Executive Summary 

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. Unfortunately, according to industry research from Conversica, only 38% of real estate companies can follow up on all their leads, and G5 found that just 14% of leads—1 in 7—were productive. This low success rate resulted in low job satisfaction, high turnover for leasing agents, and low conversion numbers. G5 wanted to solve this by using machine learning; the company knew that machine learning could help identify stronger leads that would more likely result in sales. Although G5’s product team consisted of trained statisticians and behavioral scientists, the company didn’t have dedicated data science resources to create the needed machine learning models.

 

Challenges 

The real estate marketing company found that only 14% of its call leads were productive. While machine learning offered promise in addressing this inefficiency, the realities of implementation could prove to be time consuming, expensive, and a barrier to innovation.

 

Solution Powered by H2O Driverless AI 

G5 found that H2O Driverless AI addressed its challenges with identifying the difference between a productive lead and a dead end. “At G5 we are leveraging AI to guide the decision-making process in real estate marketing with the help of our Intelligent Marketing Cloud platform that maximizes marketing effectiveness and efficiency,” said Martin Stein, Chief Product Officer at G5. “The G5 Intelligent Marketing Cloud continuously and efficiently improves its accuracy and predictive qualities.” G5’s first task was to build data sets consisting of 100,000 lead call transcripts and their scores. The company 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. These are essentially a variation of MOJO scoring, providing an easy, high-performance and scalable way to deploy and display modelling results. The model scoring and complexity was completely removed from leasing agents’ view, yielding a list of high-value leads for leasing agents to contact.

 

Results 

Using H2O Driverless AI in conjunction with H2O-3, the company derived an accurate, relevant feature table from its data sets. Based on this, the company can engineer a feature set around which it can build, test, tune, and deploy machine learning models. Results can then be scored to identify high-quality leads and improve sales conversions. The main benefits for G5 and its customers: 

  1. Model development. Using H2O Driverless AI, the G5 team was able to reduce model development time by 80%. With this, the product team estimates they are able to deliver the work of two additional senior technical employees without any dedicated data science or deployment resources. 
  2. Model results. The G5 team increased the accuracy of lead scoring to over 95%. As a result, leasing agents connect with qualified leads 85% of the time, a substantial improvement from the previous 14% benchmark. Based on these models, customers are saving $1.5M/month.
  3. Customer results. Leasing agents are better equipped to meet their sales quotas. This has a significant positive impact on job satisfaction and reducing agent turnover, which leads to dramatic cost savings in the sales process. For leasing companies, having more effective leasing agents who stay on the job longer means that they need fewer agents to meet their goals and they can deploy resources to other areas of their business.

 

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