Isomorphic Permuted Experimental Designs and Their Application in Conjoint Analysis

Abstract

This paper deals with experimental designs used in conjoint analysis. The described approach permutes the structure of the underlying fractional experimental design to make different sets of combinations. The resulting experimental designs, suggested to be called Isomorphic Permuted Experimental Designs (IPED), are statistically equivalent to each other while combining diverse sets of the variables and levels into different designs. By facilitating distinctive individual designs (for each respondent), IPEDs reduce a bias caused by some possibly unusually strong performing combinations, and allow detection and estimation of interactions among variables, as well as identification of pattern‐based segments emerging from individual models of utilities. This paper examines the theoretical foundation of the approach, formalizes the methodology for algorithmic implementation and shows a practical example of utilization. PRACTICAL APPLICATIONS Isomorphic Permuted Experimental Design (IPED) allows for overcoming multiple interlinked statistical problems that affect the traditional conjoint analysis approaches, thus leading to more reliable and targeted results in practice. IPED facilitates individual respondents' models based on unique designs, thus allowing for pattern‐based segmentation. The approach also allows for the detection of any and all interactions between the elements (features) of the experiments, thus increasing the reliability of conjoint analysis results. It has been utilized in many practical applications, such as for message optimization, early stage new product development, advertising, package and website optimization.

Empathy and Experiment™: dealing with the algebra of the mind to understand and change food habits

“…These many different vignettes prevent bias due to an incorrect choice of combinations, perhaps a combination of elements which interact with each other, unbeknownst to the researcher (Gofman and Moskowitz, 2010). The method used to uncover the impact of the different elements follows a sequence of activities shown in Fig.…”

Uncovering the mind-sets of consumers towards food safety messages

“…(Gofman & Moskowitz, 2010). The numerical value of a part-worth contribution indicates how much is added or how much of the respondent population is interested when the element is inserted into the concept (Moskowitz, Porretta, & Silcher, 2005).…”

Fat, lipids, or cheese: Understanding the algebra of the consumer mind

“…The cravability is driven by the elements. 3. Silo A, the cheese, drives the most cravability on average, with the key being the full fat cheese, 'soft, light cream coloured Brie, perfect with crusty bread (impact -14).…”