CallMiner > Case Studies > Real Time Resolutions Improves Contact Center Efficiency with CallMiner Eureka Analytics

Real Time Resolutions Improves Contact Center Efficiency with CallMiner Eureka Analytics

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Company Size
200-1,000
Region
  • America
Country
  • United States
Product
  • CallMiner Eureka Interaction Analytics
  • CallMiner myEureka Performance Feedback Portal
Tech Stack
  • Interaction Analytics
  • Performance Feedback Portal
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Cost Savings
  • Productivity Improvements
Technology Category
  • Analytics & Modeling - Real Time Analytics
Applicable Industries
  • Finance & Insurance
Applicable Functions
  • Sales & Marketing
Use Cases
  • Process Control & Optimization
  • Real-Time Location System (RTLS)
Services
  • Data Science Services
About The Customer
Real Time Resolutions (RTR) is a full-service mortgage servicer, debt collection, and business process outsourcer operating in the 1st and 3rd party marketplace. Their portfolio consists of mortgage and a variety of consumer accounts including those in bankruptcy. RTR employs 150 Recovery Agents (CSPs) across their U.S. branches. The company is committed to delivering the best possible service to its customers while also maintaining a high level of efficiency in its operations. They are constantly looking for ways to improve their processes and reduce operational costs. One of the key areas they identified for improvement was their contact center, where they saw potential for reducing After Call Work (ACW) and increasing call volumes. They recognized the need for a solution that could help them achieve these goals without compromising on the quality of service they provide to their customers.
The Challenge
Real Time Resolutions (RTR) is a full-service mortgage servicer, debt collection, and business process outsourcer operating in the 1st and 3rd party marketplace with a portfolio consisting of mortgage and a variety of consumer accounts including those in bankruptcy. RTR employs 150 Recovery Agents (CSPs) across their U.S. branches. The company was facing challenges in reducing After Call Work (ACW) and operational costs, and improving call volumes in their contact center. They identified that the agents’ average ACW had a strong relationship to product type. For one specific product type, the average call duration was 2 minutes 30 seconds, but the average after call work was 6 minutes 30 seconds. This was not an occasional occurrence. In some months, the average for call durations was only one third of ACW. The analysis also uncovered that some agents (even those who sat next to each other) had drastically different ACW times.
The Solution
RTR decided to concentrate on one or two easily identifiable business problems that could be solved with a minimum amount of effort in a short period of time. They used CallMiner’s Eureka Interaction Analytics to identify the difference between revenue-generating ‘Talk Time’ (TT) and resource-burning ‘After Call Work’ (ACW). CallMiner Interaction Analytics automatically categorises every call into existing (or expanded) codes. This automation eliminates the need for manual categorisation (saving 100 percent of that time). It also more accurately captures the data. After aggregating call attributes, and other data, the CallMiner Eureka platform categorizes all words in each call to determine reasons for the call, product or competitors mentioned, participant behaviors, presence of procedural language, outcomes or actions. It also measures acoustic signals for call duration, silence/noise, agitation, stress and tempo, and scores calls by calculating performance indicators per call.
Operational Impact
  • Identified that the agents’ average After Call Work (ACW) had a strong relationship to product type.
  • Uncovered that for one specific product type, the average call duration was 2 minutes 30 seconds, but the average after call work was 6 minutes 30 seconds.
  • Discovered that some agents (even those who sat next to each other) had drastically different ACW times.
  • Created a model that showed that by reducing ACW by 50%, total call volume could be increased by 38% with exactly the same team.
  • Provided insight to managers and agents alike, which led to a steady decrease in the company's ACW/TT ratio.
Quantitative Benefit
  • Reduced ACW by 62% in just three months
  • Reduced Average Handle Time by 60%
  • Increased daily call volumes by 82%

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