Designing trust when GPS accuracy is unreliable
This case study to describes how I apply the user-centred design process to transform complexity into validated user experience, in four big steps. Full research notes, synthesis artifacts, and iteration history are at the bottom of the page.
Summary: Applying the UCD process revealed that major pain points of existing geo-fencing pet apps were caused by the mistranslations of technology behaviour. Mapping technology constraints to user mental models made the system transparent and honest about its own limitations, reducing false alarms and loss of trust.
Mapping technology constraints to user mental models
Problem
Most pet geo-fencing apps 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.
Project goal
To design a system that's honest about its own limitations, so users can trust it even when it's wrong.
Transform complexity into a Product Vision [research]
Research summary (what users exeprience)
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.
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.
Inventory of pain points as experienced by users of existing products.
Research synthesis
Most situations fall under these three scenarios (see below matrix):
Pet indoors / courtyard (poor GPS)
Pet outdoors with obstructed sky view
Real escape events requiring action.
Define the expected user experience (& prototype it)
Defined the user story solving each pain point (persona, context, behaviour, need, action).
Drew flowchart logic of the interaction design based on each user story.
Illustrating the visions for each scenario.
Test & iterate 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.
Build & deliver the 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]