I designed and brought to life a robust analytics platform that transforms fragmented IoT and navigation data into actionable intelligence for product managers and OEM partners. Built with a deep understanding of user pain points and existing solution gaps, the platform empowers users to gain real-time product insights, reduce research time from days to minutes, and unlock revenue opportunities through behavior-based analytics.

Navigation product stakeholders struggled with:

• Disparate reporting systems: Data scattered across OEM logs, app usage stats, and third-party IoT feeds
• Slow strategic cycles: Synthesizing data for roadmap decisions took hours to days
• Lack of cross-segment clarity: Hard to compare performance across devices, regions, or firmware versions
• Minimal insight into promoted content revenue opportunities

The absence of a unified view limited the ability to act quickly on usage trends, reduce churn, or optimize monetization strategies.

Lead designer on the team.

• Conducted in-depth competitive benchmarking (TomTom, Inrix, SmartCar dashboards, etc)
• Defined user personas, use cases, and KPI hierarchies
• Collaborated with PMs, IoT data engineers, developers from Lineate and product analysts
• Created end-to-end journey maps, wireframes, and interaction prototypes

During the alpha release design monitored the the Operational Efficiency Percentage metric, ensuring measurable ROI from design interventions

Team Composition:

• 1 Product designer
• 2 Frontend engineers
• 1 Data engineer
• 1 Product Manager
• 2 Backend engineers

I followed a data-first, role-based dashboard strategy optimized for discovery, exploration, and decision support.

Key activities

• User Journey Mapping: Charted user’s workflow from event detection to strategic action

• Modular Dashboard Architecture: Designed components for system usage, regional performance, content interaction, and user segmentation

• Real-Time Event Layer: Prioritized the “Event Overview” as the landing metric—users could instantly see anomalies, top segments, and volume trends

• Filtering & Drill-down: Designed quick and smart filters (time, region, firmware version, device model) and multi-level drill-downs

• Expiremented with behavioral insight cards: Added smart-generated nudges like - “Usage of promoted routes surged in Orange county after software version 2.3.4 — investigate content placement."

• Integrating heterogeneous IoT data from legacy and modern devices

• Designing for both exploratory analysis and quick answers without overwhelming users

• Balancing OEM needs (macro analytics) with internal product roadmap needs (micro trends)

• Ensuring response times stayed under 3 seconds even for complex filters

The final platform featured a multi-layered analytics interface optimized for:

• Event Overview: Real-time system activity, high-usage patterns, failure spikes
• Content Interaction: Promoted content usage heatmaps and conversion tracking
• User Segments: Geo-device-firmware breakdowns with behavioral markers
• Performance by Region: Distribution of events and latency KPIs by geography
• Revenue Optimization: Overlay of usage patterns with sponsored content and user attention metrics
• Segment-aware filtering: Preserved state across dashboards
• Quick Compare Tool: Visualize multiple user cohorts in a easy and consumable format for the users
• Mini-insight cards: Smart contextual flags and suggestions
• Custom alert builder: Users could set behavioral or performance-based triggers based on the product insights

• Decision time dramatically dropped from a few hours to a few minutes. On an average that resulted in about ~90% faster decision making

• Research time went down  by upto ~80% and accuracy went up by a staggering 200%. It was achieved by now measuring regionwise user churn using pattern driven flags. 

• The platform had achieved a unified KPI language across OEM partners and product managers. 

• This also meant there was reduced reliance on data teams for simple insights

• It enabled proactive roadmap pivots based on behavioral intelligence

• The best part was it fostered deeper OEM collaborations due to transparent shared analytics

This platform turned raw navigation telemetry into a high-impact product and business intelligence engine, helping users make informed, timely, and strategic decisions that shape both user experiences and revenue outcomes for the business.

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