US based real estate private lender leverages Enthu’s speech AI to improve qualified leads by more than 50%

The customer was witnessing break neck growth and required a technology solution to build a scalable, replicable and predictable voice process.

Group of people working out business plan in an office
1 %
improvement in lead qualification rate
1 %
reduction in bad leads passed to loan officers
1 +
increase in calls evaluated month on month

BACKGROUND

The company was scaling up fast with a distributed voice team, both onshore and offshore. The existing process of call monitoring and quality assurance couldn’t keep pace with the number of opportunities that were coming in through inbound marketing efforts.

The company was exploring a technology solution that could enable a replicable and scalable sales process and help their voice teams produce predictable results when on the phone. The aim was to proactively monitor and learn from agent conversations and funnel the intelligence back to the agents so as to improve performance and lead qualification rates.

The customer was also looking for a conversation intelligence solution with high transcription accuracy and seamless fitment into their existing tech stack (Aircall and Hubspot).

KEY HIGHLIGHTS

#1. Replicable and scalable quality monitoring

Prior to Enthu.AI, a quality person had to manually listen to conversations, record feedback on Excel sheets and extensively rely on emails to share evaluation data and coaching inputs. The entire process worked well during initial stages but it started faltering as the company scaled up its voice operations from 3 to 30 agents.

As an example, a major chunk of leads marked as SQLs and shared
with the onshore loan officer were actually not SQLs. It was humanly
impossible for the QA to listen to each such call and identify the
improvement opportunities of the agent.

With Enthu.AI, the team started using AI driven call moments to
identify conversations with SQL/MQL signals. Evaluating calls and
sharing feedback with agents got 3X faster and more accurate. Now,
the team not just monitors 100% conversations but also evaluates
100% of SQL conversations, deriving improvements and insights on
the fly and improving their lead qualification rate by more than 50%
in just under 3 months
.

#2. Tailored agent coaching

The personalized coaching insights generated by Enthu.AI brings out opportunity areas of each agent at just a click of a button, saving
more than 20 hours
of data collection and analysis effort for the
quality & coaching team. 

With time series analysis of hits and performance of every single
agent, the quality team focuses on delivering coaching instead of
generating insights.

Feedback notes are instantly shared with agents for action and
improvement, cutting the coaching delivery time from days to just a
couple of minutes.

Since the quality team now evaluates more conversations and gives
pin pointed feedback, the agent performance has improved
resulting in better qualification of leads and more SQLs per voice
agent. 

#3. Seamless integration with 3rd party dialers and CRMs

Enthu.AI seamlessly integrates with the customer’s Aircall account, ensuring all agent conversations are redacted for sensitive data and analyzed almost instantly, without anyone lifting a finger. 

Further, all call transcriptions and analysis URLs are pushed into
Hubspot under the relevant contacts, ensuring the loan officers can
refer to the transcripts and the corresponding analysis without them
leaving
the CRM platform.

Enthu.AI enables the customer with multiple data exchanges and
cross platform intelligence sharing, without them spending even a
single minute of effort. 

ABOUT THE CUSTOMER

A real estate lender specializing in bridge loans, fix-and-flip and 30 year rental investment products throughout the United States.

CHALLENGES
Company’s existing quality and coaching process couldn’t keep pace with the rapid growth it was getting in new opportunities.

TEAM SIZE
150+

TECH STACK
Aircall + Hubspot

GEOGRAPHY
New York, USA

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What next?

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