• # Conjoint Analysis

Conjoint analysis is a statistical tool used in market research to determine how different features of a product affect the buying behavior in the consumer’s mind. The objective of this analysis is to determine the proper combination of a limited number of attributes which influences the consumer’s mind most and affects the buying behavior.

A controlled set of potential products or services is shown to consumers and by analyzing how the consumers make preferences between these products or rank different attributes, we can determine the implicit valuation of those attributes. These implicit valuations can be further used to create market models that estimate market share, revenue and even profitability of new designs.

The conjoint analysis predicts the products/services consumers/users will choose and assess the weight they give to various factors that underlie their decisions. As such, it is one of the most powerful, versatile and strategically important research techniques available for a company which plans to launch a product or modify an existing product.

Procedures

The attributes of the product are first identified and listed down. For example, a car can have attributes like power, price, performance, mileage design and style. Within these attributes there can be different levels which can segregate one car from other car.

As the number of attributes increases, the possible number of products arising out of a combination of those attributes increases exponentially. To arrive at a controlled set of profiles, a technique called fractional factorial design is used. But here we will consider only some feasible set of attributes.

Respondents would then be shown a set of products/prototypes created from combinations of these attributes and asked to rank/rate the products based on their preferences. The data is most commonly gathered through a market survey and the choices are filled up from respondents in the form of a questionnaire.

Example

Suppose a passenger car manufacturing company wants to evaluate the preference of cars among consumers for various features and has decided to perform conjoint analysis for the same. It has identified the following attributes and associated levels for the purpose:

1. Mileage (>15kmpl and <12kmpl)

2. Price (High and Low)

3. Power (High and Low)

Given these choice of attributes and their associated levels, we can crate maximum of 8 different combinations. Each of these combinations is a potential profile for a product to be launched.

 Sl No Power Price Mileage 1 Low High <12kmpl 2 Low High >15kmpl 3 Low Low <12kmpl 4 Low Low >15kmpl 5 High High <12kmpl 6 High High >15kmpl 7 High Low <12kmpl 8 High Low >15kmpl

At first glance we can see that, Product 8 will be the most preferred choice among the consumers while Product 1 will be the least preferred. The preference for the other choices will be determined based on feedback from the consumers.

Conjoint analysis can be used to determine the relative importance of each attribute or any combination of some attributes compared to others. The marketing appeal of different set of products also depends on the market segment we are looking at. High Power with lower mileage car is popular among the upper class customers while middle class customers give more importance on Mileage.

This helps the market researcher to design products/services that will be most appealing to a specific market. It can also be used to decide most effective advertising message.

Data Analysis

The data is first collected from respondents in the form of filled questionnaires where the ranks of different attributes are collected. After that, the marketing appeal of an existing product or a new product can be calculated/predicted based on the analysis of these ranking or feedback from the consumers.

Applications

1. Predict the psychological tradeoffs the consumers consider while evaluating a product
2. Calculate the expected market share for proposed new product concepts given by competitors or already launched product by the competitor
3. Predict the switching product behavior by the customers
4. Calculate competitive advantage of a product compared to other products.
5. Predict the differential consumer responses to alternative advertising strategies
6. Try to predict the customer response to alternative pricing strategies, specific price levels, and proposed price changes.
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