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 Salesforce.com 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

Source:

http://spss.co.in/

http://www.hks.harvard.edu/fs/pnorris/Classes/A%20SPSS%20Manuals/SPSS%20Statistics%20Brief%20Guide%2017.0.pdf

http://ritco.biz/files/Statistical%20Analyses%20using%20SPSS.pdf

Author: Juhi Kachhap

Roll No. 13136

Group No.: Marketing - Group 4

Football & Clusters !

First day of Business Analytics, not my first day with SPSS, but it was a refreshing way to refresh some of the knowledge imparted to us in the last semester. Heavy usage of the word refresh. Anyway, the moment I opened the file, it came as a small shock. For someone used to working with 4-5 variables at max in Microsoft Excel, this was a rude awakening. A very rude awakening.

Anyway, let me first brief a person new to the world of analytics what clustering is all about. Clustering is all about dividing data or a set of observations into homogeneous groups. Groups that within themselves hold similar data. Not in every respect but in most respects. For example, all females of the world can form a cluster and all the males could form another cluster. Valid examples these two. Thought provoking too.

Let's proceed. So a cluster is basically a set of observations that can be bundled together based on their similarity. Now while there are many ways of performing an analysis of this data, there are only a few important methods that one would most probably take.

Hierarchical Clustering: As the name suggests, the cluster is made based on data that has been ordered in some way. There are two different ways this is done as well

· Agglomerative Clustering for dummies:

§ Obtain your set of data

§ Sort it in whichever way you deem possible

§ Cluster/Collect similar data into the first cluster

§ Repeat this until you have one final cluster, the parent of all the other clusters

· Divisive Clustering for dummies:

§ Obtain your set of data

§ Do the exact opposite of the above

§ Congratulate yourself on a job well done !

K-Means Clustering: An often used form of partitioning of data that involves an iterative process where clusters are initially built randomly, their distance from the centroid computed and then re-clustered. This is done till the best fit is obtained.

Now, how do I relate this to football. Well, while I was watching the football match today evening (my passion), we (me and my friends) wondered how clustering takes place in football. (After-Effects of BA Class I guess. Or maybe because there were girls watching the football match for a change). Anyhow, here is how I perceive it. Suppose you were to step into the shoes of a manager of a professional football team, how would clustering help you? Here's how.

What exactly do you need:

• Defenders
• Midfielders
• Attackers
• And a Goalkeeper of course !

Well, there are tons of football players out there. How would you choose the best? By Divisive Clustering of course. You allot all the defenders of the world into one cluster. You whittle it down based on your requirements and your criteria till you can finally make your dream team.

It's too late for me to think of Agglomerative Clustering and its examples so I'll just leave you to figure that out for yourself.

Enough from me for now. More gyaan later !

Group Name: Finance 2

Author: Kshitij Sharma

Business Analytics : DENDROGRAMS

Dendrograms are often used for displaying relationships among clusters. A dendrogram shows the multidimensional distances between objects in a tree-like structure. Objects which are closest to each other in the multidimensional data space are connected by a horizontal line, forming a cluster which can be regarded as a "new" object. The new cluster and the remaining original data are again searched for the closest pair, and so on. The distance of the particular pair of objects (or clusters) is reflected in the height of the horizontal line. The dendrogram can also be represented in horizontal format.

Depending upon the motive of making the cluster and our convenience of decision making we decide where to cut the dendrogram. We decide the position of cutting the dendrogram intuitively also.

Interpretations of Dendrogram

The results of the cluster analysis are shown by a dendrogram, which lists all of the samples

and indicates at what level of similarity any two clusters were joined.  The x-axis is some

 measure of the similarity or distance at which clusters join and different programs use different measures on this axis.  In the dendrogram shown above, samples 1 and 2 are the most

similar and join to form the first cluster, followed by samples 3 and 4.  The last two clusters to

form are 1-2-3-4 and 5-9-6-7-8-10.  Clusters may join pairwise, such as the joining of 1-2 and 3-

4.  Alternatively, individual samples may be sequentially added to an existing cluster, such as

the join of 6 with 5-9, followed by the join of 7.  Such sequential joining of individual samples

is known as chaining. 

Determining the number of groups in a cluster analysis is often the primary goal.  Although

objective methods have been proposed, their application is somewhat arbitrary.  Typically, one

looks for natural groupings defined by long stems, such as the one to the right of cluster 1-2-3-

4.  Some have suggested that all clusters be defined at a consistent level of similarity, such that

one would draw a line at some chosen level of similarity and all stems that intersect that line

would indicate a group.  The strength of clustering is indicated by the level of similarity at

which elements join a cluster.  In the example above, elements 1-2-3-4 join at similar levels, as

do elements 5-9-6-7-8-10, suggesting the presence of two major clusters in this analysis

Posted 

Ashutosh Pratap Singh (13011)

Operations Goup 1

BUSINESS ANALYTICS

Well this was a first, to write a blog for an assignment. Given that the blog had to be connected to the subject of Business Analytics, but the feel of freedom associated with writing a blog made me excited. And then it puzzled me, what could I write? How could I write something that would solve both of my purposes?

I racked my brain for quite some time, when it finally stuck me, I was going to try and explain what business analytics is.

Well that would be easy I thought until I realized a simple fact I had no idea myself. So Google it had to be.

The name sounded simple Business Analytics; it has to do something with analysis of businesses. And with that as my starting point I moved on. A couple of searches and a few websites proved me to be quite close to the truth. But as with beauty it is never appreciated until it is polished and presented same is the case with the knowledge.

So what is Business Analytics, it is defined as the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis. What this means is that it is not a one step process but is instead carried out in various steps. And there will be repetitions of several steps as we try to understand statistical relations deeper and deeper. Second it is a methodical process meaning that the steps are not carried out in random but instead there is a method to the whole process, and the method should be clearly defined in our minds before we start with the process. And the last part of the definition talks about it being an exploration of the data of the organization. As with any exploration in any world we would be requiring some tools and for the purpose of business analytics we use statistical analysis as our tools. Well that’s quite an explanation for a single line definition.

Now what is the use of Business Analytics, in today’s competitive world where uncertainty is accentuated with every moving minute businesses are looking forward to some objective guidance. This is precisely what data and study of data does. Business Analysis is predominantly used by companies where decision making is data driven. Why are we comfortable with this approach of decision making based on data? I believe it is so because this data is collected from recent past and hence serves as a reference. Also if data analysis is done properly it serves as a completely objective source of reference.

Beyond this I also learned in the class that unlike the meandering flow of this article Business Analytics is a precise form of knowledge wherein details play a crucial part. And that the analysis in itself is not sufficient for taking decisions, one needs to have a feel about the business. To put this point in perspective the most fruitful decisions in the business scenario are made when analysis is taken into account along with experience, intuition and understanding of the business dynamics.

Group- HR1

Author- Abhas Srivastava