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1、Segmentation and Profiling using SPSS for Windows,Kate Grayson,Why Segmentation?,Used by e.g. retail and consumer product companies Trying to learn about and describe their customers buying habits, gender, age, income level, etc. These companies tailor their marketing and product development strateg
2、ies to each consumer group to increase sales and build brand loyalty. A valuable approach in Market Research, and SPSS offers some useful tools to facilitate this commercial process,Segmentation in SPSS,Most of the techniques for segmentation and profiling are exploratory There is no right or wrong
3、answer, and the results are open to interpretation Trying to make sense of the data or find patterns Iterative techniques If it does not make business sense then it is not a good model!,Segmentation in SPSS,Techniques include: Factor Analysis / Principal Components Analysis Hierarchical Clustering K
4、-Means Cluster Non-Linear Principal Components Analysis (PRINCALS/CATPCA) The new Two-Step Cluster,Which Technique to Use?,Exploratory,Confirmatory,Factor Analysis,Cluster Analysis,Categories,Discriminant Analysis,AnswerTree,Which Test to use?,Factor Analysis - to find patterns within variables Cate
5、gories - use if data doesnt fit assumptions for Factor Analysis Cluster Analysis - to find patterns between individuals Two-Step Cluster To use with both categorical and continuous variables Discriminant Analysis - to look for differences between groups, try to predict target variable AnswerTree - c
6、ombinations of data, to predict target,Multivariate Analysis,These techniques are inter-related, but dont have to use all of them Can use a combination of these techniques to segment the data,Main Considerations,Looking for patterns or trying to make predictions? Levels of Measurement of the data (c
7、ategorical or continuous) Sample size Missing values Does data fulfil assumptions for test?,Before you start. Check your data!,Handling Missing Data,Check before analysis for any patterns within missing data Check before analysis that missing values are defined as missing - otherwise may compromise
8、the model Be aware that most segmentation techniques ignore any cases with missing values - so may have less usable data than you think!,Variable and Value Labels.,It is worth checking the labels on your file SPSS may truncate long variable and value labels in the output, making it difficult to inte
9、rpret the output Make sure all the useful information is at the beginning of the variable and value labels - so even if they are truncated, the output is still easy to read,Data Coding,Check the direction of the coding scheme, and maybe consider re-coding the data if the codes are counter-intuitive
10、e.g. if have a rating scale that ranges from high to low, rather than low to high . it can be difficult to interpret output and factor scores etc. once the data has been through several transformations,Sample Data,Data = usage of underarm deodorants for men Three brands tested: Rambo: the current ma
11、rket leader Brad : second most popular Clint : recently launched product,Profiling the Customers,Clint isnt selling as well as was hoped, so the research aims to find out: Who is buying Clint? What sort of characteristics do they share? Who is buying the other deodorants tested? How might the market
12、ing campaign be changed to ensure that the correct market is targeted?,Data Collected,Ratings of a range of lifestyle attribute questions, e.g. I tend to own the most up-to-date products, My family is most important thing in my life, I prefer to dress and entertain casually etc. (34 of these) Demogr
13、aphics: age, type of work, exercise etc. Brand of D/O usually use How see yourself in relation to others, e.g. What makes you distinctive from your friends,Segmentation the steps,Run Principal Components Analysis on attribute rating questions, to see if any underlying dimension in the variables Chec
14、k using Discriminant Analysis to see if these dimensions help predict brand used Run Cluster Analysis to see if can find similarities between cases Decide if other variables need to be included, e.g. categorical demographics Run Two-Step Cluster using all variables,Factor Analysis,Factor Analysis: w
15、hat is it?,Looks for relationships between continuous variables (based on correlations), in this case attribute rating questions Derives underlying constructs or dimensions in the data Tries to reduce a large number of variables to a small number of factors which explain most of the variance in the
16、data If cant interpret the resulting solution then no good!,Run Principal Components Analysis on 34 rated attributes,Factor Analysis Results,The best solution produced 9 factors, interpreted below: F1: High computer use F2: Rules, need to conform F3: Party animal F4: Family man F5: Likes new products, experiments F6: Likes pampering, pays more for trusted brands F7: Cautious, follower rather than leader for new products F8: Relaxed, casual F9: Home
