Psychographic analysis for Amrita University

17.2.2012

In Marketing research one of the major problem is to classify the customers as marketing has to determine which segments offer the best opportunities. The process of dividing a market into distinct groups of buyers who have different needs, characteristics or behaviors, who might require separate products or marketing program, is called market segmentation.

For classification, generally, demographic data are used. It is noted that some psychological factors play critical roles in changing consumer's attitude. These are intangible variables such as need profile, personality, interests, values and life styles of potential customer. For example purchasing speedy motor car depends not only on socioeconomic condition but also one's risk taking personality trait.
The idea is that marketers can sell the product to enhance the life style of the consumers. For example, by defining bathing style of customers using bar soap, marketers push liquid soap to enhance life style of the consumers. In life style analysis, marketing researcher should study all the activities of consumers - working, shopping, holiday and social life. In analysis of behaviour, followings will be taken into account as End use, Benefits sought, loyalty, usage rate etc.Alexandre Psychographic profile data can be used for brand development
Psychographic data provide market intelligence to the company. Based on psychographic segmentation, company can modify it's width or depth of business. It is the process of exploring business opportunities or to identify specific opportunities for cross sell or up sell.

LIFE STYLE

Cell phone research:

How often

1. do you send text message?
   
2. do you talk on cell phone ?
   
3. do you use social networking site ?
   
4. do you meet person outside your school work ?
   
5. do you often talk on landline phone ?
   
6. do you use e-mail?
   
   

25.3.15

Collection from study.com


Psychographic data are important for market segmentation so that market potential across different segmented markets can be understood. Most marketing departments use multiple segmentation strategies.

Multiple Segmentation Strategies
Geography
Demographics
Psychographics
Benefits sought
Usage rate

Geographic Segmentation
One of the first variables that the team could use in their segmentation strategy is geographic. This would allow the team to break the market into sections by climate, density, market size, world or states. Many companies use climate if their products or services rely on the weather, such as snow shovels, melting pavement salt, wave runners and boats. Our Town USA is more interested in targeting geographic locations that are located near the park in a 100-mile radius. They believe some customers will fly in from out of state, so in addition, they will target large-density areas nearby.

Demographic Segmentation
Demographic segmentation is extremely important to all marketing departments since the data is easily available and does drastically affect buying patterns. Age, income, gender, ethnic background and family life cycle are all important factors of demographic segmentation. The park is going to use an age range of 2-60 years of age so they can include kids, teens, parents and even grandparents. The income level would have to be middle to upper class - $50,000 annual income or above - since park tickets are very expensive. The amusement park is not a gender-specific product, and ethnicity will also not affect the overall plan.

The marketing team is very interested in the family life cycle sub-segments. Family life cycle segmentation is a series of stages determined by a combination of age, marital status and the number of children in a household. Obviously, the park is very interested in the family life cycle of young single, young married with kids, middle-aged married with kids, young divorced with children and middle-aged divorced with kids. They plan on advertising via social media and local cable ads where parents and kids congregate.


RESEARCH DESIGN
In this analysis, the dependent variables are
how frequently customers purchase a given item, how much they spend on the item per year, and what factors cause them to purchase the item.




Some useful links are:
http://www.ehow.com/info_8244606_psychographic-data-marketing.html
Lifestyle psychographic:http://www.warc.com/fulltext/esomar/80217.htm
Big-5: http://digitalmarketingmagazine.co.uk/digital-marketing-features/psychographic-profiling-identifying-new-levels-of-customer-understanding/787
Substance abuse : http://www.ncbi.nlm.nih.gov/pubmed/24729744

SPSS training for Doctoral students of Psychology, University of Calcutta

GENERAL OUTLINE

SPSS is the statistical package for scientific researches in social sciences like psychology, sociology, economics and in engineering sciences. In social sciences, there are large number of variables and cases. Specific distribution of variable or case or association among set of variables or cases can be examined by SPSS.

    SPSS provides both menu and syntax driven approaches in analysis of data. In menu driven approach, researcher uses the icons of the SPSS tool bars. In syntax approach, researcher writes the programme in the syntax window and runs it for the output. Syntax approach is always better than menu driven,as researcher gets freedom to analysed the variables. Syntax archive helps researchers in locating specific files and analysis of data. 

There are several function in SPSS - file management, variable creation, variable transformation,data visualization,and analysis of text and numeric data.

File-Management:
SPSS accepts both MS-Exel and Text file as input and Spss output can be inserted in the MS- office files.

Variable Creation : New variable can be created in SPSS following specific scales of measurements.Saved output variables can be inserted in the original files.

Variable Transformation :Variable properties can be transformed from text to numeric or vise - versa.One numeric property of variable can be transformed another numeric through SPSS, for the same, one can create new transformed variable or replace original variable through recode into same or different variables.New variable can be created by manipulating more numbers of variables.

Case/ variable Selection: Single or multiple cases can be analysed through select cases or if command.Similarly descriptive statistics of single variables or set of variables can be extracted through SPSS.

Data Visualisation: Visual display of data is useful in examining data quality.This is possible in SPSS.Structured or unstructured ,  large text and numeric data can be summerized an visualised through graphs and tables.


Outlier Detection: Presence of outlier or the extreme data affects the distribution of data adversely.This is specifically dangerous for pervasive statistics like correlation. Through box-whisker plot location of extreme data can be identified and be manipulated to serve the quality of data.

http://www.slideshare.net/ddroy/box-whisker-show

Analysis of data,SPSS is useful for non- parametric and parametric statistics.

ANOVA: In SPSS, ANOVA follows few nomenclatures. Independent variables is called factor. This is categorical in nature. This is determined by experimental membership. Dependent list includes list of dependent variables. These are metric or scaled variables.  Results provide three sums of squares - between, within and total sums of squares; degrees of freedom, mean square, F ratio and level of significance. When more than one IV interact with each other to change in DV, it extracts interaction sum of squares.
   The effects on DV are of two types - fixed and random effects. 
Random effects cause errors. It usually happens in sampling errors, instrumental errors and environmental errors. Confidence intervals are accounted in accepting regions of random effects. 

Non-parametric ANOVA :  Kruskal-Wallis H test  is used when  original data set actually consists of one nominal variable and one ranked variable. In SPSS, it is not important to rank the scaled data when scaled data are dependent variables. SPSS provides Mean rank, chi-square, df and significance level for interpreting the result. 

see more:http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm

Regression : http://dss.princeton.edu/online_help/analysis/regression_intro.htm