Case study

Vehicle Rental: Geographic Segmentation and AI-Driven Steering

A €1.5M/year SEA account for a European vehicle-rental leader: granular geographic segmentation, AI predictive bidding and mobile-first landing pages.

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Vehicle Rental: Geographic Segmentation and AI-Driven Steering

Dominating an ultra-competitive market: geographic segmentation and AI-driven predictive steering.

Managed budget€1.5M / year
SectorVehicle rental · Europe
LeversGeo-targeted Search · Predictive AI · Mobile-first
Key resultMore bookings on the same budget

1. Context and Stakes

The engagement covered a European leader in vehicle rental, operating in a sector marked by extreme competitive intensity and real-time price volatility. With an annual budget of €1.5 million, the challenge was to maximise booking volume for a network of hundreds of physical branches while keeping cost per acquisition (CPA) under strict control.

2. The Strategic Challenge

The car rental sector faces structural challenges that our expert had to solve:

  • Geographic complexity: Managing visibility for hundreds of destinations with radically different levels of demand and vehicle stock.
  • The mobile imperative: More than 70% of searches happen on smartphones, often in urgent or on-the-move contexts, making the user experience critical.
  • Pressure on ROI: Facing aggregators and price-comparison sites, it was vital to capture users at the peak of their purchase intent to avoid a price war.

3. Strategic Solutions Delivered

The approach led by our expert moved the account from mass management to surgical steering:

A. Semantic restructuring and fine geographic segmentation

We abandoned global campaigns in favour of a granular structure segmented by geographic area and by branch. This approach adapted ad messages and budgets to the real profitability of each destination, avoiding investment in saturated areas in favour of local growth pockets.

B. Predictive steering through artificial intelligence

To optimise the €1.5M investment, we deployed AI-powered smart bidding algorithms:

  • Seasonal adjustments: Using AI to predict demand peaks (holidays, events, transport strikes) and adjust bids around the clock.
  • Optimisation by destination: Dynamic budget allocation towards the branches with the most vehicles available to rent, maximising fleet utilisation.

C. Optimising the mobile-first experience

To turn mobile traffic into confirmed bookings, we ran a technical optimisation programme:

  • Ultra-fast landing pages: Redesigning landing pages to cut mobile load time, a key factor for Quality Score and conversion rate.
  • Dynamic ads: Deploying responsive ads highlighting branch proximity and immediate availability.

D. Advanced behavioural remarketing

We set up targeted remarketing strategies to re-engage users who had viewed a specific destination without completing their booking, using personalised offers to remove the last barriers to purchase.

4. Results

Senior management of this account delivered record performance levels:

  • Significant ROI increase: A better budget split generated more bookings at constant budget.
  • Surge in mobile conversions: Thanks to landing page optimisation, the smartphone conversion rate rose dramatically.
  • Operational efficiency: Automated management supervised by an expert, allowing instant reaction to fluctuations in the automotive market.

Methodological note: This case illustrates our ability to manage complex budgets with a strong local dimension, using technology to serve a strategy of immediate results.

Premium Automotive: Localised SEA Strategy and Online-to-Offline SteeringPremium automotivePremium Automotive: Localised SEA Strategy and Online-to-Offline SteeringBudget: €1M / yearCar Manufacturer: Multi-Platform Strategy and Local Steering in EuropeCar manufacturerCar Manufacturer: Multi-Platform Strategy and Local Steering in EuropeBudget: €3.5M / yearTravel & E-commerce: Seasonal Agility and Mobile-First PerformanceOnline travelTravel & E-commerce: Seasonal Agility and Mobile-First PerformanceBudget: €7.2M / year

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