Digital twin for pricing optimisation

At a Glance

• Client: Spare Chair
• Sector: Revenue management for salons, barbers, and beauty professionals
• Scope: Simulation-based pricing experimentation and optimisation
• Key capabilities: Digital twin simulation, pricing strategy testing, data-calibrated scenario modelling
• Delivery: End-to-end design, build, and calibration of a simulation environment for pricing experimentation

Who Spare Chair Are

Spare Chair is a revenue management platform for salons, barbers, and beauty professionals. They help businesses boost chair utilisation and grow profit by intelligently adjusting prices based on demand.

Background

Spare Chair was building a pricing engine for salons and barbers. They had onboarded a handful of pilot businesses and were collecting real booking and sales data, but the volume was small.

Pricing decisions have real consequences. Drop the price of a Tuesday morning slot and it might fill faster, or it might put customers off entirely. With only a few pilot partners, every live test carried risk and took time to learn from.

The Problem

Spare Chair needed to test different pricing strategies across many scenarios to build confidence in their approach. But the pilot data couldn’t support that kind of experimentation on its own.

Too many questions remained open: How sensitive are customers to a 10% price drop? How often do they rebook? What happens when a competitor cuts their prices? A handful of real businesses couldn’t generate answers fast enough.

The Objective

Find a way to test and validate pricing strategies at scale, without needing months of additional real-world data or putting pilot partners’ revenue on the line.

What I Delivered

I designed and built a digital twin of the salon booking experience, calibrated to the real data Spare Chair had from their pilots.

The simulator modelled how customers respond to pricing changes: whether they book, when they book, and the downstream effects on chair utilisation and revenue. It captured factors a business owner can control, like pricing strategy, alongside ones they can’t, like competitor behaviour and customer preferences.

The gold standard for testing pricing strategies is A/B testing with real customers. But that requires volume Spare Chair didn’t have yet, and every live test carries real risk to the businesses using the platform if a strategy underperforms. The digital twin gave Spare Chair something close: controlled experimentation at a scale live testing couldn’t yet support, without putting real revenue on the line. It’s not a perfect substitute (simulated customers aren’t real ones) but because every scenario was calibrated to actual pilot data, the results stayed grounded in how customers really behave.

We used it to test many different pricing strategies across a range of scenarios. Each scenario varied assumptions about customer sensitivity, demand patterns, and competitive dynamics. Rather than searching for one “correct” model of how customers behave, we explored many plausible versions of reality and looked for the pricing approach that held up across all of them.

This meant Spare Chair could narrow in on the strongest strategies in simulation first, then validate them with real businesses with much more confidence about what to expect.

Outcome

• Spare Chair could evaluate pricing strategies at a scale that live testing alone could never have supported.
• We identified a pricing model that performed consistently across a broad set of market conditions, giving much stronger confidence than the pilot results alone.
• The simulation framework is reusable, so Spare Chair can run the same process again as their business grows and conditions change.

“Working with Liam has been exceptional. The external consulting support he provided to Spare Chair was genuinely invaluable. He quickly understood the complexity of what we were building and brought a level of strategic and technical clarity that made a real difference. His work gave us the ability to test and validate pricing strategies in a far more sophisticated, low-risk, and scalable way than would have been possible through live testing alone.

What stood out most was Liam’s ability to take a difficult, data-heavy challenge and turn it into something structured, practical, and highly insightful. He didn’t just give advice from the outside, he delivered meaningful work that strengthened our confidence in the product and helped move the business forward in a tangible way. The quality of his thinking, the professionalism of his delivery, and the impact of his contribution were all first-class.”

— Luke Mulhall, Founder @ Spare Chair