Call center ai · Production

How Aptive Drove Over $2 Million in Customer Retention with Cresta's Real-Time AI

The problem

Aptive Environmental's contact center faced inconsistent call handling, limited supervisor visibility into agent performance, and poor quality management that undermined customer retention in a competitive market.

First attempt

The previous QM process required supervisors to spend extensive time manually searching for individual calls, and the absence of real-time feedback left agents without in-call guidance during cancellation interactions.

Workflow diagram · grounded in source
1
Inbound cancellation call
Trigger
Customers call in for assistance, initiating the save and retention workflow.
▾ source quote
“customers were required to call in for assistance”
2
Real-time agent guidance
Ai action
Cresta equips agents with real-time prompts and suggestions to ensure consistency across customer interactions.
▾ source quote
“Cresta equips agents with real-time prompts and suggestions, ensuring consistency and standardizing customer interactions across Saves and Renewals”
3
Discovery and empathy hints
Ai action
Cresta's Hints guide agents through discovery to understand why a customer wants to cancel and offer tailored solutions.
▾ source quote
“guiding agents through discovery to understand why a customer wanted to cancel, then offering tailored solutions based on Cresta's Hints. These Hints helped agents show empathy, ask the right questions, and present the most relevant offers in the correct sequence.”
4
Offer sequence optimization
Ai action
Cresta identifies whether agents are presenting high-cost or lower-cost options first and optimizes the order of offers to maximize retention likelihood.
▾ source quote
“By identifying whether agents were presenting high-cost options first or starting with lower-cost alternatives, Cresta optimized the order of offers, maximizing the likelihood of retaining the customer”
5
Supervisor QM review
Human review
Supervisors use consolidated call reviews and accurate transcription to monitor and provide feedback quickly.
▾ source quote
“consolidated call reviews and accurate transcription, enabling Aptive's supervisors to monitor and provide feedback effectively and quickly”
6
Agent performance feedback loop
Feedback loop
The real-time feedback loop improves both agent performance and customer experience.
▾ source quote
“This real-time feedback loop has improved both agent performance and customer experience”
Reported outcome

Aptive generated $2.37 million in additional annual revenue, achieved a 9% increase in save rate on cancellation calls, improved empathy adherence from 33% to 79%, and supervisors can now review calls in under a minute.

Reported metrics
Save rate increase on cancellation calls9%
Return on investment3x
Additional annual revenue$2.37 million
Actual save rate on inbound calls46%
Show all 13 reported metrics
save rate increase on cancellation calls9%
return on investment3x
additional annual revenue$2.37 million
actual save rate on inbound calls46%
save rate goal (baseline)42.2%
empathy adherence (baseline)33%
empathy adherence (2 months)60%
empathy adherence (4 months)79%
discovery adherence (baseline)28%
discovery adherence (intermediate)40%
discovery adherence (4 months)59%
playbook adherence improvement in empathy and discoveryincreasing adherence over 10% in empathy and discovery
supervisor call review timeunder a minute
Reported stack
CrestaAgent AssistCresta's Hints
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Source
https://cresta.ai/customers/aptive
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Frequently asked questions

What did this team achieve with this AI workflow?

Aptive generated $2.37 million in additional annual revenue, achieved a 9% increase in save rate on cancellation calls, improved empathy adherence from 33% to 79%, and supervisors can now review calls in under a minute.

What tools did this team use?

Cresta, Agent Assist, Cresta's Hints.

What results were reported?

Save rate increase on cancellation calls: 9%; Return on investment: 3x; Additional annual revenue: $2.37 million; Actual save rate on inbound calls: 46% (source-reported, not independently verified).

What failed first in this deployment?

The previous QM process required supervisors to spend extensive time manually searching for individual calls, and the absence of real-time feedback left agents without in-call guidance during cancellation interactions.

How is this call center ai AI workflow structured?

Inbound cancellation call → Real-time agent guidance → Discovery and empathy hints → Offer sequence optimization → Supervisor QM review → Agent performance feedback loop.

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