The Nose Knows
-Obtain aggregate and segment-specific Conjoint part-worth estimates
-Interpret estimates to understand which levels and attributes are more desirable
-Determine each segment's ideal product
Have the slides for the Conjoint Analysis session handy, as well as the instructional videos. Not using the material learned in class will make it harder to finish the Assignment correctly.
You have recently been hired by Scentsational LLC, a new online fragrance store. Scentsational designs its own fragrances, relying mostly on the company board's intuition and understanding of the market. While some scents have been received with some success, others have failed. As such, the company's board has decided to employ a more scientific approach to developing new fragrances, and have assigned you to this task. See the memo below.
Dear Mr./Ms. X,
As you know, one of the key strategic initiatives of Scentsational is to more professionally (and quantitatively) determine how to design our new fragrances. Before your arrival, we conducted a series of focus groups, in which we determined that the following were the most relevant attributes to focus on as we design our newest women's fragrance:
Intensity: Consumers seemed interested in discussing a fragrance's intensity. Therefore, we have considered to develop either a "Fresh" scent (which is not very intense) or an "Intense" scent which is the opposite.
Price: Consumers seemed to conceptualize how fragrance prices work with .99 decimals, and on units of 10 - specifically, the prices most often mentioned were $39.99, $49.99 and $59.99.
Please help us determine how to proceed with our design given this information. Regards, Olf Acton, VP of Customer Experience, Scentsational
After being assigned this task, you constructed a Conjoint Analysis survey, with 12 profiles, in order to capture the preferences of each respondent for each profile. Your survey was delivered electronically via the Qualtrics platform to 60 current Scentsational female customers. The Rating variable (0-10) is the DV of interest - note that it was captured using a Slider scale question, and therefore it has decimal values.
Of note: these contacts were in a database that the company had previously overlooked; in this database, unbeknownst to the firm, there was an old Segment variable, which classified every individual as Segment 1 and 2. You do not know whether these segments are important or not, but you decide to experiment with the variable regardless as it might prove useful.
After deploying the survey, you prepare the data that you will use in an Excel file with the following Sheets, to be analyzed with the R package:
Profiles: Table listing the 12 profiles each consumer rated.
Levels: Text list of the levels of every fragrance attribute: Brand Name: 1. Moonlight; 2. Little Black Dress Intensity: 1. Intense; 2. Fresh
Price: 1. $39.99; 2. $49.99; 3. $59.99
Ratings_All: Consumers' ratings for each profile - everyone in the sample Ratings_Seg1: Consumers' ratings for each profile - only Segment 1 Ratings_Seg2: Consumers' ratings for each profile - only Segment 2
Candidates: A simulated consideration set among which you'll determine which product is best. These are the candidates:
1. Load the data into R.
2. Conduct a Conjoint Analysis regression as follows, and provide a professionally formatted regression output.
Rating = f(Fragrance name, intensity, price)
3. Comment on the estimates. What do you see? Which attribute levels are most preferred, and which least?
You are intrigued by the mysterious segmentation variable in the old customer database. Hence, you decide to run a separate Conjoint Analysis for each Segment.
6. Conduct a Conjoint Analysis regression as follows for each segment, and provide a professionally formatted regression output.
7. Comment on the preference structure of both segments. What is similar? What is different?
8. Using the regression results for each Segment, compute their attribute importances (name, intensity, price). Comment on your findings. Based on the similarities and differences between Segment 1 and Segment 2, create a name for each Segment.
9. Using the regression results for each Segment, determine which product would be ideal for each segment. You must provide a computation of the utility of each product for each segment.
10. Summarize your recommendations for Scentsational.
11. Using R's package, construct a fractional factorial design for Scentsational's new Conjoint study. Present this as a table with Profile numbers, and the combination of attribute levels of each profile.
Please mind the following as you answer the last question:
- Input the attributes in the order listed in the memo
- Keep all of the arguments in the function call (except for the attribute and level parts) identical to the lecture slides.
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