Granular product insights helped to fine-tune this virtual product.
Objective
Understand user behaviour to optimise conversion and product discovery.
Obstacle
Lack of consolidated data on user engagement and product interactions.
Outcome
Product and category-level insights, enabling data-driven UX improvements.
Background
BigNoo is a rapidly growing social commerce platform that blends community engagement with shopping, offering a highly curated experience that connects users through product discovery, sharing, and adding products to vaults. As the platform scaled, the team sought to better understand how users were interacting with products; what they browsed, added to the vault, and ultimately purchased. Through better understanding this they could improve on-site experience and conversion rates.
Challenges
Fragmented Data Across the User Journey: User behaviour was logged in multiple tables, making it difficult to gain a unified view of product interactions. Metrics like product views, vault activity, and time spent were tracked separately, making analysis difficult.
No Clear Visibility Into Product-Level Performance
While top-line visit numbers were available, granular insights—such as which products were most browsed, most added to vault, or most likely to convert—were missing. Without this visibility, the team couldn’t make informed decisions about product curation or layout design.
Solution
Data Stack and Transformation: Our data stack is built around Google BigQuery as the central warehouse, with dbt used for transforming raw data and GitHub Actions for orchestration. Metabase serves as the visualisation tool, while source data is pulled from Matomo. Using dbt, we modelled this data into structured, analytics-ready tables. Core entities such as visits, actions, and visit actions were joined to form a central fact table, creating a consistent foundation for downstream analysis.
Behavioural Metrics and Visualisation: From the transformed dataset, we derived a suite of behavioural metrics to better understand user engagement and product interaction. These included total visits, average browsing time, top categories and products, most added-to-vault items, and most clicked products. We also calculated key conversion metrics, such as the visit-to-vault conversion rate and the number of vaults sent via email.
The Proof Is In The Numbers...
>£100
35
4
Implementation
Improved Product Discovery: BigNoo now benefits from a unified and reliable source of truth for user behaviour, enabling the team to make more informed decisions around product placement and content relevance. By analysing which products are most frequently browsed and most commonly added to user vaults, the team can better understand what resonates with their audience. This insight allows them to prioritise high-interest products and categories, ensuring that users are consistently presented with more relevant and engaging content.
Enhanced Conversion Tracking and Iteration Speed: The introduction of detailed tracking for vault sends and user follow-through behaviour has unlocked a deeper understanding of customer intent and product desirability. By monitoring not just what users view, but also which products they save and later engage with, BigNoo can better interpret which items are likely to drive future action or purchases. This has introduced a new layer of behavioural insight, helping the team refine targeting and messaging strategies. Additionally, with self-serve dashboards powered by Metabase, cross-functional teams can now explore the data independently, validate hypotheses in real time, and make faster, data-backed decisions—eliminating bottlenecks previously caused by waiting for ad-hoc analysis.
