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Partworth utilities (also known as attribute importance scores and level values, or simply as conjoint analysis utilities) are numerical scores that measure how much each feature influences the customer’s decision to make that choice. Because partworths of attributes and levels in conjoint analysis are interrelated, in this post we will look at them using the same example of tissue paper. Suppose the company wants to find out customers’ preferences for tissue paper to re-assess its product range, as a pathway to growth. The charts below show some common attributes of the company’s (and competitors’) tissue paper:
In this example, “Texture”, “Ply”, “Scent and colour”, and “Price per 100 sheets” are attributes. “3 ply” and “2 ply” are levels of the attribute “ply”.
The index attached to each attribute shows its importance relative to others. At 48%, the “scent and colour” attribute turns out to be the most important with “ply” being the least important attribute. It appears price is not as important a factor as “scent and colour”.
This chart shows how strongly the variations of attributes affect customers' choice, but only for the levels that you chose in the design. If a more extreme level were added to one of the attributes, that attribute would likely become higher in importance. For example, if we add a more extreme price level ($1.50), customers are likely to shun it and therefore the partworth of that level will be very negative, which will in turn inflate importance of the whole price attribute.
Level partworths allow you to dive deeper to understand what specific features within an attribute drive customers' choice. In this example, recycled unscented tissue paper is strongly preferred to white scented and somewhat to white unscented.
Levels that are strongly preferred by customers are assigned higher scores, levels that perform poorly (in comparison) are assigned lower scores. The chart is scaled so that, for each attribute, the sum of all positive values equals (the absolute value of) the sum of all negative values.
Again, it is important to remember that these partworths are relative. If we include “black with velvet scent” as another level for the attribute “scent and colour”, the relative value of each level will change.
Conjoint.ly reports allow you to segment results by additional questions, GET variables, and other information about respondents (e.g., type of device or location). For example, in the table below, preferences for ply and texture are opposite between the two segments.
|Attribute||Level||Segment 1||Segment 2||Market overall|
|Scent and colour||Recycled unscented|
|Price per 100 sheets||$1.99|
|Size of segment||67%||33%||100%|
For each report, Conjoint.ly will present a “ranked list of product concepts as preferred by customers”. This is a list of up to 500 potential combinations of features and prices that represent product concepts. The column “Value to customers” will contain a single number for each concept (row). This number is calculated as the sum of partworth utility scores of all feature and price levels that comprise the concept. Consider calculations in the following example table:
|Texture||Ply||Scent and colour||Price per 100 sheets||Value to customers||Rank|
|Simple texture (10)||3-ply (4)||Recycled unscented (20)||$1.99 (13)||47 = 10 + 4 + 20 + 13||1|
|Simple texture (10)||2-ply (-4)||Recycled unscented (20)||$1.99 (13)||38 = 10 - 4 + 20 + 13||2|
|Simple texture (10)||3-ply (4)||Recycled unscented (20)||$3.50 (1)||35 = 10 + 4 + 20 + 1||3|
|Simple texture (10)||2-ply (-4)||White unscented (5)||$1.99 (13)||32 = 10 + 4 + 5 + 13||4|
|Simple texture (10)||2-ply (-4)||Recycled unscented (20)||$3.50 (1)||27 = 10 - 4 + 20 + 1||5|
In brand-specific conjoint, importance scores do not necessarily sum up to 100% within each brand. Instead, we scale then so that they can be comparable across brands.
Effectively, you can omit “%” because the scale is arbitrary. Or you can re-scale them to 100% within each brand (but that will remove comparability across brands).
Here are also some suggestions for further reading:
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