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Concept testing

Concept testing

Concept testing (to be distinguished from pre-test markets and test markets qui May be used at a later course of product development research) [1] is the process of using surveys (And Sometimes qualitative methods ) to evaluate-consumer acceptance of a new product idea prior to the introduction of a product to the market . [2] It is important not to confuseconcept testing with advertising testing, brand testing and packaging testing; as is sometimes done. Concept testing focuses on the basic product idea, with the embellishments and puffery inherent in advertising.

It is important that the instruments (questionnaires) to test the product have a high quality themselves. Otherwise, results may be biased by measurement error. That makes the design of the testing procedure more complex. Empirical tests provide insight into the quality of the questionnaire. This can be done by:

  • conducting cognitive interviewing . By asking a faction of potential-respondents for their interpretation of the questions and use of the questionnaire, a researcher can verify the viability of cognitive interviewing.
  • carrying a small sample of the questionnaire, using a small subset of target respondents. Results can inform a researcher of errors such as missing questions, or logical and procedural errors.
  • estimating the measurement quality of the questions. This can be done for instance using test-retest, [3] quasi-simplex, [4] or mutlitrait-multimethod models. [5]
  • predicting the measurement quality of the question. This is the Survey Quality Predictor (SQP) software. [6]

Concept testing in the new product development (NPD) process is the concept generation stage. The concept generation stage of concept testing can take on many forms. Sometimes concepts are generated incidentally, as the result of At other times concept generation is deliberate: examples include brainstorming sessions, problem detection surveys, and qualitative research. While qualitative, it is possible to provide an indication of the success of the new concept; this is better left to quantitative concept-test surveys.

In the early stages of concept testing, a large field of alternative concepts might exist, requiring concept-screening surveys. Concept-screening surveys provide a quick way to narrow the field of options; however, they do not provide a comparison of normative databases due to interactions between concepts. For greater insight and to reach decisions on whether or not to seek further product development, monadic concept-testing surveys must be conducted.

Monograph, sequential monadic or comparative. The terms mainly refer to the concepts are displayed:

1.) Monadic. The concept is evaluated in isolation. 2.) Sequential monadic. Multiple concepts are evaluated in sequence (often randomized order). 3.) Comparative. Concepts are shown next to each other. 4.) Proto-monadic. Concepts are first shown in sequence, and then next to each other.

“Normative database can be constructed.” [7] However, each has its specific uses and it depends on the research objectives. The decision as to which method is the most important in the field of decision making.

Evaluating concept-test scores

Traditionally concept-test survey results are compared to ‘norms databases’. [8] These are databases of previous new product concept tests. These must be ‘monadic’ concept tests, to prevent interaction effects. To be fair, it is important that these databases contain ‘new’ concept test results, since ounce consumers become familiar with a product the ratings often drop. Comparing new concept ratings to an existing product. Additionally, the concept is usually only compared to the same product category, and the same country.

Companies that specialize in this area, tend to have their own unique systems, each with its own standards. Keeping to these standards is always important to prevent contamination of the results.

Perhaps one of the famous concept-test systems is the Nielsen Bases system, which comes in different versions. Other services include Decision Analyst’s Concept Check, Acupoll’s Concept Optimizer, Ipsos Innoquest and GFK. Examples of smaller players include Skuuber and Acentric Express Test.

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 may have existed in the context 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 joint analysis (also referred to as discrete choice modeling). Various forms of joint analysis and discrete choice modeling exist. While academics stress the differences between the two, there is often little difference. These techniques estimate the importance of product attributes by means of an alternative design, and then use the responses to these alternatives. The results are often expressed in the form of a ‘simulator’ tool which enables customers to test alternative product configurations and pricing.

Volumetric concept testing

Volumetric concept test et testes et de la recherche et des tests et de la recherche et des tests et de la recherche et de la recherche du simulants. The aim is to provide ‘approximate’ sales volume forecasts for the new concept prior to launch. They have other variables beyond just the concept test survey itself, such as the distribution strategy.

Examples of volumetric forecasting methodologies include ‘Acupoll Foresight’ [9] and Decision Analyst’s ‘Conceptor’. [10]

Some models (more Properly Referred to as ‘pre-test market models’ or ‘simulated test markets’) [11] 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. Some such as Decision Analyst, include discrete choice models / conjoint analysis.

See also

  • marketing research
  • proof of concept


  1. Jump up^ Wind, Yoram (1984). NEW-PRODUCT FORECASTING MODELS AND APPLICATIONS . Lexington Books. ISBN  0-669-04102-5 .
  2. Jump up^ Schwartz, David (1987). Concept Testing: How to Test New Product Ideas Before You Go to Market (1st ed.). American Management Association. ISBN  978-0814459058 .
  3. Jump up^ Lord, and F. Novick, MR (1968). Statistical theories of mental test scores. Addison – Wesley.
  4. Jump up^ Heise, DR (1969). Separating reliability and stability in test-retest correlation. American Sociological Review, 34, 93-101.
  5. Jump up^ Andrews, FM (1984). Construct validity and error components of survey measures: a structural modeling approach. Public Opinion Quarterly, 48, 409-442.
  6. Jump up^ Saris, WE and Gallhofer, IN (2014). Design, evaluation and analysis of questionnaires for survey research. Second Edition. Hoboken, Wiley.
  7. Jump up^ Thomas, Jerry. “Concept Testing (And The” Uniqueness “Paradox)” . Decision Analyst . Decision Analyst . Retrieved 21 April 2017 .
  8. Jump up^ Thomas, Jerry. “Concept Testing (And The” Uniqueness “Paradox)” . Decision Analyst . Decision Analyst . Retrieved 21 April 2017 .
  9. Jump up^ “ForeSIGHT ™ Going-Year Volume Estimates” . Acupoll . Retrieved 21 April 2017 .
  10. Jump up^ “Conceptor® Volumetric Forecasting” . Decision Analyst . Retrieved 21 April 2017 .
  11. Jump up^ Wind, Yoram (1984). NEW-PRODUCT FORECASTING MODELS AND APPLICATIONS . Lexington Books. ISBN  0-669-04102-5 .