Qogita

Creating a smoother wholesale trading experience for first-time and returning customers, boosting GMV and retention.

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Overview
I led a number of  key features at Qogita, turning a complex AI‑driven wholesale engine into a simple, trustworthy buying experience for retailers across Europe.
Role — Lead Product Designer
Key results
+18% first‑time checkout completion.
2.2× average monthly repeat orders (from 1.0).
1.8× monthly GMV growth.
50% fewer reports of buyer confusion.
Discovery
Why do customer research? Checkout is a many‑to‑many problem, so we needed to link specific problems, users, and solutions for both light and power users.
To understand wholesale buyers, I ran interviews, surveys, and behavior analysis. We found they think like traders, driven by scarcity, risk, and margins. Marketplace buyers are opportunistic and data‑driven, while retailers want stable prices, reliable stock, and trusted suppliers.

We learned that end users in a two‑sided marketplace follow the same core workflow: they research, screen, diligence, decide, execute, manage, exit, and learn. This pattern shows up in any market where people evaluate assets under uncertainty.
Learnings
Buyers behave like traders, optimizing around scarcity, risk, and margins.
Marketplace buyers are opportunistic, data‑driven, and comfortable with volatility.
Retailers prioritize stability: consistent pricing, dependable stock, and trusted suppliers.
Marketplace buyers seek upside from scarce, discounted, or competitive deals; retailers stick with known products.
Problem
Buyers placed large orders on Qogita but kept seeing prices and availability change without explanation. This exposed a wider lack of transparency and control across the wholesale buying journey.

I worked with PMs and engineers to give buyers more clarity, control, and confidence by improving navigation, simplifying checkout, and strengthening watchlists to drive re‑engagement.
Insights
10–15% of cart items couldn’t be checked out because of hidden supplier constraints.
Buyers didn’t understand why Qogita’s AI changed prices and quantities, leading to frustration and abandonment.
The order flow felt opaque and confusing, with low perceived transparency.
Buyers struggled to discover key categories and brands due to weak catalog visibility and navigation.
A handful of badly priced products damaged trust in the wider catalog.
Solution
We shipped 100+ releases to make checkout clearer, give buyers more control, and cut confusion.
We designed flows for each profile: new buyers got a simple, mobile-friendly path for test orders, while power users got advanced tools like grouped suppliers and MOV tracking for large, complex carts.
Key features
Fixed Pricing to lock prices during allocation so carts stop “moving around” at checkout.
Supplier grouping and MOV tracking so buyers can adjust quantities per supplier and still hit order thresholds.
Supplier inventory view to quickly add or swap items inside the locked portion of the cart.
Price and quantity alerts that prompt buyers to update carts when products hit their target levels.
Takeaways
Prioritize purpose before process or pixels.
Anchor software design strategy in systems that empower human control.
Maintain rigorous quality standards ruthlessly, but go lean.

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