Conjoint Analysis-2

Marketing in Analytics? Analytics in Marketing? :P

“How much are you willing to pay for a product and what are you willing to trade off”

Conjoint pricing is a very innovative marketing strategy because it segregates the functionalities, the utility as well as the price and attributes for the different consumers in the market. It does not particularly focus just on the price or just on the features, it rather focuses on what a consumer is willing to let go to for getting something else...for e.g. if a customer is willing to pay Rs 10,000 more for a laptop which has a better battery life then he is trading off on the price of the laptop. He could have spent Rs 10000 less to get a laptop if he didn’t bother about the good battery life.

So basically, conjoint pricing is a bracket or range of offerings what you are willing to pay for.

Let’s look at an example to understand this further...

Sony Bravia is a premium TV brand. There are so many features that you get- but the question is how much do you feel is the utility of all those features...

Let’s say you don’t want to pay for a feature that the company is offering to you, but at the same time you get other features which you definitely want in the product...hence you are looking at the utility that the product brings to you and this is because different features are looked at by different kinds of customers...

 

Here we have a TV which offers all this features and functionalities, and can combine these features into several groups depending on the demand criteria by the different types of customers...we can actually focus on just the size, slim, colour and pixels for customers who are more concerned about the “looks” of the television. Like this we can make several other groups and develop ad campaigns and promotional events that could be targeted at those specific utility quotients perceived by those segments of customers.

This method is very useful as it focuses on the different consumer segments more accurately and more effectively. As the people are paying for the product which is giving them the best utility according to them and not confusing them with too much information not valued by them. 

Chayan Ray

Marketing 2

Conjoint Analysis

Conjoint analysis

 Conjoint analysis is the most widely used multivariate research technique for establishing product attribute and price levels for both new and mature products which is typically used to measure consumers’ preferences for different brands and brand attributes. It revolves around one key idea; to understand the purchase decision best.   This methodology was developed in the early 1970’s. It has become one of the most widely used quantitative tools in marketing research.   It is described in many published journal papers. Green & Rao (1971) first introduced the research concept and Batsell & Elmer (1990) discussed its application to pricing and demand forecasting. 

 There are many different conjoint methods; adaptive conjoint analysis (ACA), full profile conjoint analysis (CVA) and choice based conjoint (CBC). Respondents in a market research interview (i.e. phone, in-person or web) are asked to make either choices or rankings of preference regarding hypothetical product profiles.  A group of products and the products corresponding profiles are presented to a respondent (i.e. written description, pictures or physical products).  The respondent is asked to select or rank the different individual product attributes or individual products.  This process is repeated several times with the levels  of each product/service attribute (and sometimes price) varying in each scenario.  By analyzing the tradeoffs that respondents make in choosing their preferred products it becomes possible to see which product features respondents are willing to give up to obtain others. 

 The conjoint analysis yields two measures i.e. the relative importance of each attribute and the relative importance of each attribute level. If price is included in the conjoint test it becomes another attribute. This analysis produces information that gives strategic direction for marketing managers. It can be helpful in developing forecasting models.  Price is presented as one of many product attributes. This helps minimize price bias and game playing.  Conjoint testing holds all extraneous real world factors constant (i.e. advertising, new product entries, stock outs, promotions, distribution, etc.).  It can be used for both new and established products.  CBC employs choice based methods.

Too many additional attributes besides price can dilute subject attention to price. This can result in an underestimation of the relative importance of price in an actual buying situation.  Adaptive Conjoint Analysis is commonly known to underestimate the importance of price. In fact researchers refer to it as the ‘ACA Price Effect’. Researchers have attempted to develop heuristic solutions to deal with the ACA Price Effect.  Some evidence is presented that demonstrates the relative value of price versus brand changes depending on the number of scenarios a respondent is exposed to. 

Deepak Sharma

Marketing 2

Fractional factorial Orthogonal design used in Conjoint

The approach louverie and woodworth devloped involved constructing conjoint choice experiments with the use of 2^j designs when there are j possible alternatives , obtained by generating all possible combinations of attribute levels.If there are , for instance , two attributes each with two levels, four alternatives can be constructed. The 2^j design used then contains all combinations of the four alternatives present or absent in the choice set. From the full 2^j design an orthogonal main effects experimental design is selected such that a relatively small number of choice set remains for estimation purposes.A disadvantage of 2^j fractional factorial designs is that when there are many alternatives (J), this approach will result in large tasks for respondents where choice set can contain too many alternatives. A more general version of the 2^j fractional factorial design can be used when each choice set contains a fixed number of alternatives (M) and each alternative has S attributes with each L levels. In that situation a L^m-s main effects, orthogonal, fractional factorial experiment design can be used to create joint combinations of attribute levels.In case the number of levels is not equal for all alternatives a L^m-s design still can be used, where L now represents the maximum number of levels present in the study.

Kartik Singhvi

13082 (Fin Grp-6)

The lesser used coefficient- Kendall’s Tau

All of our knowledge of correlation coefficients so far has been limited to the Pearson’s correlation coefficient- the much used and analyzed and commented upon ‘R’. However, when going through the output of one of the exercises done in class, I came across a never before read about co-efficient- the Kendall’s Tau. So my ‘googling’ skills in tow, I have set out to figure out what this new animal is.

The Kendall’s Tau is a measure of rank correlation: that is, the similarity of the orderings of the data when ranked by each of the quantities. A non- parametric coefficient, it is an alternative to Spearman’s correlation coefficient and is much easier to interpret.

Although the actual calculation is a bit arduous and needs more than just googling abilities to figure out, the interpretation of this coefficient is done as below:

The Kendall tau correlation represents the difference between two probabilities – say that Sonia and Manmohan are raters, who are rating some object on some characteristic. Sonia says that A has a higher score than B. The tau-a correlation is the probability that Manmohan will say that they are in the same order minus the probability that he will say that they are in the opposite order. If the two are in complete agreement, this will be 1 – 0 = 1. If they are in complete disagreement, this will be 0 – 1 = -1, and if both Sonia and Manmohan are random, this will be 0.5 – 0.5 = 0.

The main advantages of using Kendall's tau are that the distribution of this statistic has slightly better statistical properties. However, the difficulty in its computation makes t a lesser used correlation coefficient.

References: http://www.jeremymiles.co.uk/usingstatistics/chapter8/kendalltaua.html

http://www.blackwellpublishing.com/specialarticles/jcn_10_715.pdf

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Jyoti Maheshwari

Finance

Conjoint Analysis

In today’s class we learnt about Conjoint analysis, which is also called multi-attribute compositional models or stated preference analysis. It’s basically 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.

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 principle behind conjoint analysis is to break a product or service down into its constituent parts then to test combinations of these parts to look at what customers prefer. 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).

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

For example, a television may have attributes of screen size, screen format, brand, price and so on. Each attribute can then be broken down into a number of levels. For instance, levels for screen format may be LED, LCD, or Plasma.Respondents would be shown a set of products, prototypes, mock-ups, or pictures created from a combination of levels from all or some of the constituent attributes and asked to choose from, rank or rate the products they are shown. Each example is similar enough that consumers will see them as close substitutes, but dissimilar enough that respondents can clearly determine a preference. Each example is composed of a unique combination of product features. The data may consist of individual ratings, rank orders, or preferences among alternative combinations.

As the number of combinations of attributes and levels increases the number of potential profiles increases exponentially. Consequently, fractional factorial design is commonly used to reduce the number of profiles that have to be evaluated, while ensuring enough data is available for statistical analysis, resulting in a carefully controlled set of "profiles" for the respondent to consider

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.

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

Conjoint analysis also forms the basis of much pricing research and powerful needs-based segmentation. From the understanding obtained from the class, 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. The problem facing the decision maker is how to trade off the possibility that option X is better than option Y on attribute A but worse than option Y on attribute B, and so on.

Group : Marketing 5

Author : Mihir Sikka

Conjoint Analysis

Conjoint analysis is a technique used by market researchers to make customers narrow down on choices. Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of the given choices. To improve the predictive ability of this analysis, research participants should be grouped into similar categories. Conjoint is used 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. For 40 years researchers from a variety of disciplines - economics, operations research, psychology, statistics, marketing and business--have studied aspects of the multi attribute choice problem. Conjoint analysis is concerned with the day-to-day decisions of consumers--what brand of toothpaste, automobile, or mobile.

The theoretical work of Luce and Tukey (1964), conjoint analysis was introduced to the marketing research community in the early 1970s. Conjoint has been one of the most documented methods in marketing research. Judging by the thousands of conjoint applications that have been conducted since 1970, it has become the most popular multi attribute choice model in marketing. One use of conjoint analysis can be shown by this example; a real estate developer is interested in building a high rise apartment complex near an university. To ensure the success of the project, a market research firm is hired to conduct focus groups with university students. Students are segmented by academic year (freshers, seniors and graduate studies) and amount of financial aid received. Study participants are given a series of index cards. Each card has 6 attributes to describe the potential building project (proximity to campus, cost, and facilities like gymnasium, garden, laundry options, floor plans, and security features). The estimated cost to construct the building described on each card is equivalent. Participants are asked to order the cards from least to most appealing. This forced ranking exercise will indirectly reveal the participants' priorities and preferences, in this conjoint analysis can be used in determining participant’s priorities.

Raghvendra Singh

13031, Finance Group – 6

Reference - http://en.wikipedia.org/wiki/Conjoint_analysis

Conjoin Analysis and Selection of CellPhones !

Conjoint analysis is a statistical technique used primarily by marketers to determine how people value different features that make up any particular product. The main aim of this analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. The attributes are developed in such a way that it covers all the possibilities. It is widely used technique to test the customer acceptance of new product designs, assessing the appeal of advertisements and also in service design. The attributes form a wide

variety of choices to the customers from which they can choose their interests. This will help the company or the manufacturer to exactly know the preferences of the customers. This is also useful in finding the poor combination of attributes which will be a very beneficial for the company.

Conjoint analysis requires research participants to make a series of trade-offs. To make these revelations even better, the companies group the research participants into similar segments based on objectives, values and other factors.

For example, if we want to describe a mobile telephone in terms of attributes like weight, battery life and price, we give different choices to the participants in every attribute to know the preferences of the participants

.

For instance would you choose phone A or phone B?

attributes	Phone A	Phone B
Weight	200g	120g
Battery life	21 hours	10 hours
Price	7000	9000

The analysis from the above data can be as follows:

“Phone A is bulkier, but has the battery life and lower cost, but Phone B is smaller and neater yet more expensive and with lower battery life. Lighter weight is worth more than the loss of battery life, and it’s probably worth the extra 2000, so I’d choose B in this instance.”

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.

Posted by : Finance - 2

Author : Abhishek Reddy