AI Chat Bot for Jacklyn De Ciccio
Duration
1 month
Project Type
Contribution
UX/UI Researcher, UX Strategist
Tools Used
Figma, Adobe Illustrator, Photoshop, Notion
Overview
We partnered with Jacklyn De Ciccio, a well-established real estate broker in the Greater Toronto Area (GTA), to design and implement an AI-powered chatbot aimed at enhancing her client engagement and streamlining communication. Jacklyn's clients typically browse real estate listings during evenings or weekends when she is not available to respond instantly. This delay in responses negatively impacted client satisfaction, leading to missed opportunities for engagement and conversion.
The Jacklyn AI Chatbot was designed to address these challenges by responding to inquiries in real-time, managing appointment bookings, and handling common client questions, freeing up Jacklyn’s time to focus on high-value tasks like closing deals. The chatbot significantly reduced the need for manual intervention and streamlined the communication process, all while maintaining a conversational and personalized tone to ensure users felt connected.
Challenges
Jacklyn's communication channels, particularly Instagram and her website's live chat, were underperforming, resulting in delayed responses to potential clients, especially during non-working hours. This delay caused frustration for potential clients and reduced overall engagement.
Jacklyn had to manually manage inquiries, which became overwhelming during peak hours and led to missed opportunities. Instagram’s messaging system required an accepted invite before multiple messages could be exchanged, while the live chat on Jacklyn’s website was rarely used due to the slow, manual response times.
Solution
The solution was an AI-driven chatbot capable of answering inquiries instantly, whether the user interacted via Instagram or the website. The chatbot not only responded to common questions about listings but also provided personalized property suggestions based on user preferences, such as budget, location, and property type. Additionally, the chatbot synced with Jacklyn’s calendar, allowing users to schedule property viewings without manual input.
Immediate engagement with users, answering inquiries about property details, availability, and legal procedures. Tailored suggestions based on user input to help clients quickly find properties matching their needs. Seamless appointment booking by syncing with Jacklyn’s calendar, allowing clients to book viewings and consultations in real-time. Integrated into both Instagram and the website, ensuring consistent and fast responses across multiple channels.
Research
The research phase began with an analysis of Jacklyn’s existing communication bottlenecks. It became evident that her response times were causing her to lose potential clients. Her reliance on manual communication methods, particularly via Instagram and website chat, was inefficient. Furthermore, peak browsing times—during evenings and weekends—exacerbated the problem, as Jacklyn was not available to respond outside of office hours.
We conducted surveys and interviews with Jacklyn’s clients to better understand their expectations. The feedback clearly indicated that users browsing real estate listings at non-standard hours expected quick responses, especially when making decisions on high-value purchases such as homes.
User Insights
The user research revealed key insights:
- Browsing Habits: Clients primarily browse listings in the evenings and on weekends.
- Expectation of Quick Responses: Clients expected responses within minutes, especially when they had immediate questions regarding properties or viewing schedules.
- Frustration with Delays: The delay in responses caused many users to abandon their inquiry or seek out other brokers who offered faster responses.
These insights solidified the need for a system that could handle inquiries autonomously, 24/7, and still provide a personalized, human-like experience.
Competitive Analysis
We examined how other real estate professionals were managing client communication. While many had implemented basic chatbots, these solutions often lacked depth and personalization. The analysis showed that more sophisticated, human-like interaction was missing in the market. This gap presented an opportunity for Jacklyn’s chatbot to differentiate itself by offering a more personalized and conversational user experience, with intelligent responses that mimicked human communication.
Persona
Elly, a 30-year-old marketing coordinator, is the ideal client persona for the Jacklyn AI Chatbot. Elly is a first-time homebuyer who feels overwhelmed by the real estate process. She is unfamiliar with the pricing, neighborhoods, and legalities involved in buying a property. Elly is looking for fast, clear, and reliable answers as she navigates the housing market. She appreciates the chatbot’s ability to offer personalized property recommendations based on her input and schedule viewings without the back-and-forth usually required for such tasks.
Implementation
Ideation
During brainstorming sessions, we focused on creating a chatbot that could handle common real estate queries and deliver a conversational experience. The chatbot needed to be more than a simple answering machine; it needed to understand user preferences and deliver curated property recommendations. Furthermore, it had to integrate seamlessly with Jacklyn’s calendar for real-time appointment scheduling.
We discussed the potential user flow and interaction scenarios, ensuring that the chatbot felt intuitive and human-like, yet efficient in providing quick answers and booking appointments.
Wireframes/Prototypes
We began with low-fidelity wireframes to map out the chatbot interaction flow. Key features included an easy-to-use interface for property searches and appointment scheduling. Users could quickly input their preferences and receive property suggestions. The initial user tests highlighted the need for a simple, step-by-step process to avoid overwhelming first-time users.
We iterated on the designs to enhance usability, especially focusing on making appointment scheduling as straightforward as possible. The chatbot’s fallback options allowed users to request a direct call or email if their needs were not met.
Design
The final chatbot design emphasized a smooth, user-friendly experience. Clients were greeted with a personalized message that collected their property preferences (such as budget and location). Based on this input, the chatbot offered relevant property listings. From there, users could schedule viewings directly, with the chatbot syncing with Jacklyn’s calendar to avoid double bookings.
We paid particular attention to the chatbot’s tone, ensuring it felt conversational and friendly, while maintaining the efficiency of an automated system.
Feedback & Iteration
Through multiple rounds of testing, we found that users initially had trouble understanding how to schedule an appointment. We refined the process by making the call-to-action clearer and breaking the scheduling into simpler steps. Additionally, the chatbot’s tone was adjusted to ensure it didn’t feel too robotic, offering a more conversational experience.
Key Testing Feedback: Users appreciated the chatbot’s quick responses and real-time availability but needed clearer guidance when booking appointments.
Key Takeaways
- Improved Client Engagement: The AI chatbot significantly improved client engagement by reducing response times. Clients no longer had to wait for Jacklyn to respond, making the interaction feel seamless and continuous.
- Efficient Lead Management: Jacklyn could now manage more inquiries and appointments without additional manual work, allowing her to focus on converting leads.
- Balancing Automation with Personalization: One of the biggest challenges was ensuring the chatbot didn’t feel too automated. We achieved a balance by allowing the chatbot to ask personalized questions and deliver recommendations that aligned with each user’s needs.
Challenges & Solutions:
- Challenge: Users found the appointment scheduling process initially confusing.
- Solution: We simplified the scheduling process by breaking it down into smaller, more manageable steps. This resulted in a 40% increase in successful bookings.
- Challenge: Ensuring the chatbot didn’t feel overly robotic.
- Solution: We adjusted the chatbot’s tone, making it more conversational, which improved overall user satisfaction and engagement.
Live Prototype
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