Introducing RenPref sm Preference Modeling from

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