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Research Background & Objectives

A marketing technology (MarTech) company conducted a pricing and packaging diagnostic to better understand discounting levels for its products and buying behaviors of their customer base. Primary findings from the diagnostic were that extreme discounting was occurring for its core product and customers were not buying into the company’s product packages as they were currently structured.

Anecdotally, they were also hearing that their pricing was significantly higher than competitive offerings, and the deals they lost to competitors had been primarily due to price.

The MarTech company was looking for research to help them reset list prices and their overall packaging strategy for their core product.

Specific questions they were looking to answer include:

    • How should we bundle features to create adoptable packages that have clear value, offer transparent options to buyers, and drive competitive differentiation?
      • Which features are most and least important?
      • Which features differentiate us from our competitors?
      • Which features are table stakes and which could be paid or premium add-ons?
      • How should our service offerings be integrated into packages, if at all?


  • What pricing lever will optimize revenue, align to value and ultimately meet buyer expectations?
    • What do buyers view as the ultimate price lever?
case study research

our approach

We recommended a quantitative online survey that included a Kano Q-Sort exercise, a MaxDiff exercise, and a Gabor-Granger pricing exercise. Through the use of these three advanced analysis techniques, our client would be able to understand which product features were most and least important, and subsequently, how sensitive customers were to increases or decreases in price.

  • 200 completed responses in the US
  • The online survey was approximately 15 minutes in length
  • Respondents were sourced from online panels
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the outcome

  • The results helped our client completely redesign their packaging using the MaxDiff data to inform which features are most important and differentiated (should be included in the packaging) and which features are less important or polarizing (should be offered as add-ons).

  • Our client was able to reset all of their list prices, using a combination of the survey data and competitive pricing they were able to collect through secondary research. The value of the survey data around price was in understanding total willingness to pay, to keep them from dipping prices too low. The results clearly showed buyers are willing to pay a premium for the features they need, so this became an anchor as they fine-tuned how much to reduce costs.

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