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Quantify Customer's Buying Decisions for Application Integrated Keurig Machine

Keurig Project

This is a ​generative research self-started project. The high level objective of the study is to understand user's mental models when they brew, in order to inform application features' design direction. Quantify whether application features will motivate target users X to buy. 

Background and Goals

A few years ago, Keurig created product A in effort to appeal to target users X. This initiative worked to some extent. However, there are other initiatives that could boost this appeal to the target group.


One of these initiatives is an application integrated coffee machine. Prior to application development, some key goals to investigate are:

Generative Research Goals

  • Understand our user's mental models and workflow.

  • Understand pain points throughout brewing process.

  • Craft application features from ethnographic study.

  • Quantify value of application features as utility scores. 


  • Ethnographic study*

  • MaxDiff survey*

*For more information about specific ethnographic study and MaxDiff analysis, please contact

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Figure 1: User Persona from Ethnographic Study

Crucial Insights

  • There are sub-personas within the main target group.

  • Therefore were are sub-mental models within the main mental model of the target group.

  • Ethnographic study provided rich data about user personas' values.

  • The value of these ethnographic findings were quantified by Max Diff survey utility scores.

For additional findings and learning, please contact

Research Impact

Generative Impact

  • Understand how to support mental models with application features.

  • Understand how to provide provide value through features.

  • Increased subscriptions and donations.

  • Increased user involvement in forum and quiz feature.

  • Increased efficiency in completing tasks.

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Figure 2: Map of Workflow

Strategic Impact

  • Determine which application features are worthwhile to develop based on MaxDiff customer feedback.

My Learnings

  • User's values are embedded in mental models; ethnographic data is rich enough to uncover this.

  • Quantitative insights are great at weeding through grey area in the discovery phase.

  • Quantitative insights are great at providing the "what" and qualitative insights are great at providing the "why" behind it. 

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Figure 3: Results of MaxDiff Survey

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