methodology for Moldova

Date: 2016-01-28

www.gemius.com

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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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