Conjoint-Analysis

Conjoint-Analysis: Because the whole is greater than the sum of its parts

The conjoint-analysis is a proven method of marketing research that has been in use for decades.

MWResearch is one of the institutes that established the conjoint method in Germany. With CARES (Conjoint Analytical RESearch) we offer you a state-of-the-art portfolio fit for many marketing questions to identify the optimal way to address the target group – both in the consumer goods and in the service industry.

Areas of application for CARES

Whenever it’s necessary for decision-making in marketing to learn more about preferences and purchase decisions conjoint analyses can be used for a range of applications, such as:

Product and services design: Optimization of existing product and services portfolio in the competitive set.
Pricing policy: How much can a new product or a new product feature or a new service cost compared to the competition?
Market/customer segmentation: Which product or service features generate the highest benefit in the different market segments?
Market/sales scenarios: Which influence does the introduction of a new product (or service) have on market share (of the competition)? Are there any cannibalization effects?

Every purchase decision is different!

The two most important aspects of a preference and a purchase decision are the consumer’s involvement and the complexity of the situation in which the decision is made. As a consequence a wide spectrum of methods is necessary in order to reproduce realistically the situation of the purchase decision.

By using the suitable conjoint analysis you can check…

which marketing mix factor has the strongest impact on purchase intention,
what is the relative importance of the performance dimensions/variables of a factor,
which product offer or service package has the biggest market potential, also compared to competing offers.

Which form of analysis is chosen for the problem at hand depends on several factors and should be considered carefully. Generally, the method that simulates the decision making process of the category consumer best is to be preferred.

With CARES MWResearch offers a comprehensive range of methods for analyzing purchase decisions and the underlying consumer preferences. These methods simulate the purchase decision process in different ways with various points of emphasis. The following table can be used for a first estimation which method is best for your particular questions.

CBC – for simpler products and purchasing decisions

The Choice Based Conjoint Analysis (CBC) is perfect for less complex purchase situations, in which products can be described comprehensively by a small number of relevant features. Respondents repeatedly have to choose their preferred product from different sets of products. CBC simulates purchase decisions realistically and is therefore often used during pricing studies. For more complex purchase decisions – e.g. when many product alternatives or complex products are relevant – the CBC is not optimal because respondents can suffer a cognitive overload during the interview.

For complex structured products and decision processes: ID CBC

With the development of the Information Display CBC (ID CBC) method it was possible to combine the advantages of the CBC with the requirements of the evaluation of more complex products.

During the interview respondents need to choose actively which information they regard as relevant. This prevents cognitive overload of respondents while allowing tracking information seeking processes at the same time. Moreover, the ID CBC helps to identify so called must-haves and “unacceptables” that many customers use to simplify complex purchase decisions. Comparative studies have shown that the ID CBC has a much stronger prediction power than the CBC method.

AHPlab

The Analytic Hierarchy Process with Mouselab (AHPlab) facilitates the measurement of preferences for complex products, such as technical products or services that can only be described realistically be a big number of features.

Initially, features that are really relevant as well as those that have a lower influence on the purchase decision are identified. In the subsequent preference evaluation only those features relevant for the respondent are taken into consideration. This leads to a substantial shortening of the interview.

AHPlab can test products with up to 25 features adequately and is also apt for price research.

Understanding customer needs and predicting buying behavior

Relevant topics

MaxDiff Price Research Market Share Forecast Packaging Test