CASE STUDY 01 — PRODUCT & UX LEADERSHIP

Competitor
Intelligence
Cockpit

How I helped senior leadership go from days of waiting to instant competitive insight — by building a product from the ground up.

Large UK Retail Group
Product & UX Lead
2024 – 2025
Internal Analytics Product
01The Problem

Senior execs were flying blind on competition

Imagine being the CFO of one of the UK's largest retailers. You want to know, right now, how your stores are performing against a key competitor — across products, customers, and store footfall.

The old process meant reaching out to an analyst team, explaining what you needed, waiting days while they pulled data from multiple internal systems and external providers like Kantar — then going back and forth on clarifications. A simple question could eat up a week. By the time the insight arrived, the window for action had often closed.

The data existed. The problem was there was no self-serve way to access it — quickly, confidently, and independently.

This wasn't a data problem. It was a product problem. And it needed a product solution.

02My Role

Ideation to launch — across product, design, and engineering

I joined at the ideation phase. My contribution spanned the full product lifecycle: helping define what the product should do, shaping the data narrative it would tell, deciding which indicators would genuinely matter to leadership, and co-designing the experience in Figma before a line of code was written.

Once wireframes were validated with actual business users, I led technical delivery — architecting the backend data layer, integrating 5+ internal and external sources, and guiding the engineering team to ship something that felt fast and trustworthy.

01

Problem Framing

Mapped leadership's actual decision-making needs, not assumed ones

02

Ideation

Defined data stories, KPIs, and which indicators actually mattered

03

Wireframing

Designed end-to-end in Figma before engineering began

04

Validation

Reviewed with actual business stakeholders. Incorporated feedback.

05

Build & Ship

Led technical delivery end to end and launched

03Key Decisions

The hard calls that shaped the product

We navigated real ambiguity at every stage — from what to measure to how to build it. These were the decisions that mattered most.

DECISION 01

MVP on Tableau First

Rather than over-engineer upfront, we validated the concept on Tableau. Only once we confirmed value did we rebuild as a scalable web application. Classic MVP thinking — learn before you invest.

DECISION 02

Bridging External + Internal Data

The hardest integration challenge: making Kantar (external market data) talk to internal systems. We architected a unified aggregation layer that made this invisible to the end user.

DECISION 03

What to Measure

We debated standard industry metrics vs. proprietary indicators specific to this retailer's competitive context. We ultimately built both — industry benchmarks for comparison, custom metrics for insight.

DECISION 04

AI-Powered Narrative Insights

We added an LLM layer — feeding structured data to generate plain-language narrative insights alongside charts. The "so what" became instantly visible, removing the last mile of friction.

04Impact

From days of waiting to instant answers

The Cockpit launched successfully and was immediately adopted across the senior leadership team. The response validated the product thinking — and generated new questions we hadn't anticipated, which shaped the next iteration.

Days → Hours Reduction in time-to-insight for competitive analysis
5+ Disparate data sources unified into one seamless experience
Zero Manual analyst requests needed for competitive intelligence post-launch

Directors didn't just use it — they engaged deeply enough to ask new questions. That's the signal that a product created real thinking, not just answered old ones.

Team Excellence Award — November 2025
05Learnings

What I took away

  • Shipping an imperfect MVP on Tableau first was the right call. The validation made every subsequent engineering decision more confident and more precise.
  • The most valuable product decisions weren't technical — they were about what not to build. Defining the right indicators mattered more than the architecture underneath them.
  • User validation before build isn't just a best practice — it rewrites the engineering brief. We went in far more aligned because of those early Figma conversations.
  • AI-generated narrative insights fundamentally changed how leadership consumed the product. Combining data with plain-language interpretation removed the last mile of cognitive friction.
  • When users start asking new questions after launch, the product has done its real job — it created thinking, not just delivered answers. That's the bar worth aiming for.