Fizbot is a platform that connects real estate agents and individuals looking to purchase properties with the right listings and people. The platform’s primary users are real estate offices and agents.

Fizbot asks real estate office staff to input potential buyers’ requests and preferences into the system, enabling the platform to generate a list of properties that match the desired features and budget. Through its integrations, it automatically retrieves property information from real estate applications in its operating country.

The primary goal of the product is to enable real estate offices to quickly and easily input buyers’ requests and information into the system, allowing them to see suitable matches instantly. Similarly, when sellers’ information is entered into the system, the platform aims to match them with potential buyers.

Currently agents prefer to manually note down information and manage transactions outside the system. This disrupts information sharing among real estate agents, narrows the portfolio, and significantly slows down the process.

The proportion of users who regularly enter data into the system is quite low. To understand this issue and its root causes, we conducted a study. The study revealed that the biggest problem is that agents avoid using the platform because entering customer data into the system is laborious and out of their usual practices.

We first focused on addressing semantic inconsistencies in the structure of the listing page. By reorganizing the filtering and categorization system, we made it more intuitive and user-friendly while maintaining a more clear hierarchy.

Additionally, we collaborated with the product team to reevaluate the information that needs to stand out in the rows, prioritizing them accordingly. The UI adjustments were completed in alignment with these improvements.

As a result of these improvements, users can now easily identify which leads are prioritized or delayed when they view the list. They can also filter and take action based on this information much more efficiently.

One of the most critical aspects of the potential buyer and seller addition process was the use of a left-side panel design, which was unconventional for user habits. To address this issue, we updated the design according to user expectations and located it in a more appropriate place.

During the addition process, users often experienced confusion about what steps to take. The primary cause of this was the lack of distinction between mandatory and optional information. Moreover, since the fields were interdependent, the order of information entry was crucial. To resolve this, we separated mandatory and optional fields very clearly. Additionally, we removed or made optional the information that was less significant for the user regarding potential buyers and sellers.

Thanks to these adjustments, now users could quickly input the necessary information. In initial user tests, the addition rate for potential buyers and sellers increased by approximately 12%.