Tensor Projects Competitor Review - Researching and discovering insights and patterns to build mega-website for popular ML platform
Optimizing for increased user engagement and conversion
Tensor Projects, an ambitious initiative, envisions a comprehensive ecosystem for ML/AI tools and resources. As a UX Strategist/Researcher, I collaborated with a content strategist on a three-month journey to conduct a competitor review. Our goal was to uncover insights and propose recommendations for Tensor Projects, aiming to position it as a leading ML technology brand.
Client: Tensorflow
My Contribution: UX strategy and research
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Our challenge was to create a valuable resource on ML companies' marketing and UX strategies. With seven specific questions, a conventional heuristic evaluation was impractical. The focus was on understanding how similar companies structure their websites.
The Approach
We adopted a sprint-like format, conducting a Competitive Analysis to address our key questions. I concentrated on UX-related inquiries, while my partner delved into content-focused aspects. Each question underwent a two-week cycle—research followed by synthesis—culminating in a comprehensive slide deck for stakeholders.
Key Questions Investigated
- Presentation of "umbrella" brand hierarchy in similarly structured organizations.
- Documentation experience for open source and commercial libraries.
- Information design of libraries/documentation.
- Characteristics of best-in-class product search and discovery.
- Presentation of communities, social, and blogs by ML tooling companies.
- Well-designed "learning" modules for the web.
We looked at a number of open source software websites providing ML/ AI resources and education
UX Strategy
Rather than relying on numerical ratings, I evaluated competitors based on a set of criteria aligned with UX best practices. This approach provided a nuanced understanding of the UX landscape.
There were several dimensions that helped define the extent and reach of our research in order to keep a focus on ML//AI and open source software.
Scopes and constraints
- No access to analytics or statistical data for the reviewed websites.
- Focused on documentation IA/navigation.
- Examined only open source software and ecosystems.
Insights & Recommendations
To streamline our findings, we organized them into color-coded themes, such as documentation experience, search and discovery, and tool promotion. Recommendations were linked to evidence-rich slides, featuring visuals and gifs for enhanced comprehension
We linked each recommmendation to the slides that contained more evidence to support the claim such as gifs and photos from the websites we reviewed.
Outcomes
The final presentation showcased the depth of our research and recommendations. Stakeholders praised the detail, though some desired more data-driven evidence, a consideration beyond our project scope. There's an expressed interest in building this with our team and seeking UX guidance.
Positive Reception:
Stakeholders embraced proposed features like educational modules, user dashboards, community features, and interactive homepage modules.
Future Prospects:
As of January 2023, there are no reported updates on progressing with the Tensor Projects website. However, the project remains an exciting prospect, presenting an opportunity to create a complex ecosystem of significant value for ML/AI developers and researchers.