MULTIVARIATE

It allows the analysis of more than 2 variables of a single measure. It determines the multiple relationship at the same time. Ex. Sale = f (demand, population, marketing mix, purchasing power, advertising) It helps us to build the hypothesis for research.

Types of multivariate analysis techniques

MULTIPLE REGRESSION it is the analysis of dependence. It predicts the value of criterion variable by developing an equation. it tests and explains causal theory by the way of path analysis

FACTOR ANALYSIS it is the analysis of interdependence.it reduces the variable to manageable number. It aims to describe large no of variables or questions by using a reduce set of underline variables called factors. factor analysis is done with the help of Principle component analysis, by calculating the communalities, factor analysis by rotation. Ex. A car dealer has asked for several customer preference rating on a variety of car models made by Mercedes, bmw, Toyota using factor analysis the researcher was able to identify 3 main factors which were style considerations, price, performance.

Application of factor analysis in Market ResearchData reduction- it summarizes perceiver of a service or a product starting from a multi item questionnaire. Ex. the value of a bank service has been summarized into 3 dimensions professional service, marketing efficiency, effective communication. Identifying new marketing opportunities. Ex. A company wants to measure end users preference wrt. Light dairy products and identify opportunity for new product development- strawberry yogurt.Market Segmentation a company wants to segment a computer gamers segment with the help of factor analysis they found out 3 main drivers of gaming behaviours attitude, playing habits, buying habits, this type of Factor analysis will help in segmenting the gamers into 2 categories 1) hard-core gamers 2) Casual gamers. CLUSTER ANALYSIS COJOINT ANALYSIS

Cluster Analysis Statistical classification. Are sub divided into groups or into clusters such that the item in the cluster are very similar to one another and very different to the items in the otherStages 1- Select the objective for cluster analysis 2- Select a similarity measure 3- Make your assumptions 4- Determine the method of clustering 5- Interpreting the clusters 6- Validation with selected outcomes and profiling with additional descriptive variables.The governing principle for cluster analysis is maximum homogeneity within the cluster. And max. heterogeneity across the cluster.Main decisions how to measure similarity. How to form cluster. How many cluster.Examples or applications of cluster analysis You need to identify ppl with similar patterns of past purchase so that u can tailor your marketing strategies You have been assigned to grp television shows into homogenous categories based pn you viewers characteristics It can also be used for market segmentation. You want to cluster your customers with the diagnosis of diabetes to sell wheat flour, biscuits, etc. to different types of Different methods of clustering k cluster Hierarchical cluster Discrimant analysisThis analysis ..

Ex. Distinguishing btw innovators and non innovators according to their demographics and psychographics.Good credit customer vs bad credit customer.The mail purpose of discriment analysis is to predict group membership the end resuld allows .and also helps in understanding of data.

Ec. Discriment analysis used to predict the successful completion of PG course on the basis of cat mat and gre scores.