challenge
As our organization delved into the complexities of workforce management within a large industrial setting, we encountered the task of deciphering vast amounts of data over time.
Leveraging our existing capability to store support call audios, we recognized a significant opportunity to enhance value through NLP technology. Thus, our team embarked on a mission to address this challenge head-on.
As the sole designer within this pod, alongside ML researchers and AI scientists, our aim was clear: to develop a solution that streamlined workforce understanding. Our goal was not only to simplify data analysis but also to empower users with actionable insights derived from complex data sets, ultimately transforming the way industrial organizations manage their workforce
process
In addressing the challenges, our team embraced the renowned Double Diamond design process, a methodical framework that emphasizes divergent and convergent thinking to generate innovative solutions. By uniting the expertise of design, product management, and data insights, we fostered a collaborative and adaptive environment conducive to creative problem-solving
Market Research
We conducted extensive market research to identify existing solutions and assess their strengths and weaknesses. Through this research, we discovered that there were a number of tools and platforms available for call analysis and optimization, but none of them provided a comprehensive solution that could cover all of our clients needs. Furthermore, we found that many of the existing platforms had user interfaces that were overly complex and difficult to navigate.
User Research and Persona
Before starting any design work, we conducted in-depth user research to better define our target user persona and understand their pain points.
Feature Prioritization
One of the key steps in the development process for Call Insights was feature prioritization. To determine which features to include in the platform and in what order, we worked closely with stakeholders to understand their needs and priorities, and created a set of user stories to guide our decision-making.
Outcome
The Challenges
Working on the Call Insights project presented a number of challenges, including building the platform as we went along with many periods of investigation in between to figure out if features were feasible, working with evolving natural language processing technology, and setting up collaboration strategies as a newly created team
Wireframes and Early Prototypes
During the initial phase of the project, our team developed several early prototypes to test the feasibility of the platform and explore different design approaches. These prototypes ranged from simple mockups to functional demos that allowed us to gather feedback from users and stakeholders.
Discovery Sessions
Throughout the design process for Call Insights, I worked closely with the project manager to schedule regular demo sessions at different stages. These sessions were designed to gather feedback from clients and users to ensure that the features we were building provided value to them.
Key Performance Indicators
As with any project, it was important to establish clear Key Performance Indicators (KPIs) for Call Insights in order to measure its success and impact. These KPIs were based on the project objectives and included metrics such as user engagement, feedback and satisfaction, and cost savings for call center operations.
The Solution
The Call Insights platform was designed to provide users with valuable insights from their workforce by leveraging call data. Our team used a variety of techniques, including sentiment analysis, topic modelling, call summarization, and entity recognition, to extract meaningful information from call data and present it in a user-friendly format.
The solution implemented for Call Insights proved highly successful, as evidenced by the overwhelmingly positive qualitative feedback from our clients.
Furthermore, the strong market validation was underscored by the fact that 32% of our early enterprise users expressed a willingness to pay an additional fee specifically for this service.
