THE STORY OF CONJOINT ANALYSIS

Hi all,

Today again, I have been given a chance by my group coordinator to write whatever I want on Conjoint Analysis. Oooh, so whatt is Conjoint Analysis...??? If you have been following the blog, the definition, meaning, advantages, disadvantages, application, etc, everything has been explained thus far.

But do you still, understand how this magic wand functions? Well I have not completely understood how it functions, but today yet again our very own Prof. Bhate in his 20^th lecture, mesmerised me with the way in which this quant tool simulates the choice of an individual.

Now when I came back from the lecture, I tried to read it from several other blogs and websites as to how actually this technique works. The First step involved in it is to describe products in attributes and levels. An attribute is a general feature of a product or service. Each attribute is valued down into specific levels. For example, we might describe an object in attributes like - durability, looks and price. A specific level for the same can be defined as anything like say 8 years, Good Looking and say Rs. 50/-

Conjoint analysis takes these attribute and level descriptions of product/services and uses them in interviews by asking people to make a number of choices between different products. So this we had done in class the day before and had collected the information/data in an excel sheet. By asking for enough choices (and with good design to minimise the number of choices), we worked out numerically how valuable each of the levels is relative to the others around it – this value is known as the utility of the level. Then came the time to know the outcome. However, we can also compare across attributes to see which attributes make have the greatest impact in making a choice. We can therefore say which the most important attribute is and measure importance by taking the relative impact of one attribute compared to the others.

In Toto we learnt from the entire exercise that conjoint analysis is a sophisticated technique and there are technical issues that needed to be considered. In particular, the design of attributes is a crucial step in a conjoint project as choices between poorly defined levels can render the exercise meaningless.

RAJEEV RANJAN

FINANCE - GROUP 1

Dealing with Trade- Offs..

Life is all about trade-offs. The jeans you really need- or the pretty black dress you’ve been eyeing for the longest time ever. A wife with assets (financial ofcourse), or one with the brains. A dinner date, or the movies with your buddies. So how do you make these all important decisions while always maximising your utility. Atleast for us the stakes aren’t so high. The same decision making accuracy could cost Apple its brand, and the Product Manager his job. Whether the objective is increased market share, profit margin or revenue, every product manager makes trade-offs—quality vs. cost, time to market vs. breadth of features, richness of the offering vs. ease of use

So, how do you know what the market wants? What market segments exist? What those segments prefer? What will they pay? In short, how do you know what trade-offs to make? The answer is to get the market to make the trade-offs for you. Not the entire market, ofcourse, just a representative sample of the market.

By using conjoint analysis, you, as a product manager, can do just that: understand the trade-offs you should make by understanding the trade-offs your market will make. Then, apply your increased market insight to your revenue, profit or share objective.

Conjoint analysis is one of many techniques for dealing with situations in which a decision maker has to choose among options that simultaneously vary among two or more variables. It is both a trade-off measurement technique for analyzing preferences and intentions-to-buy responses and a method for simulating how consumers might react to changes in current product/services or the introduction of new products into an existing competitive array.

So the questions a Conjoint Analysis could seek to answer for you as a product manager are:

· Do i have the right pricing strategy?

Conjoint analysis helps determine how customers trade off different price levels with the features of your product they most desire- without asking them directly. That’s the magic.

· How important are new features?

Conjoint analysis tells you the must have features of your product by segment, so you can tailor your marketing efforts to a particular demographic or behavioural profile.

Does my brand matter?

Conjoint Analysis reveals what factors drive consumer behaviour: price, brand or features- and whether your brand can command a premium

Why it works...

The direct survey question “ how much would you pay for ABC?” would throw up unreliable and misleading results. So instead, by taking note of the consumer’s opinion through a series of products with differing features over a range of prices, accuracy is sought.

Techniques such as the use of regression analysis to compute mathematical values that explain consumer behaviour- how much value is placed on value, location, features etc; and then correlate this data to demographic, lifestyle or other consumer profiles.

APARNA RAMACHANDRAN

FINANCE - GROUP 1

FACTOR ANALYSIS AND HUMAN RESOURCE MANAGEMENT

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 observed variables are modelled as linear combinations of the potential factors, plus "error" terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Factor analysis estimates how much of the variability is due to common factors.

Factor analysis originated in psychometrics, and is used in behavioral sciences, social sciences, arketing, product management, operations research, and other applied sciences that deal with large quantities of data.

Factor analysis is also extensively used in the domain of human resource management. It can also be used to identify the work motivation and the job satisfaction dynamics in the employees in an organisation. Factor analysis can help to analyse the effectiveness of psycho – social, economic, organisational and managerial tools over individual’s motivation and the job satisfaction of the employees in the business.

The four factors that are instrumental in determining the perceived level of motivation are:

1. Organisational and managerial tools:

Employment of tools of development such as training, courses, etc. Maintenance of participation into decision making processes, sufficient sensitivity of the management towards the problems about work, sufficient sensitivity of the management towards the problems of the employees, the existence for opportunities for promotion, fairness of rewarding and punishment.

2. Psycho-social tools:

Sufficient appreciation of human being and respect for private life, existence of exposure to negative factors such as noise, brightness, radiation or smoke rising,

3. Economic tools

Fairness of waging system, use of rewarding success and fairness of promotion system

4. Tools for maintaining balance between authority and responsibility:

Fairness of benefiting from opportunities such as training, courses, etc. having returns for overtime, application of a policy of assigning more authority, responsibility and independence

Managers may apply to encouraging their employees by monetary awards when they act in the way expected from them. The effect of such awards is still limited like other motivating tools and they may be useless or even become a damaging factor in the case that they are not used carefully and in a fair manner or not individualized sufficiently. The function of economic awarding used by many enterprises today in various ways in motivating people cannot be denied. However it is seen that appeal to the motivating economic tools and expecting from them more than needed does not seem to result in success very much. Therefore, in planning rewarding, encouraging economic tools should be employed in accordance with employees’ needs. In fact, there are ways of increasing employees’ work motivation and satisfaction other than monetary tools.

Today people spend most of their lives in business organizations and satisfy many of their material social and even sentimental needs at these organizations. Therefore, members of the organizations become more dependent on their organizations in satisfying some of their needs. Social security and retirement plans invented to prevent people from being negatively affected by this dependency should

include various forms of economic protection that would provide life-long revenue such as retirement, or accident, disease, life and unemployment insurances. However it is possible for organization managers to shape these tools into more useful forms and employ them as tools of motivation. Opportunity of retirement under better conditions, more convenient health security, paid or unpaid vacation opportunity offered without any problem when necessary, food, fuel or child aids and other similar tools that would render the enterprise more attractive compared to others contribute to the personnel’s sense of satisfaction from job and safety.

Group- HR1

Author- Bithi Ghosh

Conjoint Analysis

Should I buy Samsung Galaxy II or HTC Desire HD? Samsung Galaxy looks a bit expensive but it’s lighter than HTC. So how to trade off the possibility that choosing Samsung is better because it is lighter than HTC or choosing HTC is better because Samsung is expensive. In this kind of situation Conjoint (trade-off) analysis has become one of the most widely-used quantitative methods in Marketing Research.

Conjoint Analysis is concerned with understanding how people make choices between products or services or a combination of product and service, so that businesses can design new products or services that better meet customers’ underlying needs.

Rather than directly ask survey respondents what they prefer in a product, or what attributes they find most important, conjoint analysis employs the more realistic context of respondents evaluating potential product profiles. Each profile includes multiple conjoined product features (hence, conjoint analysis).

Conjoint analysis takes attribute and level descriptions of product/services and uses them in interviews by asking people to make a number of choices between different products.

For instance would you choose phone A or phone B?

Phone A Phone B

Weight 200g 120g

Battery life 21 hours 10 hours

Price $70 $90

In practice you can see how difficult some of the choices can be. By asking for enough choices (and with good design to minimise the number of choices you need to ask), the researcher can work out numerically how valuable each of the levels is relative to the others around it – this value is known as the utility of the level.

At the end of the conjoint exercise we can plot the utility for each of the levels on a graph.

In this instance we can see that for this customer, the optimum weight is 80g. 40g is too light and more than 80g is too heavy. In designing a mobile phone for this customer therefore, we can see that there is no benefit in spending development money to bring the weight of the phone below 80g.

However, we can also compare across attributes to see which attributes make have the greatest impact in making a choice. We can therefore say which the most important attribute is and measure importance by taking the relative impact of one attribute compared to the others. For example:

In this dummy example, getting the weight right is more than twice as important as looking at the battery life.

Knowing the values of the utility values of each of the levels, but what we really want to know is how our product/service compares to our competitors and how we can best optimise the value we give to the customer.

To do this we can total up the utility value our product is giving the customer and compare it to the value for the competition (in practice we do this via modelling as we typically look at the choices of 100s of customers at a time).

In the example below, utility values are in brackets. Notice that a lower price has a higher utility (we typically prefer cheaper goods)

Ours Theirs

Weight 200g (15) 120g (35)

Battery life 21 hrs (15) 10 hrs (10)

Price $70 (25) $90 (15)

Total utility 55 60

In this example we are 5 utility points behind the competition. If we reduced the weight of the phone to 160g we would gain 10 utility points which would mean we would expect to be chosen over the competition. Alternatively, we could to reduce the price a little to have the same impact.

For a business making the choice of what to change or improve thus comes down to understanding the cost impact of making the change balanced against the extra value to the customer. Would you get a better return spending more on development to bring the weight of the phone down, or would it be worth bringing the price down despite the lost profit margin (usually the former!)?

Author:- Rinzing

Group operations3

Conjoint Analysis

It is essentially a technique used to determine the tradeoffs people make in choosing between products or anything for that matter (not just a product). Speaking about products and attributes makes it look like a marketing tool. However this analysis is more than just a “marketing research technique.”

Conjoint analysis is typically used to identify the most desirable combination of features to be offered in a new product or services (e.g. what features should be offered in a new public telephone booth?). In such studies, respondents are told about the various combinations of features under consideration and are asked to indicate the combination they most prefer, to indicate the combination that is their third preference, and so on. Conjoint analysis uses such preference data to identify the most desirable combination of features to be included in the new product or service.

It applies a complex form for analysis of variance to the data obtained. This analysis then calculates a value for each feature. Features which have the highest values are taken to be most important to respondents.

This is applied to categorical variables, reflecting different features or attributes of the product under consideration. Since it is applied only to these kind o variables it is different from cluster or factor analysis.

Conjoint analysis is like cluster and factor analysis in the sense that these methods try to identify the interdependencies which exist between a numbers of variables. In the example involving a new public telephone booth, the variables are the features that can be designed into the new system and also tries to measure the relative importance of various combinations.

Harshala D

No 13174(Finance, group 5)

Conjoint Analysis

Concept

A conjoint analysis applies a complex form of analysis of variance to the data obtained from each respondent. This analysis calculates a value (or utility) for each feature. Features with the highest values are judged the most important to respondents. It tries to identify the interdependencies which exist between a numbers of variables.

Method

First we need to create an orthogonal design feeding in the factors and defining various levels for each one of them. This design then needs to be saved as a Plan File and another Data file needs to imported from the excel. After running the syntax file we get a lot of tables and charts depicting the utility of individual subjects for different factors as well the group as whole along with an importance summary explicitly stating the importance of each factor as ranked or rated by the subjects. The factors can be rated, ranked or arranged by the subjects.

Application

Conjoint analysis can be used to investigate the attributes that influence individual investors when they make a decision to buy shares. In deciding to buy a particular stock, financial measures, such as dividend and price–earnings ratio are relevant. However, they are less important than the company's management or recent movements in the share's price. These variables can be factored in and then analysed according to find out whether these people are speculators or investors.

Conjoint analysis can also be used to probe into the factors that are responsible for Venture Capitalists decision making. Decision making is central to the ability of venture capitalists to predict those new ventures likely to succeed.

Conclusion

Conjoint analysis is a useful tool that helps 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.

NAME: ASMITA KARANGE

FINANCE_3

A Joint with Facebook and Conjoint analysis

Usually we used write our learning’s for the day in this blog, for a change i am gonna write a prelude to what we are going to learn tomorrow.

Tomorrow we are going to learn about conjoint analysis for which we had taken a case which deals with preferences that people consider while choosing their spouse. Each and every student in the class had provided the data with his/her own preferences and the final data was generated. The preferences were based on looks, education, family background,  experience (this is my favorite attribute :)), religion... etc. In tomorrow’s class we are planning to analyze this data, having said that i still have a doubt on the data that was generated today. As per my understanding which is as unclear as a Lokpal bill, the data generation was only partially right. The reason is as follows

The best way to understand a person is to analyze his Facebook profile.

Both guys and girls don’t have similar requirements

Please note this is just a humorous take on the task that we did today, no offences meant on any particular gender....(going by the rate at which i am commenting on the female audience, i might even get sued by some women activists)

The point which i am trying to emphasize is, no matter how many kinds of analysis we do and no matter how many statistical tools we use, Girls will be girls and Guys will be Guys.

Cheers,
Ragu


Group: Marketing 5
Author: Ragunathan A
Roll no: 13032

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 is a less expensive and more flexible way to address these issues.

Let’s understand Conjoint Analysis with the help of a simple example.

Suppose we want to buy a laptop. We know from experience that it has 3 important product features – Weight, Battery Life and Price. We further know that there is a range of feasible alternatives for each of these features, for instance

Weight Battery Life Price

3kg 5 years Rs 20,000/-

5kg 4 years Rs 35,000/-

6kg 2 years Rs 40,000/-

Obviously, the market’s “ideal” laptop would be

Weight Battery Life Price

3kg 5 years Rs 20,000/-

and the “ideal” laptop from a cost of manufacturing perspective would be:

Weight Battery Life Price

6kg 2 years Rs 40,000/-

Now the problem here would be that it would be quite easy to sell the first laptop whereas no one would buy the second one. The most viable product would lie somewhere in between. This is what Conjoint Analysis lets us find out.

A traditional research project might start by considering the rankings for Weight and Battery life

Fig 1

Rank Weight Rank Battery Life

1 3 1 2

2 5 2 4

3 6 3 5

This type of information doesn’t tell us anything that we didn’t already know about which laptop to buy.

Now consider the same two features taken conjointly. The two figures below show the rankings of the 9 possible products for two buyers assuming price is the same for all combinations

Fig 2 : Buyer 1

Weight	Battery Life
		5 years	4 years	2 years
	3 kg	1	2	4
	5 kg	3	5	6
	6 kg	7	8	9

Fig 3 : Buyer2

Weight	Battery Life
		5 years	4 years	2 years
	3 kg	1	3	6
	5 kg	2	5	8
	6 kg	4	7	9

Both buyers agree on the most and least preferred laptop but as we can see from their other choices, Buyer 1 tends to trade-off Battery life for Weight, whereas Buyer 2 makes the opposite trade-off.

The knowledge we gain in going from Figure 1 to Figures 2 and 3 is the essence of conjoint analysis

Next, we figure out a set of values for Weight and a second set for Battery Life so that when we add these values together for each laptop they reproduce Buyer 1's rank orders. Now, we figure out the trade-offs Buyer 1 is willing to make between Battery life and price. Finally, we get a complete set of values (referred to as “utilities”) that capture Buyer 1's trade-offs.

We use this information to determine which laptop to buy based on the estimates of preferences of the buyers.

The three steps--collecting trade-offs, estimating buyer value systems, and making choice predictions-- form the basics of conjoint analysis.

Group 3 : Marketing

Author : Sahil Kotru