Business Analytics - Summary

Over the course of 24 lectures, we learnt to use the SPSS software to analyse data and deduce meaningful interpretations from them. The course included general use of SPSS software to familiarise ourselves with the various tools available in SPSS. Some of the tools used for analysis are K-Means, Discrminant Analysis, Factorial Analysis, PerMap, Bubbles Graph, Chart Graph and Conjoint Analysis.

K-means clustering is a method of cluster analysis which aims to partition 'n' observations into 'k' clusters in which each observation belongs to the cluster with the nearest mean. It attempts to find the centers of natural clusters in the data as well as in the iterative refinement approach.

Discriminant analysis is a method used in statistics, pattern recognition and machine learning to find a linear combination of features which characterize or separate two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors. Factor analysis searches for such joint variations in response to unobserved latent variables. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset.

Perceptual mapping is a graphics technique used by marketers that attempts to visually display the perceptions of customers or potential customers. Typically the position of a product, product line, brand, or company is displayed relative to their competition. Perceptual maps can have any number of dimensions but the most common is two dimensions.

A chart is a graphical representation of data, in which the data is represented by symbols. Charts are often used to ease understanding of large quantities of data and the relationships between parts of the data. Charts can usually be read more quickly than the raw data that they are produced from.

A bubble chart is a type of chart where each plotted entity is defined in terms of three distinct numeric parameters. Bubble charts can facilitate the understanding of the relationships between the different entities.

Conjoint analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service. The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined.


Author: Karthikeyan Dakshinamurthy (13081)

Group: Marketing - Group 4

Factor Analysis

Factor analysis is a method that reduces the number of variables based on the underlying unobservable variables that are reflected in the observed variables.

There are 2 types of factor analysis:

• Exploratory Factor Analysis – it attempts to discover the nature of the variables that are influencing the responses.
• Confirmatory Factor Analysis – it confirms something that is already known

In marketing, factor analysis is more commonly used to study interrelationships among variables in an effort to find a new set of variables which express what is common among all the original variables. Factor analysis is used:

• To reduce the number of original variables while maximizing the amount of information in the analysis i.e. the new variables now account for most of the variance.
• To search for distinctions when the amount of data is very large.
• To test a hypothesis.

However, factor analysis is not an end in itself; the factors need to be subjected to further analysis (such as discriminant analysis etc).

In marketing, factor analysis is generally applied by changing one variable to see what effect it has on the outcome. An infinite number of marketing variables can exist which is why it is necessary to alter one variable at a time. Eg. Marketing variables influencing the sales of a product include the product, the product packaging, the size of the product and the colour of the product. The price, distribution channels and marketing strategies may also be variables of the product that can be changed to see how the change makes a difference in the sales of the product.

Factor analysis in marketing is important because it reflects the perception of the buyer of the product. By testing variables, it is possible for marketing professionals to determine what is important to the customers of the product. It is imperative to use factor analysis in marketing to create the ideal product for customers, which in turn, would increase the sales of the product.

Companies test variables with factor analysis in marketing using tools such as focus groups and surveys. This is because making changes to the product in order to test the variables on a big sample size can be expensive. Thus companies choose small groups which include a combination of past users, current users and non-users. Studies are conducted in the form of surveys and focus groups which allows companies to gather pertinent information without drastically increasing the cost to manufacture the product. Focus groups and surveys allow companies to gather perceptual information from this sample.

Ref: http://smallbusiness.chron.com/importance-factor-analysis-marketing-1698.html

Author: Makushla Marion Santimano

Group: Marketing - Group 4

Conjoint Analysis

Conjoint analysis is a popular marketing research technique that marketers use to determine what features a new product should have and how it should be priced. Conjoint analysis became popular because it was a far less expensive and more flexible way to address these issues than concept testing.

Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors.

The exercise can be administered to survey respondents in a number of different ways. Traditionally it is administered as a ranking exercise and sometimes as a rating exercise (where the respondent awards each trade-off scenario a score indicating appeal).

In more recent years it has become common practice to present the trade-offs as a choice exercise (where the respondent simply chooses the most preferred alternative from a selection of competing alternatives - particularly common when simulating consumer choices) or as a constant sum allocation exercise (particularly common in pharmaceutical market research, where physicians indicate likely shares of prescribing, and each alternative in the trade-off is the description a real or hypothetical therapy).

Analysis is traditionally carried out with some form of multiple regression, but more recently the use of hierarchical Bayesian analysis has become widespread, enabling fairly robust statistical models of individual respondent decision behavior to be developed.

When there are many attributes, experiments with Conjoint Analysis include problems of information overload that affect the validity of such experiments. The impact of these problems can be avoided or reduced by using Hierarchical Information Integration.

Author: Gayathri Nair

Group: Marketing - Group 4

SPSS - A Brief Summary

I would like to summarize the learning’s from the BA classes by writing a summary about SPSS and its uses in the field of marketing:-

SPSS is of great use for marketing surveys like large scale demographic surveys, marketing surveys require at least two stages of rigorous treatment. Careful data collection, cleaning and finally data mining is required to make the dataset ready for the analysis. In both data management and the analysis part SPSS can be regarded as the most useful software tool. SPSS has a spreadsheet interface for data management, which can be manipulated by state-of-the-art syntax coding. Together with the spreadsheet SPSS has advanced statistical tools and graphics engine that can be used to analyze the survey data. Key utilities that can be in used in SPSS in dealing with marketing surveys are as follows:

1. Cross tabulation – to make custom build summary statistics of different subgroups.
2. Missing data analysis – to fill up missing data or cleaning the missing data from the original dataset
3. Forecasting and trend analysis – to predict future trend of products
4. Regression analysis – to understand the effect of factors on something in question
5. Discriminant analysis – to separate one seemingly related factors with another

One of great SPSS utilities is that it has user friendly database management tools in it. Within a very short time, without writing huge amount of codes, we can summarize and process the survey results with its help. Let’s have an example using a SPSS sample dataset. Let’s assume our thesis writing service made a survey with different questions including demographic information. Now we want to see how the race of the respondent differs in family planning issues. We can do a cross-tabulation having race in the rows and number of children in columns.

To interpret the survey descriptive statistics more rigorously, SPSS offers a number of sophisticated statistical measures.

SPSS is an excellent tool especially for managing and great insights from the data collected and is a useful tool especially for people in the field of marketing and thus helping management to get great insights from the data collected.

Author: Krunal Patel

Group: Marketing - Group 4

CONJOINT ANALYSIS

Conjoint Analysis is a research technique used to measure the trade-offs people make in choosing between products and service providers. It is also used to predict their choices for future products and services. Conjoint Analysis assumes that a product can be “broken down” into its component attributes. For example, a car has attributes such as color, price, size, miles-per-gallon, and model style. Using Conjoint Analysis, the value that individuals place on any product is equivalent to the sum of the utility they derive from all the attributes making up a product. Further, it assumes that the preference for a product and the likelihood to purchase it are in proportion to the utility an individual gains from the product.

There are three phases in the analysis of conjoint data: collection of trade-off data through a questionnaire, statistical analysis of the data, and market simulation. Conjoint analysis is based on the fact that the relative values of attributes considered jointly can better be measured than when considered in isolation.

Steps in Developing a Conjoint Analysis

Developing a conjoint analysis involves the following steps:

1. Choose product attributes, for example, appearance, size, or price.
2. Choose the values or options for each attribute. For example, for the attribute of size, one may choose the levels of 5", 10", or 20". The higher the number of options used for each attribute, the more burden that is placed on the respondents.
3. Define products as a combination of attribute options. The set of combinations of attributes that will be used will be a subset of the possible universe of products.
4. Choose the form in which the combinations of attributes are to be presented to the respondents. Options include verbal presentation, paragraph description, and pictorial presentation.
5. Decide how responses will be aggregated. There are three choices - use individual responses, pool all responses into a single utility function, or define segments of respondents who have similar preferences.
6. Select the technique to be used to analyze the collected data. The part-worth model is one of the simpler models used to express the utilities of the various attributes. There also are vector (linear) models and ideal-point (quadratic) models.

Depending upon the type of conjoint survey conducted, statistical methods like ordinary least squares regression, weighted least squares regression, and logic analysis are used to translate respondents' answers into importance values or utilities.

Group HR1

Author- Rupali Varshney

Kano Model and Permaps

The first impression that I got after learning about perceptual mapping is that it is exclusively a marketing tool and that it would have meagre implications in the field of operations. And I am sure many people from the operations clan might have felt the same way. But, first impression is not always the correct impression.

On researching about permaps, I couldn’t help notice a stark similarity between permaps and another tool that we used in total quality management- ‘The Kano Model’.

Now, for the un-initiated, Kano Model is used in total quality management wherein we tend to define whether our product attributes are in alignment with the customer requirements. The Kano model defines customer needs into three parts:

1)The bottom curve, labelled basic needs, represents needs that are taken for granted and typically assumed by the customer to be met (i.e., these are needs that “must be” satisfied). “The camera works out of the box, the camera is safe, the battery can be recharged by plugging into any outlet” are examples of basic needs for a digital camera. These needs are the “order qualifiers”. Completely meeting basic needs cannot greatly increase customer satisfaction, but if they are absent or below par customers will not react favourably.

2)The middle curve, labelled performance needs, represent needs for which customer satisfaction is roughly proportional to the performance exhibited by the product or service (i.e., these needs are “linear” in that “more is better’). For example, longer battery life in a digital camera and more internal memory for image storage are preferred

3) The upper curve, labelled exciting needs, represent needs that the customer does not expect to be satisfied. Thus, if this need is completely addressed the customer is delighted but if not, the customer does not really care. These needs are the “order winners” for customers. For example, side airbags, global positioning systems, air-less tires that never get flat for automobiles might be exciting needs today.

Now the idea of linking perceptual mapping may seem a little far-fetched, but in practice the underlying concept remains similar. Permaps could be used to understand which consumer needs are perceived as basic, performance or exciting by the consumer himself. One of the major functions of a permap is to uncover facts hidden in a complex data set. If a customer survey is done to enlist different consumer needs and then grading is done according to the intensity of needs, one could classify the needs in the above mentioned three categories. Again, one may study which needs are closer to each other and hence may be inserted into one category. So just like different products are perceived vis-à-vis product attributes, different needs may also be perceived differently.

Information obtained from such a permap may help in designing a product with suitable attributes taking into account ‘order qualifiers’ and ‘order winners’.

Posted by: Anish Diwadhkar

Operations _ Group 2

FINAL BLOG - A Summary

Being our final day and lecture of Business analytics, we were summarized upon the learning of all the 24 sessions took place. We started with general use of SPSS software, later in to details of the same.

The later part consists of K-Means, Discrminant Analysis, Factorial Analysis, PerMap or perceptual Mapping, Bubbles graph, chart graph and at last conjoint analysis.

K- Means clustering is an excellent technique for clustering points when the number of clusters is known. We present an implementation of the algorithm. We also implement the k-Means intialisation method which finds the global optimum much more frequently than a random initialization.

Discriminant analysis attempts to identify which variables or combinations of variables accurately discriminate between groups or categories by means of a scatter diagram or classification table.

How it is being used by people: Discriminant analysis has applications in finance, for example, credit risk analysis, or in the prediction of company failure (in bankruptcy prediction), and in the field of marketing, for market segmentation purposes.

Conjoint analysis takes the attributes and level description of product/ services and uses them in

Interviews by asking people to make a number of choices between different products.

Siddhartha Khandelwal

Finance Grp 6