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Rail · Revenue management UK long-distance operator RMS build & transformation

Rebuilding rail revenue management from the ground up — and beating the targets.

A long-distance rail operator was about to outgrow its revenue management system. I led the programme to replace a failing legacy platform with a purpose-built RMS — designing the approach, proving the analytics and embedding it with the team. It delivered a 3% revenue uplift against a 2% target, with pricing automated across more than 80% of journeys.

3%revenue uplift, against a 2% target
15xreturn on investment over three years
>80%of journeys with pricing fully automated
35%improvement in demand forecast accuracy
The operator

A high-volume operator with rising expectations on both sides.

The business carried a mix of commuter and leisure passengers across thousands of unique origin–destination journeys, generating annual revenue of more than £650 million. Like every modern operator, it had to hold two things in tension at once: ambitious commercial growth targets, and a customer promise of fair fares, genuine choice and no overcrowding.

A new reservation system was coming that would multiply the number of price points to manage by a factor of 100. On the existing tools and processes, that was simply not a battle the revenue management team could win. The system that had carried the business this far was about to become the thing holding it back.

The challenge

A legacy RMS that had quietly stopped managing revenue.

The legacy system had drifted from an active pricing engine into little more than an upload tool. Four problems were leaving money on the table every week.

01

Pricing on autopilot

With no time to review recommendations journey by journey, the team relied on high-level business rules to "set and forget" pricing. When demand moved, the upside was missed.

02

Revenue management by spreadsheet

The RMS automated almost nothing, so the real work happened manually in Excel. Skilled analysts spent their days uploading allocations instead of making commercial decisions.

03

Manual resets at every change

Schedule changes and engineering work shift capacity constantly. The legacy system could not adjust for them, so analysts re-set fares by hand every time — slow, and error-prone.

04

Forecasts no one trusted

Demand forecasts were weak and price sensitivities had not been refreshed in years. The result was a quiet collapse of trust: recommendations were routinely overridden or ignored.

My role

Building the system — and the confidence to use it.

This was not a software-swap. It was a commercial transformation: defining what "good" looked like, building an analytics capability the team would actually trust, and changing how revenue was managed day to day. I led that work across four phases.

1 Define & design

Mapped the team's real processes and objectives, then specified an RMS configured around how revenue management actually needed to work — not a generic template.

2 Build & prove the analytics

Stood up an extensive analytics library — 1,500+ predictive models across demand forecasting and price sensitivity — and tested rigorously so recommendations made commercial sense.

3 Optimise per journey

Put in place model selection that fits the best-performing approach to every individual journey, so pricing maximised both revenue and ridership rather than trading one off against the other.

4 Embed & hand over

Ran in-depth training and a managed transition so the team moved off spreadsheets with confidence, and the new way of working stuck after go-live.

The hard part of revenue management transformation is rarely the technology. It is rebuilding trust between the analysts and the numbers.

The results

Targets exceeded — and verified independently.

An independent auditor evaluated the new system's performance against the original business case.

3%

Revenue uplift

Ahead of the 2% target set in the business case — recurring, not one-off.

15x

Three-year ROI

The programme returned fifteen times its cost over three years.

+ridership

More passengers, too

Demand shifted to quieter trains and availability opened on previously constrained journeys — revenue and ridership rising together.

Pricing automated across more than 80% of journeys, freeing analysts to focus on the decisions that need judgement.
Capacity changes handled automatically — engineering work and schedule changes no longer mean manual fare resets.
Demand forecast accuracy improved by more than 35%, rebuilding trust in the recommendations.
Special-event demand handled far more effectively, capturing peaks the old rules-based approach missed.
Price sensitivities refreshed on a defined schedule, so the model stays current instead of decaying.

Figures reflect independently-audited outcomes of this revenue management programme. The operator is not named to respect commercial confidentiality, and some details have been generalised for the same reason.

What changed for the team

From uploading allocations to making decisions.

The clearest measure of success was not on the revenue line — it was in how the team spent its day. Analysts moved off manual spreadsheet work and onto strategy, with software that surfaced the data they needed and pointed them to where their expertise mattered most.

That is the outcome I aim for in every engagement: not a system that impresses in a demo, but one a commercial team trusts, uses and keeps improving long after the programme closes. The operator left it set up not just to hit a revenue target, but to keep hitting them.

More work

Selected engagements across the travel sector.

Hotels · Portfolio revenue strategy

Revenue strategy across a large London hotel portfolio

Led revenue strategy across roughly 30 hotels, sharpening pricing discipline and local execution through close work with General Managers and head-office teams.

In the pipeline

Further case studies coming soon

Additional pricing and commercial transformation engagements across rail, airlines and travel technology will be added here. Want to discuss whether your situation is a fit? Let's talk.

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