Company— Aurora Solar
Project role— Lead Designer
Responsibilities— Design strategy, user research, problem definition, design, testing


Aurora Solar primarily lets you create a highly accurate solar design without having to visit the site, saving time and money during the quoting process. When I joined, LCAI (Lead Capture AI), a product of Aurora Solar, had been operational for about six months with limited functionalities. LCAI seamlessly integrates into clients' platforms, allowing homeowners to receive instant solar estimates and a 3D model of their potential solar roof by answering a few questions, thereby generating leads for the client.


Define to product strategy by contributing through design lens

Driving design-led culture

Meet diverse client needs

Balancing client and homeowner needs

Process overview

As a new product, it was crucial to ensure that it effectively addressed the intended problem and resonated with its target market. Unmoderated user testing was conducted to gather feedback on the existing product, while service design was employed to identify any flaws in the product's sales process. Additionally, feedback collected from recorded calls was incorporated into this analysis. By examining competitive landscapes, we gained a deeper understanding of the leads space, particularly the lead funnel. These efforts collectively contributed to shaping the roadmap of the product and enhancing its overall effectiveness.

User testing

The testing revealed that homeowners found the product easy to use, but expressed a desire for additional features. Specifically, they sought more functionality related to solar education, cost breakdowns, payment options, and the ability to refer a friend. These findings challenged some of the initial assumptions made during the product's conceptualization phase.


Service design map

Given that LCAI is positioned as an add-on, it was crucial to assess its sales process to explore opportunities for improving its service design. Through this examination, we identified several challenges. Demos frequently encountered issues, hindering effective showcasing of the product. Additionally, many existing clients remained on V1, complicating the sales of LCAI's V2 capabilities. Customers exhibited hesitancy towards purchasing due to the product's novelty, while implementation often faced delays, requiring extensive support from our pods' Project Managers or Engineer.


Future thinking

In my approach to work, I strive to balance short-term objectives with long-term goals. While identifying immediate impactful features and enhancements, I also aim to develop concepts that serve as a north star for LCAI's future. In this discovery process, I emphasized the importance of solar education, explored innovative ways to visualize costs and associated factors, and reimagined the lead index based on key user insights. These initiatives not only generated excitement around our pod's work but also fueled our collective aspirations, keeping both my pod and the wider team motivated and engaged in our work.


Shipped features samples

Service region: Customers were being billed for leads outside of their designated service area. Subsequent research was conducted to gain insight into how customers perceive their regions. We explored various factors, including whether customers define regions based on postal codes, AHJs (Authorities Having Jurisdiction), cities, or whether they use custom names for each area. Additionally, we investigated the level of granularity required to define a region, considering factors such as how micro or macro the selection of area needed to be.

Form customizability: The upgraded form builder offers a more dynamic experience, empowering users to create custom questions tailored to their specific needs. This facilitated the creation of a new section dedicated to showcasing customer insights derived from the gathered information. With the ability to gather targeted information aligned with individual requirements, customers can now effectively qualify leads and advance them through the sales funnel with greater precision and efficiency.

Lead to product conversion: The enhancement of lead to product conversion was a significant step forward, albeit one that required careful navigation of backend complexities, leading to a longer development timeline. While customers could initially convert leads to projects, there was a critical gap in passing along gathered information. Upon completion, we prioritized transparency by ensuring users were fully informed about the data being transferred to projects for the automatic generation of solar models. This transparent process streamlined the conversion process and improved overall efficiency.

Solar system design defaults: In response to the varying estimation methods across solar companies, we introduced customizable defaults within the LCAI platform. Recognizing the multitude of factors influencing estimates, we provided clients with greater control by allowing them to set defaults for each region or associated form. Leveraging existing research from another pod, we identified key factors influencing estimates and conducted additional research to determine the required accuracy level for preliminary estimates. This approach aimed to offer clients more tailored and accurate estimations while maintaining flexibility within the system.

Max-fit and irradiance: The maps provide valuable data within the lead index, empowering admins to assess the solar potential of each home. This initiative was a pivotal aspect of enhancing our lead qualification process within the platform.