CASE_STUDY / PEG_B_TECHNOLOGY

Peg B Technology

Resolving a foundational B2C-versus-B2B disagreement with market data, then building the design token architecture and testing loop that shipped a localized e-commerce platform on a compressed timeline.

Jump to outcomes & impact
RoleUX Designer & Product Owner
MethodKantar BI, usability testing, A/B testing
ContextDubai, UAE
DurationJul 2024–Jan 2025
−40% Design iteration time Internal tracking
−25% User error rate Post-launch behavioral analytics
+10% Customer retention Post-launch A/B testing
Tools & methods Figma Tokens Studio Style Dictionary W3C DTCG token architecture Odoo Kantar Business Intelligence Usability testing Journey mapping A/B testing Behavioral analytics Agile / Scrum

Problem & research background

Peg B Technology needed a localized e-commerce MVP for luxury retail markets across the UAE. The scope was equivalent to a six-month senior engagement, compressed into an intensive delivery cycle, which meant there was no room for a slow start.

The slow start almost happened anyway. Before any interface work could begin, the team was split on a question that wasn't cosmetic: should the platform serve a B2C model or a B2B one? That single decision would determine the information architecture, the checkout logic, and the pricing structure underneath everything else. Building before resolving it would have meant designing the wrong product faster.

I brought in Kantar Business Intelligence data to ground the decision in evidence rather than whoever argued loudest in the room. That gave stakeholders a shared reference point to commit to a direction, instead of re-litigating it mid-build.

Evidence note: the specific market segmentation finding from the Kantar data isn't something I have recorded in detail here. What's confirmed is that it was the evidence base the stakeholder disagreement was resolved against, not the exact figures.

My role, process & key decisions

Key design decisions

  • Primitive → Semantic → Component token architecture, built in Tokens Studio and Style Dictionary. Brand and UI decisions lived in a system, not hardcoded across screens, so a change at the primitive layer could propagate everywhere it needed to without a manual rebuild.
  • Odoo as interim ERP. Rather than waiting on a bespoke backend, the team worked against a known system, keeping design and engineering moving in parallel instead of stalling on infrastructure.
  • Evidence-first resolution of the B2C/B2B question, instead of a design-by-committee compromise that would have hedged on both models and served neither well.

Artifacts & evidence

Ikigai Labs — Source honesty note

How this page is styled

This flow is a generic reconstruction: a plausible luxury-retail e-commerce journey built to exercise the actual decisions the case study names, not a recreation of Peg B's confidential UI. Treat it as a design brief, not an artifact of record.

Generic Ikigai Labs bento design reference render. Not Peg B's delivered interface, and not a recreation of any part of this engagement.
Ikigai Labs bento design reference render. Not Peg B's delivered interface, not a recreation of any part of this engagement.
Ikigai Labs design solution

User Flow: Peg B Technology

View the canvas in Figma (opens in a new tab)

Outcomes & impact

The three figures above appear here again with full source detail - the proof tiles up top are the at-a-glance version, this is the cited version.

Measured outcomes for Peg B Technology, with the source of each figure
MetricResultSource
Design iteration time (internal handoffs)−40%Internal tracking
User error rate−25%Post-launch behavioral analytics
Customer retention+10%Post-launch A/B testing

The platform launched on time and within budget. More importantly, the improvements didn't stop at go-live: the error rate and retention figures came from behavioral data gathered after launch, not projections made before it.