Exploring Millions of User Interactions with ICEBOAT: Big Data Analytics for Automotive User Interfaces

TL;DR

This paper introduces ICEBOAT, a visualization tool designed for automotive UX experts to analyze large telematics datasets, supporting data-driven design decisions for In-Vehicle Information Systems.

Contribution

The paper presents the design, development, and evaluation of ICEBOAT, a novel interactive visualization tool tailored to automotive UX analysis needs.

Findings

ICEBOAT enables efficient analysis of telematics data.

UX experts can make better data-driven design decisions.

The tool improves understanding of driver interactions with IVIS.

Abstract

User Experience (UX) professionals need to be able to analyze large amounts of usage data on their own to make evidence-based design decisions. However, the design process for In-Vehicle Information Systems (IVIS) lacks data-driven support and effective tools for visualizing and analyzing user interaction data. Therefore, we propose ICEBOAT, an interactive visualization tool tailored to the needs of automotive UX experts to effectively and efficiently evaluate driver interactions with IVISs. ICEBOAT visualizes telematics data collected from production line vehicles, allowing UX experts to perform task-specific analyses. Following a mixed methods User-centered design (UCD) approach, we conducted an interview study (N=4) to extract the domain specific information and interaction needs of automotive UX experts and used a co-design approach (N=4) to develop an interactive analysis tool. Our evaluation (N=12) shows that ICEBOAT enables UX experts to efficiently generate knowledge that facilitates data-driven design decisions.