Case studies

We’d love to talk you through the cool things we’ve done over the last 20 years! Here is a sample of a few ways we’ve applied UX and human factors research and design methods.

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Explore our areas of expertise:

Conduct global, large-scale mixed methods research with conversational & generative AI

A global technology company sought to understand how best to apply AI offerings across a suite of products.

Challenge

We were tasked with understanding how humans form meaningful connections with AI characters, and, conversely, what factors drive attrition. Across global markets, we explored why and when users find AI such as ChatGPT compelling so that learnings could be applied to competing products.

Approach

For this global study, we employed a mixed-methods approach utilizing co-design, usability testing, panel research, IDIs, diary studies, and surveys. Beginning with exploratory co-design sessions and IDIs, this body of work expanded to mixed methods with multiple 200+ participant cohorts (1400 in all) completing diary entries, IDIs, and surveys.

Outcome

Our insights directly impacted our client’s design and development of conversational characters and was recognized as a roadmap for future exploration. This nimble program pivoted to provide competitive insights on AI adoption in the US and globally, informing our client’s strategy in this fast-changing space.

Industry:

AI, Consumer technology

Method/Process:

Co-creation workshop, Diary study, Exploratory / Foundational, In-depth interview, Usability testing

Stimuli:

AI-enabled product
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Explore collaborative intelligent technology

A global technology company sought to investigate ways to collaborate with intelligent technology to create customized and impactful outcomes for their users.

Challenge

We were tasked with creating a research environment that enabled ordinary people to engage in dialogue about concepts that don’t yet exist. Our goal was to identify best use cases for broad mass market appeal of the technology with a focus on integrating this technology into future products and services.

Approach

Integrating group interviews and aspects of service design, our approach involved identifying friction points and collaboration opportunities. By grounding the team in current behaviors and envisioning imagined futures, we optimized task delegation for effective testing across diverse scenarios.

Outcome

We delivered envisioned future scenarios of smart technology collaboration and marketing use cases for diverse segments. Additionally, our deliverables included detailed engagement journeys and insights into points of resistance to delegation, covering aspects like safety, trust, and sharing limits.

Industry:

AI, Consumer technology

Method/Process:

Exploratory / Foundational, Focus group

Stimuli:

AI-enabled product
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Explore opportunities to improve in-product support features

A global technology company wanted to collect feedback on potential in-product support features, scenarios, and concepts.

Challenge

A global technology company wanted to explore opportunities to improve in-product support features and concepts for support across products. To achieve this goal, research was needed to understand ideal user in-product support experiences across the product ecosystem, identity preferences for support features, and uncover perceptions of AI-generated support help.

Approach

We conducted 90-minute one-on-one interviews in four different countries. The interviews had participants share their opinions and rate 20 potential in-product support features. After identifying preferred features, they were presented high- and low-urgency scenarios and asked to describe how they would use those features to navigate each scenario. Finally, they were asked provide feedback on concepts incorporating those features.

Outcome

Through analysis of participant feedback and rankings, our team provided the client with insights on which features participants preferred, which concepts offered them the support options they wanted, and their perceptions of AI-generated support options. We also provided insight into how support features would factor into participants’ approaches to navigating both high- and low-urgency scenarios.

Industry:

AI, Consumer technology

Method/Process:

Exploratory / Foundational, In-depth interview

Stimuli:

AI-enabled product
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Explore the integration of AI and human interaction across a full product lifecycle

A global technology company wanted to drive buy-in for a new service that challenged existing assumptions.

Challenge

We collaborated with stakeholders that included UXRs, designers, marketers, and engineers to inform the development of a premium product. The team wanted to understand and respond to user mental models about which tasks are most appropriate for humans and which for AI.

Approach

Our research supported the client across multiple stages of a P0 to P1 product. We engaged 180 users over 6 months to identify gaps and differentiation opportunities for the product relative to competitors. Through diary, survey, and IDIs, we identified task types and moments of truth required for conversions over time. Additionally, we planned, ran, and reported on a series of 2-week iterative design and testing sprints.

Outcome

We identified and leveraged the moments of truth that drove conversion or churn at multiple stages of the user journey. Our long-term engagement resulted in a variety of deliverables and insights including quarterly roll-up reports to facilitate high-level understanding of findings and impact across multiple methods and a task taxonomy for strategic user onboarding and alignment with user journeys.

Industry:

AI, Consumer technology

Method/Process:

Diary study, Exploratory / Foundational, In-depth interview, Survey

Stimuli:

AI-enabled product
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Iterate simulation testing of an AI-enabled EMR

A Fortune 50 healthcare organization was developing an AI-enabled electronic medical record designed to improve providers’ situational awareness, communication, collaboration, and response.

Challenge

We were tasked with guiding the evolution of an AI-enabled electronic medical record (EMR). In addition to recruiting and delivering a large sample set of voices and utterances for training the Large Language Model, we were also asked to provide recommendations for incremental improvements to system usability and design a roadmap for both the EMR and the AI-agent.

Approach

Our 4-year program of research involved usability testing, iterative design research, and simulated-use environments. We designed and operated both low-fidelity (Wizard of Oz) and high-fidelity (scenario-based trauma center simulation) test environments over the course of 10+ projects carried out in both the United States and United Kingdom.

Outcome

In addition to suggesting incremental improvements, we offered guidance on aligning notifications with users’ information needs and communication patterns. We collaborated on multiple professional conference presentations and conducted a summative evaluation, which was submitted to the FDA for 510(k) pre-market approval.

Industry:

AI, Consumer technology, Health

Method/Process:

Formative / Evaluative, Global research, Simulated environment, Summative / Validation, Wizard of Oz

Stimuli:

AI-enabled product, Clinician-centered digital app, Mobile and PC prototype
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Understand users’ expectations for balancing information access and privacy when using AI-enabled tools

A global technology company sought to understand user privacy and safety thresholds in a global qualitative & quantitative research study.

Challenge

We were asked to determine when and how much users expect to be shielded from false, misleading, or offensive content when searching for information in any platform. Additionally, we sought out how various design factors influence users’ expectations for access vs. safety, and their expectations about our client’s role in protecting them from harmful content.

Approach

We conducted 30,000 quantitative surveys and 100 qualitative interviews with users from Brazil, Germany, Japan, Nigeria, and the United Sates to understand the relative importance of specified attributes (e.g., information source, private vs shared device, suggested vs requested information etc.) that contributed to people’s sense of how to balance information access versus safety.

Outcome

We delivered risk rankings to capture the total picture of how the attributes sat on a spectrum of where people place these attributes. We also provided global research partner management and a consolidated report spanning the entire scope of research.

Industry:

AI, Consumer technology

Method/Process:

Exploratory / Foundational, Global research, In-depth interview, Survey

Stimuli:

AI-enabled product
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