Table.ai
Designing an intuitive causal analysis platform experience
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Data Analytics
Platform
Causal AI
E-Commerce
Table.ai is a platform that empowers third party resellers on Amazon and Shopify with AI powered causal insights, allowing them to understand the root causes of their brand’s performance.
I led the end-to-end design process of the core experience, making it easy for brand managers to understand their brands’ health at a glance and intuitively deep dive their data to understand the drivers.
End user: Brand managers who manage multiple brands selling on Amazon and Shopify. Brand managers currently need to access at least 4 different platforms that track various metrics.
Oftentimes, the brand managers will manually input the data into a homebrew excel spreadsheet in order to piece together the full picture of how their brand or product is doing. Due to the significant time they dedicate to data aggregation, they often find themselves spending the remaining time addressing urgent issues rather than strategizing on improving their brands and products.
Our solution assists brands in consolidating data from multiple platforms, enabling brand managers to efficiently triage and prioritize their tasks.
Using machine learning and AI, Table.ai empowers brand managers with an understanding of the causal drivers for their brand or product performance.
In the UX/UI design, I surfaced the most relevant and urgent insights using cards at the top of the page and implemented a folder-like view that allows users to deep dive their data. This flexible display allowed the interface to be clean and minimal while retaining the ability to explore more.
Through user research and behavioral analytics using FullStory, we gained valuable insights into user workflows and pain points. By identifying key product opportunities, we implemented strategic design improvements that contributed to a 433% increase in client acquisition.
Additionally, we revamped the platform’s visual design and restructured its site architecture, enhancing usability and discoverability. These improvements led to a 1,500% increase in monthly traffic, significantly boosting user engagement and platform growth.
Conducted 5 thinking aloud shadowing user research sessions paired with an interview segment to understand existing workflows and pain points.
Analyzed 5 platforms on 2 main dimensions, ease of use and analytics capabilities
The key insight from the research was that while these platforms provided brand managers with detailed information about various aspects of their business, the holistic picture is scattered across several different platforms. Inundated with data, brand managers found it hard to identify how to prioritize their time.
Having call out cards of important metrics at the top enable our platform to be glanceable.
Flexible and intuitive method to dive into their data and see how we derived our insights
Each brand manager prioritized different metrics, leading to some variation in their data needs and decision-making processes. Given the small sample size we worked with, identifying overarching patterns was challenging.
If I were to approach this again, I would aim for a larger sample size to improve the reliability of insights. Additionally, I would incorporate a card-sorting exercise to better understand how brand managers categorize and interact with their data. This method could reveal larger trends in their mental models and organizational strategies, ultimately informing more intuitive and scalable design solutions.