How a Top 5 US Financial Services Company Saved $380K and Lifted NPS Across a 300-Agent Coaching Pilot

How a Top 5 US Financial Services Company Saved $380K and Lifted NPS Across a 300-Agent Pilot

A Top 5 US financial services company — one of the most recognized names in credit card servicing in the country — runs a large-scale contact center operation where the customer experience is a competitive moat, not a cost line. The brand is built on premium service: card members expect a different standard, and the operation is engineered around delivering it.

That commitment is exactly what made the operating problem so urgent. Average handle times were creeping up across card servicing programs. Team leaders were spending the majority of their week stitching together performance reports instead of coaching the agents behind the numbers. And the coaching that did happen lived in notebooks and one-off documents — invisible to leadership, hard to follow up on, and impossible to measure for impact.

AmplifAI was selected through a competitive RFP evaluating AI-powered platforms against the demands of a regulated, large-scale financial-services contact center. A seven-month pilot across 300 frontline agents then proved the operating thesis: when team leaders are equipped with unified data, AI-driven coaching priorities, and visibility into their own coaching effectiveness, the operation gets cheaper and the customer experience gets better at the same time. The pilot delivered a 14-second AHT reduction, a +1 NPS lift, and $380,600 in annualized savings — and converted the engagement into an enterprise rollout to all 10,000+ frontline agents in the contact center organization.

The Problem: Rising AHT, Manual Coaching, Invisible Follow-Through

Card member servicing is unforgiving. The volume is high, the issues are nuanced, and the brand promise leaves no room for shortcuts. Three structural problems were quietly compounding inside the operation:

• Rising average handle times. Calls were getting longer. In an operation handling tens of millions of interactions a year, every added second translates directly into payroll cost, longer hold queues, and a customer-experience penalty that the brand cannot afford.
• Manual coaching mechanics. Team leaders were spending significant share of their week consolidating call data, building scorecards in spreadsheets, and preparing for coaching conversations that hadn't happened yet. The prep work was the job. The coaching was whatever time was left.
• Coaching follow-through gaps. Feedback delivered in a session lived in a notebook or a one-off document. There was no shared record of what was coached, what action plan was set, or whether the agent was actually progressing against it. Leadership couldn't see the coaching, so they couldn't manage to it.

Leadership recognized that adding more team leaders wasn't the answer. The right move was to give the existing leaders their week back — and to make the coaching they did deliver visible, prioritized, and measurable.

The Opportunity: An AI-Driven Performance Enablement Stack

The team ran a rigorous RFP, evaluating AI-powered platforms against the specific demands of a financial-services contact center: regulated environment, large-scale operations, blended in-house and partner sites, and a customer base with high expectations for both speed and accuracy.

AmplifAI was selected for its AI-powered performance enablement capabilities — a platform that could optimize both frontline-agent and team-leader efficiency from the same data foundation, rather than treating them as separate problems with separate tools.

The Solution: Four Capabilities Working Together

The deployment focused on four capabilities that compounded into a single coaching operating system.

1. Data Unification

AmplifAI pulled disparate contact center data — telephony, QA, CSAT/NPS surveys, CRM activity — into a centralized hub with frequently refreshed metrics. The manual report stitching that team leaders had been doing every Monday simply went away. So did the variance arguments about whose numbers were "right." There was one source of truth, available on demand to every role.

2. Performance Transparency

Agents and team leaders gained on-demand access to clear performance data. Agents stopped waiting for their leader to tell them how they were doing — they could see it themselves, between shifts, on the same dashboard their leader was looking at. Ownership followed visibility. The most competitive agents started using the platform as their own performance tool, asking for coaching rather than waiting to be coached.

3. Next Best Coaching Action

The platform analyzed agent performance and recommended specific coaching priorities to each team leader: which agent, which behavior, which metric, this week. Recognition opportunities surfaced alongside performance gaps, so coaching time was distributed where it would actually move a metric. Team leaders stopped guessing what to coach and started executing against an evidence-based queue.

4. Team Leader Coaching Effectiveness

This was the lever that changed the conversation. AmplifAI tracked the percentage of coaching sessions that produced measurable agent improvement — separating team-leader effectiveness from the raw performance of their team. A high-performing team with a low-effectiveness coach is a hidden risk; a struggling team with a high-effectiveness coach is a signal to invest. With both numbers visible side by side, leader development became a targeted investment rather than a generic training spend.

The Pilot: 300 Agents, Seven Months

The pilot was scoped tightly enough to prove the model and broadly enough to be representative of the full operation. Three hundred agents. Seven months. A control group of non-pilot agents kept running on the legacy coaching motion, providing the cleanest possible comparison.

Agent Efficiency: A 14-Second AHT Drop with NPS Moving Up

Pilot agents reduced average handle time by 14 seconds per call while improving Net Promoter Score by one point. Over the same window, non-pilot agents drifted 3 seconds longer on AHT — a 17-second swing per call between the two populations, attributable to the coaching program.

The combination matters. Faster calls and lower customer-experience scores would have been a worse outcome, not a better one. The pilot delivered both efficiency and experience in the same direction — exactly the dual outcome the brand competes on.

Cost Savings: $380K Annualized Across the Pilot

Translating 14 seconds per call across the 300-agent pilot population produced $380,600 in annualized call-handling savings. The math is unforgiving and unambiguous: less time per call, multiplied across the population, equals real money — and the customer experience went up while it happened.

Coaching Capacity: 15 Minutes Back Per Session, 53,000 More Calls

The cleanest operational outcome lived at the team-leader level. Team leaders saved an average of 15 minutes per coaching session with prep, scoring, and documentation handled by the platform. That recovered time translated to roughly 53,000 additional calls of capacity per year at the pilot site — and a credible path to reduce coaching staff by up to 30% as the model scales without losing coaching coverage.

The platform did not replace coaches. It removed the work that was preventing coaches from coaching.

Enterprise Expansion: From 300 to 10,000+

Pilot success drove an enterprise commitment. The Top 5 financial services company signed on to roll the platform out across all 10,000+ frontline agents in the contact center organization. The pilot wasn't a proof-of-concept that quietly ended in a slide deck. It was the trigger event for the operating model going forward.

Why It Worked: Coaching as the Operating System

The lesson the team is most explicit about is straightforward. Coaching is the highest-leverage activity in the contact center, and almost nothing in a traditional stack is built to measure whether it's actually working. Counting sessions on a calendar is a vanity metric. The right number is the percentage of sessions that produce agent improvement — and that number is the single biggest predictor of whether handle time, conversion, and CX will move in the direction leadership wants them to move.

AmplifAI made that number visible at the team-leader level for the first time. Once it was visible, it could be managed. Once it could be managed, the underlying business metrics moved with it.

What This Means for Other Financial Services Operations

The lessons from this pilot generalize cleanly across financial services, card servicing, and other regulated contact operations:

• Stop adding coaches. Free up the ones you have. Manager time savings of 15 minutes per coaching session, multiplied across a 300-agent population, is the structural ROI of the deployment. The pilot site is now running with materially more coaching capacity without adding a single coaching head.
• Measure coaching effectiveness, not just coaching activity. Sessions-per-month is the easy number to track. Percentage-of-sessions-that-produce-agent-improvement is the right one.
• Efficiency and CX are not opposed. A 14-second AHT reduction with NPS moving up in the same window is the cleanest possible refutation of the false trade-off between speed and experience. Targeted coaching, prioritized by AI, delivers both.
• Pilot small, prove the math, then scale fast. Three hundred agents was enough to expose the cost line and the customer line. With both numbers moving in the right direction, the case for 10,000+ wrote itself.

For financial services contact center leaders facing rising AHT, stretched team leaders, and coaching that no one can see clearly enough to manage, the structural case is simple. The constraint is not coaching capacity. The constraint is coaching visibility — and AmplifAI is built to remove it.