Analytical methods in marketing research

L 12

Ing. Jiří Šnajdar 2013

Analytical methods in marketing research  Methods used for analysis of data obtained by marketing research could be divides to :

•data analysis of secondary character, •data analysis of primary character.

This segmentation is only working. In practise appear some further stated processes in both spheres.  

Possibilities of analysing of secondary data Among main directions of data analysis belong :

analysis of time sequences, frame by frame analysis and combination of frame by frame and time analysis.  •Analysis of time sequences

  

Evaluation of absolute increases

The first step at analysis of time sequences is investigation of movement of absolute values of given variable in time.

Methodically is for it used research of absolute increases, sliding averages, relative values, regressive analysis of time sequences.

Sliding totals, sliding averages

Other analytical view on time sequences offer sliding averages, first stage of which are sliding totals.

 

  

• sliding totals = moving sums of few adjoining values (can be even or uneven)

• sliding averages = averages of sliding total in period

Advantages of sliding total, averages is that reduce influence of extreme values,

express better tendency.

Interesting analytical possibility is possible combination with analysis of absolute

differences (we use sliding averages of first or second differences).

 

  

Moving averages Averages from last „n“ periods moving with last period. Value of sliding average can be set also in last period.

Exponentially weighed moving averages (EWMA)Are moving averages with possibility of giving an other weight to historically more remote data than data from last periods.In statistical programmes these processes are called exponential smoothing.  

 

 

  

Evaluation of relative increases, values Investigation of progress of time sequences is appropriate to in context, so to apply extent of change in absolute statement to total basis. It is possible to use either the relative increases or their adapted forms, chain indexes.

Relative increases are absolute differences related to value of given period.

Chain indexes express rate of values of two adjoining periods (relative increase + 1)

 

 

 

  

Constant and plus value of chain index means, that progress runs in progressive increase.

For evaluation of relative progress of time sequence is used average rate of increase = geometrical average of chain indexes, or n-1 radix from basic index. Basic indexes = rate of value of last period of given time sequence to value of first period.Relative rates are space-less characteristics. Are presented in basic or percentage (point) score. The average is comparability among indicators measured with different units.

 

 

 

  

- Regressive analysis (equalising) of time sequences

Most often is with equalising of time sequences meant usage of regressive analysis. With help of method of least squares we try to afflict a curve, that best expresses existing movement of followed quantity in time. The most often mentioned functions of time sequences smoothing :

linear: Y = a + btquadratic, cubic : Y = a + bt + ct2

Y = a + bt + ct2 + dt3

hyperbolic : Y = a/t exponential : Y = abt

 

 

 

  

Choice of concrete type should be justified by exactness, with which follows progress of followed values in time. By following of time sequences have in marketing research specific position functions of equipage – for example Gompertz function and the most famous logistic function (both functions belong to sphere of so called limit functions). kForm of logistic function : Y = ---------

1 + be –at

Parameter “k” means top limit of saturation. Critical is inflexion point - transition form progressive to regressive increase.

 

 

 

  

Progress of logistic curve is commented from view of possible phases of equipage process (from phase of development of initial equipage over phase of quicken increase, fast increase, slow increase after phase of saturation).

With help of logistic function we try to apprehend market situation from view of its saturation, primary at so called things of long-term consumption.

 

 

 

  

Mostly we cancel out no absolute value of number of articles in household, but so called degree of equipment (number of households, that own any number of given PDS to number of all households).

In addition to this evaluation of equipment comes in useful so called extent of equipment – number of all things of followed type of PDS to all households.

In addition to degree and extent of equipment we follow age of things, structure, recondition cycle, or average price. 

 

 

 

  

- Frame by frame analysis  

We try to analyse structure of followed phenomenon according to chosen points of view.

Frame by frame analysis can be based for example on :•features of consumers (see segmentation criterions) :- sphere of traditional criterions (demo-, geo-),- also behaviour criterions (for example sphere of expenses in statistic of family budget), 

 

 

 

  

• structure of distributors and their offers,• structure of product mix (own, competitors´),• structure of communication mix,• price structure. At following of structure we try to catch also the

rate of tightness or conversely dispersion of values according to followed variable. To this serve characteristics of variability (dispersal, standard deviation, variable coefficient).

 

 

 

 

  

Exist some areas of market research and consumer behaviour, where are these methods used (binding demand – incomes, price flexibility,…). One of specific method of generalising of binding between incomes and demand are so called Törnquist curves.Three function bindings, each characterises certain group of goods.

goods of indispensable character : goods of dispensable character : goods of luxury character : 

 

 

 

 

 

  

 - Combination of frame by frame and time analysis It is analysed progress of structure in time. - One of specific view is so called cohort

analysis.Cohort in demographic meaning are social categories of individuals, by whom happened in the same time (or time interval) the same event.

The most used are cohorts, where the same event is birth. Sense of cohorts´ usage at investigation of consumer behaviour explains following hypothetical example. 

 

 

 

 

 

  

 We have partly numerousness of people in age groups 20-29, 30-39, 40-49 years. Further we know their expenses for books. Following of cohorts according to date of birth enables show relations between numerousness, extent of cohorts, groups (say segments defined by age, because cohort represents de facto possible market segment), their movement in time and their purchase behaviour (towards books).

The most delicate proving of cohorts movement is going of “baby boomers” (children of after war boom of birth rate) generation through age categories.

 

 

 

 

 

 

  

 Correlation and regress analysis

The natural form of combination of time and frame by frame analysis is use of correlation and regress analysis for two or more phenomenon in time.

Content analysis

With its character on relative border between analysis of secondary and primary data and together between phase of collecting and processing of data is method, assigned as content analysis. It is objective and quantitative analysis of any announcements.

  

 

 

 

 

 

 

  

 Base of content analysis is :•decision about type of investigated announcements and media,•decision about recorded elements :-decision about entry units (content positions of

given problem)- decision about contextual units (conditions of occurrence of entry units – at modification of content analysis for marketing it is characteristics of advertising product, type, category, mark and media characteristics).- decision about categories (possible forms of given unit).•registering of occurrence of elements into database•own analysis of database. 

  

 

 

 

 

 

 

  

Positions and analysis of primary data

Before it is possible to analyse obtained data, must be transferred into appropriate form. It means to edit individual records, forms (questionnaires), sort codes, tabulate and enter data into database.-Editing

Purpose of editing is to examine completeness, legibility, answers and their consistence,•continuous editing in terrain – own data collection of interviewer,•central : at taking of forms, questionnaires in research agency. 

 

  

 

 

 

 

 

 

  

 - Coding

•determination of categories, classes, groups (in case of processing of open questions),•sorting of codes (best numerous) to classes of answers 

-Tabulating and entering

Forming of database structure. Entering is transmitting of individual data in (computer) database of research.At creation of database structure it is necessary to decide about width of intervals for followed signs, about number of entries (columns) for individual questions in database, about categories to enter.

 

  

 

 

 

 

 

 

  

* Basic directions of analysis After data entering and their control we can do own analysis of research results :•summarisation,•following of differences,•following of dependence.

* Summarisation

also designated as analysis of first grade, general evaluation of individual questions, recorded items etc. Are used these main positions :frequencies, rates (most often in %)

 

  

 

 

 

 

 

 

  

• Basic central moments :- modus = the most frequent category,- median = value that reaches the middle by

ascending order of categories (usable for scale from ordinals below),

- average : usable only for interval and ratio scales (sometimes is defined also by ordinals – with more problematic interpretation).

 • Measurement of variability : variation span,

dispersion, standard deviation, variation coefficient (if used scales allow it).

  

 

  

 

 

 

 

 

 

  

- Types of scales

• Nominal (also categorical) scales : category of objects (man, woman) – nominal scales work only on modus level.

• Ordinals – ordinal : we are able to determine order of signs (for example : very fast, fast, slow, very slow).

• Intervals : contrary to ordinal scales are known rates of distance between intervals, but does not exist natural zero – only arbitrary.

• Ratio : the most ideal from view of possible quantification (age, weight,…)

  

 

  

 

 

 

 

 

 

  

Nominal and ordinal scales are sometimes assigned as non-metric (non-metric data), interval and ratio as metric scales (metric data).  Practical usage of scales is in basic lines, from these develop concrete modifications. In marketing is for example successfully used series of scales in form of so called semantic differential. It proceeds from Osgood knowledge that each notion from view of its features is possible to characterise with different intensity, typical for this motion.   

 

  

 

 

 

 

 

 

  

 * Following of difference between primary data  Analytical processes in scope of difference analysis and relation analysis can be partially classified according to few criterions, fulfilment of which leads to possibility of use of certain technique.

These criterions are :•number of samples (selections)•independence of selections, samples on each other (yes, no)•number of variables :

uni-variant techniques multi-variant techniques

  

 

  

 

 

 

 

 

 

  

  

 

  

 

 

 

 

 

 

  

• assumptions of technique, tests : types of separation knowledge of dispersion in basic complex Primary data are based on selective relations.

To this purpose are used two groups of tests : non-parametrical tests and parametrical tests

(non-parametrical tests are less demanding on assumptions).

- Non-parametrical tests Chí-square (χ2) – test of good coincidence. Purpose : differs observed frequency enough

(statistical) relevantly from others values (characterising conditions of basic complex) ?

  

 

  

 

 

 

 

 

 

  

McNemar test : for two dependent samples (pre-test, post-test) It is a modified test of good coincidence.

Example : We have to evaluate effectiveness of advertisement campaign for chocolate Milka Nestlé. To disposal we have records about chocolate purchase before campaign and after obtained by interviewing.

 

  

 

  

 

 

 

 

 

 

  

Koglomorov-Smirnov test : For ordinal data in questionnaires. Used among others for evaluation of preferences, utilities etc.Example : Producer of bicycles interests whether exist bigger preferences for darker shades. Following data were obtained from sample 100 persons : black preferred 35, dark 25, neutral 20, light 10 and very light also 10. - Parametric tests •group of tests used primarily in cases of interval and ratio scales.•based on assumption, that data have normal

classification.

 

  

 

  

 

 

 

 

 

 

  

* Measurement of association – following of connections  - Cross tables Basic entering tolls of investigation of connections between primary data are cross tables. * Contingent cross tables Phenomenon having more than two situation alternatives. All rates with help of which we assume current occurrence of two phenomenon, proceed from comparison of observing and expecting (in means of regular spreading) of state. Rate of mutual occurrence we can compare on different levels.Possible is verbal interpretation of data, contingent spread in table.

 

  

 

  

 

 

 

 

 

 

  

* Measurement of association – following of connections  - Cross tables Basic entering tolls of investigation of connections between primary data are cross tables. * Contingent cross tables Phenomenon having more than two situation alternatives. All rates with help of which we assume current occurrence of two phenomenon, proceed from comparison of observing and expecting (in means of regular spreading) of state. Rate of mutual occurrence we can compare on different levels.Possible is verbal interpretation of data, contingent spread in table.

 

  

 

  

 

 

 

 

 

 

  

- Dispersion analysis The process follows connection between

phenomenon on the basis of relation between inner-group, between-group and total dispersion. On basis of dispersion analysis is followed F-statistic :

explained dispersion (between-group)F = -------------------------------------------------------

non-explained (inner-group) dispersion - Multi-variant analysis Are used for simultaneous following of connection of more variables. Classification of multi-variant techniques :

 

  

 

  

 

 

 

 

 

 

  

• Follow dependence on other variables. For example :

- for interval and ratio scales : multiple regressive and correlative analysis

- for nominal and ordinal scales : discrimination analysis, regression analysis with binary

variables• Is not clearly defined side of dependent variables

and independent variables. For example : factor analysis, cluster analysis, conjoint analysis, multi-dimensional scaling (if proceeds for example

from factor analysis or probability schemas) 

 

  

 

  

 

 

 

 

 

 

  

* Nature of chosen multi-variant techniques - Usage of binary variables in multiple regressive analysis (for nominal scales) : 

Y = a + b1x1 + b2x2 + … + bkxk

 Basic thought : substitution of qualitative variables by binary. Number of variables is about 1 smaller than number of degrees of scale. If we follow for example influence of sex, independent variables are : x1 … man (x1 = 0) x2… woman (x2= 1)

 

 

  

 

  

 

 

 

 

 

 

  

- Factor analysis  Sense of factor analysis is :

•to find deeper assigned “coefficients”, wider than individual criterions of influencing phenomenon,•to lower number of variables, criterions (similar influencing and working criterions, entering similar the same factor),

Factor is understood as variable that is not directly noted. It is the “coefficient” which is necessary to uncover.

 

 

  

 

  

 

 

 

 

 

 

  

Example : we follow what advantages are connected with products for dish washing (we follow binding between utilities – variables).5 followed criterions (variables) of expected values :k1 – price, k2 – effectiveness, k3 – shine,k4 – aroma, k5 – colour

Correlation matrix : enter into use of factor analysis – we follow what correlation (correlation coefficients) achieve all pairs of variables at respondents´ answers.  Note : if in matrix are low values only, the factor analysis has no practical sense.  

 

 

  

 

  

 

 

 

 

 

 

  

From view of given criterions at evaluation of products for dish washing oscillated answers of respondents in two levels, factors. Similar testified criterions of price, effectiveness and shine (rationality factor). Criterions of aroma and colour create mainly second factor.  Some important characteristics : •burden factors : correlation between factor and variable•factor´s score : result of each respondent at each factor•communality : share of dispersion of variable•explained dispersion : how much from total dispersion of all variables explain given factor  

 

 

  

 

  

 

 

 

 

 

 

  

- Cluster analysis

The sense of cluster analysis is finding of clusters, objects according to simultaneously used variables. Assumption are at least ordinal scales or conversion on binomial variables (yes/no … marked/not marked)

 The basic idea of cluster analysis is usage of

distance between objects in individual criterions. 

Own process of objects clustering can use different methods of clustering, their basic lines are :

hierarchical clustering (from upper – from most distant to closest or from under – from closest to most distant), K – averages, FQ analysis (“factoring” are not variables, but objects).

 

 

 

  

 

  

 

 

 

 

 

 

  

Designation of numbers of clusters for purposes of uncovering of market segments is conditioned by marketing strategic assignment : what marked homogeneity inside of segment we require, how open, low homogeneity inside segment we allow.

- Discrimination analysisIs used in case of simultaneous operation of more variables of nominal or ordinal character to search that what differentiates different groups of consumers.

Dependent variable is membership in a group.

 

 

 

  

 

  

 

 

 

 

 

 

  

Sense, purpose of discrimination analysis is :to find out total effect on differentiating of membership, to find out which variables influence most.Technically it is usage of regress relation for nominal and ordinal scales. 

Example for two variables : 

Y = v1X1 + v2X2

Y … frequency of magazineX1 … character of residenceX2 … education

Y = 0,05 X1 + 0,1 X2

 

 

 

  

 

  

 

 

 

 

 

 

  

Education has two times bigger discrimination weight for categorisation among readers of given magazine than residence.Discrimination analysis is also used to expand profile of market segments.  -Conjoint analysis

Conjoint analysis investigates preferences against certain combination of characters. Entering data are based on how respondents evaluate different combinations of product characters.

Methodical process is based on iterations. It is used mainly for determination of suitable characters of product.

 

 

 

  

 

  

 

 

 

 

 

 

  

- Multidimensional scaling Purpose of multidimensional scaling is to find out

how is the object perceived in multidimensional space.

The base of process are two phases : to determine dimensions of space, to place followed objects in given dimensions.

Methods of multidimensional scales are based on perception :

• according to characters :- usage of factor analysis- usage of discrimination analysis

 

 

 

  

 

  

 

 

 

 

 

 

  

• not according to characters :- on base of perception of similarity- on base of preferences.

Usage of methods of multidimensional scaling concerns in consumable marketing for example the knowledge of marks image, ideal objects, market segmentation.

Ways of data mining  - Data mining – introducing and meaning In last years comes to fast expansion of information

technologies. Efficiency of computers growths permanently and possibilities of systems are expanded.

 

 

 

  

 

  

 

 

 

 

 

 

  

Large expansion of information systems enables to collect, process and keep enormous number of data. Companies and institutions of different branches keep in these systems data of different aspects of their activities – production companies record data from stock items, over information of production character, to data about sales, customers, accountant data, different health organisation collect wide data about health condition of population, financial institution keep detail data about their clients, business events, etc.

 

 

 

  

 

  

 

 

 

 

 

 

  

Contrary development of information technologies offers large number of new ways how to analyse data. Current computers enable fast running of demanding algorithm for data analysis and presentation of results of these analysis.

Information are at present the most valuable business commodity. One of ways for effective usage of these systems and obtaining of important, valuable, interesting and new information is data mining.

Exist many definitions of data mining, according to author. Very accurate is for example definition : 

 

 

 

  

 

  

 

 

 

 

 

 

  

 “Data mining is analysis of (often large) observation data with aim to find out unsuspected relations and summarise data in new ways so, that are understandable and useful for their owners.”

This definition contains few basic parts, that demonstrate meaning and biggest advantages of data mining.

Definition talks about “analysis of large data”. 

 

 

 

  

 

  

 

 

 

 

 

 

  

The task of data mining is to find interesting dependence hid in these large data (for example what types of clients have more problems with payment than other types of clients etc.) and so provide look into data bringing maximal number of useful information. In data mining it is possible to use methods that enable assigning of potentially interesting hypothesis in general, as sphere of potentially interesting hypothesis. With help of special algorithm the system for data mining itself defines and formulates all hypothesis from given circle of hypothesis and automatically proves in data their validity and in output displays list of hypothesis valid (hidden) in data and renews it on statistical characteristics.   

 

 

 

  

 

  

 

 

 

 

 

 

  

In relatively short time it is possible to define and test automatically hundreds and thousands of hypothesis and to find “unsuspected”, surprising dependence hidden in data.

Other essential part of above mentioned definition is “…are useful and understandable for their owners.”

The task of data mining is not only to find information in large data, but also to provide the found information in form, understandable and useful for owner of data. Results of data analysis is necessary to interpret properly, that the founded conclusions would be properly understood and could be used correctly by the person who ordered the analysis.   

 

 

 

  

 

  

 

 

 

 

 

 

  

* Methodology CRISP-DMData mining is not only method of data analysis, but it is a process, that consists of many partial phases. At solution of data mining project it is necessary to make complex of tasks and operations.

The idea to create methodology CRISP-DM arisen in the year 1996 as result of cooperation among people from firms Daimler-Benz, SPSS and NCR.

In the year 1997 was founded consortium CRISP-DM, financed and supported by European committee. The name CRISP-DM is an acronym, originated from words Cross Industry Standard Process of Data Mining.

 

 

  

 

  

 

 

 

 

 

 

  

Its target is simulating of process of knowledge extracting from databases as standard universal process, independent on industry branch, in that are data of mining analysis performed.  CRISP-DM simulates life cycle of each data of mining project in 6 phases :Business understanding, Data understandingData preparation, Modelling, Evaluation, Deployment

Purpose of business understanding phase is to define a problem, that will be solved with help of data mining, to determine targets and tasks, that should the project fulfil.

 

 

  

 

  

 

 

 

 

 

 

  

Data understanding phase – its task is to collect enter data for performing of data analysis, leading to fulfilment of targets determined in preliminary phase of project.

In phase of data preparation are all enter data transformed into form necessary for performing of own modelling. This phase of data mining project is usually the most time demanding.

 

 

Following phases of data mining project – own modelling – selection of suitable methods and algorithms for knowledge obtaining and model creating wilt help of these algorithms.

Results obtained it the modelling phase is necessary to interpret – phase of model evaluation. It is necessary to assess whether the found results are correct and contribute to fulfilment of targets, defined in introductory phase of the whole project.

Only the proved models obtained in previous phases are possible to use in practice, in the deployment phase.   

 

 

• Data mining in market researches

Data mining is used for data analysis from any sphere, if there is to disposal sufficient amount of data.

At market research especially at quantitative researches is done large data collection. Researches are often done in periodically repeated waves, large number of respondents is interviewed.

The advantage of data obtained in typical quantitative researches is own form and format of obtained data.

  

 

 

At data mining is maximal relief of work in phase of data pre-processing, it is not necessary to transform data from different time periods to mutual format. All collected data are in the same format and therefore maximal comparable.

Data obtained in quantitative market researches are appropriate for usage of data mining methods if there is to disposal sufficient amount of data, where is possible to look for hidden information, potentially interesting and useful.

Data mining is today very modern and appears in offers of institutions, dealing with data analysis and institutions for market research.