Energy

Case Study

Objective
As the brand entered Singapore’s competitive fuel market with four new petrol stations, the goal was to increase brand awareness and encourage trial among active drivers. Traditional location-based targeting could not sufficiently distinguish real drivers from general consumers, so the focus was on building a data-driven driver audience framework using real-world behavioral signals.

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Data Strategy

1. Driver Identification via Behavioral Signals

   • We analyzed mobile activity patterns to identify users with limited programmatic events during morning and evening rush hours—a behavioral marker for drivers in transit.

   • This data layer helped isolate genuine road users from general commuters or passengers, creating a verified audience of active drivers.

2. Vehicle Ownership via Location Intelligence

   • We geofenced government-approved vehicle inspection and servicing centers across Singapore.

   • Devices appearing at these locations were classified as vehicle owners, forming a core audience segment with verified ownership behavior.

   • Devices consistently appearing at these locations were blacklisted, as these likely belonged to employees of these inspection and servicing centers.

3. Mobility Insights for Commercial Drivers

   • To identify ride-hailing and taxi drivers, we analyzed mobility data to find devices with at least 10 programmatic events spread across multiple districts in a day—an indicator of island-wide driving patterns.

   • This allowed the brand to reach high-mileage, fuel-dependent audiences critical for station adoption.

4. Intent Signal Enrichment

   • Behavioral and contextual signals were layered to detect users searching for fuel discounts, loyalty programs, and fuel-saving tips.

   • These high-intent audiences were prioritized for engagement, ensuring relevance to the brand’s promotional offerings.

5. Hyperlocal Awareness Activation

   • Each new petrol station was surrounded by a geofenced radius, allowing the brand to target drivers residing or frequently commuting nearby.

   • This approach created localized awareness and convenience-driven behavior—encouraging trial among the most accessible potential customers.

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Results

• Verified Driver Segments: Combined behavioral, location, and mobility data to define multiple driver personas—private, commercial, and local.

• High-Intent Discovery: Identified drivers seeking fuel-related promotions and rewards.

• Localized Engagement: Delivered targeted awareness among residents and commuters within station catchment zones.

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Impact
Through the integration of mobility intelligence, intent signals, and behavioral analysis, the brand achieved an unprecedented level of precision in reaching active drivers across Singapore. This data-led approach enabled efficient awareness generation for its new station network, improved visibility among high-value fuel users, and set the foundation for sustained customer acquisition in a new market.