Editing Concept testing (section)

==Determining the importance of concept attributes as purchase drivers==

The simplest approach to determining attribute importance is to ask direct open-ended questions. Alternatively checklists or ratings of the importance of each product attribute may be used.

However, various debates have existed over whether or not consumers could be trusted to directly indicate the level of importance of each product attribute. As a result, correlation analysis and various forms of multiple regression have often been used for identifying importance - as an alternative to direct questions.

A complementary technique to concept testing, is conjoint analysis (also referred to as discrete choice modelling). Various forms of conjoint analysis and discrete choice modelling exist. While academics stress the differences between the two, in practice there is often little difference. 
These techniques estimate the importance of product attributes indirectly, by creating alternative products according to an experimental design, and then using consumer responses to these alternatives (usually ratings of purchase likelihood or choices made between alternatives) to estimate importance. The results are often expressed in the form of a 'simulator' tool which allows clients to test alternative product configurations and pricing.

'''Volumetric concept testing'''

Volumetric concept testing falls somewhere between traditional concept testing and pre-test market models (simulated test market models are similar but emphasize greater realism) in terms of the level of complexity. The aim is to provide 'approximate' sales volume forecasts for the new concept prior to launch. They incorporate other variables beyond just input from the concept test survey itself, such as the distribution strategy.

Examples of volumetric forecasting methodologies include 'Acupoll Foresight'<ref>{{cite web|title=ForeSIGHT™ Going-Year Volume Estimates|url=http://www.acupoll.com/volume-forecasts|website=Acupoll|accessdate=21 April 2017|ref=4|archive-url=https://web.archive.org/web/20170331015648/http://www.acupoll.com/volume-forecasts|archive-date=31 March 2017|url-status=dead}}</ref> and Decision Analyst's 'Conceptor'.<ref>{{cite web|title=Conceptor® Volumetric Forecasting|url=https://www.decisionanalyst.com/analytics/volumetric/|website=Decision Analyst|accessdate=21 April 2017|ref=3|date=2015-12-28}}</ref>

Some models (more properly referred to as 'pre-test market models' or 'simulated test markets')<ref>{{cite book|last1=Wind|first1=Yoram|title=NEW-PRODUCT FORECASTING MODELS AND APPLICATIONS|date=1984|publisher=Lexington Books|isbn=978-0-669-04102-6|ref=5|url-access=registration|url=https://archive.org/details/newproductforeca0000unse}}</ref> gather additional data from a follow-up product testing survey (especially in the case of consumer packaged goods as repeat purchase rates need to be estimated). They may also include advertisement testing component that aims to assess advertising quality. Some such as Decision Analyst, include discrete choice models / conjoint analysis.