AI Market Mix + 5G Targeting (Huawei)

Huawei · Sep 2020 – Feb 2023

Role

Data Scientist

Timeline

Jul 2020 – Jan 2023

Team

Strategy Directors, Network Engineers, Marketing Leads

My Focus

Geospatial Analytics, Causal Inference, Strategic Planning

PythonXGBoostGeospatialMarket Mix ModelingOptimization

Business impact: $40M; +39% targeting
Scale: Multi-market deployment

AI Market Mix + 5G Targeting (Huawei) — system preview

The Challenge

The Challenge: The Infrastructure Risk

Deploying 5G infrastructure is capital intensive. Huawei needed to ensure that every new tower location would generate maximum ROI across Latin America.

The Bottleneck: Traditional planning relied on simple population density data, which failed to account for purchasing power or "willingness to pay."
The Goal: Develop a precision-targeting engine to identify high-value "Lifestyle Zones" rather than just high-population areas.

The Architecture

I built an end-to-end Market Mix Model that combined geospatial data with economic behavior:

Data Ingestion: Integrated OpenStreetMap data with internal network usage logs to create a rich geospatial dataset.
Lifestyle Clustering: Developed a custom "Lifestyle Zones" algorithm using K-Means and DBSCAN to segment areas based on commercial activity and housing types.
Optimization: Applied Linear Programming and XGBoost to predict demand and recommend optimal site placements under budget constraints.

graph TD
    A[Geographic Data<br/>OpenStreetMap] --> B[Clustering Algorithm<br/>K-Means + DBSCAN]
    B --> C[Lifestyle Zones<br/>Segmentation]
    C --> D[XGBoost<br/>Demand Prediction]
    D --> E[Linear Programming<br/>Site Optimization]
    E --> F[Pricing Model<br/>Elasticity-Based]
    F --> G[PowerBI<br/>C-Level Dashboard]

    H[Demographic<br/>Data] --> C
    I[Network<br/>Coverage Data] -.->|Constraints| E

    style A fill:#2563eb,stroke:#1d4ed8,stroke-width:2px,color:#fff
    style B fill:#60a5fa,stroke:#2563eb,stroke-width:2px,color:#fff
    style C fill:#2563eb,stroke:#1d4ed8,stroke-width:2px,color:#fff
    style D fill:#60a5fa,stroke:#2563eb,stroke-width:2px,color:#fff
    style E fill:#2563eb,stroke:#1d4ed8,stroke-width:2px,color:#fff
    style F fill:#60a5fa,stroke:#2563eb,stroke-width:2px,color:#fff
    style G fill:#2563eb,stroke:#1d4ed8,stroke-width:2px,color:#fff
    style H fill:#666,stroke:#444,stroke-width:1px,color:#fff
    style I fill:#666,stroke:#444,stroke-width:1px,color:#fff

System architecture — data flow from ingestion to serving

The Impact

The insights from this model directly influenced the deployment strategy for the 5G rollout.

Metric	Legacy Planning	AI-Driven Strategy
Targeting Method	Census/Population Density	Behavioral "Lifestyle Zones"
Precision	City/Regional Level	Hyper-local Block Level
Efficiency	Standard Conversion	+39% Targeting Efficiency
Financial Value	Baseline Revenue	+$40M Incremental Revenue

Collaboration & Strategy

This project was not just about code; it was about influencing executive strategy:

Stakeholder Management: I presented findings directly to C-Level executives via interactive PowerBI dashboards, translating complex clustering logic into actionable "Go/No-Go" maps.
Cross-Market Adoption: The "Lifestyle Zones" methodology was so successful it was adopted as the standard planning framework across multiple Latin American markets.

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