E-commerce

Objective
The client, Singapore’s premier ergonomic furniture brand, sought to strengthen awareness and customer acquisition during the highly competitive 11.11 shopping season. Traditional broad targeting was inefficient during high-demand retail events, so the goal was to use data intelligence to identify consumers with genuine purchase intent and optimize engagement in real time.

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Data Strategy

1. Intent & Behavioral Signal Mapping

   • We analyzed behavioral data to identify users actively searching for ergonomic desks, workspace furniture, and competitor products.

   • Using browsing, search, and interaction patterns, we isolated high-intent shoppers likely to engage with premium workspace solutions rather than generic furniture.

2. Life-Stage Segmentation via Location Data

   • We layered in location intelligence to identify new homeowners, a key segment likely to invest in home office setups.

   • Mobility data helped distinguish new residents visiting furniture outlets, renovation hubs, and home improvement retailers, enabling accurate life-stage targeting.

3. Contextual & Category Affinity Enrichment

   • Contextual data from online content and app interactions was analyzed to build audience clusters around home improvement, productivity, and workspace design interests.

   • This ensured the brand reached consumers browsing relevant categories at the precise decision-making stage.

4. Dynamic Optimization Through Data Feedback

   • Audience clusters were continuously updated based on real-time engagement signals.

   • As new behavioral data came in, our system dynamically re-weighted audience relevance—allocating more focus to segments showing strong interest and interaction.

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Results

• Precision Discovery: Identified and engaged new homeowner and workspace-intent audiences with verified purchase behavior.

• Contextual Relevance: Mapped engagement across key digital touchpoints where ergonomic and productivity content performed strongest.

• Scalable Intelligence: Built a dynamic dataset for future audience reactivation beyond the sale period.

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Impact
By integrating behavioral, contextual, and location-based intelligence, the brand was able to identify who their next customers were—before they even began actively shopping. This data-first approach replaced guesswork with precision, enabling smarter engagement, higher-quality traffic, and long-term audience intelligence the brand could continuously build upon.