Conjoint Analysis

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What is Conjoint Analysis?

Conjoint Analysis is simply  "trade-off" analysis. It is a disciplined way to isolate what it is that a customer really wants to buy, and that knowledge allows the organisation to 'engineer' prime buying triggers into the product and prune out unnecessary features and other costs.

Conjoint analysis helps marketers deliver more satisfying solutions to customers

Conjoint Analysis is a statistical technique systematically varying customers' preferences among real and hypothetical configurations, to see what attributes are more important than others.

Used to identify actual physical differences rather than perceptual ones, Conjoint Analysis helps identify and prioritise determinant attributes to deliver better products and improve marketing mix decision making. (What price? What channel/s? What advertising & promotion? What brand personality? What product characteristics?)

Conjoint Analysis determines what factors target audience members will trade off or give up for other factors that make up the total product formed by the marketing mix. For example, Conjoint Analysis for one client discovered that customers simply did not find any value in a layer of packaging when they acquired a new product. This allowed the client to remove that layer and generate a 12.5% reduction in COGS!

The Concept of Conjoint Analysis

Marketing managers are faced with numerous difficult tasks directed at assessing future profitability, sales, and market share for new product entries or modifications of existing products or marketing strategies. These specific tasks include:

1. Predicting the profitability and/or market share for proposed new product concepts given the current offering of competitors.

2. Predicting the impact of new competitor products on profits or market share if we make no change in our competitive position.

3. Predicting customer switch rates either from our current products to new products we offer (cannibalism), or from our competitors products to our new products (draw).

4. Predicting the differential response of items 1-3 by key market segments purchasing our product.

5. Predicting competitive reaction to our strategies of introducing a new product. Specifically, should a new product be introduced, and if so, what is the optimal design configuration for this new product? Further, should pricing or other attributes of our current products be modified in response to the competition).

6. Predicting the impact of situational variables on customer preference.

7. Predicting the differential response to alternative advertising strategies and/or advertising themes.

8. Predicting the customer response to alternative pricing strategies, specific price levels, and proposed price changes.

9. Predicting competitive response to distribution strategies studying such diverse problems as determining the optimal channel of distribution, number or type of outlets, vendor selection, or sale person quotas.

Each of the identified management problems may be addressed and solved using the conjoint analysis methodology. In addition, a conjoint based competitive strategy may be implemented by modifying the marketing mix, i.e., new product/concept identification, pricing, advertising and distribution. This competitive strategy may focus on new segments or product re-positioning.

In addition to product and corporate strategy, conjoint research has been applied to family decision making; Tourism, tax analysis; time management; direct foreign; and medicine.

How does Conjoint Analysis Work?

Conjoint analysis involves the measurement of psychological judgments (such as consumer preferences, or acceptabilities) or perceived similarities or differences between choice alternatives.

The name "Conjoint analysis" implies the study of the joint effects. In marketing applications, we study the joint effects of multiple product attributes on product choice.

Alternative Conjoint Analysis Methodologies

• Stimulus Construction: Two Factor at a Time; Full Factorial Design; Fractional Factorial Design
• Data Collection: Two Factor at a Time Trade-off Analysis; Full Profile Concept Evaluation
• Model Type: Compensatory and Non-Compensatory Models Part Worth Function; Vector Model; Mixed Model; Ideal Point Model;
• Measurement Scale: Rating Scale; Paired Comparisons; Constant Sum; Rank Order
• Estimation Procedure: Metric and Non-Metric Regression; MONANOVA; PREFMAP; LINMAP; Nonmetric Tradeoff; Multiple Regression; LOGIT; PROBIT; Hybrid; TOBIT; Discrete Choice
• Simulation Analysis: Maximum Utility; Average Utility (Bradley-Terry-Luce); LOGIT; PROBIT

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