Background

Initiated to address the challenge of efficiently identifying and targeting potential customers who are likely to move and may need new roofing solutions. GAF, a leading roofing manufactures, sought to enhance their marketing strategies and sales outcomes by leveraging advanced data analytics and predictive modeling.

Engagement Goal:

• Collect and analyze diverse data sources to predict customer behavior and optimize marketing efforts.

Long-term Goal:

• Leverage predictive models to enhance customer targeting and increase sales of roofing products.

Solution

The solution integrated components into a seamless workflow that enabled GAF to leverage predictive analytics to enhance their marketing efforts, resulting in improved customer targeting, higher engagement rates, and increased sales.

1 Comprehensive Data Collection:

• Aggregated data from social media, housing records, zip codes, demographics, locations, and news feeds.

2 Predictive Modeling:

• Developed models to identify individuals likely to move and need roofing solutions.

3 Data Storage and ETL:

• Implemented robust data storage solutions and ETL processes to ensure data quality and accessibility.

4 Business Intelligence:

• Utilized Power BI to visualize data insights and support decision-making.

5 Marketing Optimization:

• Created a targeted marketing strategy to offer discounts to identified potential customers.

Conclusion

• Increased marketing efficiency and reduced costs through targeted campaigns.
• Boosted sales and customer satisfaction by offering relevant discounts to potential movers.
• Strengthened GAF’s competitive edge in the roofing market by leveraging advanced analytics.

The end-to-end analytics solution accurately identifies potential customers likely to move and in need of roofing products. This targeted approach led to higher customer engagement, increased sales, and enhanced overall marketing efficiency.