AI Market Mix + 5G Targeting (Huawei)
Huawei • Sep 2020 – Feb 2023
Role
Data Scientist
Timeline
Sep 2020 – Feb 2023
Team
Strategy Directors, Network Engineers, Marketing Leads
My Focus
Geospatial Analytics, Causal Inference, Strategic Planning
Business Impact
$40M; +39% targeting
Scale
Multi-market deployment
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.
System Architecture Diagram
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:#0066ff,stroke:#0052cc,stroke-width:2px,color:#fff
style B fill:#4C9AFF,stroke:#0066ff,stroke-width:2px,color:#fff
style C fill:#0066ff,stroke:#0052cc,stroke-width:2px,color:#fff
style D fill:#4C9AFF,stroke:#0066ff,stroke-width:2px,color:#fff
style E fill:#0066ff,stroke:#0052cc,stroke-width:2px,color:#fff
style F fill:#4C9AFF,stroke:#0066ff,stroke-width:2px,color:#fff
style G fill:#0066ff,stroke:#0052cc,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:#fffThe Impact
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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