Monday, 29 August 2011

SPSS Basics and Its Applications in Marketing

The “Statistical Package for the Social Sciences” (SPSS) is a package of programs for manipulating, analyzing, and presenting data; the package is widely used in the social and behavioral sciences. SPSS is a comprehensive and flexible statistical analysis and data management solution.

SPSS can take data from almost any type of file and use them to generate tabulated reports, charts, and plots of distributions and trends, descriptive statistics, and conduct complex statistical analyses.

The science of business analytics is used to evaluate organization-wide operations. Each department, from sales to marketing and product development to customer service, can benefit from a closer look at customer behavior.

Importance of SPSS in Marketing:

  • Conduct sophisticated analyses of customers or contacts easily – and with a high level of confidence in your results. Choose from Recency- How recently a customer has purchased? Frequency- How often he purchases? And Monetary - How much does he spend value (RFM) analysis, cluster analysis, prospect profiling, postal code analysis, propensity scoring and control package testing.
  • Identify which customers are likely to respond to specific promotional offers
  • Develop a marketing strategy for each customer group
  • Select potential business locations
  • Connect to to extract customer information, collect details on opportunities and perform analyses

· Predictive analytics technology improves business processes by giving organizations consistent control over decisions made every day.

· Detailed knowledge of customers and customer segments can be obtained and used to create effective strategies for customer acquisition and customer retention, and to increase customer lifetime value

SPSS Statistics data files are organized by cases (rows) and variables (columns). In this data file, cases represent individual respondents to a survey. Variables represent responses to each question asked in the survey.

The Data Editor displays the contents of the active data file. The information in the

Data Editor consists of variables and cases.

· In Data View, columns represent variables, and rows represent cases (observations).

· In Variable View, each row is a variable and each column is an attribute that is associated with that variable.

Variables are used to represent the different types of data that you have compiled. A common analogy is that of a survey. The response to each question on a survey is equivalent to a variable. Variables come in many different types, including numbers, strings, currency, and dates.

The Variable View spreadsheet serves to define the variables. Each variable definition occupies a row of this spreadsheet. As soon as data is entered under a column in the Data View, the default name of the column occupies a row in the Variable View.

There are 10 characteristics to be specified under the columns of the Variable View:

1. Name — the chosen variable name. This can be up to eight alphanumeric characters but must begin with a letter.

2. Type — the type of data. SPSS provides a default variable type once variable values have been entered in a column of the Data View.

3. Width — the width of the actual data entries.

4. Decimals — the number of digits to the right of the decimal place to be displayed for data entries. This is not relevant for string data and for such variables the entry under the fourth column is given as a greyed-out zero. The value can be altered in the same way as the value of Width.

5. Label — a label attached to the variable name.

6. Values — labels attached to category codes. For categorical variables, an integer code should be assigned to each category and the variable defined to be of type “numeric.”

7. Missing — missing value codes. SPSS recognizes the period symbol as indicating a missing value.

8. Columns — width of the variable column in the Data View.

9. Align — alignment of variable entries. The SPSS default is to align numerical variables to the right-hand side of a cell and string variables to the left.

10. Level of Measurement - Categorical. Data with a limited number of distinct values or categories (for example, gender or marital status). Also referred to as qualitative data.

Nominal - Categorical data where there is no inherent order to the categories. Ordinal - Categorical data where there is a meaningful order of categories, but there is not a measurable distance between categories. Scale - Data measured on an interval or ratio scale, where the data values indicate both the order of values and the distance between values.

Missing Data - Missing or invalid data are generally too common to ignore. Survey respondents may refuse to answer certain questions, may not know the answer, or may answer in an unexpected format.

The Statistics Menus - They allow manipulation of the format of the data spreadsheet to be used for analysis (Data), generation of new variables (Transform), running of statistical procedures (Analyze), and construction of graphical displays (Graphs).

Data File Handling - Merge files allow either Add Case or Add Variable to an existing data file.

Crosstab and Chi square test: the cross tabulation of two variables shows us the distribution of one variable separately for each category of another variable (the "conditional distributions"). This is equivalent to preparing separate frequency distributions of one variable for cases having each particular value of the other variable.

The chi-square test of independence plugs the observed frequencies and expected frequencies into a formula which computes how the pattern of observed frequencies differs from the pattern of expected frequencies.

If the significant value is less than 0.05 then there is a significant relationship between two variables. We reject the null hypothesis i.e., there is no relation between the two variables


Author: Juhi Kachhap

Roll No. 13136

Group No.: Marketing - Group 4

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