Increasing Effectiveness of Real Estate Marketing with H2O.ai
Customer Company Size
Large Corporate
Region
- America
Country
- United States
- Canada
Product
- H2O Driverless AI
- G5 Intelligent Marketing Cloud
- Amazon S3
- Amazon EC2
- AWS Lambda
Tech Stack
- Machine Learning
- H2O Word2Vec
- MOJO scoring
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
- Customer Satisfaction
- Employee Satisfaction
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Maintenance
Services
- Data Science Services
- System Integration
About The Customer
G5, Inc. is a leading marketing optimization company for the real estate industry. Through its Intelligent Marketing Cloud, G5 helps customers optimize advertising and lead management to increase marketing efficiency and effectiveness. G5 works with more than 7,000 properties in the United States and Canada. Its customers are leasing companies for large apartments, senior living, and self storage complexes. G5 employs leasing agents who follow up on leads through phone calls. The company's goal is to increase the productivity of these leads and improve the efficiency of their leasing agents.
The Challenge
G5, Inc., a leading marketing optimization company for the real estate industry, was facing a challenge with its lead generation process. The company found that only 14% of its call leads were productive, resulting in low job satisfaction, high turnover for leasing agents, and low conversion numbers. G5 wanted to solve this by using machine learning to identify stronger leads that would more likely result in sales. However, the company didn’t have dedicated data science resources to create the needed machine learning models. The implementation of machine learning could prove to be time consuming, expensive, and a barrier to innovation.
The Solution
G5 found that H2O Driverless AI addressed its challenges with identifying the difference between a productive lead and a dead end. The company built data sets consisting of 100,000 lead call transcripts and their scores, stored these data sets on Amazon S3, and powered its machine learning with the compute capacities of Amazon EC2. G5 then used H2O Word2Vec to analyze the data sets and generate a table of features to serve as the underpinnings of the emerging machine learning model. Having a preliminary matrix of the model, G5 used H2O Driverless AI to further engineer the model’s features, and train it using the existing data sets. As a result, the model identified high-quality leads with increasing accuracy. Lastly, G5 needed to make its results production-ready and usable by leasing agents. To do so, the company ran the modelling results on AWS Lambda and passed them through H2O Driverless AI’s automatic scoring pipelines.
Operational Impact
Quantitative Benefit
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