LeaseHawk: No Sales Agent or Apartment Unit Left Unoccupied
Challenge overview
LeaseHawkʼs mission is to simplify the leasing process for the multifamily industry. They do this by creating intuitive software solutions that drive leads to apartment owners and leasing agencies. Allowing them to track prospects, optimize marketing activities, and close leases faster via data-driven decision making. Headquartered in Scottsdale, Arizona, in the growing “Silicon Desert” the company generates $88.7K in revenue per employee.
In 2016 the company partnered with TechFabric to develop an umbrella analytics project. LeaseHawkʼs “Digital Conversational Analytics” that calculates a lead score using AI/Machine Learning, and to further enhance their CRM System.
“It sometimes takes a leasing agent up to 48 hours to respond to a lead. That’s just unacceptable in the eyes of today’s consumer and it should be unacceptable to multifamily execs, too,”
60
Second response time
Turning hot leads to sales
97%
Accuracy rate
Using Natural Language Processing with Machine Learning
100%
Satisfied customers
Delighted apartment residents
Solutions
Hot leads to sales within 30 seconds
TechFabric built a Customer Relationship Management (CRM) portal and implemented the latest in NLP (Natural Language Processing) technologies to connect agents and available units with motivated apartment hunters faster. TechFabric helped LeaseHawk automate and integrate their telecom infrastructure (which records and tracks all calls and voicemails coming into the system) with the enhanced CRM portal. Much like Amazonʼs Alexa, LeaseHawkʼs new “HawkEye Business Intelligence platform” uses NLP technology, recognizes speech, and uses machine learning to identify next steps.
LeaseHawk execs have been scoring calls for years and were well equipped to help train machine learning models to listen for these nuances. There are few things more valuable to a leasing agent than being able to identify a prospect versus a non-prospect. The AI-driven lead scoring gives them the insights they need to generate sales more quickly (and gain more happy customers.)
Results
Results
Before automation, LeaseHawk was using a 30-person team to listen to the audio files and classify/prioritize the call as either a prospect or current customer. The cost of the manual approach was close to $700K per year, and because it was manual, the risk of misclassifying the call was high and required a 24-hour turnaround.
Accuracy was also an issue. Using Natural Language Processing with Machine Learning, LeaseHawk was able to achieve a 97% accuracy rate.
Bottom Line: properly classified leads in the hands of sales people in less than 60 seconds of call received – vs 24 hours, resulted in:
• Better Classification
• Faster response time
• Reduced vacancy rates for LeaseHawk customers
• Delighted apartment residents
Together TechFabric and LeaseHawk transformed a time-consuming, error-prone, and expensive process into an efficient and productive system generating increased revenues and satisfied customers for LeaseHawk, its customers, and agents.
Technologies Used:
