The Future of Intelligent Quality Monitoring
1 Jan, 2007
By: Erik LaurenceIn today’s highly competitive business environment, where customers can switch vendors or buy competitive products with the click of a mouse, customer service is a key competitive differentiator. The quality of customer interactions can make the difference between satisfied, long-term customers and unhappy customers who are likely to defect and are costly to replace. Contact center managers need to better understand the factors that impact quality, performance and customer satisfaction so they can reinforce positive agent behaviors and produce better contact outcomes. And, as customer satisfaction and long-term value is shaped not only by the contact center, savvy managers need to better understand how departments throughout the enterprise, from sales and marketing to billing and shipping, affect the customer experience.
Most organizations have some quality program in place to ensure that certain standards are being met and customers are generally satisfied. However, with traditional quality monitoring solutions and practices, organizations see only part of the picture and the data they gather often don’t reveal the key insights that can have a positive affect on customer loyalty and business success.
Challenges of Traditional Quality Monitoring
Traditional quality monitoring has been based primarily on evaluating a random sample of calls, assessing them against quality standards and then generating a monthly report of average quality scores. Most companies evaluate less than one percent of their calls using a manual, time-intensive process. While this approach provides a certain level of quality assurance, it alone is not sufficiently effective in providing the highest impact and most actionable insights the contact center and business need. Results are focused on what is happening, but do not necessarily reveal how to improve those areas, making it difficult for companies to bring their quality standards to a higher level.
A traditional quality monitoring approach can provide important insights about quality and performance indicators and help companies address contact center issues. But this traditional approach may not address issues unrelated to the contact center that nevertheless impact performance. And while traditional quality monitoring can tell you what is happening in the contact center, it usually does not indicate why, leaving companies to guess at the root causes of poor business outcomes and the best ways to improve them. For example, a business may know that customers are closing accounts without knowing the underlying reason for these customer defections. This leaves the business poorly equipped to address this critical business situation.
Not All Interactions Are Created Equal
The other problem with random sampling is that not all customer interactions have equal value to a company. Certain customer segments and transactions impact profitability more significantly than others and some customer interactions provide greater insights about performance strengths and weaknesses. A large majority of inbound calls are related to average everyday situations. In the future, many of these repetitive transactions may be self-served by Web or speech IVR solutions. The key focus for these interactions should be on improving efficiencies and reducing costs. However, the other 20 percent of these are “moment of truth” interactions --- those that make or break a customer relationship. These interactions reveal a turning point that either cements customer loyalty or significantly damages it. These types of pivotal moments shape customer loyalty and can yield valuable insights for strengthening customer relationships. Companies need to dig deeper and find those calls that matter most to their business.
A quality monitoring solution that automatically focuses on the most important calls ¯ the calls most relevant to business objectives ¯ delivers a significantly higher return on investment and helps align the contact center with important business goals.
A Focused-Quality Approach
Moving beyond traditional quality monitoring can deliver greater enterprise intelligence and value. To stay competitive and provide enhanced customer service, companies are adding a more focused approach to their quality programs. So, in addition to randomly sampling calls, companies can focus on the calls that matter the most to their business and customers. Automatically analyzing a wide variety of calls ensures that nothing falls through the cracks and that any underlying trends or issues are revealed. Calls with successful or failed up-sell attempts, calls with poor or high customer satisfaction, and repeat and unresolved calls can provide key insights into overall business performance.
Beyond prioritizing and evaluating calls that matter most, companies are also leveraging advanced quality monitoring applications to improve efficiencies, such as synchronized audio, screen and quality forms in one window for evaluations. Features such as automated inboxes save evaluators time by automatically pushing calls of interest to them. Specific calls based on customizable criteria are automatically delivered directly to the evaluators’ desktops according to duration, direction, DNIS, ANI or CRM data or findings from speech, screen or performance analytics. Leveraging this call data and speech, screen and performance analytics, companies can drive the calls that matter most into the quality process ¯ from customer complaints and competitive threats to calls that do not meet first-call resolution guidelines.
Employing a focused-quality approach not only enables agent quality, but delivers the “power of why,” so organizations can understand the reasons behind the issues impacting business performance and take actions that drive enterprise results. Based on what customers are saying, as identified and categorized with speech and performance analytics, customer interactions can be mined to identify and suggest the root cause of customer dissatisfaction. This advanced data mining technology uncovers subtle trends and patterns that users might never have considered --- actionable intelligence that might never otherwise be revealed.
Intelligent Quality Monitoring
The next generation of quality monitoring is transforming contact centers into enterprisewide intelligence centers. It is moving beyond the traditional approach of identifying issues in the contact center alone to a more holistic view of performance enterprisewide. With this approach, companies can identify the “why” behind contact center performance and outcomes and, based on this information, determine the best courses of action to improve them. And it can provide important insights to departments that shape the customer experience — in the contact center and across the company.
One way to focus these efforts is through call categorization --- the ability to classify large volumes of calls into business-related categories based on call content. For example, a large New England-based provider of financial services was able to maximize productivity by taking a more focused approach to its quality management and using call categorization to find the right calls to evaluate. Its existing quality program required each supervisor listen to a certain number of calls per agent each month where the customer was trying to transfer funds, close an account or set up a new account. Its approach to finding these types of calls was less than efficient. An evaluator would pick a call at random and listen to it for three to four minutes to determine whether the call did not meet the QM criteria. Then the evaluator would pull another random call and continue this tedious process until he found the right one to evaluate.
The financial services company was wasting precious time and resources with this hit-or-miss approach. So it decided to integrate an analytics-driven quality monitoring solution and, using features such as call categorization, it was able to search specifically for all calls that pertained to transferring funds and opening or closing an account. The system easily identified these calls and automatically delivered them to the company’s inbox for review. This saved evaluators a significant amount of time; so instead of spending half of their day searching through random calls to find the ones that were relevant, they were available for review at their convenience. Evaluators now had more time to monitor a wider array of calls and to focus on training and coaching their teams.
Leading contact center managers are applying advanced analytics to reveal trends impacting key metrics, such as first-call response, high call volume, average hold time, successful up-selling and cross-selling. These analytics automatically prioritize key metrics by their impact on the business, so resources can be focused on these areas first.
Why Are My Customers Calling?
Speech analytics, in particular, can reveal key insights into the root cause of customer problems and complaints. For example, a large wireless carrier was experiencing unusually high call volume and didn’t understand why. In addition to being a large expense for the company, the high call volume was leading to unhappy customers and increased customer churn. The company wanted to understand why its customers were calling and find out if there was a trend or common issue among these calls that needed to be addressed. The company implemented a speech analytics solution to enable them to understand the root cause of the high volume of customer calls so they could reduce costs and increase efficiency. The solution was deployed across its enterprise to gain in-depth insight on a broad range of issues.
The wireless carrier focused its speech analytics solution on identifying those calls that were leading to the high volume. After analyzing thousands of calls, the carrier was able to identify that six percent were customers complaining about handsets. Further analysis revealed that the vast majority of these complaints were specific to one vendor’s handsets. The data were presented to the handset vendor, along with a sample of calls to substantiate and quantify the issues. The handset vendor resolved the problem and provided a substantial credit back to the wireless carrier. The speech analytics solution helped this customer identify a pervasive but hidden problem, revealed its root cause and saved the wireless carrier both money and valuable customers. The company has since expanded this new approach to quality monitoring enterprisewide.
Achieving Stronger, More Profitable Customer Relationships
Shifting to a focused quality approach provides a company with the power to understand why things are really happening. Focused, analytics-driven quality monitoring programs help organizations improve quality enterprisewide, in every department that shapes customer satisfaction and value. With this actionable intelligence, companies can realize significant return on their quality monitoring investments. This customer intelligence can then be shared across the organization, helping to align the entire enterprise more closely with the behaviors that impact customer and business success.
Intelligent quality monitoring is transforming contact centers into knowledge centers and giving companies more power to make changes that will positively impact business performance. Companies are gaining a competitive advantage through advanced quality monitoring in the contact center and across the enterprise, leveraging this actionable intelligence for stronger, more profitable customer relationships.
