We're thrilled to announce the launch of our new and improved version of Kotae, our AI chatbot designed specifically for small and medium-sized businesses who manage high volumes of customer inquiries. This marks a major step forward in our commitment to providing intuitive and user-friendly AI solutions.
This blog post is the first of a series that will give a peek into the design and development process behind Kotae v3. Today, we'll focus on the first step: defining problems and conducting research.
Understanding User Needs
Our journey began with a comprehensive review of feedback from our MVP users and internal stakeholders. We recognized areas needing refinement and aimed to elevate Kotae's usability and overall user experience. In particular, we had a high drop rate in our onboarding flow. We hypothesized that users were experiencing friction in signing up for a free trial and set out to identify potential areas of improvement.
The scope of our research included the following questions:
And our primary goals were to:
Employing Robust Research Methodologies
To achieve these goals, we employed two standard user research methods:
Measuring Usability: Effectiveness, Efficiency, and Satisfaction
We evaluated usability using three key metrics:
Identifying Critical User Paths (Red Routes)
The tasks we assigned were "red routes"—critical paths nearly all users must take to utilize Kotae:
Key Findings: Room for Improvement
Our usability assessment revealed that while Kotae was effective (task completion rate was high), it lacked efficiency. For example, one task was completed in approximately 5 minutes by expert users, while new users took an average of 17 minutes. This highlighted the need for a more streamlined onboarding process.
User satisfaction was also below our target, with an average SUS score of 56.9, indicating low satisfaction.
We then analyzed our observations from the usability tests and categorized usability issues into the following six groups:
This provided a framework for identifying the most problematic aspects of our product, prioritizing problems, and eventually ideating solutions to these issues.
In Part 2 of this series, we'll explore how we addressed these usability challenges and delivered a more satisfying experience for our users.
