Research Methodology

Focus Group Discussion

A focus group is a form of qualitative research in which a group of people are asked about their perceptions, opinions, beliefs, and attitudes towards a product, service, concept, advertisement, idea, or packaging. Questions are asked in an interactive group setting where participants are free to talk with other group members.

A focus group discussion (FGD) is a good way to gather together people from similar backgrounds or experiences to discuss a specific topic of interest. The group of participants is guided by a moderator (or group facilitator) who introduces topics for discussion and helps the group to participate in a lively and natural discussion amongst them.

The strength of FGD relies on allowing the participants to agree or disagree with each other so that it provides an insight into how a group thinks about an issue, about the range of opinion and ideas, and the inconsistencies and variation that exists in a particular community in terms of beliefs and their experiences and practices.

FGDs can be used to explore the meanings of survey findings that cannot be explained statistically, the range of opinions/views on a topic of interest and to collect a wide variety of local terms. In bridging research and policy, FGD can be useful in providing an insight into different opinions among different parties involved in the change process, thus enabling the process to be managed more smoothly. It is also a good method to employ prior to designing questionnaires.

Conjoint Analysis

Conjoint analysis is a technique for measuring consumer preference about the attributes--such as price or package design--of a product or service. It relies on surveying subjects with a representative set of attribute combinations--for example, a particular package design and price--which the subjects rank or score according to preference. Analysis then yields quantitative information that can be used to model consumer preference for any combination of the attributes.

A study utilizing conjoint analysis consists of choosing a representative set of attribute combinations, administering them to a group of subjects, and analyzing the rankings or scores recorded by the respondents. In conjoint analysis, attributes are referred to as factors, and attribute values--like a particular price or package design--are called levels.

Conjoint uses the full-profile approach where respondents rank alternative products defined by specific levels of all factors. Even after careful selection of the factors and levels for a study, the total number of potential product combinations is frequently too large for subjects to judge. For instance, with 5 factors and 3 levels for each factor, the number of combinations is 243 (3 × 3 × 3 × 3 × 3)

Orthogonal Design

To solve this problem, the full-profile approach uses what is termed a fractional factorial design, which presents a suitable fraction of all possible combinations of the factor levels. The resulting set, called an orthogonal array, is designed to capture the main effects for each factor level. Interactions between levels of one factor with levels of another factor are assumed to be negligible.

Each set of factor levels in an orthogonal design represents a different version of the product under study, and should be presented to the subjects in the form of an individual product profile. This helps the respondent to focus on the product version currently under evaluation. The stimuli should be standardized by making sure that the profiles are all similar in physical appearance except for the different combinations of features.

Part-Worths

Analysis of the data results in a utility score, called a part-worth, for each factor level. Part-worths provide a quantitative measure of the preference for each factor level, with larger part-worth values corresponding to greater preference.

Part-worths are computed using the Conjoint procedure (available only through command syntax), and are expressed in a common unit. This allows them to be added together to give the total utility, or overall preference, for any combination of factor levels. The part-worths then constitute a model for predicting the preference of any product profile.

In this research project, we are interested in observing the Von Restorff effect in any FMCG products. Thus, we wish to examine the influence of five factors on consumer preference—package design, package material, cover design, outer color, and marketing campaign. There are four factor levels for package design; three package materials (protective, convenience, eco-friendly); two cover designs; and two levels (either no or yes) for the marketing campaigns. The following table displays the variables used in this study, with their variable labels and values.

Variable name	Variable label	Value label
pkg_design	Package Design	Design1, Design2, Design3, Design4
pkg_material	Package Material	Protective, Convenience, Eco-Friendly
cover	Cover design	Simple, Smart
color	Outer Color	Color1, Color2, Color3, Color4
marketing	Marketing Campaign	No, Yes

There could be other factors and factor levels that characterize an FMCG product, but these are the only ones of interest as analyzed from the qualitative analysis. This is an important point in conjoint analysis. We’d want to choose only those factors (independent variables) that we think most influence the subject's preference (the dependent variable). Using conjoint analysis, we will develop a model for customer preference based on these five factors.