Leading lease-to-own finance company saves over $600,000 per year by resolving 60% of customer requests with agentic AI

Since 1999, this national lease-to-own finance company has helped millions of consumers with limited or imperfect credit get access to furniture, electronics, and appliances through flexible payment plans at some of the largest retailers in the United States. The business is built on reducing barriers — making it possible for people to get what they need when traditional financing isn’t an option.

But with millions of customer interactions each year across chat, phone, IVR, and email, the company recognized a gap between the experience they wanted to deliver and what their existing technology could support. 

The company’s existing chatbot was built on intent-matching: it looked for keywords, routed to a pre-written answer, and stopped there. When a customer’s question didn’t fit a known pattern — which happened constantly — the Genesys chatbot failed or repeated the same answer. Customers got frustratingly generic responses or nothing at all, and they either dropped off or called in.

The stakes were highest in the application process where many customers dropped off when asked to enter their bank account information. The existing solution couldn’t explain why the information was needed, reassure a hesitant applicant, or guide them through the step. It could only present the field and wait. Every drop-off was lost revenue — and at a company where a 1% improvement in application completion translates to more than $1 million in annual revenue, that friction was expensive.

Specific pain points the team was facing:

• Application drop-off at the most sensitive step. Customers abandoning the ACH/bank account entry step had no way to get reassurance or guidance from the legacy Genesys chatbot. 
• A bot that couldn’t handle nuance. Lease questions are rarely generic. A customer asking about their specific lease, their payment status, or whether a particular item qualifies for the program needed an answer tied to their situation — not a generic FAQ.
• No ability to take action. The old bot could answer (sometimes) but couldn’t do anything past that. A customer who wanted to make a payment still had to call. Thousands of calls per month were driven entirely by transactions the channel should have been able to handle.
• Volume without a release valve. With millions of interactions per year across four business divisions at different stages of maturity, the support operation needed to scale. Adding headcount every time volume grew wasn’t sustainable.
• An incumbent that wasn’t agentic. The company had been using Genesys. When they evaluated it against the new requirement — a true agentic AI platform that could understand context, integrate with third-party data, and act autonomously — it fell short. Genesys had also failed to deliver for one of the company’s sister brands. They needed a different answer.

After running a formal RFP with seven AI vendors, the company chose Quiq.

Quiq deployed a fully automated agentic AI agent to handle the company’s entire customer-facing chat operation. 

The system was built to:

• Handle the application process proactively, not reactively. The AI agent can detect when an applicant appears to be stuck — including at the bank account entry step — and offer specific guidance before the customer gives up. Rather than waiting to be asked, it anticipates the friction point and addresses it.
• Understand and respond to lease-specific questions. When a customer asks about their specific lease, their payment schedule, or whether a financed item qualifies for coverage, the AI agent draws on the company’s knowledge base and connected systems to give an answer tied to that customer’s actual situation, not a generic version of the question.
• Process payments directly through chat. Customers who previously had to call to make a payment can now complete the transaction through chat. The AI agent collects what it needs, processes the payment, and confirms the outcome — end to end, without a phone call.
• Operate 24/7 across the full volume. With conversation volume growing month over month (70,000+ conversations a month and growing), the AI agent scales automatically. There is no staffing ceiling.
• Analyze every conversation to assess quality. This company set up Quiq’s AI Analyst to review 100% of AI agent conversations, surfacing patterns like where customers drop off in the payment flow, which lease types generate the most confusion, where the knowledge base has gaps, and what’s actually driving satisfaction scores. The team gets a complete, real-time picture of what to do to continue to improve the experience. The value of the AI agent shows up most clearly in the interactions that used to generate phone calls.
• Bank account entry during applications. This was the highest drop-off point in the application process. When an applicant hesitates at the step requiring their bank account information, the AI agent can now engage proactively — explaining why the information is needed, addressing concerns about security, and walking the applicant through the step. The company showed a 1% increase in application starts and completions; at their volume, that improvement represents more than $1 million in annualized revenue.
• Payment processing through chat. A customer who needs to make a lease payment no longer has to call. The AI agent authenticates, identifies the account, presents the payment options, processes the transaction, and confirms completion — 3,500 to 4,000 times per month. Those are calls that never happen, and about $500,000 a month in payments that flow through the chat channel instead of the phone queue.
• Lease-specific questions. Customers frequently ask questions that depend entirely on the details of their specific lease: when their next payment is due, whether they’re eligible for early buyout, what happens if they miss a payment. The AI agent connects to the relevant systems, identifies the specific lease, and answers in the context of that customer’s actual account — not a generic FAQ response.
• Routing when escalation is needed. For situations the AI agent can’t fully resolve — complex disputes, unusual lease situations, cases requiring human judgment — the system identifies the need quickly and gives the customer the right number to call. The goal isn’t to prevent every call; it’s to make sure the calls that do happen are the ones that actually require a human.

The value of the AI agent shows up most clearly in the interactions that used to generate phone calls.

Bank account entry during applications. This was the highest drop-off point in the application process. When an applicant hesitates at the step requiring their bank account information, the AI agent can now engage proactively — explaining why the information is needed, addressing concerns about security, and walking the applicant through the step. The company showed a 1% increase in application starts and completions; at their volume, that improvement represents more than $1 million in annualized revenue.

Payment processing through chat. A customer who needs to make a lease payment no longer has to call. The AI agent authenticates, identifies the account, presents the payment options, processes the transaction, and confirms completion — 3,500 to 4,000 times per month. Those are calls that never happen, and about $500,000 a month in payments that flow through the chat channel instead of the phone queue.

Lease-specific questions. Customers frequently ask questions that depend entirely on the details of their specific lease: when their next payment is due, whether they’re eligible for early buyout, what happens if they miss a payment. The AI agent connects to the relevant systems, identifies the specific lease, and answers in the context of that customer’s actual account — not a generic FAQ response.

Routing when escalation is needed. For situations the AI agent can’t fully resolve — complex disputes, unusual lease situations, cases requiring human judgment — the system identifies the need quickly and gives the customer the right number to call. The goal isn’t to prevent every call; it’s to make sure the calls that do happen are the ones that actually require a human.

The company’s support operation runs differently now. The AI agent handles the entire chat channel without a human in the loop, which means the support team is not triaging chat conversations. The calls that do come in are, by definition, the ones that couldn’t be resolved through chat — which makes the phone queue more focused and the work more meaningful.

The AI Analyst adds another dimension. Rather than reviewing a sample of conversations after the fact, the team sees a complete picture of what’s happening in real time: which topics are generating the most volume, where the knowledge base needs to be updated, and what’s actually driving the metrics that matter. Continuous improvement is no longer a quarterly exercise; it happens as the data comes in.

Looking ahead, the company and Quiq are expanding the partnership to bring the AI agent to the voice channel, extend API integrations to resolve a higher share of transactional inquiries, and deploy SMS for collections and authentication use cases.

By replacing a rigid, intent-based chatbot with a fully automated AI agent, this lease-to-own company turned its chat channel from a triage tool into a transaction engine — one that resolves customer needs, processes payments, and improves the application conversion rate that drives their core business.

Key outcomes:

• ~60% conversation resolution rate across 70,000+ conversations in the past quarter, fully automated with no human agents in the loop 
• 3,500–4,000 payment transactions per month processed directly through chat, capturing an estimated $500,000 in monthly lease payments that previously required a phone call [CONFIRM]
• ~$56,000/month in deflected call costs based on volume and cost-per-call assumptions [CONFIRM $4/call rate with CSM]
• 1% increase in application completion during POC, representing more than $1 million in annualized revenue
• Complete visibility into every conversation, with Conversation Analyst reviewing 100% of chats and surfacing real-time improvement opportunities