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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 interactive service wishes to optimize its initial entry and starting point. That this service could comprise different type of windows, colours, proportion of sections, claims and price levels. The combinations of all these combinations are numerous!
Let's assume we are searching an optimal service presentation, and that we can produce various alternative executions for the presentation of the menus. 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:

Screen P1 Screen P5
Rounded windows
 Rectangular windows
Blue print/Grey background Grey print/White background
Section A %: 50
Section B %: 30
Section C %: 20		 Section A %: 40
Section B %: 40
Section C %: 20
Product photo Testimonials
Subscription price 25$ Subscription price 14$
   
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 TV/PC 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 screen may be simulated.
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