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Product optimization
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Scope
Product optimization can seek optimal audience, subscription and purchase behaviour.

By measuring various product feature contributions, motivations are modelled for product optimization.
Product optimization is done through conjoint analysis which considers jointly product features and interests.

Conjoint analysis enables to fine tune features to maximize 'up take'. Conjoint techniques can also be addressed for other problems relative to profitability, competitive scenarios and strategic or tactical options
Techniques
Various techniques may be used depending on:
Depending on the chosen technique, a sample size is determined, in general between 60 and 300 interviews.
Respondents "liking" or "choices" are probed for a number product features or alternatives.

These successive questions enable to determine preferences for each of the analytical features.
Once these preferences have been studied, an optimal solution is determined or simulated.


Showcase: Interactive service menu/window optimization example
Case study
Let's imagine a new appliance manufacturer wishes to optimize a given first price model. That this appliance could comprise different type of windows, colours, proportion of sections and price levels. The combinations of all these combinations are numerous!
Let's assume we are searching an optimal product/design presentation, and that we can produce various alternative executions through renderings. Product optimization uses various conjoint techniques and in this case we would recommend Adaptive Conjoint Analysis (ACA).
Methodology and sample
Respondents are probed on their “choices” of alternative executions with different analytical features that are then modelled. ACA offers an efficient way to optimize concepts, products and designs.
For example, a respondent might be asked to choose between two initial menu combinations, such as:

Appliance A1 Appliance A5
Rounded windows
 Rectangular windows
Blue/Grey Grey/White
Section A %: 50
Section B %: 30
Section C %: 20		 Section A %: 40
Section B %: 40
Section C %: 20
Price 175$ Price 164$
   
This example is repeated a number of times until the preferences have all been determined according to an algorithm on combinations/comparisons.
This technique does not require large samples. Therefore sub samples of n=60 could help optimize an execution, even among various countries.
Presentation of alternative executions would be projected onto a plasma screen. The program would, depending on respondent choices, carefully select further screens to be compared.
Reporting
The answer to these successive questions would be used to determine the respondent's preferences for each of the analytical features. Once these preferences have been determined, an optimal entry model may be simulated.
[Print PDF]