NCCHR

🏛️ Visitor Prediction + Micro-Targeted Ad Campaign — National Center for Civil and Human Rights (NCCHR)

🌍 Overview

This project combines AI-driven visitor prediction and micro-targeted ad optimization to increase local engagement for the National Center for Civil and Human Rights (NCCHR). By blending historical attendance, social insights, and demographic data, it builds a forecasting and targeting model that improves awareness, ad ROI, and visitor turnout.

📘 View Campaign Dashboard + Full Case Study →

🧩 Problem Statement

NCCHR sought to boost museum foot traffic and digital engagement through smarter audience targeting. Traditional marketing lacked data precision, leading to uneven attendance and inefficient ad spend.

The organization needed to:

✅ Predict visitor inflow based on seasonality, events, and campaigns

✅ Identify high-engagement audience clusters by zip code and demographics

✅ Design micro-targeted ad campaigns that optimized cost per visitor

🔍 Approach

1️⃣ Define Campaign Objective

Partnered with NCCHR’s marketing and community outreach teams to align goals — increasing in-person visits, event attendance, and digital engagement, while maintaining cost efficiency and brand integrity.

2️⃣ Data Collection + Preparation

Integrated historical visitor records, ticket sales, and local demographics. Combined social and weather data to assess engagement drivers. Cleaned and standardized all datasets using Python and AWS Glue pipelines.

3️⃣ Develop AI Prediction Model

Trained regression and clustering models using Amazon SageMaker and Scikit-Learn. Predicted high-traffic periods, repeat visitor probabilities, and geo-segment performance. Generated daily visitor forecasts and campaign responsiveness scores.

4️⃣ Launch Micro-Targeted Campaigns

Deployed Meta Advantage+ ad campaigns targeting predicted high-engagement zones. Tailored creatives by region and demographic segment for maximum relevance. Integrated budget automation based on predicted conversion rates.

5️⃣ Monitor + Optimize Performance

Visualized campaign metrics using Amazon QuickSight dashboards. Tracked real-time ROI, engagement, and conversion data from Meta Ads API. Adjusted budget and targeting weekly for continuous improvement.

⚙️ Tech Stack

AWS Services:

Amazon SageMaker

AWS Lambda

Amazon S3

Amazon QuickSight

AWS Glue

Amazon CloudWatch

⚙️Technical Tools:

Python

Pandas

Scikit-Learn

Meta Ads Manager

⚙️Skills Applied:

Predictive Analytics

Audience Segmentation

Ad Optimization

Data Visualization

📈 Results

Key Outcomes:

📊 Engagement Lift: 35% increase in campaign CTR

🎯 Ad Spend Efficiency: 28% reduction in cost per conversion

🗺️ Local Awareness: Reached 3× more new households in target zip codes

⚡ Visitor Forecast Accuracy: Improved predictive accuracy to 92%

🧠 Business Impact

The predictive and micro-targeting model transformed NCCHR’s digital marketing into a data-driven engine for community awareness. It empowered the organization to: Reach new audiences with localized, AI-optimized messaging

Reallocate budgets toward high-yield visitor segments

Establish a reusable forecasting framework for ongoing campaigns and museum events