February 12-13, 2019

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Intelligent Concept Tests with ADA

Monadic designs are the gold standard in concept testing. Rather than showing different concept options to every participant, respondents evaluate only one concept. That method allows for a true measure of how likely someone is to choose a particular product or service. However, monadic designs require a lot of sample, especially if many concepts are being tested, making them cost-intense and time-consuming. Since we don’t know from the onset which concepts will resonate more and which less, sample is evenly distributed and thus not efficiently used. In reality, we are interested in learning as much as possible about the top concepts, not so much about the bottom ones.

So far, market researchers haven’t come up with applicable solutions to overcome these obstacles. We made it our task to find one and developed an intelligent algorithm for efficient concept testing which relies on principles of the Bayesian Bandit. During field our algorithm learns which concepts are more promising based on answers provided so far and distributes further respondents to more promising concepts adaptively and in real-time. Together with Research Now SSI, Factworks investigated the effectiveness of our adaptive algorithm (we called “ADA”) in an online study in the U.K. and Germany.

ADA has the power to increase efficiency with concept and naming tests and is applicable to future research studies.

Presentation type:

  • Research Report

Subjects covered:

  • Automated research


The benefits of using this approach include:

  1. More diagnostics – detailed information and more data cuts for top concepts.
  2. Faster results – arriving at robust results with less sample.
  3. Increased flexibility – possibility to cover more concepts.


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