How I initiated and evangelized a data-driven process to gauge clients' strategic fit using the Sisense BI tool. This quickened the products team's time to insight by 75%, making road mapping, user research recruiting, and NPS discovery affordable and engaging.
A new internal predictive BI tool based on Sisense BI capability became available to the product team. The beta launch aimed to empower product roadmapping and streamline daily research. The outcome exceeded expectations, achieving a 75% reduction in insight generation time, improved research participant recruitment, and enhanced accessibility of NPS data. This journey began with an unrefined idea: leveraging a "bird's-eye view" of client strategic fit through predictive analytics. It became a reality when a data scientist and engineer joined forces with me. I fostered cross-organization collaboration, and many have helped to turn an informal knowledge base into a powerful, communal tool inspired by the Gartner quadrant.
My Role: I was the senior product designer on the UX team of five designers. I was assigned to push forward research capabilities within the local branch. I recruited a data scientist and a data engineer from remote branches to use Sisense's BI tools to streamline research and provide an out-of-the-box evaluation method to gauge clients' relevancy and satisfaction. I encouraged early ideation and collaboration with the rest of the product team while collaborating with stakeholders throughout the organization to understand the full scope of the problem. Tools:
Miro, Figma, Gong, Salesforce, Sisense BI platform
Methods:
Stakeholder interviews, former user interviews analysis, NPS analysis, ideation, analytics, dashboard design, and process analysis.
Timestamp: 2022-2023 | One year
Situation (the problem)
The Sisense UX team could not easily access users for UXR purposes. As a senior product designer, I was assigned to improve that. I decided to review the holdups preventing the team from scheduling client meetings. Obtaining customer service (CS) approval was mandatory and time-consuming. To resolve this, I attempted to create a pre-approved pool of clients but faced difficulty classifying them due to insufficient data and criteria. Additionally, I learned from professional services (PS) leaders and product managers (PMs) that known issues remained unaddressed by the product team because of the absence of centralized reporting and frequent staff changes, leading to a loss of essential, hard-earned informal knowledge.
With new competitors entering the market and user satisfaction with the Sisense legacy platform declining, we knew UXR was essential for prioritizing which of the pressing UX issues to tackle first. Failure to do that would increase frustration among users and could result in a loss of market share.
How Might we utilize Sisense BI tools to assess client relevance and satisfaction, thereby prioritizing feature development and enhancing UXR support for better feature improvement?
I’ll share how I turned an authorized private initiation into a company project by enlisting the support of cross-functional stakeholders to demystify Sisense’s complexity. Sourcing the collaboration with a data scientist and a data engineer to leverage Sisense's BI tools and AI and develop a unified data model and a new classification system. This system eased user interview candidate allocation and formed the foundation for an AI-driven roadmapping tool accessible to the entire product team.
Furthermore, it addressed the challenge of prioritizing user issues for the product team, ensuring an ongoing, inclusive approach that bridged the knowledge gap for new Sisensers.
