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Distinguishing Product Attribute Importance with Advanced Preference Modeling Techniques

Understanding product attribute importance is crucial for market research, yet traditional techniques like Monadic Attribute Ratings and Conjoint Analysis have limitations. RenPref.sm provides innovative alternatives that balance discriminating power with ease of administration. Key methods include Multiple Paired Comparison (MPC), which offers continuous preference scores through paired comparisons, and Maximum Difference Analysis (MaxDiff), which creates interval-level importance ratings with less respondent burden. These techniques ensure reliable data collection while simplifying the choice process for participants.

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Distinguishing Product Attribute Importance with Advanced Preference Modeling Techniques

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  1. IntroducingRenPrefsmPreference Modelingfrom

  2. How can you distinguish the importance of product attributes? • Traditional techniques have disadvantages: • Monadic Attribute Ratings: Easy to administer, but: • Subject to “yea-saying” and “straight-lining” • High intercorrelation makes it hard to find their true order of importance • Conjoint Analysis: Forced choice makes attribute importance easy to distinguish, but: • Requires cumbersome design • Burdens respondents • Limits the number of items you can test

  3. How can you distinguish the importance of product attributes? • With RenPref sm, we offer alternatives that combine discriminating power with ease of administration: • Multiple Paired Comparison (MPC): Turns a series of two-way comparisons between product attributes into a continuous preference score. • Maximum Difference Analysis (MaxDiff): Creates interval-level importance ratings using a small number of easy-to-answer preference tasks.

  4. Multiple Paired Comparison (MPC) • An MPC analysis consists of three stages: • Design • Administration • Analysis

  5. Multiple Paired Comparison (MPC)Design • We provide the design for you to administer: • Each item is paired with every other item • Full design contains pairs • Design can be blocked • Each respondent sees a limited number of pairs • All pairs covered across entire sample • Each pair should be seen by enough respondents for adequate statistical power (usually 100+)

  6. Multiple Paired Comparison (MPC)Administration and Analysis • Respondents are shown a series of pairs, asked to choose the one they prefer • We analyze the data, preference-scoring the items • Average percent chosen • Normalized score • Conjoint-like “utilities” for each item, using logistic regression

  7. MPC Example Output

  8. Maximum Difference (MaxDiff) Analysis • MaxDiff analysis is an extension (and improvement) on MPC • Allows testing more items on a smaller total sample • Like MPC, consists of three stages: • Design • Administration • Analysis

  9. MaxDiff Design • A set of n-way combinations is formed from the item list • n is greater than 2, but recommended less than 5; four-way combinations are the “default” • Through our algorithm, we create a Partially-balanced Incomplete Block (PBIB) design • A subset of all possible n-way combinations • All items are exposed as equally as possible • All pairs of items are exposed as equally as possible • Design is blocked: not all respondents see the same set of combinations • Block size should be small enough to limit respondent burden • Sample size should be large enough for sufficient statistical power (usually 100+)

  10. MaxDiff Administration • Combinations are exposed to respondents in turn • In each combination, respondent is asked to check off which item is the best (most preferred), and which is the worst (least preferred):

  11. MaxDiff Analysis • MaxDiff results can be analyzed in two ways • Counting Analysis • Via a Discrete Choice Model

  12. MaxDiff Analysis • Counting Analysis • For each choice set, the “best” choice is assigned a value of 100; the “worst”, a value of –100. Choices not used are assigned 0 • Values for each item are averaged across the choice set; the averages form a rank-ordered set of preference scores from –100 to +100

  13. MaxDiff Analysis • Discrete Choice Analysis • Multinomial Logit (MNL) analysis is used to regress the probability of choosing an item as “best” or “worst” on the composition of each choice set • Produces utilities for each item that can be used to assess the item’s relative preference and predict the choices made for any combination of items • Rescaled to a 0-100 scale

  14. MaxDiff Attribute Importance

  15. If you have any questions, or would like to discuss a RenPref sm analysis, please call Paul Gurwitz at(212) 319-1833, or email pgurwitz@renaiss.com

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