A leading consumer robotics company engaged us to build scalable analytics capabilities that support new product and feature launches. Each release required robust data practices and seamless cross-functional coordination. By leveraging established KPI baselines, we enabled deep insight into product performance and ensured every rollout was both measurable and efficient.
- Outcome 1: Established a comparative metric system across nine dimensions, including technical performance, user engagement and mobile app usage
- Outcome 2: Created four product-specific measurement domains to assess feature value and guide future investment decisions
- Outcome 3: Enabled deductive metric completion for products lacking intrinsic performance or engagement tracking
The challenge
Our client, a pioneer in consumer robotics and a key contributor to the advancement of AI, needed to continuously evolve its products to stay ahead in a fast-moving, competitive market.
The main challenge was the rapid and ongoing evolution of consumer robot capabilities, which are critical to maintaining a competitive edge. Their innovative engineering teams required timely feedback on newly developed product capabilities, necessitating the creation of fresh metrics to measure user engagement and feature effectiveness.
This all had to be achieved while preserving core product functionalities – also requiring measurement – and ensuring meaningful comparison across successive releases. The analytics function had to balance maintaining existing metrics with building new frameworks for performance evaluation.
The approach
We focused on building persistent, scalable metrics that could adapt to product changes while remaining comparable across releases. These metrics were designed to reflect real-world user interaction and evolving product capabilities.
Our approach began with a deep understanding of the underlying data – what it represented, how it was generated and how it related to metrics from prior releases. We refined data pipelines to align with updated requirements and implemented a medallion data architecture across multiple layers.
Using a test product population, we ensured that KPIs were accurately captured and ready to scale to the production fleet. This method supported robust analytics while allowing for rapid adaptation to product changes.
The value delivered
Our work enabled the creation of a resilient metric system capable of supporting multiple product releases within short timeframes. New metrics could be easily added to existing dashboards, giving decision-makers real-time visibility into performance.
All metrics were structured by product area and linked to relevant entities such as users, robots, software and mobile apps. This framework enabled cross-functional comparisons, offering a holistic view of product performance.
Combined with the medallion architecture and deductive metric capabilities, our solution allowed metrics to be developed in parallel with pipeline completion – accelerating time to insight and supporting more agile product rollouts.