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

Factor Analysis and Conjoint Analysis

Factor analysis is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables. There are two types of factor analysis: exploratory and confirmatory. Exploratory factor analysis (EFA) attempts to discover the nature of the constructs influencing a set of responses while Confirmatory factor analysis (CFA) tests whether a specified set of constructs is influencing responses in a predicted way.

Both types of factor analyses are based on the Common Factor Model. This model proposes that each observed response is influenced partially by underlying common factors and partially by underlying unique factors. The strength of the link between each factor and each measure varies, such that a given factor influences some measures more than others.

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. These implicit valuations can be used to create market models that estimate market share, revenue and even profitability of new designs.

In a conjoint analysis, the respondent may be asked to arrange a list of combinations of product attributes in decreasing order of preference. Once this ranking is obtained, a computer is used to find the utilities of different values of each attribute that would result in the respondent's order of preference. This method is efficient in the sense that the survey does not need to be conducted using every possible combination of attributes. The utilities can be determined using a subset of possible attribute combinations. From these results one can predict the desirability of the combinations that were not tested.


Author: Karthikeyan Dakshinamurthy (13081)

Group: Marketing - Group 4

Market Research with Conjoint Analysis

Market research is frequently concerned with finding out which characteristics of a product or service is most important to consumers. The ideal product or service, of course, would have all the best characteristics, but realistically, tradeoffs have to be made. The product with the most expensive features, for example, cannot have the lowest price.

Conjoint analysis is a technique for measuring consumer preferences about the attributes of a product or service. There are two general approaches to collecting data for conjoint analysis—the two-factor-at-a-time tradeoff method and the multiple factor full-concept method. With the tradeoff method, respondents are asked to rank the cells of a series of matrices, each matrix crossing the levels of one factor with the levels of another.

Why Use Conjoint Analysis?

• Effective market research is integral to the design, manufacture, and sale of successful products. It identifies the needs and wants of target markets, ensuring that products will sell because they meet the needs of buyers.
• Conjoint analysis is a market research tool for developing effective product design.
• Using conjoint analysis, the researcher can answer questions such as: What product attributes is important or unimportant to the consumer? What levels of product attributes are the most or least desirable ones in the consumer’s mind? What is the market share of preference for leading competitors’ products versus our existing or proposed product? Answers to these questions are of crucial importance in the design and launch of a successful product.
• The virtue of conjoint analysis is that it asks the respondent to make choices in the same fashion as the consumer presumably does—by trading off features, one against another.

For example, suppose that you want to book an airline flight. You have the choice of sitting in a cramped seat or a spacious seat. If this were the only consideration, your choice would be clear. You would probably prefer a spacious seat. Or suppose you have a choice of ticket prices: $225 or $800. On price alone, taking nothing else into consideration, the lower price would be preferable. Finally, suppose you can take either a direct flight, which takes two hours, or a flight with one layover, which takes five hours. Most people would choose the direct flight.

Steps In The Application Of Conjoint Analysis

The main steps involved in the application of Conjoint Analysis are following:

1. Determination of the salient attributes for the given product from the points of view of the consumers

2. Assigning a set of discrete levels or a range of continuous values to each of the attributes.

3. Utilizing Fractional Factorial Design of Experiment for designing the stimuli for experiment.

4. Physically designing the stimuli

5. Ranking or Rating data collection

6. Conjoint analysis and determination of part worth utilities.

7. Applying conjoint analysis output for different marketing decisions

How does Conjoint Analysis Work?
Conjoint analysis involves the measurement of consumer preferences, or acceptability between choice alternatives. The name "Conjoint Analysis" implies the study of the joint effects. In marketing applications, we study the joint effects of multiple product attributes on product choice. When asked to do so outright, many consumers are unable to determine the relative importance that they place on product attributes. For example, when asked which attributes are the more important ones, the response may be that “they all are important”.

It is difficult for a survey respondent to take a list of attributes and mentally construct the preferred combinations of them. The task is easier if the respondent is presented with combinations of attributes that can be visualized as different product offerings. Fortunately, conjoint analysis can facilitate the process. Conjoint analysis is a tool that allows a subset of the possible combinations of product features to be used to determine the relative importance of each feature in the purchasing decision; the relative values of attributes considered jointly can better be measured than when considered in isolation.


Author: Juhi Priyanka Kachhap (13136)

Group: Marketing - Group 4

Conjoint Analysis

Conjoint analysis, also called Multi-attribute Compositional Models or Stated Preference Analysis, is a statistical technique that originated in mathematical psychology. Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is not to be confused with the theory of conjoint measurement.

In conjoint analysis, the respondent may be asked to arrange a list of combinations of product attributes in decreasing order of preference. Once this ranking is obtained, a computer is used to find the utilities of different values of each attribute that would result in the respondent’s order of preference. This method is efficient in the sense that the survey does not need to be conducted using every possible combination of attributes. The utilities can be determined using a subset of possible attribute combinations. From these results one can predict the desirability of the combinations that were not tested.

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. These implicit valuations (utilities or part-worth’s) can be used to create market models that estimate market share, revenue and even profitability of new designs.

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.

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).

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.

Advantages

§ Estimates psychological tradeoffs that consumers make when evaluating several attributes together

§ Measures preferences at the individual level

§ Uncovers real or hidden drivers which may not be apparent to the respondent themselves

§ Realistic choice or shopping task

§ Able to use physical objects

§ If appropriately designed, the ability to model interactions between attributes can be used to develop needs based segmentation

Disadvantages

§ Designing conjoint studies can be complex

§ With too many options, respondents resort to simplification strategies

§ Difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features

§ Respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to

§ Poorly designed studies may over-value emotional/preference variables and undervalue concrete variables

§ Does not take into account the number items per purchase so it can give a poor reading of market share

Author: Krunal Patel

Group: Marketing - Group 4