Case Study

A complaint-reduction juggernaut for a financial services major

Deep-dive analytics and better agent training were the key

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Who we worked with

A Fortune 500 financial services company with over $95 billion in total assets

What the company needed

A vastly improved customer experience, a more effective workforce, and a speedier return on technology investments

How we helped

We built a suite of cutting-edge customer experience solutions powered by AI and advanced analytics. Then, we put them to work.

What the company got

Happier customers. An upgraded workforce. A bottom-line impact.

If the customer is always right, this financial services firm was almost always wrong. Clients were bombarding the firm with criticism, especially about its co-branded credit card business. Grievances poured in about payments, charges, fees, fraud — even promotional programs. Judging by complaints and satisfaction scores, the customer experience was getting steadily worse year over year. Inefficient internal operations and ineffective customer interactions added to the pain. So the bank turned to Genpact to design, build, and implement an analytics-led transformation program for customer service operations.

Challenge

Get a handle on a wide range of customer complaints — and eliminate the causes.

The bank's leadership knew that to make customers happier, it needed a detailed understanding of their preferences and pain points in order to improve strategic and operational decision making. The bank had invested in some new technologies, such as voice-to-text, but it hadn't realized any benefits. The main reason: Only a few people had the training to use these tools and the firm hadn't collected any analytics to determine if the technologies were working well.

Solution

AI- and analytics-led transformation for inbound customer service operations

We'd provided data support for the bank in the past, but now it asked us to take on a much larger role. It wanted us to design, build, and implement an analytics-led transformation program for inbound customer service operations. We had specific goals for the new system, affecting 25 consumer finance/retail portfolios in the bank's co-branded credit card market:

  • Develop a data-driven method of measuring customer experience that spanned all interaction channels: agent, interactive voice response, e-service, chat, and social media
  • Quickly detect sub-par agent performance and best-practice sharing opportunities across 15+ sites to improve service efficiency and identify cross-sell opportunities
  • Use the existing voice-to-text technology platform to accurately capture customer complaints and uncover the reasons behind their frustrations
  • Offer technical and advisory support for projects, measure post-roll out performance, and recommend timely solutions by collaborating across client teams

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The assignment was broad, but we rose to the challenge, focusing on the following three key areas:

Customer experience management
We extracted, and rationalized structured and unstructured customer interaction data. We then mapped customer behavior across channels and functions. Applying a predictive customer satisfaction model, we studied 100% of customers' conversations as well as other channels. Then, our proprietary analytics algorithm measured the customer experience across multiple touch points and compiled the results into a customer effort score. In doing so, we discovered that web crossovers and repeat calls were the biggest problems. So we gave agents additional training to improve first call resolution and reduce call handling times. As a result, satisfaction scores improved in just four months!

Agent performance optimization
We centralized all KPIs into a single digital agent performance management platform. We then used our AI-powered automated call quality assessment tool to generate the Agent Performance Index — a single metric directly linked to cost savings and revenue enhancement opportunities. By ranking agents within and across sites, products, and teams, we identified targeted training needs.

Figure 1: Agent Performance Index - a comprehensive measurement system that combines multiple disjointed agent KPIs into a singular metric

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Advanced speech analytics
We used text mining to study over 2,500 call transcripts. We also used speech tuning and automated capture and classification to sort complaints. We found more than 15 categories of complaints in all, including payment, fraud, charges/fees, and promotion. Based on what we learned, we gave agents training to proactively handle customer concerns. That resulted in more than a 10% lift in complaint capture accuracy and a 15% reduction in overall complaints. Customer satisfaction scores improved by more than 300 basis points.

Figure 2: Our speech analytics solution helped the bank reduce customer complaints

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Impact

Complaints are rapidly decreasing

Overall, agent productivity and efficiency skyrocketed. But perhaps the greatest impact was in the customer experience across all channels:

  • 20%-25% reduction in customer effort
  • More than 15% reduction in customer complaints
  • Greater than 5% improvement in customer satisfaction scores

Improved productivity and effectiveness produced about $3 million in annual savings. What's more, the bank experienced:

  • A decrease in agent handling time by 10%-15
  • An improvement in first call resolution rate by 8%-10
  • The elimination of manual complaint capture process
  • Fewer manual audits, improved QA coverage and reduced sample bias

The bank also enjoyed greater revenue, with up to 10% improvement in offer and net sales rates across sites.

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Thanks to our expertise, the bank later replicated these solutions across other banking functions, moving us up the client's value chain from data support to key decision influencer!