Conjoint Analysis: Simple but Crucial

Introduction

 Conjoint Analysis is an advanced statistical tool used by marketing research companies in business and finance research. Market research companies use the technique to determine how people value various features in a product or service. The main objective behind using conjoint analysis is to find out what combination of a limited number of attributes influences decision making most. It is tested by marketing research companies directly on people. A defined set of products is shown to them and their way of preferring one product over the other is analyzed. Conjoint analysis determines the implicit valuation of all those individual elements making up a product. These valuations help structure models that further help estimate market share, revenue and profitability of new designs. Marketing research companies work with organizations from different industries.

Applications

Some of the realms of research they use conjoint analysis in are marketing, product management, operations research, pharmaceuticals and analytics. It is frequently used to test how customers receive new product designs, how advertisements appeal to masses and how one successfully positions products. Conjoint Analysis is a set of techniques. Market Research Companies use the set as a whole in research. Also known as multi-attribute compositional modelling, these tools include the likes of brand-price-trade-off, evolutionary algorithms and rule developing experimentation. Their statistical nature has also earned them the monikers ‘discrete choice modelling’ and ‘stated preference research’. Market research companies use multiple forms of conjoint analysis. The first form used is full profile studies, where a small set of attributes are used for creating profiles shown to respondents on individual cards. Respondents rank these profiles. Through dummy variable regression analysis, implicit utilities for the levels are calculated. The other type of analysis is hybrid conjoint analysis. The objective behind this technique is to use a form of self-explication to handle conjoint tasks. Conjoint analysis in market research helps estimate psychological trade-offs consumers make when they evaluate attributes. The analysis helps measure preferences at an individual level. One gets to uncover otherwise hidden drivers. Model interactions between attributes become possible, resulting in needs based segmentation.