Macquarie MotoMe

Re-inventing the car buying process

A bit of context

MotoMe is a whole new way to buy a car. It’s a single-page web application that features several handy tools to match you with your ideal car. The interactive platform guides customers through a process of car selection, custom option extras, booking a test drive and learning about finance options. We conducted extensive research into what consumers are looking for in the journey of purchasing a car, ensuring a first-of-a-kind multi-brand experience for potential customers. Once you have decided on a car, Motome’s team of car-buying specialists will negotiate a deal for you and can help set you up for finance. In 2018 MotoMe opened a totally new, first in Australia store concept in Westfield Hornsby.

An experimental startup within a corporate setting

Prior to my involvement, a small team consisting of three software engineers, a product owner and designer, had ventured off to San Francisco with the objective to build something special: the future of car buying. The team partnered with one of the best agile software development in the industry at Pivotal Labs in Silicon Valley and came back to Australia with a basic concept (MVP) they had developed in just 3 months. On their return, the team set up their own incubator lab in Sydney and continued to evolve the product and also advocate the new product development and design practices they had learned to other teams. The team hired a data scientist and started looking for a design lead to start refining the product. This is when I got involved.

  • Motome Main Landing
  • Motome Car Browsing
  • Motome Car Detail
  • Motome Car Comparison

The challenge

My task was a broad as I was involved in all aspects: the end-to-end experience, the brand, the UI and later, the retail store. However my initial goal was clear and simple: help the product become more mature, beyond MVP and define a clear customer experience. I collaborated with the product owner, our strategic lead and various stakeholders to map out the product roadmap along with three different experience design goals:

  1. Improve the overall experience
  2. Refine and integrate MotoMe's features
  3. Refine the customer value proposition

Our Approach

When I started, the team was very healthy in terms of delivery as well as culture. The Lean principles that the core people had brought over into Macquarie from their time at Pivotal Labs were well integrated into a quick development cycle. This meant that capability of the platform was excellent. However, I felt that the features that were implemented were not well integrated into the a single, coherent experience. The analytics confirmed this: e.g. the bounce rate of various landing pages was very high. As a multi-disciplinary designer I was also curious about the backstage: how were things behind the scenes? I interviewed a recent customer who had bought a car using the service and learned about their frustrations.

Customer Research

We did research in various ways to obtain quantitive data to learn from, e.g.:

  • A research firm was briefed to to market research
  • We collected and summarised existing, published car market research
  • We used Google Analytics to gather data and held weekly Analytic hour to discuss our findings
  • We ran Google surveys to learn specific behaviours, e.g. how do you people decide on accessories?

We also used various methods to gather more qualitative insights:

  • We talked to our own customers to learn about their pain-points
  • We ran user tests to validate our ideas for design concepts
  • We used Full Story to observe users while they used the service
  • We recruited recent car buyers to map out their end-to-end journeys


After our initial market research we learned what our car buyers key pain points were:

Interacting with the dealer

Some customers actively tried to avoid dealers as they found them too agressive.

Complexity in information-gathering

Some customers would maintain their own Excel sheets to be able to compare car details.

Value for money hard to judge

Customers struggled to understand whether the deal they were offered was a good one or not because of its complexity.

Lack of knowledge about finance

Customers seemed to lack understanding of how finance worked.

MotoMe's Customer Principles


You can trust us to get a great deal


We'll save you time by avoiding the dealer


We'll help you understand the deal in a clear way


Our car buying experts will answer your questions

No strings attached

We don't limit your choice in any way


We aim to be transparent without hidden costs

The design process

Data Science meets UX

With our Data Scientist I often explored ideas where we mixed my creative insights with his scientific approach. Using R software he was often able to help me understand what was feasible with the data we had and what wasn’t. I then collaborated with the product owner and one of our other designers in sketching sessions in which we explored our ideas further.

A car match-maker concept based on a Machine Learning algorithm that was developed in-house by our Data Scientist

Lean UX & Agile Development

At MotoMe the developers followed the Agile feedback loop: Learn - Build - Measure. During my time with the team I introduced Lean UX principles and new team rituals. We discovered a balanced way of working with various design methods in which the developer were involved e.g. our weekly Design Reviews during which the developers would check design concepts for feasibility.

  • Capturing comment during design reviews
  • Live collaboration during concept sessions
  • Using Inspect for developer handover
  • Rapid Prototyping
  • Rapid Prototyping
  • The MotoMe Design System
  • Style Guide using the Design System

User Testing

Concepts for new features were always validated with recent car buyers. Most tests were run by two staff members: one being the facilitator, the other note-taker and assistant. Sessions were then captured with lookback.io for reference purposes

We used mixed techniques for our prototypes. Some were created with software such as Flinto, Sketch or Invision.

In other cases we had to hand-code prototypes that were very rich in interaction, such as our MotoBot chatbot concept.

An integrated experience

MotoMe Westfield Hornsby Retail store

As the MotoMe grew, it expanded into the retail word and a second service was added. In 2018 we opened a store in Westfield Hornsby, I helped in planning how this experience would work together with the website. I also drew sketches for the floorpans. We made sure the branding was aligned and we were able to reuse features designed for the site on in-store iPad kiosks.

Finance Widget

To address our customer needs to better understand finance we started to explore a second product: a finance widget. This affordability calculator was intended to help customers understand what their repayments would be on a certain car and how they could adjust their loan variables

Finance widget minimised

Finance widget expanded

Planning IA

Feature Roadmap

Part of the experience was booking a test-drive delivered to your location.

Various designs for cards with repayments displayed.

We followed the same process as we had done with MotoMe, applying what we had learned at Pivotal Labs plus our own insights we had gathered over the years.

The widget was designed in a modular way and theme-able way so that it could be used on MotoMe as well as on partner websites, adopting custom colour schemes.


In my first few months I was successful in drastically lowering the bounce rates of various landing pages.

MotoMe received a lot of media attention when it opened its Hornsby store. For the first time ever in Australia cars were being sold in a shopping mall!

What MotoMe did as a startup within the bank was truly unique and we were able to inspire other teams with our Lean/Agile ways of working and design culture. The API services we designed at MotoMe were re-used internally and some even externally by car dealers / brokers.

An interesting learning for myself was how powerful collaboration between data science (e.g. machine learning) and experience design can be.