Tech

Case Study

Objective
The client—a Thailand-based fintech startup specializing in microloans—wanted to increase awareness and adoption of its lending services among underserved populations. The goal was twofold:

1. Identify high-intent individuals actively searching for financial assistance, and

2. Broaden discovery among those unfamiliar with microfinance options but showing consumer behavior patterns indicative of financial need.

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Data Strategy

1. Loan Intent Identification

   • Using search and behavioral intent signals, we identified individuals actively looking for loans, credit services, or financial aid across Thailand.

   • This core audience represented high-intent prospects who were already in-market for lending solutions.

2. Discovery Through Lifestyle and Purchase Behavior

   • To reach audiences who may not have been aware of microloan services, we expanded targeting to include individuals searching for aspirational or high-ticket items—such as new smartphones, luxury goods, or short domestic getaways.

   • These behavioral indicators suggested a potential need for short-term financing or flexible payment options, creating a bridge between financial aspiration and product access.

3. Anonymized Lookalike Modeling

   • Leveraging anonymized client data, we built a lookalike audience based on the startup's existing borrowers.

   • This model provided a clear behavioral and demographic profile of typical borrowers, allowing us to identify similar users across the country who fit the same financial and lifestyle patterns.

4. Location Intelligence & Employment Proxies

   • We geofenced high-density commercial and shopping districts to identify workers frequently present in these zones, while blacklisting devices appearing inconsistently (tourists or casual visitors).

   • This technique isolated urban blue-collar and service workers—key target segments for microloans.

   • The strategy was extended to rural communities, helping the brand reach populations that often lack traditional banking access but could benefit from digital microfinance options.

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Results

• High-Intent Audiences: Identified verified users searching for financial products across Thailand.

• Expanded Reach: Built awareness among new audiences through behavioral and aspirational data patterns.

• Data-Driven Inclusion: Reached both urban and rural populations underserved by conventional banks.

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Impact
By combining behavioral intent, mobility, and lookalike intelligence, the fintech startup gained a deep, data-backed understanding of Thailand’s financially underserved communities. This precision approach helped the brand balance awareness with inclusion—connecting genuine financial need with accessible digital solutions while maintaining ethical, anonymized data practices throughout.