Index C contains definitions for words that describe customer or consumer attributes, such as consumer brand attitude, consumer loyalty, consumer brand affinity, and customer insights. Index C also contains definitions for advanced research techniques and approaches, such as conjoint analysis and confidence intervals.
The Dynamics of Consumer Response to Brand
Consumer responses to brands are characterized by two important consumer dynamics. Consumer responses span a continuum from negative to positive, and the beliefs that consumers hold in relation to brands are firmly believed—by the consumers—to be accurate. Consumers act on their beliefs about products and services, and they tend not to alter those beliefs without good cause.
Speaking of cause, consumer responses to brands are shifting to include corporate social responsibility as a part of brand. Research indicates that the actions and activities in which a company engages as part of their social responsibility campaigns play a substantive role in the choices and brand evaluations consumers make. In fact, the presence of prior positive attitudes toward a particular social cause can have a substantive influence on brand success and brand-cause alliances.
The higher the affinity for the cause, the more likely the consumer is to make a purchase of the brand, and the more likely the consumer will have a high positive brand attitude. Consumers differ in their interest in brands that promote corporate social responsibility. Consumers with high cause affinity exhibit greater overall concern about brands with a social dimension. Consumers view a greater congruence between themselves and a company that develops a corporate social responsibility program. As one might guess, this perception of congruence is made even stronger in situations where the consumers are more supportive of the social cause. In these instances, the consumer evaluates the company higher and achieves a stronger level of identification with the company or brand. Strong identification with a brand tends to translate into stronger customer loyalty.
Conjoint Analysis & Key Drivers
Products and services are characterized by many features that contribute to their brand appeal. Figuring out just which combinations of those features are valued—and to what degree they are valued—is the purpose of conjoint analysis. The key is to determine, from a limited number of characteristics, which attributes have the most influence on a consumer’s decision to purchase a product or service.
Conjoint analysis works like this: A limited number of promising products or services are shown to study participants. The criteria that these participants use to make choices and express preferences are analyzed. In some studies, participants actually assign a value to the criteria, while in other instances, the valuation is implicit and is derived by noting the choices that participants make. In market research vernacular, these implicit valuations are referred to as utilities or part-worths. Utilities are often used by market researchers to create models that estimate the potential revenue, profitability, and market share of the preferred product designs.
The value or utility (preference score) that a consumer assigns or perceives is part of a process of the trade-offs that consumers make when they form a decision to purchase or prefer a product or service. Analyzing these trade-offs with conjoint analysis is a decompositional technique. A consumer’s overall evaluation or preference of a product or service is decomposed to provide a utility for each of the attributes (or predictor variables) and to provide a utility for each level of the predictor variable.
A conjoint analysis has two main purposes: To determine the contributions of various attributes to the dependent variable, and to establish a predictive model that can be used with different (new) combinations of values based on the attributes. Bayesian Belief Models are often used to create these predictive models of consumer preference and purchase decisions.
Steps in Conjoint Analysis
- 1. Select a model of preference Example: Part-worth
- 2. Data collection method Example: Full profile
- 3. Stimulus set construction Example: Additive compensatory model
- 4. Stimulus presentation Example: Written instructions
- 5. Measurement scale for the dependent variable Example: Metric
- 6. Estimation method Example: Ordinary Least Squares
Loken, B., Ahluwalia, R., and Houston, M. J. (Eds.) (2009). Brands and Brand Management: Contemporary Research Perspectives, [Chapter 2, Branding and Corporate Social Responsibility (CSR)]. Psychology Press.
Schaupp, C. and Bélanger, F. (2005). A conjoint analysis of online consumer satisfaction. Journal of Electronic Commerce Research, 6(2).