Customer Analytics - Preference Measurement Explicit

What is the purpose of Maxdiff scaling?

What is the implication of pij in the preference measurement?

What is the formula for the log importance difference?

What is the next step after reducing the attribute set using rating scales?

What is the focus of measuring preference strengths?

What is the sum of the normalized importance of each attribute?

The Kano Method categorizes product features into four types: Must-be, More-is-better, Delighter, and Neutral.

MaxDiff Scaling is also known as worst-best scaling.

Scale Rating is a method used to prioritize customer preferences by ranking features in order of importance.

Paired Comparisons is a method used to compare customer preferences between multiple options.

Rank Ordering is a method used to measure customer attitudes and opinions on a numerical scale.

What is the main advantage of MaxDiff Scaling?

What is the primary purpose of Scale Rating?

What is the main advantage of Rank Ordering?

What is the primary purpose of Paired Comparisons?

Explicit Response Approach is used to infer importance valuations indirectly through preference ordering of products.

Constant Sum scale is used to eliminate less important attributes.

The main purpose of Preference Measurement is to identify customer attitudes and opinions on a numerical scale.

Paired Comparisons is a method used to prioritize customer preferences by ranking features in order of importance.

The statement 'Chinese is twice as good as Indian' is an interpretation of an interval scale.

Rank Ordering is used to refine the set of attributes after eliminating less important ones.

The ratio scale interpretation implies that the difference between Mexican and Chinese is three times the difference between Chinese and Indian.

The ordinal scale only provides information about the relative ranking of the attributes, but not their relative differences.

The scale type determines the type of analytics that can be done with the collected data.

The scale type affects how the data is summarized and interpreted.

As the number of items per set increases, the cognitive load also decreases.

The optimal number of items per set is 3 or 6.

The methodology of 'Balanced Incomplete Block Designs' is used to determine the optimal number of items per set.

The logit probability is used to model the probability of the customer's picks based on the difference in importances between the two attributes in the pair.

The log importance difference is calculated as L_i - L_j = log pij / (1-pij)

The purpose of paired comparisons is to identify customer attitudes and opinions on a numerical scale

The differences in preference strengths for various attribute-levels within the most important attributes do not matter much.

The ratings should be rescaled so that the most preferred attribute-level has a rating of 1 and the least preferred attribute-level has a rating of 7.

The MaxDiff approach is used when there are few attribute-levels for a certain attribute.

Stated importance methods can provide accurate results without considering the range or set of values the attribute can take.

The Preference Measurement Explicit method is used to prioritize customer preferences by ranking features in order of importance.