is done irrespective of each
Reporting Panellist’s age. The size of the population to which results are estimated is at a general
level (Internet Users) and at a site-specific level (the number of visitors (real users) to each
Website).
The general population size will be determined as the number of Internet Users (or if requested All
Users and Non-Users) above a certain age (as provided for in the definition of a Reporting Cookie
Panellists), that make use of the Internet from any location (e.g. at-home, at-work, etc.) as provided
for in the Structural Study. Demographic universe will be calculated based on the Structural Study.
The population size at a specific level will be determined as the number of visitors (real users) for
a given Node in a given time period, based on the Site-centric Audit, in accordance with the
algorithm for calculating the number of visitors (real users) after accounting for Cookie deletion.
Universes for sections or groups of Websites will be calculated based on the Media Tree using

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identifiers of Site-centric Audit scripts. The Media Tree will be defined in accordance with the
procedures provided in this document.
Limitations in Estimating Results to the Population
The method for collecting data from Cookie Panellists via the use of the Site-centric Audit makes it
possible to conduct measurements only if and when a Cookie Panellist accepts (does not block)
cookies.
Due to legal limitations, recruitment difficulties and usage patterns in some access locations (i.e.
large numbers of people of varying demographic characteristics making use of one computer, for
example at an Internet café) the Research will not include the following:
 people connecting less frequently than once every 30 days (non users will be only surveyed for the
purposes of projection of the figures for the Whole Population in the measured country)
 people connecting from locations other than at-home or at-work.
Due to limitations in the scope of data available from the Structural Study, the Research will not
include those:
 provided for in the definition of a Reporting Cookie Panellist.
The factors described above represent a limitation of the ability to estimate data from the
Research to the entire Internet user population in the measured country.
The Cookie Panel Module
An Internet User, who filled out the Recruitment becomes a Cookie Panellist.
Measuring Online Activity
The activity of Cookie Panellists is measured using site-centric scripts that are embedded in the
html code of web pages. The online activity of Cookie Panellists only includes Websites that have
site-centric measurement.
For the set of Reporting Cookie Panellists, Activity Logs are prepared for all monitored with site-
centric Websites from data from the Site-centric Study. Activity Logs contain:
 time of Page View
 Cookie number
 Website number
 Web Page number

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Such an Activity Log can be sent to a program which calculates Visit Time and Visits for Internet
users.
Preparing Demographics
The file containing respondent demographics is created on the basis of databases prepared and
collected automatically throughout the research process. The demographic characteristics used
in weighting are extracted from these databases and data is filtered according to defined
demographic validation rules. Then grouped demographics are generated (if they are required) and
additional demographic columns are added for weighting. The resulting file contains precisely one
row for each respondent containing his or her characteristics.
Preparing the Set of Reporting Cookie Panellists
A group of Reporting Cookie Panellists is created as a sub-group of Cooke Panellists that belong to
the set of „good” Cookies in the selected month. The group of “good” Cookies is derived by taking
all persistent Cookies from the examined country in the selected month and any preceding
months.
Structural Study
Explanation of the Structural Study
The goal of the Structural Study is to determine the population to which research results are to be
estimated at a general level. Specifically, the study is meant to:
 determine the size of the population using the Internet and the size of the Internet User population
in the measured country,
 determine the structure of the Moldavian population using the Internet and its component Internet
User population in general and depending on their place of Internet usage (home, work, school,
other),
 determine Internet usage habits (e.g. frequency of overall Internet use depending on place of
Internet use, Internet usage history, etc.),
 as the results of the research should be also projected to the non-users population, the Structural
Study should determine its size.
The results of the Structural Study are the basis for structural weighting so as to ensure
demographic accuracy.

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Demographic Universes
Demographic universes are calculated on the basis of data from the Structural Study. The
universes can contain more aggregated responses than those present in the data from which they
are calculated (data collected from the Cookie Panels). The responses of individual Panellists are
aggregated into analogous groupings to those present in the Structural Study. Subsequently,
universes are calculated for these defined groupings from the Structural Study.
Site-centric Audit
Explanation of the Site-centric Audit
The goal of the Site-centric Audit is to:
 determine the usage of Websites measured via the Site-centric Audit, usage being understood as the
number of page views, the number of visits, time spent on Websites and the number of visitors
connecting to the Website, identified and differentiated according to their Cookie identifiers,
 determine the reach of specific Websites, their component sections or groups of Websites as per the
method of calculating Website reach using the real users indicator.
The method of calculating Website reach while accounting for the inflation introduced by Cookie
deletion is contained in the chapter below.
The exact values of the number of page views, visits, time spent and number of visitors (real users)
can be calculated only for Websites that are measured with the Site-Centric Audit.
A combination of data from the Structural Study and the Site-centric Audit will be used to calculate
weights for Reporting Cookie Panellist. The weights of all Reporting Panellists will be contained in
the weight databases.
Procedures for Embedding Site-centric Scripts
1. Websites participating in the Site-centric Audit embed scripts into their web pages in
accordance with the Researcher’s instructions. Not adhering to these instructions will result in
exclusion of the Website from the Site-centric Audit.
2. Each individual web page is allowed to contain only one Site-centric Audit script at one point in
time, unless otherwise instructed by the Researcher.
A given Website can only use scripts that are dedicated for this Website. Each Website is
measured by using at least two script identifiers – for the main page and for other pages. It is not
allowed to monitor advertisements (including those embedded in pop-ups) with site-centric
scripts.

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Behavioural Universes
Behavioural universes are calculated on the basis of the Media Tree, archived Site-centric Audit
data and two sets of Cookies – that set for which there is certainty that they were not deleted in
the reporting period (so-called „good Cookies”) and a set containing all Cookies. The mentioned
data sources are prepared separately for each month.
The set of good Cookies is calculated as the combined set of Cookies present in the current
month, the preceding months and the following month. Only Cookies reported as being from the
measured country are used (determined by IP address). Cookies that are not from the measured
country and their page views are not included in the behavioural universes (such cookies and page
views are excluded).
Site-centric Audit data are subjected to subsequent processing which assigns page views to the
Media Tree structure
Universes are calculated on such a file. For each Node defined in the Media Tree the number of
visitors (real users), the number of page views, the number of visits and the time spent on that
Node are calculated from the Site-centric Audit. The behavioural universe is calculated only for
Website that have Site-Centric scripts correctly installed for the whole reported month.
The algorithm for calculating these behavioural universes takes into account those situations in
which the Media Tree contains parent-child relations.
Algorithm for Calculating the Number of Visitors (Real Users) of
Websites in the Behavioural Universe.
Since the number of cookies registered on a Website through the Site-centric System is not
equivalent to the number of Internet Users (due to deletion of cookies, multiple people using one
computer, one person using multiple computers), it is impossible to credibly calculate the reach of
Websites basing directly on the number of cookies which have been registered. In order to solve
this problem, the researcher has developed the following algorithm:
a. It is being calculated the minimum number of cookies for which it is certain that they must
have been existed to generate all page views on every of the studied Websites (taking into
consideration the fact of cookie deletion and that one deleted cookie is replaced with a new
one).
b. For each cookie from a given country registered at any of the studied Websites the
following values are determined:
Cc - timestamp of cookie creation
Cl – timestamp of the last page view generated on any of the Websites

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c. Values of Cc, Cl of cookies are sorted and iterated by the sequence: supplementary variable
x is set up to 0. For every Cc the value of x is incremented by 1 and for every Cl the value of
x is decremented by 1. After the finished iteration the value of x equals 0.
d. The minimum number of cookies that must have been existed to generate page views for
the group of all studied Websites equals to the maximum value of x in point c) – let’s call
this number MinAll.
e. For every of Websites the group of cookies that generated at least one page view is
examined. For such groups of cookies the operation of sorting Cc and Cl are being done
and the maximum values of x are calculated - according the description in points c) and d).
Let’s call this number MinWebsite
f. It is being calculated the relative reach RW of visitors (real users) on a given Website in the
chosen time period using the formula: RW = MinWebsite/MinAll.
g. If P is the size of the Internet user population in a given time period and R is the reach (in
percentages) of the Website connected to the Site-Centric Audit in the given country, then
the number of Visitors (real users) UW for that Website in the given time period can be
calculated using the formula: UW = RW * P * R.
h. If UW is larger than the number of Visits from all cookies from given country on the given
Website in the time period, then UW is set to be the number of all Visits on the given
Website in the given time period.
These calculations are made on the following assumptions:
The percentage of the Internet population from the given country that visits at least one of the
measured Websites is known and equals R (so the sum of all people reached by the Site-centric
System in the time period is equal P*R).
The method used to calculate the number of Visitors (real users) of Websites and Internet
Applications does not allow calculating the estimation error. The method is based on the
calculation of the number of unique Internet browsers (real browsers or real cookie) and was
developed by and is proprietary to the researcher. The method uses the results of the Site-centric
System and information about the overall Internet user population from the Structural Study.
Data Integration
The Data Integration module combines data collected & analyzed in the all the modules.
The Media Tree
The first stage in data processing is the development of the Media Tree for Websites participating
in the Research.The publisher can define a level of nesting. Then domain coverage is verified -
traffic is accepted only from websites with domains which are in study.

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Data Quality Control
Validation is a process by which the quality of data gathered during the Research and their value to
achieving the Research Goals is assessed. Validation procedures are meant to verify if collected
data are logical, internally consistent and credibly present the analyzed reality. During the
validation process, the dataset is „cleaned” through the removal of data likely caused by error or
conscious manipulation of the research.
Each Website is responsible for the proper embedding of site-centric scripts in accordance with
the Researcher’s instructions. Page views that have been generated by non-allowed mechanisms
will be rejected. Only traffic generated by Moldavian Cookies will be reported.
A sample control procedure is used to verify the logical consistency of gathered data. By verifying
logical consistency, data that are logically inconsistent will be ignored during weighting and will not
enter into the research results until they are either verified or corrected.
Panellist Demographic Data
A file containing demographic data provided by the Panellists is generated on the basis of the
Panellists Database. Those demographic characteristics used in the weighting process (see
below) are extracted from the Panellist Database, which are subsequently filtered according to the
demographic validation rules (see below). Then, groupings of demographic characteristics are
generated (as applicable) and then additional columns are added determining whether the given
characteristic is utilized in weighting. The resulting file contains precisely one row for each
Panellist with a list of his/her characteristics.
Structural & Behavioural Weighting
Due to differences between the structure of the Internet User population and the structure of the
Panels in terms of socio-demographic variables and behavioural variables, the Panels will make
use of wreath-weighting.
The weighting process will adjust the structure of the Cookie Panel, specifically to:
 adjust the structure of the Cookie Panel to equal the socio-demographic structure of the group
of Internet Users in the measured country. The Cookie Panel will be adjusted to the socio-
demographic variables in the Structural Study. Cookie Panel can describe the behaviour of
internet users only on Websites that have Site-Centric Audit and the demographic structure of
the audience of this group of Websites differ from the demographic structure of all Websites
(including Websites that don’t have Site-Centric Audit)
 adjust the structure of the Panel to equal the visitation structure of Websites taking part in the
Site-centric Audit. The Panel will be adjusted to behavioural variables in the Site-centric Audit.
Weighting is conducted using the wreath weighting method. Each demographic characteristic used
in the weighting process is called a wreath. The weighting process begins by applying initial

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weights. Then for the first wreath, subsequent weights are generated for the sum data from the
Panel following correction to equal the universes calculated for that characteristic. This is
accomplished by dividing the universe for that wreath by the sample size and multiplying Panel
data by the number received. After adjusting the weights for the first wreath, these weights are
subsequently adjusted for the second wreath and so on until the final wreath. Modifying the
weights of each additional wreath adjusts all previous weights. After running through all rows the
scale or error is calculated for the entire matrix and if it is higher than the agreed minimum (eg:
0,1%) then the entire process is repeated. During subsequent repetitions of the weighting process
the error evidences a tendency to decline and so weights are subsequently more accurate. The
iterative process is repeated until weights are acceptable.
Research Results
Research Results will be produced from data from the Reporting Cookie Panel. Reporting periods
for Panellist activity will be one month and one week. Data will be produced approximately within
the first 20 days of the month following the month being reported (e.g. data for January will be
available at the end of February).
Basic data (number of visitors (real users), number of page views, time spent, socio-demographic
characteristics) are to be the foundation for calculating the analytical indicators available in the
gemiusExplorer Analytical Software.
Figure 2 - gemiusExplorer user interface
gemiusAudience results - traffic numbers and socio-demographic profile of the users - are made
available through Gemius proprietary analysis software – gemiusExplorer. This offline application
provides users with the ability to analyze gemiusAudience results such as website’s
reach/frequency data for specific target groups as needed. It is possible to export data to csv/xls
format for outside analysis as well. Media planning data is updated and published once a month
and reports data at a daily, weekly and monthly level in gemiusExplorer.
Results can be analyzed within a daily/weekly or monthly split.

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It is also important to note that gemiusAudience can report usage and demographic data for entire
websites as well as groups of websites and their sub-sections/sub-pages. This means that media
planners and media sellers can prepare media plans based on audience measurement that
matches publisher’s rate card structures.
It is an important advantage of the research that the media planning data can be presented not
only in the form of rankings, but also by categories depending on the content or the ownership
structure, depending on the market requirements.
All indicators in gemiusAudience can be filtered according to custom-defined target groups.
Link to a demo of gemiusAudience results (along with gemiusExplorer analytical application):
https://audience.gemius.com/en/research-results/demo-data/
Appendix I - Weighting process of single panel
Algorithm input data:
- behavioural data (from site-centric system and the structural study)
- demographic data (from the structural study and data gathered by the
questionnaire)
Algorithm way of working:
- setting the weight for each panellist from the sample for a given period of
time (month, week or day)
Algorithm output data:
- set of weights of all panellists from the sample
INPUT
Behavioural data
- for each node in the tree number of real users, pageviews, visits and time is known from
site centric study
nodeA ruA pvsA visitsA timeA
nodeB ruB pvsB vistsB timeB
nodeC ruC pvsC visitsC timeC
... … … … …

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- for each panellist time, number of pageviews and visits which he made on a particular node
is known
panellist1 Panellist2 panellist3 …
nodeA timeA1 pvsA1 visitsA1 timeA2 pvsA2 visitsA2 timeA3 pvsA3 visitsA3 …
nodeB timeB1 pvsB1 visitsB1 timeB2 pvsB2 visitsB2 timeB3 pvsB3 visitsB3 …
nodeC timeC1 pvsC1 visitsB1 timeC2 pvsC2 visitsB2 timeC3 pvsC3 visitsB3 …
... … … … … … … … … … …
so for each panellist there is information which node he was active on
panellist1 panellist2 panellist3 …
nodeA actA1 actA2 actA3 …
nodeB actB1 actB2 actB3 …
nodeC actC1 actC2 actC3 …
... … … … …
where act can have one of below values:
act = 1 (a given panellist was active on a given node)
act = 0 (a given panellist was not active on a given node)
Demographic data
- for each target group (characteristic) there is a weighting conducted to adjust the panel to
meet the representativeness given by the structural study
CharacteristicA PopA
CharacteristicB PopB
CharacteristicC PopC
… …
- for each panellist there is information given which target group he belongs to:
Panellist1 panellist2 panellist3 …
characteristicA isA1 isA2 isA3 …
characteristicB isB1 isB2 isB3 …
characteristicC isC1 isC2 isC3 …
… … … … …
where is can have one of the following values:
is = 1 (a given panellist has a given characteristic)
is = 0 (a given panellist does not have a given characteristic)

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RIM WEIGHTING ALGORITHM
Weighting process should find weights of a particular panellists w1, w2, w3 which meet the
following rules:
nodeA: w1 * actA1 + w2 * actA2 + w3 * actA3 + ... = ruA
nodeB: w1 * actB1 + w2 * actB2 + w3 * actB3 + ... = ruB
nodeC: w1 * actC1 + w2 * actC2 + w3 * actC3 + ... = ruC
...
characteristicA: w1 * isA1 + w2 * isA2 + w3 * isA3 + ... = popA
characteristicB: w1 * isB1 + w2 * isB2 + w3 * isB3 + ... = popB
characteristicC: w1 * isC1 + w2 * isC2 + w3 * isC3 + ... = popC
...
where nodeX is a whole website; sections are excluded from RIM weighting process.
Weighting is conducted using a technique called wreath (or RIM) weighting. Each demographic
and behavioral characteristic, which is used for weighting, is dubbed a wreath. The process of
weighting begins with applying initial weights w1, w2, w3 to each panelist. Then, for the first
wreath, weights are generated such that the sum of all weights applied to panelists will be equal to
the value of the universe for that characteristic. This is done by dividing the universe for that
wreath by the sample size and then multiplying data from panelists by the number received. After
applying the weights from the first wreath, subsequent wreaths are weighted in order as above.
Adjusting the weights for each subsequent wreath adjusts all weights. After weighting all rows, the
error for the entire matrix is calculated and if it exceeds a defined minimum (0,1%), then the entire
weighting process is repeated. During subsequent weightings, the size of the error has a decidedly
decreasing tendency and thus weights are increasingly accurate. This process is an iterative
process.
Algorithm stops when a) maximum error for ru on a node for every characteristic is not greater
than x% (eg 0,1%) or b) after y iterations (eg. 1000 iterations).
Important: only number of ru and demographic data are weighted for roots of websites, other
statistics like time, pageviews and visits are corrected by coefficients (see below).
Number of real users on a given node in a given target group is calculated in the gemiusExplorer
analytical application as a sum of the weights of all those panellists, who belong to a selected
target group and visited a selected node.

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COEFFICIENTS CALCULATION
For every node (including section nodes) the value of time coefficient, page view coefficient and
visits coefficient is being calculated by the formulas present below.
nodeA:
time_coeffA = timeA / (w1 * timeA1 + w2 * timeA2 + w3 * timeA3 + ...)
pvs_coeffA = pvsA / (w1 *pvsA1 + w2 * pvsA2 + w3 * pvsA3 + ...)
visits_coeffA = visitsA / (w1 *visitsA1 + w2 * visitsA2 + w3 * visitsA3 + ...)
nodeB:
time_coeffB = timeB / (w1 * timeB1 + w2 * timeB2 + w3 * timeB3 + ...)
pvs_coeffB = pvsB / (w1 * pvsB1 + w2 * pvsB2 + w3 * pvsB3 + ...)
visits_coeffB = visitsB / (w1 * visitsB1 + w2 * visitsB2 + w3 * visitsB3 + ...)
nodeC:
time_coeffC = timeC / w1 * timeC1 + w2 * timeC2 + w3 * timeC3 + ...)
pvs_coeffC = pvsC / (w1 * pvsC1 + w2 * pvsC2 + w3 * pvsC3 + ...)
visits_coeffC = visitsC / (w1 * visitsC1 + w2 * visitsC2 + w3 * visitsC3 + ...)
Number of page views on a given node in a given target group is calculated in the gemiusExplorer
analytical application as a sum of the weight of every panellist, who belongs to a selected target
group by the number of page views he generated on a selected node; than the result is multiplied
by a value of pvs_coeff for a given node.
In a similar way the value of time and visits is being calculated.
Note – in gemiusExplorer application the number of page views on a selected folder is a sum of
page views on child nodes.

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