Translating technology complexity into better UX

This case study describes how a user-centred design approach helps transforming technology complexities into better user experiences. Full research notes, synthesis artifacts, and iteration history are at the bottom of the page.

Summary: Most pet geo-fencing app designs assume ideal conditions. But real users spend most of their time indoors, in courtyards, parks, or wooded areas — where accuracy degrades. When this happens, interfaces continue to behave as if data were precise, creating false alarms, anxiety, and loss of trust. Major pain points are caused by the mistranslations of technology behaviour. Mapping technology constraints to user mental models makes the system transparent and honest about its own limitations.

Goals

  1. To design (for a pet-finding app) a system communicating transparently its own limitations, so users can trust it even when it's wrong.

  2. To learn the “[...] ability to take an empathetic view of the user, and to interpret that into a systematic design solution.”

— Peter Merholz

Role

Independent case study, initiated within a UX Research bootcamp. Challenge designed by UX researcher, as it requires solving technical constraints within real contexts. Sole designer, mentored by tutor.

  1. Research & Synthesis

Research summary

User interviews

Interviews with dog owners to find out how people think and act during stressful events (e.g. losing a pet); what they expect the system to do when something goes wrong. Key users groups. Personas. Empathy and journey mapping.

Understand the conceptual model of technology

Learning how GPS signal quality works depending on context (indoors, trees, buildings, sky visibility) and how it causes mismatch between users' mental models and reality.

Desk research; competitive analysis of pain points (various apps)

Inventory of pain points as experienced by users of existing products.

Research synthesis

Most situations fall under three contexts (see below matrix):

  • Pet indoors / courtyard (poor GPS) -> most often triggering false alarms.

  • Pet outdoors with obstructed sky view -> usually triggering false alarms.

  • Real escape events requiring action, often in areas where GPS coverage / signal is poor.

  1. Defining UX: from user story to flowchart to prototype

For each of the three scenarios, I've sketched the research sysnthesis in three steps:

  1. Defined the user story solving each pain point (persona, context, behaviour, need, action).

  2. Drew flowchart logic of the interaction design based on each user story.

  3. Prototyping the Interaction Design fulfilling each story.

Scenario 1 — pet indoors

Scenario 2 — pet in the courtyard

Scenario 3 — pet in the park / woods, roaming freely

For each of the three scenarios, I've scketched the research sysnthesis in three steps:

  1. Defined the user story solving each pain point (persona, context, behaviour, need, action).

  2. Drew flowchart logic of the interaction design based on each user story.

  3. Illustrating the visions for each scenario.

  1. Rounds of testing the prototype with users

Key findings

  • Users did not want technical explanations.

  • Users expected the system to learn and stop repeating false alarms.

  • Trust increased when uncertainty was acknowledged instead of hidden.

Each iteration reduced friction, clarified intent, and simplified decision paths.


Key findings

  • Users did not want technical explanations.

  • Users expected the system to learn and stop repeating false alarms.

  • Trust increased when uncertainty was acknowledged instead of hidden.

Each iteration reduced friction, clarified intent, and simplified decision paths.


  1. Final UI

Learning points

  • Good interaction design aligns mental models with real-world constraints.

  • Users don’t need explanations — they need clarity.

  • Usability testing needs measurable goals.

Design process

Full research notes, synthesis artifacts, and iteration history [View as PDF]

© 2026 Marius Grigore

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