INTELLIGENCE DATA SYSTEMS - AI
Actionable Retail Intelligence: NIQ Dashboard Leakage Tree - 5-level drilled down funnel to prevent loss, with Confidence Indicator, Citation and Smart Alerts
Retailers generate enormous volumes of transaction data. What they rarely have is a clear line from performance drop to recoverable opportunity — and the trust to act on what they're seeing.
I designed the NIQ Retail Vantage dashboard to close that gap: a leakage tree workflow that makes five levels of insight navigable on a single surface, paired with a trust architecture — confidence indicators, verifiable citations, and rule-based alerts — that was already speaking the language of AI before we had words for it.
23K Global B2B clients
Case Study Map
THE WHOLE STORY, FAST
5 Min Read Version
TL;DR
Context
NIQ's Vantage dashboard gives enterprise retailers a single view of category performance — built on 60B daily data points across 100+ products and 95 countries. I designed the Leakage Tree analytics view and Smart Alert system for the NIQ Retail Dashboard.
Insights
The real gap wasn't data volume — it was trust and actionability. Retailers could see a KPI drop. They couldn't trace where the lost sales went, and the system never surfaced signals until someone went looking. Two compounding problems, one surface to solve them on.
Solution
Designed the Leakage Tree and Smart Alert system in Vantage. The Leakage Tree traces where lost sales went — a five-level drill-down that carries full context from top-line performance to root cause without a single context reset. Backed by three trust layers: verifiable data citations, inline confidence indicators, and rule-based smart alerts delivered in-app and by email.
Impact
This unified experience reduced analysis time from hours to minutes by eliminating context-switching and mental fatigue. It proved immediately scalable, driving real-time, data-backed decisions for over 20,000 global enterprise users.
Context
NIQ Retail Dashboard is built on one of the world’s largest retail transaction datasets — millions of UPCs, hundreds of markets, and 900+ metrics. Vantage is where retailer users land first, before reaching any module in the suite — it reveals not just how a retailer performs within their own stores (T-log - transactional data), but how they compare against the broader market (benchmarking).
Vantage hence covers a broad view of 5 distinctive retailer personas before users dive deeper in next detailed-report level modules: Advanced Analytics, Assortment, Data Portal, HUB (Custom-report), Insights, Price, Promo, and Supply Chain.
The Vantage dashboard surfaces key KPIs with seamless drill-downs into three core views: Performance Overview, Vendor Scorecard, and Leakage Tree. This case study focuses on the KPI alerting system and the Leakage Tree experience.
Prototype | NIQ Retail Dashboard side-navigation with 8 modules
Challenges
Retailer users faced two compounding gaps: an operational blind spot obscuring where lost shoppers went, and a reliance on lagging data.
Their own transaction data revealed what sold inside their stores — but nothing about where lost shoppers went. And when key metrics shifted, they found out too late: reporting cycles meant users were always reacting, never anticipating. The work required moving users from passive monitoring to proactive action, real-time metric triggers that enabled immediate, data-driven action.
The design challenge matched the complexity: Answering these questions required a five-level analytical workflow, but existing design patterns couldn't hold it. Decomposition trees broke under screen constraints. Sequential drill-downs lost context between levels. Everything needed to live in one surface, simultaneously, without page navigation or cognitive overload.
Solutions
The Leakage Tree
Four directions were explored. Option A - separate tabs — caused visual context loss between levels, and silently dropped selection parameters at navigation boundaries. Option B, C - a decomposition tree — broke under screen width and height constraints with no clear action path.
The decision, the Leakage Tree, resolved both: five drilled-down levels of analytical hierarchy visible simultaneously, in one surface, with no navigation event and no context drop.
The result in one sentence: 236M shoppers → 20M shop at Walgreens → buy Hair Care → 99% convert → but only 3% share of wallet stays → the rest goes to specific competitors.
Prototype | End -to-end Dashboard user journey experience
Prototype | End-to-end Leakage Tree taskflow
Solutions: Embed future-proof AI UX and Infrastructure:
Smart Alerts, Citation, Confident indicator and Conversational language
Building the Trust Layer: Pioneering AI UX Patterns Beyond the Leakage Tree, I embedded foundational patterns throughout the experience to build a robust layer of user trust.
Data Transparency: Source Citations ➔ Grounded output with clear attribution.
Smart Alerts: Rule-Based Monitoring ➔ Agentic monitoring and triggered insight delivery.
Confidence Indicators: Sample Size Signals ➔ Surfacing uncertainty and confidence at the point of consumption.
Conversational Language: Data Selection Summaries ➔ Bridging the semantic layer. Structuring queries in a natural, logical hierarchy laid the exact infrastructure that later supported 19 distinct AI agents.
All three were designed in 2021. The vocabulary arrived later. The architecture was already there.
Prototype | AI UX patterns
NIQ massive global commerce data layer is cleansed, harmonized, and semantically structured so autonomous AI systems can ingest, reason over, and execute decisions in real time. Both the infrastructure and the workflow across the NIQ suite closely mirror how modern AI systems operate — and the data structure itself is AI-agent-ready:
Clean semantic layer: Data are already mapped to the business logic so agents instantly understand product attributes, hierarchies and metrics
Real-Time Data Velocity: NIQ’s data streams continuously and in near-real-time — critical for immediate action
Agent-to-Agent and API Connectivity: The whole platform is built on modern integration protocols, so even external AI systems can plug in directly to the intelligence stack
Massive scale & Proven models: Backed by data covering billions in consumer spend and thousands of AI models running daily, the foundation provides the precise, trustworthy ground-truth data that agents need to function reliably in production.
HOW I ACTUALLY SOLVED IT
Deep Dive
At a Glance
This case study focuses on two features within Vantage — a smart alert system and the Leakage Tree — built to move users from passive monitoring to confident, localized action.
Duration
4 weeks
Role
Product Designer Lead
Design System owner
Scope
Design Vantage view - Leakage tree to all 5 retailer personas & across 8 modules
Integrate Alerts - a NIQ platform feature
Scale & Impact
Platform/ Systems level
Costco, Walmart + B2B retailers
60B data points, 95 countries
Pilot launch with: Rite Aid, Jumbo, CVS
Positioned as the strategic launchpad of the retail user journey, Vantage connects top-line visibility with deep-dive analytics. Before navigating to detailed modules like Assortment or Supply Chain, users are equipped with a high-level KPI overview driven by real-time smart alerts. By surfacing market benchmarks, vendor scorecards, and leakage tree opportunity gaps upfront, Vantage empowers users to pinpoint critical action areas before seamlessly transitioning into targeted reporting.
User journey | Vantage Dashboard as launchpad before user takes action or navigates to child modules
NIQ Retail Platform—is designed to handle massive amounts of omnichannel consumer data. Because it offers everything from high-level market measurement to granular 1:1 personalization, it serves several distinct roles within both retail organizations and Consumer Packaged Goods (CPG) companies.
NIQ Retail | 5 personas
Alerts and the Leakage Tree are the combined core of Vantage — this case study covers both, with the deep dive centered on the Leakage Tree.
Alerts appear directly on the Retail dashboard when users land. They play a critical role in triggering immediate action for each persona. By surfacing risks and anomalies the moment they emerge — and tailored to every user’s own conditions and thresholds — smart alerts help prevent lost sales and eroding market share. They give each role the seconds that matter to deploy a strategic response, readjust their parameters, or refocus on the right shoppers and buyers.
NIQ Retail | Design alerts to each persona
Alerts - Design & Prototypes - Create Alerts
Flow 1: Create alerts
I made the decision to make alerts customized and tailored-made to each user. The alert creation flow is chosen to start right from the key KPI, which are tied to each users' preference.
Alerts | Create Alerts prototype
Alerts - Design & Prototypes - View & Manage Alerts
Flow 2: View & Manage alerts
Alerts | View & manage Alerts prototype
Leakage Tree - Context
Within the platform’s ecosystem, standard views like the Performance Overview and Vendor Scorecard effectively tell retailers what is driving their current performance. However, a critical analytical gap remained: pinpointing exactly where lost sales were going. To solve this for multiple distinct user personas, I designed the Leakage Tree—a cascading diagnostic framework where each progressive layer answers a specific behavioral question. Rather than forcing users to pull and synthesize dozens of fragmented reports, this single view immediately surfaces points of risk and revenue loss, enabling users to transition instantly from high-level analysis to targeted, real-time action.
Leakage Tree| Structure & data
Leakage Tree to each persona
This 5-level leakage tree is a powerful diagnostic framework. In UX terms, it’s a funnel that tells the user exactly where money is walking out the door and to whom.
Because this tree relies on comparing a specific retailer ("their store") to the rest of the market ("elsewhere"), the value of this data shifts depending on whether the user works for the retailer or the brand.
Leakage Tree| Design Leakage Tree to each persona
Leakage Tree - Three options were removed, and here is why
Option A: Independent Tabs — Maximized Space vs. Fragmented Context
This approach utilized a tabbed structure to navigate between the layers of the leakage tree. While this successfully solved the screen real-estate constraints—providing ample room to cleanly present complex graphs and charts—it introduced a critical usability flaw. During testing, I observed that the intended drill-down workflow felt disjointed. Users struggled to maintain the mental connection between data points across different tabs, ultimately losing the broader context of the tree.
Leakage Tree| Option A: Independent Tabs — Maximized Space vs. Fragmented Context
Option B &C : Decomposition Tree — Relational Clarity, User's Cognitive Load vs. Spatial Constraints
This approach utilized an existing decomposition tree structure, where top-level data physically branches out into its detailed, drill-down containers. While this successfully preserved the contextual relationship between data points—addressing the primary flaw of Option A—it introduced severe real-estate limitations. The branching layout constrained the visualizations both vertically and horizontally, forcing users to rely on excessive scrolling and preventing them from seeing the complete picture at a glance. Additionally, surfacing all hierarchical information upfront created a significant cognitive load, overwhelming the user rather than guiding their analysis.
Leakage Tree| Option B: Decomposition Tree — Relational Clarity, User's Cognitive Load vs. Spatial Constraints
Leakage Tree| Option C: Decomposition Tree — Relational Clarity, User's Cognitive Load vs. Spatial Constraints
Design Decision
Leakage Tree - Decision 1: Resolving Cognitive loads via Progressive Disclosure
To solve the spatial issues inherent in my earlier models, I converted the horizontal tabs into a vertically collapsible component. This gave users the flexibility to manually open or close relevant layers, keeping them in control of their workflow. Not only did this successfully fit all five critical layers within the initial viewport, but it also minimized cognitive load by preventing the user from being overwhelmed with upfront data.
Prototype 1: Progressive Disclosure in Action This interaction allows users to manually expand and collapse individual layers, keeping them in control of their workflow and minimizing upfront cognitive load.
Leakage Tree| Solution: Progressive Disclosure in Action
Leakage Tree - Decision 2: Resolving Spatial Constraints via Interaction Design
By dividing the screen into two halves and treating each level's container as a clickable area itself, users can access relevant charts by interacting directly with each layer. This keeps the relationships between levels intact while maximizing the actual screen real estate users have available for each level's data.
Prototype 2: Interactive Data Containers By treating each level as a clickable area, users can interact directly with the layer to instantly access its full-scale, contextual graph without leaving the main view.
Leakage Tree| Solution: Interactive Data Container
Leakage Tree - Final Prototype - Effectiveness & Flexibility
Combining my spatial and interaction decisions resulted in a final prototype that maximizes both effectiveness and user flexibility.
In this final iteration, the interface balances cognitive load with optimized real estate. Users are no longer required to dive into dozens of separate reports; instead, they are given a holistic view of opportunity gaps that surfaces root problems and drives immediate action (e.g., triggering a personalized retention campaign to regain lost market share).
Through progressive disclosure, users have full control to expand, collapse, and drill down into specific layers. By treating each level as an interactive container, clicking a layer instantly reveals the associated full-scale graphs. This allows users to easily access top-level data while maintaining a clear understanding of where they are within the overall tree.
Prototype 3: The Complete Hybrid Workflow Prototype The final solution combines both interactions. Users can freely expand specific layers and click to reveal relevant graphs simultaneously, delivering maximum analytical flexibility within a single, optimized viewport.
Leakage Tree| Solution: Interactive Data Container & Progressive Disclosure
WHAT IT MOVED, WHAT I LEARNED
Impact & Reflection
Impact
Platform Impact: Transforming Data into Immediate Action
As foundational, platform-level features, the Leakage Tree and Smart Alerts served as the primary entry point for the retail user journey. Together, they shifted user behavior from passive, fragmented data exploration to guided, real-time strategic decision-making.
Quantitative Business & Usability Impact
Time on Task Reduced (Hours ➔ 15–30 Minutes): Replaced manual exploration across 45–70 isolated report views with a single, unified view, enabling users to isolate root causes in minutes.
Risk Mitigation Speed (Days ➔ Hours): Automated real-time alerts processing billions of data points across 21M–22M stores, empowering users to adjust strategies instantly before losses compounded.
Cognitive Load Minimization: Compressed multi-layered data hierarchies into a single viewport, eliminating context-switching and mental fatigue.
Global Enterprise Scale (20K–23K B2B Clients): Proved immediate value with tier-one global retailers (including Walmart, CVS, Rite Aid, and Jumbo), leading to platform-wide adoption across the NIQ client base and integration into the flagship NIQ Discover platform.
Qualitative Experience & Trust Layer
End-to-End Opportunity Funnel: Delivered a unified visual funnel that directly isolates lost sales, shopper behavior shifts, and KPI-segmented performance gaps.
From Static Data to Interactive Dialogue: Leveraging embedded AI patterns—source citations, rule-based monitoring, confidence indicators, and conversational language summaries—the interface turned standard reporting into a responsive, two-way interaction model.
Architected Trust for Executive Decisions: Surfacing confidence signals and data origins directly at the point of consumption gave retail leaders the trust and clarity needed to execute high-stakes commercial actions immediately.
Reflection
Balancing complex data with intuitive UX across 5 distinct user personas brought critical takeaways:
Architecting Trust Across Personas (Sample Size > Citation Alone): Designing for 5 distinct user personas revealed that while source citation builds initial credibility, leaders still hesitate without knowing data volume. Embedding confidence indicators and sample size signals directly at the point of consumption gave users the explicit trust needed to make high-stakes commercial decisions.
Progressive Disclosure & Intentional Workflows: Early design iterations failed because they attempted to surface every data point simultaneously. Anchoring the user journey to a clear narrative flow—moving intentionally from Signal ➔ Detailed Analysis ➔ Comparative Data via progressive disclosure—dramatically improved user comprehension and task completion.
Mastering High-Density Information Hierarchy: Resolving extreme screen constraints required aggressive visual chunking and hierarchical organization. Guiding the user’s eye to the meta-level first, while keeping granular data scannable, proved to be the single most critical factor in preventing cognitive overload.
The Symbiosis of Graphs and Figures: Data visualisations and raw numerical tables shouldn't compete for real estate; they serve distinct cognitive functions. Raw figures state immediate facts, while graphs communicate historical trends and broader narrative context. For this high-level executive dashboard, both elements are essential to turn raw data into strategic action.