As the global fashion market becomes subject to ever-faster shifting customer demand, the pressure is on for retailers to make smarter, faster pricing decisions. Our client, an S&P 500 fashion brand, was struggling to keep prices aligned with changing market conditions.

QuasiScience created a practical pricing model that helped them adjust prices confidently, incorporating currency fluctuations, stock levels, and expected demand. This increased revenue by 4%.

FX and regional demand are exerting a significant pressure on margins

— Jean‑Jacques Guiony, CFO of LVMH, an industry leader

Fashion Fades

The fashion industry, and in particular fashion’s eCommerce landscape, is becoming harder to navigate. Prices are updated rapidly and global currency movements affect the real value of every transaction. For retailers selling across borders, small shifts in exchange rates or demand can have a massive impact on profits.

Our client faced this challenge daily: prices set one day no longer made sense the next, and differing stock levels across regions meant some products sold out too quickly while others were overproduced. Without a clear way to stay ahead of these shifts, the team was leaving value on the table.

Evergreen Solution

Using advanced simulation and optimisation mathematics, QuasiScience developed an easy-to-use pricing model that enabled our client to adjust prices as conditions changed. Our model provides:

• Automatic price optimisation: Practical recommendations that balanced attractiveness to customers with revenue goals and stock availability.
• Scenario-based options: Simple scenario-based views so the team could model how different pricing choices might affect revenue as the context changed.
• Evidence-based decision making: Easy-to-use alerts indicated when and how prices should be updated.

The tool took the guesswork out of pricing optimisation, and enabled our client to navigate an uncertain market with confidence.

Know Your Assets

The tooling developed by QuasiScience transformed the team’s work. It led to:

• 4% revenue increases during peak seasons and high-volatility periods.
• More consistent pricing decisions, reducing the guesswork that previously led to lost margin or missed sales.
• Better alignment between teams helping teams maximise revenue across the organisation.
• A stronger long-term pricing foundation, opening the door to broader revenue-management improvements.

And this tool is not only relevant in retail - it has broad applications across every B2C sector. QuasiScience is now developing similar tools for clients across industries.