Business Implications
Steps Performed
Developed and deployed an AI-based forecasting and targeting pipeline that predicted visitor inflow, guided hyperlocal ad spending, and boosted museum attendance using precision digital marketing.
1.
Define Campaign Objective
Partnered with NCCHR marketing leadership to identify primary goals—boost in-person visits and digital engagement—while keeping budget efficiency and brand awareness as core success metrics.
2.
Collect And Prepare Data
Integrated historical visitor records, event attendance, and social media insights with local demographic and weather data using AWS Glue and Python preprocessing pipelines.
3.
Develop AI Prediction Model
Used Amazon SageMaker and Scikit-Learn to train regression and clustering models predicting high-traffic days and visitor segments most likely to convert from online ads.
4.
Launch Micro-Targeted Ads
Designed Meta Advantage+ campaigns focusing on predicted high-engagement zip codes, tailoring creative tone and timing for each demographic cluster to maximize CTR and ticket conversions.
5.
Analyze And Optimize Performance
Monitored real-time results through Amazon QuickSight and Meta dashboards, dynamically adjusting budget and audience weights to achieve continuous growth in visitor acquisition.
AWS Services Used
Amazon SageMaker
AWS Lambda
Amazon S3
Amazon QuickSight
Amazon CloudWatch
AWS Glue
Python
Pandas
Scikit-Learn
Meta Ads Manager
Technical Tools Used
Predictive Analytics
Audience Segmentation
Campaign Optimization
Data Visualization
Skills Demonstrated
Visitor Prediction + Micro-Targeted Ad Campaign
AI-Driven Audience Growth For Civil Rights Awareness
Built an AI-powered visitor prediction model and integrated it with a micro-targeted Meta ad campaign for the National Center for Civil and Human Rights to increase local awareness, optimize ad spend, and attract new visitors through data-driven personalization.
