GAF Predictive Analytics Platform

A first-of-its-kind predictive analytics engine that detects customers most likely to need roofing services before competitors even realize they've moved.

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GAF Predictive Analytics - Customer Targeting Case Study
Analytics Machine Learning Enterprise
Client: GAF

Roofing Manufacturing | Home Improvement | Construction

About

In an industry where timing is everything, GAF—the largest roofing manufacturer in North America—needed more than just traditional marketing. They needed predictive intelligence. With shifting housing markets, regional weather patterns, and evolving customer expectations, identifying potential buyers before they even raise their hands was the holy grail.

On Beat Digital delivered exactly that.

By building a predictive analytics engine that synthesized real-world behavioral signals and integrated it directly into GAF's marketing infrastructure, we helped GAF anticipate the customer—not just reach them. The result? A first-of-its-kind system that detects who's most likely to need a new roof before competitors even realize they've moved.

The Challenge

For a product like roofing, intent is everything. Customers don't shop for a new roof casually—they need one when they move, renovate, or face storm-related damage. But the biggest hurdle wasn't reaching customers—it was identifying them before the need became obvious.

GAF wanted to:
  • Predict which households were most likely to move
  • Target those individuals with timely, relevant offers
  • Optimize marketing budgets by focusing on highest-likelihood prospects
  • Turn data into a strategic weapon for lead generation and sales enablement

This wasn't just another CRM segmentation task. This was full-on data science meets demand generation.

Our Solution

On Beat Digital engineered a complete end-to-end predictive marketing solution—combining data ingestion, machine learning, business intelligence, and integrated campaign activation.

1. Massive, Multi-Source Data Aggregation

We unified datasets from:

  • Housing transaction records
  • Demographics & census data
  • Location signals & zip code migration trends
  • News & economic indicators
  • Social media behavior & sentiment signals

This data was piped into a highly secure, scalable ETL framework that allowed us to continuously clean, normalize, and structure data for modeling and real-time analysis.

2. Custom Predictive Modeling: Movers, Not Clickers

We built predictive models trained to:

  • Score households based on likelihood of relocation
  • Cross-reference regional homeownership patterns with roofing lifecycle
  • Flag indicators such as recently listed homes, updated permits, or new mortgages

This wasn't lead scoring. This was proactive lead discovery—delivering intelligence on customers weeks or months before competitors could even see them coming.

3. Enterprise-Grade Data Infrastructure

Behind the scenes, we deployed:

  • A robust cloud data warehouse for model outputs
  • Automated ETL pipelines to refresh insights weekly
  • Role-based access control for sales and marketing teams
  • GDPR- and CCPA-compliant data governance at every layer

4. Business Intelligence & Visualization

Using Power BI, we created stunning, interactive dashboards that:

  • Showed heatmaps of "mover hotspots" across regions
  • Allowed marketing teams to segment, filter, and drill down into likely buyers
  • Provided campaign managers with direct feedback loops on conversion performance tied to model scores

5. Campaign Activation & Real-Time Optimization

We then helped GAF deploy predictive scores across channels:

  • Targeted email and SMS campaigns with early-bird incentives
  • Regional discounts surfaced only to high-score leads
  • CRM enrichment to inform local reps of high-intent prospects in their zones

Every piece of outreach was driven by data, timed for action, and measured for ROI.

The Results

While detailed metrics remain confidential, the business impact was unmistakable:

Increased Sales Volume

Significantly increased sales volume from high-score regions

Marketing Efficiency

Improved marketing efficiency, with reduced cost-per-acquisition

Higher Engagement

Higher engagement rates through tailored messaging and timing

Customer Satisfaction

Elevated customer satisfaction by meeting prospects with solutions before they asked

Competitive Advantage

Strengthened GAF's competitive advantage with a proprietary data asset that's still evolving