Category Assistant
Single store
“Which products are underperforming in my store?”
Data need: local performance vs. benchmarks
Low fluency
Retail analytics platform
Buyer Insights & Store Insights
Bringing customer-led data to every decision-maker across 2,000 Woolworths locations — from store assistants to national buyers
Client: Woolworths Australia Role: Design & Product Impact: 10+ user roles, 2,000 stores
Fragmented data. Siloed insights. Decisions made on incomplete information.
Across Woolworths — from a single store to headquarters — teams were making decisions without access to the data they needed. A buyer at head office used one analytics system. A store manager used another. A category assistant had none. Data existed, but it was trapped — locked in backend systems, inaccessible to the people who needed it most.
The challenge was not to build separate products for each user. It was to build one coherent system that could answer a store assistant's simple question (“Am I out of stock?”) and a buyer's complex question (“Should we alter our range strategy regionally based on customer segments?”) — using the same data, presented differently for each decision-maker.
The user ecosystem
| User Role | Location | Core Problem | Data Need | Technical Fluency |
|---|---|---|---|---|
| Category Assistant | Single store | “Which products are underperforming in my store?” | Local performance vs. benchmarks | Low |
| Merchandise Manager | Store / Regional | “Are we maximizing our shelf space?” | Regional range performance | Medium |
| Store Manager | Single to Regional | “What's driving sales in my region?” | Store performance, promotions | Low |
| Buyer | Head office | “Should we cut this product nationally?” | Deep category analytics | High |
| National Category Manager | Head office | “Optimal range for each region?” | Customer segmentation, regional data | High |
| Supplier | External | “How is my category performing?” | Product performance, trends | Low |
Merchandise Manager
Store / Regional
“Are we maximizing our shelf space?”
Data need: regional range performance
Medium fluency
Store Manager
Single to Regional
“What's driving sales in my region?”
Data need: store performance, promotions
Low fluency
Buyer
Head office
“Should we cut this product nationally?”
Data need: deep category analytics
High fluency
National Category Manager
Head office
“Optimal range for each region?”
Data need: segmentation, regional data
High fluency
Supplier
External
“How is my category performing?”
Data need: product performance, trends
Low fluency
The design challenge: Build a system that scales from simple to complex, using the same data, without feeling like multiple products.
The solution
One data source. Multiple entry points.
Store Insights
Simple. Fast. Actionable.
Store managers and category assistants need to understand what's happening in their stores right now. Store Insights starts simple — showing sales velocity, stock health, promotional impact, and customer demand in four clear cards. No jargon. No navigation. Just what matters.
Quick Insights Module: The entry point — sales trends, out-of-stock rates, promotion performance.
Core Checkout Module: The deep dive — customer profiles, basket analysis, store benchmarking, promotional ROI.
Four-card entry (concept)
Sales velocity
Stock health
Promotions
Customer demand
Buyer Insights
Data-driven. Category-focused. Commercial.
Buyers and category managers need to understand category performance across 2,000 stores and make strategic range and promotional decisions. Buyer Insights starts with strategic questions: “Should we keep or cut this product? Where is this category underperforming? What's the optimal range for each region?”
Strategic Dashboards: Category performance, regional breakdowns, promotional ROI.
Quantum Checkout: Customer behavior segmentation, cross-category insights, competitive intelligence, forward indicators.
Strategic dashboards (concept)
Shared data foundation
Same data, different perspectives
Unified architecture (concept)
Multi-level design
How we scaled a system across 10 different user types without feeling like multiple products
01
Consistent Data Semantics
Every user — regardless of level — saw the same underlying data. A “sales velocity” metric meant the same thing for a store assistant and a buyer. We just presented it at different aggregation levels.
02
Progressive Disclosure
Show the right complexity at the right moment. A store manager saw their store first. When they asked “Why?”, we showed regional context. When they asked “Why that?”, we showed customer data.
03
Role-Based Layouts, Not Separate Products
The system recognized who was logging in and presented the relevant entry point. But every user could navigate to other perspectives. A supplier could see category performance. A buyer could drill into a single store. The system was coherent.
04
Standardized Interaction Patterns
Hover for detail, click to explore, filter to narrow, export to use elsewhere. Users who knew one module could navigate another without relearning.
Hover
Click
Filter
Export
Design for retail decision-making
Fast. Intuitive. Mobile-first where it matters.
Fast to Parse
Colour coding: green (good), amber (watch), red (action) — users recognised patterns in seconds. Data hierarchies: most important insight first. Scannable layouts: never more than 4–5 key metrics on screen.
Intuitive Without Training
Chart types matched mental models: time series for trends, bar charts for comparisons, heatmaps for regions. Labels in plain English. Every metric explained on the page.
Line — trends
Bars — compare
Heat — regions
Mobile-First
Store managers checked insights on phones. Core dashboards were designed mobile-first; desktop was enriched. Touch-friendly, readable on small screens.
Extending insights beyond headquarters to suppliers
Suppliers needed visibility into how their products performed at Woolworths — which stores, which customer segments, promotional effectiveness. But they couldn't see overall category strategy or Woolworths' internal decisions.
We built a supplier portal structurally identical to Buyer Insights, but with role-based access controls: only their products visible, only their assigned categories visible, performance data visible, strategy data hidden.
From a supplier's perspective: deep, actionable insights about their business. From Woolworths' perspective: airtight data governance.
Impact & outcomes
Measurable impact across four stakeholder groups
Store Teams
10s
From “request a report” to insight
- Speed to insight: 2–3 days → 10 seconds
- Empowerment: data-driven decisions without HQ
- Action: same-week response to underperformance
Buyers
2,000
Stores analyzed simultaneously
- Confidence: decisions grounded in customer data
- Scale: optimise range nationally without manual grind
- Speed: 4-week cycles → 2-week cycles
Suppliers
Transparency
First-time visibility into actual performance
- Visibility into real-world performance
- Partnership: data-informed conversations
- Commercial: stronger range → stronger shelf share
Woolworths
Competitive
Advantage through speed of decision-making
- Customer-led decisions across the network
- Operational speed at scale
- Margin: smarter range, promotions, faster exits
Beyond the product
Bringing design thinking to an operationally-focused business
Retail doesn't naturally think in “user experience” terms — it thinks in operational efficiency and margin. The work here involved:
- Building empathy for decision-makers — designing for actual fluency, not an idealised analyst persona.
- Making data accessible, not simplified — right complexity when needed.
- Scaling design thinking across role levels — coherent for assistant and national buyer alike.
- Partnering with commercial and operations — grounded in how retail actually works.
Why this matters
What this work demonstrates
Multi-Stakeholder Product Thinking
Designing for 10+ distinct user types with competing needs — from a single store to 2,000 locations; technical to non-technical audiences.
Complex Information Architecture
Structuring data to scale from simple to sophisticated without feeling like multiple products — progressive disclosure and consistent semantics.
Data Product Design
Translating complex analytics into actionable insight for non-specialists — retail domain expertise and measurable commercial impact.
Product design for scale.
Data experience for everyone.
This case study shows how to bring sophisticated data insights to decision-makers at every level of a complex, distributed organisation — without compromising either depth or usability.
Let's talk →