Powering London Northwestern Railway through intelligent architecture

London Northwestern Railway (LNR) entered FY25/26 with a paid search programme built for a different era. Built on historical structures that predate today's machine learning tools, the accounts were no longer equipped to compete in an increasingly sophisticated search landscape and progressively competitive, price sensitive market.

As a long-distance rail operator, LNR operated in a market contested by competing transport providers, coach operators and third-party aggregators. The strategic challenge was to win new leisure passengers and make rail the obvious choice over car and coach - whilst ensuring budget wasn't wasted on brand terms or commuter keywords where passenger intent was already firmly in LNR's favour.

The account was grounded in traditional PPC fundamentals: brand bidding, single keyword ad groups, manual bidding, location targeting overlaps, and over 150 campaigns. Strong on paper, with an average monthly ROAS of 5.5, it was delivering, but it was increasingly misaligned with how modern search works and how leisure travellers actually behave.

Rather than optimising around the edges, LNR, along with our partner agencies, overhauled the entire account structure with a test and learn approach. Simplifying the account didn't just improve performance; it unlocked sharper insights, faster decision-making and the full capability of AI-driven Smart Bidding.

Despite a 59% reduction in campaign volume, we achieved £2.9M in revenue (+62% YoY) and an 8.0 ROAS (+25% YoY). PPC efforts saw ticket purchases grow by 50%, driven not by inflated traffic volumes but by meaningfully higher quality demand.