(02)
(02)
(02)
Lead Product Designer
Lead Product Designer
Lead Product Designer
12 Weeks
12 Weeks
12 Weeks
SaaS
SaaS
SaaS
B2B
B2B
B2B
UX Research
UX Research
UX Research
Branding
Branding
Branding
ShopLevers is an all-in-one data visualisation dashboard and analytics tool for auto-repair shop owners. I was tasked with the end-to-end creation of a smart dashboard MVP that delivers intuitive, data-driven interfaces to optimise operations for repair shop owners. This dashboard uses data visualisation to present real-time insights and actionable recommendations, streamlining decision-making for the user.
ShopLevers is an all-in-one data visualisation dashboard and analytics tool for auto-repair shop owners. I was tasked with the end-to-end creation of a smart dashboard MVP that delivers intuitive, data-driven interfaces to optimise operations for repair shop owners. This dashboard uses data visualisation to present real-time insights and actionable recommendations, streamlining decision-making for the user.
(The Problem)
(The Problem)
Auto repair shop have fragmented data: revenue, parts, labour, efficiency, GP — no single view to track performance.
Without actionable insights, decision-making is reactive rather than strategic.
(The Goal)
(The Goal)
Create a unified, easy-to-use dashboard that consolidates fragmented data into one view.
Provide performance insights through clear visualisations and priority metrics.
Help shop owners move from reactive decision-making to proactive growth through benchmarking and active recommendations.
View prototype
View prototype
View prototype
1
1
1
Research
Research
Identifying the user's pain points with Design Thinking
To understand how auto repair shop owners track performance, we conducted user interviews and created shop-owner personas based on their workflows. Owners were asked to describe how they currently interpret and act on data when making operational decisions.
Through this process, we identified recurring challenges:
difficulty tracking KPIs like average repair order or gross profit over time;
lack of comparisons to historical performance or peer benchmarks;
and the need for simple, actionable recommendations rather than raw numbers.
To understand how auto repair shop owners track performance, we conducted user interviews and created shop-owner personas based on their workflows. Owners were asked to describe how they currently interpret and act on data when making operational decisions.
Through this process, we identified recurring challenges:
difficulty tracking KPIs like average repair order or gross profit over time;
lack of comparisons to historical performance or peer benchmarks;
and the need for simple, actionable recommendations rather than raw numbers.
I created several personas for this project in order to fully understand and empathise the users' pain points:
I created several personas for this project in order to fully understand and empathise the users' pain points:
Key insights from the research phase:
Key insights from the research phase:
(01)
(01)
Managing data across multiple shops was time-consuming, requiring manual exports from different systems.
Managing data across multiple shops was time-consuming, requiring manual exports from different systems.
(02)
(02)
Even in instances where data was readily available, users lacked the time to interpret it.
Even in instances where data was readily available, users lacked the time to interpret it.
(03)
(03)
A need for a centralised dashboard emerged, surfacing actionable insights quickly and clearly.
A need for a centralised dashboard emerged, surfacing actionable insights quickly and clearly.
(04)
(04)
Users focused mainly on high-level metrics like revenue and car count, missing drivers of profitability.
Users focused mainly on high-level metrics like revenue and car count, missing drivers of profitability.
(05)
(05)
Reporting was fragmented, often needing multiple PDF reports and spreadsheets to compile insights.
Reporting was fragmented, often needing multiple PDF reports and spreadsheets to compile insights.
(06)
(06)
Stakeholders wanted to empower staff to interpret data, reducing their own daily involvement.
Stakeholders wanted to empower staff to interpret data, reducing their own daily involvement.
2
2
2
Design
Design
Defining the structure of the Smart Dashboard
- Levers — key operational metrics such as revenue, labour, and efficiency;
- Insights — system-generated analytics that highlight trends, risks, and opportunities.
- Levers — key operational metrics such as revenue, labour, and efficiency;
- Insights — system-generated analytics that highlight trends, risks, and opportunities.
The idea behind this devised flow is to provide equal emphasis on data as analytics.
The idea behind this devised flow is to provide equal emphasis on data as analytics.
Phase 1 revolved around the idea of an Insights popover, so that the user is never viewing analytics without the context of the underlying data.
3
3
3
Phase 2
Phase 2
Evolving the dashboard with deeper analytics and scalability
Following the success of the MVP, I was recommissioned to design Phase 2.
In order to give shop owners greater control, the dashboard was redesigned to show performance trends directly on the home screen, alongside a new Priority Metrics system.
Users could pin up to three KPIs most relevant to their business, creating a personalised, at-a-glance view.
Following the success of the MVP, I was recommissioned to design Phase 2.
In order to give shop owners greater control, the dashboard was redesigned to show performance trends directly on the home screen, alongside a new Priority Metrics system.
Users could pin up to three KPIs most relevant to their business, creating a personalised, at-a-glance view.
A comparison of the original (Phase 1) dashboard versus the Phase 2 version.
A comparison of the original (Phase 1) dashboard versus the Phase 2 version.
Supporting multi-shop
Phase 2 also introduced multi-shop functionality, enabling owners to compare levers such as revenue and efficiency across different locations. Interactive charts and a breakdown section helped identify which shops were underperforming or exceeding benchmarks.
This feature addressed a key user need uncovered during research: larger operators required not just individual shop data, but also the ability to spot trends across their network at a glance.
Phase 2 also introduced multi-shop functionality, enabling owners to compare levers such as revenue and efficiency across different locations. Interactive charts and a breakdown section helped identify which shops were underperforming or exceeding benchmarks.
This feature addressed a key user need uncovered during research: larger operators required not just individual shop data, but also the ability to spot trends across their network at a glance.
The goal of Phase 2 was to easily allow analytics across multiple repair shops.
The goal of Phase 2 was to easily allow analytics across multiple repair shops.
3
The Final Prototype
The Final Prototype
At-a-glance analytics, always in context
At-a-glance analytics, always in context
The task of managing complex shop data has been distilled into a streamlined dashboard. Priority metrics give owners focus on their most important levers, while insights flag trends across revenue, labor, and efficiency. Users can monitor performance at a glance, and dive deeper only when needed.
The task of managing complex shop data has been distilled into a streamlined dashboard. Priority metrics give owners focus on their most important levers, while insights flag trends across revenue, labor, and efficiency. Users can monitor performance at a glance, and dive deeper only when needed.
4
4
4
Evaluation
Evaluation
How do we measure the success of the MVP?
How do we measure the success of the MVP?
I was not present for the full implementation of Phase 2 of this product, but I did lead early prototype testing with shop owners. Feedback on this was encouraging — in particular, users reported that the dashboard redesign gave greater clarity on at-a-glance performance trends.
In addition, the extra granularity on data visualisation, including industry benchmarking, added considerable value.
I was not present for the full implementation of Phase 2 of this product, but I did lead early prototype testing with shop owners. Feedback on this was encouraging — in particular, users reported that the dashboard redesign gave greater clarity on at-a-glance performance trends.
In addition, the extra granularity on data visualisation, including industry benchmarking, added considerable value.
Onboarding feedback through survey and Mixpanel data:
Onboarding feedback through survey and Mixpanel data:
(01)
(01)
Users asked to rate the redesigned dashboard gave an average score of 8/10 for clarity and usability.
Users asked to rate the redesigned dashboard gave an average score of 8/10 for clarity and usability.
(01)
(01)
Prototype testing showed a 22% faster time-to-insight, with shop owners finding key metrics more quickly.
Prototype testing showed a 22% faster time-to-insight, with shop owners finding key metrics more quickly.
(01)
(01)
Shop owners valued industry benchmarks, reporting they added useful context for evaluating performance.
Shop owners valued industry benchmarks, reporting they added useful context for evaluating performance.
(02)
(02)
Users noted that multi-shop provided clearer comparisons between shops, reducing reliance on spreadsheets.
Users noted that multi-shop provided clearer comparisons between shops, reducing reliance on spreadsheets.
5
5
5
Learning Outcomes
Learning Outcomes
This project reinforced the value of designing for clarity in data-heavy products. When designing for real users who are often time-poor, it is important to balance depth vs. simplicity — giving power users access to detail should they wish, while still providing at-a-glance insights for everyday decisions.
Additionally, working across two phases taught me how to scale a design system over time, reusing existing components and expanding on them as their use also expanded. This ensured consistency while adding new functionality. Iterating with users highlighted that even small interaction details, like how insights are surfaced in context, can make the difference between data that is ignored and data that drives action.
This project reinforced the value of designing for clarity in data-heavy products. When designing for real users who are often time-poor, it is important to balance depth vs. simplicity — giving power users access to detail should they wish, while still providing at-a-glance insights for everyday decisions.
Additionally, working across two phases taught me how to scale a design system over time, reusing existing components and expanding on them as their use also expanded. This ensured consistency while adding new functionality. Iterating with users highlighted that even small interaction details, like how insights are surfaced in context, can make the difference between data that is ignored and data that drives action.