Trade off methodologies

Max-diff and conjoint analysis are both statistical techniques used in trade-off analysis. However, the methods provide different insights. Max-diff determines the most preferred features for a product or service. Conjoint on the other hand determines feature combinations with the highest preference share.

Trade off methodologies

Max-diff and conjoint analysis are both statistical techniques used in trade-off analysis. However, the methods provide different insights. Max-diff determines the most preferred features for a product or service. Conjoint on the other hand determines feature combinations with the highest preference share.

Trade off methodologies Max-diff & Conjoint

Max-diff and conjoint analysis are both statistical techniques used in trade-off analysis. However, the methods provide different insights. Max-diff determines the most preferred features for a product or service. Conjoint on the other hand determines feature combinations with the highest preference share.
The below illistartate with a familiar example of a car purchase. An important set of attributes for a car purchase may include items shown in Figure 1 (left). Max-diff analysis informs the relative preference of these attributes (Figure 1 right).

On the other hand, a choice-based conjoint exercise allows one to dig deeper into user pref- erences. Let’s consider a more complex choice scenario (Figure 2) where each of the attri- butes (e.g. engine size) has its own levels (1.3 L, 1.8L, 2L and 2.5L).
For simplicity, a smaller set of attributes is considered.

A consumer is now guided to make a choice between different product configurations (i.e. product 1 and 2, Figure 3, below), as in a real purchase scenario. The subsequent analysis identifies product concepts with high market share along with relative importance of the attributes and their levels.

Trade off methodologies
Max-diff & Conjoint

Max-diff and conjoint analysis are both statistical techniques used in trade-off analysis. However, the methods provide different insights. Max-diff determines the most preferred features for a product or service. Conjoint on the other hand determines feature combinations with the highest preference share.
The below illistartate with a familiar example of a car purchase. An important set of attributes for a car purchase may include items shown in Figure 1 (left). Max-diff analysis informs the relative preference of these attributes (Figure 1 right).

On the other hand, a choice-based conjoint exercise allows one to dig deeper into user pref- erences. Let’s consider a more complex choice scenario (Figure 2) where each of the attri- butes (e.g. engine size) has its own levels (1.3 L, 1.8L, 2L and 2.5L).
For simplicity, a smaller set of attributes is considered.

A consumer is now guided to make a choice between different product configurations (i.e. product 1 and 2, Figure 3, below), as in a real purchase scenario. The subsequent analysis identifies product concepts with high market share along with relative importance of the attributes and their levels.

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