ClimaPlan AI
A responsive website that helps users identify and assess climate risk data for a portfolio of real estate assets with actionable insights.
Background Research
How are Climate Risks linked to Financial and Socio-economic Risks?
Climate risk is intrinsically linked to financial risk because the physical and transitional impacts of climate change can directly affect the value, operational costs, and insurability of assets. Physical risks, such as extreme weather events, flooding, and rising temperatures, can damage infrastructure, disrupt operations, and increase maintenance costs. These risks can lead to reduced asset value, higher insurance premiums, or even loss of insurability.
What is the role of analysts and planners?
Real estate analysts and planners use climate risk data analytics platforms to assess the potential impacts of climate change on properties and urban areas. By integrating data from sources like GIS maps, climate models, and regulatory frameworks, analysts can predict long-term risks and assess a property's resilience. However, they face challenges such as difficulty in interpreting complex data, limited access to accurate localized information, and the need for platforms that can effectively integrate with existing tools.
Product Design Lifecycle
Brainstorm
User Research
Competitor Analysis
Empathy Maps
Personas
Journey Maps
Ideate
Ideation
How might we?
Value Proposition
Storyboarding
User Flows
Design
Information Architecture
Wireframing
Lo-Fidelity Prototype
Hi-Fidelity Prototype
Design Systems
Test
Usability Study
User Testing
Affinity Diagrams
Analyzing Findings
Accessibility Check
Refine & Develop
Refine Mockups
Reiterations
Take Aways
Future Scope
Hand over to Engineers
Problem Statement
" Real Estate Analysts and Urban Planners need to easily identify and analyze climate risk data because they need to be able to assess this for regions and property portfolios. "
Brainstorm
Competitor Audit
Our Research process started with studying various climate risk data analytics platforms that exist currently in the market. What are these platforms currently offering? Who are the key users? How to earn money? These applications are majorly used by real estate analysts, sustainability specialists, investors and some urban planners.
We wanted to understand what are the existing features, pricing, business sizes and their unique value proposition. We also conducted a User experience audit to assess and compare the user experience and interactions in these platforms.
What I found was that most of these platforms can benefit from:
-
A better mapping platform
-
Consistent onboarding experience
-
The addition of AI features which can automate some tasks.
Along with this, I also understood how analysts and planners currently use these platforms and how the user base can be expanded to other professionals within the AEC industry.
User Survey
After this, we wanted to understand what a majority of these users think and feel while using such platforms. Are they enjoying the current process? What can be improved? What are their major paint points? What could be potential features that users want? Thus, before jumping into user interviews, we conducted a survey from 44 people - Real Estate Analysts and Urban Planners, all across the globe who answered and seemed like most users did not use know and use these platforms, and those who did were extremely overwhelmed.
71%
Difficulty visualizing long-term climate impacts on assets.
61%
Overwhelmed with the amount of information present across the dashboards
31%
Time-consuming process of combining diverse datasets
25%
User-friendly dashboards with real-time climate risk visualization
20%
Need an understanding of how to classify a climate risk
User Interviews and Empathy Mapping
To specifically dig into issues, around 6 user interviews of Real Estate Analysts, Architects, Urban Planners and Sustainability Specialists were conducted in order to understand and empathize with what are their frustrations and pain points in using existing climate risk analytics platforms. Based on which we created an empathy map of what the users say, think, feel and do.
" I am so overwhelmed with the complex Climate Data that I need it simplified. "
"I wish there was a 'how to' page so that I can understand "
" I wish AI would do the redundant tasks for me as it would make my life easier "
User Pain Points
Based on the user interviews and surveys, we identified four key pain points which were highlighted by all the users. This was done by affinity mapping and categorization of opinions of different users as we received a lot of repetitive ones. This led to the creation of the product's problem statement.
1
Users need easy to understand data
Most of climate data is overwhelming and is not super transparent for users and hence, needs an easy to understand user flow.
2
Users need Insightful Maps
Analysts need actionable insights for next actions and maps provide direction on where can they invest/divest or identify vulnerable areas
3
Users need trustful methodology
Users need to build trust in order to make decisions through the risk insights and hence, methodology that is easy to understand is needed
4
Users need help and assistance
While using these platforms, users have constant doubts and rely on supervisors or consultants to solve those issues.
User Stories
Based on the problem statement, user stories were created which were a potential product descriptions from the perspective of our end users who would use this product. These helped us provide value proposition for the product.
" As a real estate analyst who wants to maximize returns while assessing climate risks for a portfolio of assets, I want to access and understand different climate risks for different regions, time frames and different emissions scenarios, so that I can create reports for the portfolio. "
" As an urban designer, who is identifying safe locations for development and planning strategies for the next 20 years, I want to easily visualize climate risk GIS data for the next 50 years, so that I can visualize how it impacts the site and how can I design better. "
Product Goal
ClimaPlan AI will let users identify, understand, and analyze climate risk data easily which will affect real estate analysts and planners by allowing them to form decisions for mitigation and adaptation. We will measure the effectiveness by analyzing user satisfaction and engagement metrics.
Personas and User Journey Maps
The user pain points and gain points along with a streamlined product vision and goal helped the most while creating user personas. Two personas were created. The first one is Alex, the real estate analyst who uses such apps and finds it super overwhelming and complex to understand climate data. The other one is Ethan, the urban planner, who doesn't use such apps yet but would benefit a lot by integrating maps in their climate adaptation goals for cities.
User Journey Maps were created for both of these personas to understand their current user flow for tasks, identifying pain points and associated feelings and areas of new opportunities for introduction and improvement, along with accessibility considerations.
Alex Cierra
Age: 31
Education: MBA
Hometown: London
Occupation: Real Estate Analyst
Alex is a real estate analyst who analyzes climate risk data for all the property portfolios at risk in her company so that the firm can decide where to invest or not. She needs insights actionable maps with some help and assistance
Goals:
-
Identify investment or divestment opportunities
-
Maximize returns on investments while minimizing risks
-
Act on insightful maps with suggestions that are data driven and impactful
-
Comply with ESG to attract investors
Frustrations:
-
Limited time to analyze complex climate data
-
Needs actionable maps for decision making
-
Needs trustable data to decide
-
Needs insightful maps to take actions for climate change mitigation and adaptation
“We need to link climate change risks to financial risks”
User Journey Map
Ethan Jules
Age: 31
Education: MS Urban Planning
Hometown: New York
Occupation: Urban Planner
Ethan is an urban planner who wants to access and analyze climate risk data in his city but can’t find the latest climate geospatial data and doesn’t understand the emissions scenarios to model any existing climate risk data.
Goals:
-
Ensure City Zoning aligns with climate goals
-
Identify vulnerable areas of communities at high risk
-
Integrate climate risk data into planning frameworks
-
Identify areas for infrastructure upgrades
Frustrations:
-
Access granular data at a block level in a city
-
Have to do a lot of data search online for maps
-
Can’t find climate geospatial data
-
Doesn't understand IPCC scenarios to model
“The most vulnerable are affected by climate risks”
User Journey Map
This led us to understand the features of the product and what are the opportunities where ClimaPlan could be beneficial in the current user journey flow.
Product Roadmap
Ideate
Then, we move to the next phase of the design process, where we started the ideation process by forming a "How might we?" statement based on our Hypothesis and creating a value proposition by identifying all the future potential features, considerations and costs which we can include based our user research study.
How might we? and Value Proposition
How might we make climate risk assessment easier to understand and actionable?
Key features which were highlighted included:
-
Customized and easy to understand dashboard for identifying the various climate risks for a property of assets or a region.
-
Actionable insights from analyzing the data and acting on it with adaption and mitigation strategies for different climate risks.
-
AI features were introduced to aid this process by automating various tasks which a user might do.
-
AI summaries and chat help AI chatbots for users in their user flows.
Storyboarding and Crazy 8s
To understand the user flow of the product, storyboards were created where the study included how the user would go about trying to do their current task and an app like this would help. Sketches were done in order to understand screens and their user flows.
To generate more ideas, an approach called Crazy 8s was adopted where a limited amount of time was given to sketch in 8 boxes. These ideas were iterated upon and the exercise was repeated.
User Flows
Following the sketching sessions, user flows were created based on scenarios, in order to create a seamless user experience and pave the future wireframing design. According to these user flows, there was a better understanding on how to create the product screens.
Risk Identification Flow
This flow shows the user flow to identify a particular climate risk factor in an area or for a portfolio of assets.
Asset Risk Analyze Flow
This flow shows the user flow to analyze patterns and insights from the graphs and act on them
Design
Then, we move to the next phase of the design process, where we started the designing by creating an Information Architecture Site Map for the product which informed the wireframes and prototypes. This was a crucial stage to involve design thinking principals like Gestalt Principals and making sure the design was WCAG accessibility compliant.
Information Architecture
The Information Architecture was crucial to understand the site layout and what screens would follow each, post the user flows.
Wireframing and Mid-Fi Prototype
After creating the IA, a low fidelity mockup was created by wireframing and understanding how the screens would function and how the onboarding process would happen for users to make it a seamless experience. To make it responsive, two prototypes were created, a desktop one and a mobile phone which were tested on 4 users for a usability test.
Usability test Study Findings
Two prototypes were created, a desktop one and a mobile phone which were tested on 4 users for a usability test. The pilot testing was done with 5 key tasks to achieve and see what users felt about the user experience in this process. An Affinity diagram was made to find common themes and patterns amongst different users for getting user insights.
Design System and Style Guide
Before starting a Hi-Fi prototype, a style guide comprising of the typography, iconography, color scheme, and other guidelines was set up along with a design system of major components were designed.
The Logo was designed while keeping in mind the aspect of the earth as an important element for Climate Change and involving it with the letter C and P. The colors were intentionally chosen as cooler colors in order to show how this product can cool the globe
Hi-Fidelity Prototype
Two Hi-Fi prototypes were designed, one for desktop and one for laptop, which were reiterated with usability studies and user feedbacks.
Onboarding & AI Chatbot
This process involves sign up, uploading a list of assets, onboarding and using the AI Chatbot for any questions.
Identify
This process involves putting in the filters, identifying the type of risk, generating portfolio PDF summary
Analyze and Act
This process involves analyzing the filtered assets wrt risks and generating savings from adaptation or mitigation measures
Accessibility Considerations (WCAG)
Next, an accessibility audit was done with compliance to the WCAG guidelines
Annotations and Hierarchical Headings
Labels for screen reader
_edited.jpg)
Key Performance Metrics and User Testing
Once the prototype was ready, the KPIs were measure to measure the impact of the design on the users and the business. This was done by a usability testing on usertesting.com with 3 users and 6 tasks to complete.
Usability
Time on Task: Reduced time to assess risk by 35%
Task Success Rate: Achieved 90% task success rate
System Usability Rate (SUS) of 85 indicating good UX.
Customer Feedback
Customer Satisfaction Score (CSAT): 90% of users rated their experience as excellent
Qualitative Feedback: Users reported that product simplified the process a lot.
" I like how easy and visible the onboarding thing is. "
" I like how the risk maps are visualized and easy to download"
" Maybe you can add a carbon emissions calculator in future too "
Collaboration with Engineers
Optimizing Heavy GIS Data for Performance
Challenge: Climate risk filters required real-time GIS data rendering, causing slow load times.
Solution: Implemented Map Tiling + Progressive Disclosure, loading lower-resolution data first and refining results dynamically.
Project Management
Learnings and Future Scope
What I learned from this project:
1. Importance of iterations and usability
2. Advantages of metric based studies
3. How user flows can guide the project
4. Attention to detail
5. Personal Growth and Collaboration
The future scope of this project will be exciting as I had planned some additional features which will come in future:
1. AI based Map Generation
2. On-ground Act notifications
3. Carbon emissions calculation
4. Carbon footprint calculator
4. Collaboration with organizations
