CHOOSING A WIFE USING CONJOINT ANALYSIS!!!

Inspired by the last few lectures of Business Analytics, I thought to come up with a story of how a man can use Conjoint Analysis in the process of arrange marriages and get himself the most suitable wife.

So the story goes like this...

After a lot of persistence from his parents, Dhruv agreed to start looking for a suitable girl for marriage. But he was not sure as to how would he be able to know if the girl was “perfect” for him. He was looking for a guaranteed method that he could use in the process.

Just a day before he was about to start meeting girls, he miraculously got what he was looking for. During his Business Analytics class he was taught Conjoint Analysis. Conjoint Analysis is a market research technique in which consumers make tradeoffs between two or more features and benefits of a product on a scale ranging from 'Most Preferred' to 'Least Preferred.' coupled with techniques such as simulation analysis, conjoint analysis helps in evaluation of different points.

Just during this lecture, Dhruv got an idea that by using Conjoint Analysis he could cut down on meeting all the 15 girls his mother wanted him to meet. Right after the class he prepared a list of possible attributes that he wanted in his life partner. The list contained things like looks, family background, intelligence, occupation, education, household skills etc. After putting all the attributes and the options for each attribute, (for e.g. Attribute – looks, Options – Very good looking, good looking, average) he came up with all the permutations and combinations possible.

Once his list was final, he sat with his mom to discuss what all attributes that every girl had. He prepared an excel sheet with all the data he collected. Now the next step was to rate all the girls out of 10 on the attributes that were similar to his dream girl. During the rating only, he was sure that he didn’t want to meet 8 girls as they were not at all compatible with him. Once the rating was done, he put the data in SPSS and analyzed it. Now after analyzing the data, he came to know what all factors were more important to him than the others. Now he was sure about what attributes to look for in the girls he would meet.

Now was the most important thing – look for the attributes in the girls. One by one he met the remaining 7 females and analyzed them on the attributes that were most important to him. In less than 4 days he had met all the 7 girls and now he had to see who fit the bill the most. Some of them were close to what he was looking for and some of them missed the target by a mile. He finally shortlisted 2 girls (both of them were ranked 10 by him earlier) that he thought were the closest to what he wanted. He thanked his Conjoint Analysis and SPSS software to make his task so much simpler. The software had done its job; it would be of no help any further.

Now Dhruv will have to trust his instincts and GOD to get his soul mate. Tomorrow he will be meeting both the girls once more and then decide who would he want as his wife. Let’s all of us wish him all the best for one of the most important decision of his life.

Written by: Ajvad Rehmani

Group: 13003

Perceptual Analysis on " FlitterIn"

Objective : of this blog post is to study in very simple terms how the three stalwarts of the social media space – Facebook, Twitter and Linkedin are perceived by a common user.

Purpose : of carrying out this analysis is to debate whether there

· is a gap in the respective company’s desired positioning vis-à-vis the actual perception

· are gaps that the new entrants like Google Buzz or Google Me can exploit.

Method : Perceptual Mapping are easy to interpret graphs that visually display the perceptions of customers about a brand. I have used this

technique to display the relative position of Facebook, Twitter and Linkedin across certain key attributes.

Before we embark upon the analysis, it is important to put in perspective certain key stats about the three companies.

Analysis by way of Perceptual Maps

Perceptual Map 1 : Personal Vs Professional Networking

· Facebook scores very high on personal networking but low on professional networking.

· Facebook users tend to look for and interact more with old friends/acquaintances.

· Twitter has much better use of professional networking with corporate in the fray too.

· Twitter users are more open to making new friends – who had been total strangers till they met on Twitter.

· Linkedin’s perception is that of a business/professional networking site. That’s how it has been officially positioned as well.

Perceptual Map 2 : Knowledge Based Vs Relationship Based Updates

Twitter scores the highest in knowledge based updates. This includes links to news, opinions and blogs.

· Personal updates on Twitter are not that common. In fact, they are despised, with a threat of unfollowing looming large in case they are used more often.

· Linkedin is low on updates of any kind. That’s something Linkedin management is trying to address through measures like provision for article links, the ‘like’ button etc.

· Facebook is high on personal updates but low on knowledge based ones.

·

Perceptual Map 3 : Privacy Vs Downtime

· Linkedin has neither faced privacy issues nor any serious downtimes.

· Twitter is pathetic in terms of downtime.

· Twitter has also faced privacy issues, with the hacking menace giving jitters to users every now and then.

· However, since Twitter has been positioned as an open/public site, too much personal information is neither desired nor available on the website that could cause any serious privacy issue.

· Facebook is facing serious privacy issues but seems to be doing fine as far is downtime is concerned.

Conclusion

There are sufficient gaps in the social media space that Google can exploit, viz

· Perceptual Map 1 : There is no website that is high on both professional and personal updates at the same time. Is this an opportunity for Google, or an invitation to confused positioning?

· Perceptual Map 3 : Can Google Me offer excellent privacy with minimal downtime? Google is a powerhouse, and can achieve both! (Only if it does not botch up accidentally as it did with Google Buzz!)

As per Perceptual Map 2, both twitter and Facebook are comfortably placed in their respective quadrants, and it would be difficult to nudge past them!

Disclaimer – These maps are based on my perceptions of the three companies.

Author:- Lincy Thomas

Group:- Operations 3

Conjoint Analysis

Conjoint Analysis is a procedure for measuring, analyzing, and predicting customer’s responses to new products and to new features of existing products. It enables companies to fester customer’s preferences for products into part-worth utilities associated with each option of each attribute or feature of the product category. Companies can then recombine the part-worth to predict customer’s preferences for any combination of attribute options, to determine the optimal product concept or to identify market segments that value a particular product concept highly.

Factors and their values are defined by the researcher in advance. The various combinations of the factor values yield fictive products that are being ranked by the interviewed persons. With Conjoint Analysis it is possible to derive metric partial utilities from the ranking results. The summation of these partial utilities therefore results in metric total utilities.

Conjoint Analysis

· Independent variables: Object attributes.

· Dependent variable: Preferences of the interviewed person for the fictive products.

· The utility structure of a number of persons can be computed through aggregation of the single results.

Conjoint Analysis

· Factors and Factor Values

o Important for the choice of factors and their values are

§ Relevance

§ Interference

§ Independence

§ Realisable

§ Compensatory relationships of the various factor values

§ They do not constitute exclusion criteria

§ Terminable

Conjoint Analysis

Possibilities of rating of the incentives

· Ranking

· Rough classifications into groups of different utility with succeeding ranking within these groups.

· Aggregation of these results leads to a total ranking. Used when there are a large number of incentives.

· Rating scales

· Paired comparison

Conjoint Analysis

Estimation of the utility values

Conjoint Analysis is used to determine partial utilities (partworths) for all factor values based upon the ranked data. Furthermore, with this partworths it is possible to compute the metric total utilities of all incentives and the relative importance of the single object attributes.

Individual Conjoint Analysis: For each person utility values are computed.

Combined Conjoint Analysis: Only one value for each factor category.

Conjoint Analysis

Estimation of the utility values of target criterion for the determination of the partial utilities:

The resulting total utilities should yield a good representation of the empirically ranked data. Related procedure for the determination of the partial utilities: monotonous analysis of variance.

Author:- Ishan Tupe

Group :- Operations 3

REGRESSION

Rather than discussing about the various methods taught in class, I thought today I’ll review some of the facts related to regression; which is the basic knowledge required to draw conclusions from the output of the “Discriminant Analysis”.

Regression analysis is used to produce an equation that will predict a dependent variable using one or more independent variables. This equation has the form

Y = b1X1 + b2X2 + ... + A

where Y is the dependent variable being predicted;

X1, X2 and so on are the independent variables being used to predict it;

b1, b2 and so on are the coefficients or multipliers that describe the size of the effect the independent variables are having on the dependent variable Y, and

A is the value Y is predicted to have when all the independent variables are equal to zero.

Suppose we have a regression equation for the dependent variable,

PRICE = -294.1955 (mpg) + 1767.292 (foreign) + 11905.42; telling us that price is predicted to increase by 1767.292 when the foreign variable goes up by one, decrease by 294.1955 when mpg goes up by one, and is predicted to be 11905.42 when both mpg and foreign are zero.

Coming up with a prediction equation like this is useful, only if the independent variables in the dataset have some correlation with the dependent variable. So in addition to the prediction components of our equation--the coefficients on our independent variables (betas) and the constant (alpha)--we need some measure to tell us how strongly each independent variable is associated with our dependent variable.

When running the regression model, we are trying to discover whether the coefficients on our independent variables are really different from 0 (so the independent variables are having a genuine effect on our dependent variable) or if alternatively any apparent differences from 0 are just due to random chance. The null (default) hypothesis is always that each independent variable is having absolutely no effect (has a coefficient of 0) and we are looking for a reason to reject this theory.

Coefficients

In simple or multiple linear regression, the size of the coefficient for each independent variable gives the magnitude of the effect that variable is having on the dependent variable, and the sign on the coefficient (positive or negative) gives the direction of the effect. In regression with a single independent variable, the coefficient tells how much the dependent variable is expected to increase (if the coefficient is positive) or decrease (if the coefficient is negative) when that independent variable increases by one. In regression with multiple independent variables, the coefficient tells how much the dependent variable is expected to increase when that independent variable increases by one, holding all the other independent variables constant. We would have to keep in mind the units which our variables are measured in.

R-Squared and overall significance of the regression

The R-squared of the regression is the fraction of the variation in the dependent variable that is accounted for (or predicted by) the independent variables. (In regression with a single independent variable, it is the same as the square of the correlation between your dependent and independent variable.)

With this blog, I hope to have covered some of the intrinsic underlying facts behind the regression model and its analysis.

Posted by-

EMI JAVAHARILAL (13134)

Operations Group 2

Discriminant Analysis – Application Area

Yesterday, I had written about conjoint analysis. A few days back, when Discriminant analysis was being taught in class, due to some reasons I had to skip the class. So today I thought, I would read on discriminant analysis and would write on that topic.

From what I could understand, the major application area of discriminant analysis is where a distinction between two or three set of objects, or people,, based on knowledge of some of their characteristic is required. For Ex. For the selection process of a job, the admission process of an educational programme in a college, or dividing a group of people into different classes, like buyers and non buyers. Discriminant Analysis can be, and is in fact, used by credit rating agencies to rate the credit risk. In fact being students of finance, we had done an analysis by taking a paper where Risk analysis of SMEs was done using Discriminant Analysis and even a Risk model was developed. This technique can be very handy in deciding and classifying them into good lending risk and bad lending risk.

From what I could make out by going through various articles and blogs on the net, that discriminant analysis is very similar to the multiple regression technique. It also gives quite a similar equation as we get in a regression equation, where Y is the dependent variables and x1 and x2 are the independent variables. K1 and k2 are the unstandardised discriminant function coefficients.

To summarize, we can use linear discriminant analysis when we have to classify objects into two or more groups based on knowledge of some variables related to them. Typically these groups would be users/non-users, potentially successful salesman/potentially unsuccessful salesman, high risk/low risk customers or can be on any such similar lines.

RAJEEV RANJAN

FINANCE - GROUP 1

My Choice : Factor Analysis

Today’s class covered revision of almost all the topics and techniques covered in Business Analytics course so far. It was a good learning to understand the application of different techniques appropriately to analyze the given data and to come up with strategies.

As the freedom is to us to choose any topic for blog today, I prefer to write about Factor Analysis as it is my favorite topic. Factor analysis is a good technique to reduce data/variables to a smaller number of factor or components. Let us take for example we want to examine banking preferences of customers : There will be around 30-40 variables like which would define customer preferences. However it gets time consuming to analyze so many variables. Thus, factor analysis helps to reduce these variables into dimensions in such a way that variables which have high degree of correlation come under one dimension. Thus, in this example 30-40 variables can be reduced to say around 7 or 8 variables under the label: quality, reliability, accessibility, user friendliness, etc.

With the help of SPSS, factor analysis can help us find out the correlation between different variables and also define the extent to which one variable can be extracted to form a single component of different variables. Another advantage of this technique is that correlation between groups can be found.

Once the variables are reduced to components, various other statistical tools and techniques can be applied to groups to come up with findings and solutions. In our earlier example, we can find the preferences for customers for various dimensions and thus come up with strategies.

Let us take an example where an insurance company wants to understand the responsiveness of customers with respect to product communication. It can prepare a questionnaire and collect data from a set of customers. For example: Will you buy insurance product if a celebrity endorses? , Product communication is better through brochures, product communication is better through audios, etc.

Thus, these independent variables can be grouped under various heads having high correlations. The main labels can be: Communication through flyer, communication through videos, communication through website, etc. Thus, after grouping data under various heads, we can find customer preferences of different set of customers for product communication in insurance industry.

The study can help us find out if people in rural areas prefer communication through agents? People in metro prefer communication through website, etc. These conclusions can help the company decide their product communication and marketing strategy.

SAPNA BATRA

FINANCE - GROUP 1

The saga of Neural Networks

All of us while doing the assignments for the Business Analytics might have come across the concept of Neural Networks. Let’s have a sneak peek into the basics of Neural networks and some of its applications.

Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal.

Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style.

Neural networks do not make any forecasts. Instead, they analyze price data and uncover opportunities. Using a neural network, you can make a trade decision based on thoroughly analyzed data, which is not necessarily the case when using traditional technical analysis methods. For a serious, thinking trader, neural networks are a next-generation tool with great potential that can detect subtle non-linear interdependencies and patterns that other methods of technical analysis are unable to uncover.

Some of the application of Neural networks are

1. Character Recognition : The idea of character recognition has become very important as handheld devices like the Palm Pilot are becoming increasingly popular. Neural networks can be used to recognize handwritten characters.

2. Image Compression – Neural networks can receive and process vast amounts of information at once, making them useful in image compression. With the Internet explosion and more sites using more images on their sites, using neural networks for image compression is worth a look.

3. Stock Market Prediction -The day-to-day business of the stock market is extremely complicated. Many factors weigh in whether a given stock will go up or down on any given day. Since neural networks can examine a lot of information quickly and sort it all out, they can be used to predict stock prices.

4. Traveling Saleman's Problem- Interestingly enough, neural networks can solve the traveling salesman problem, but only to a certain degree of approximation.

5. Medicine, Electronic Nose, Security, and Loan Applications - These are some applications that are in their proof-of-concept stage, with the acception of a neural network that will decide whether or not to grant a loan, something that has already been used more successfully than many humans.

6. Miscellaneous Applications - These are some very interesting (albeit at times a little absurd) applications of neural networks.
Source:

http://www.investopedia.com/articles/trading/06/neuralnetworks.asp#axzz1XCqoAslo

http://www-cs-faculty.stanford.edu/~eroberts/courses/soco/projects/2000-01/neural-networks/Applications/index.html

Author: Surya Narayana Adiga

Group : Finance 2