AI is in the news more than ever, thanks to ChatGPT and generative AI. Businesses across all industries are in the process—or have already—made plans to strategically invest in AI.
In the contact center space, 99% of companies recently surveyed by NICE, say they plan to invest in AI analytics-driven quality management. This dramatic percentage reflects the fact that organizations are aware that their current method(s) of assessing agent performance is sub-par. More crucially, some of these outdated sampling practices have led to misinformed decision making. Other studies agree—according to Aberdeen, 75% of executives want to make better use of their interaction data (by using AI). AI technologies are pivotal in enhancing the customer experience in contact centers by providing real-time insights and feedback, predicting future customer needs, and thereby improving agent performance and boosting CSAT.
Let’s take a closer look at how AI can improve how businesses gather and use data. Many contact centers rely on random sampling of interactions to evaluate the performance of their agents and gain insights from customer interactions. They use these samples to help identify areas of improvement for agents, ensure that agents are adhering to call scripts or regulatory requirements, and identify common issues that can inform the development of training materials and targeted coaching. In some cases, these results even impact agent compensation.
Random sampling is not without its challenges, however. Chief among them is the problem of inadequate or unrepresentative sampling. NICE commissioned a survey of 400 senior decision-makers—supervisors, managers, directors and VPs who work in customer care, customer service, or contact center departments with at least 200 agents across all industries in the U.S. and the U.K.— to better understand the relationship between agent soft skills, customer satisfaction, and the potential of artificial intelligence (AI) to revolutionize how we evaluate agent performance.
One of the key focuses of the survey was the sampling practices of the contact centers, as well as their perception of how AI could improve those practices and CX goals and outcomes. Here’s what we learned.
Sampling is inadequate: Contact centers rely on skewed or random data to make critical contact center performance decisions
Contact centers may not sample every interaction, but they often implement strategies to ensure a representative and meaningful sample. This can include random sampling, stratified sampling based on interaction types or customer segments, or sampling that’s targeted for another specific evaluation purpose. The goal is to strike a balance between resource constraints, operational efficiency, and the ability to gain reliable insights that can be used to drive continuous improvement in customer service. Analyzing agent-customer interactions is crucial for gaining valuable insights into performance, adherence to quality standards, and areas for personalized coaching to enhance agent and customer satisfaction.
In reality, however, sampling performed in most contact centers is far from representative—it encompasses a very small percentage of the overall interactions that are typically handled each month. According to our survey, the average contact center measures just 14 voice and digital interactions each month, and more than a quarter of them currently measure fewer than 10 interactions each month. Given that all of the respondents work for contact centers with more than 200 agents, this is an insignificant sample size, statistically speaking, and not representative of agent performance.
In addition, nearly two-thirds of the contact center leaders we surveyed choose samples based on post-interaction customer satisfaction surveys, which are known for attracting either highly satisfied or highly unsatisfied customers, further skewing the sampling process. CSAT surveys also tend to have a relatively low response rate, representing a small sample of customers.
Other methods of selecting interactions for evaluations include:
- Targeted based on speech analytics categories (55%)
- An automatically selected random sample (51%)
- Targeted based on specific data points (48%)
- Targeted based on desktop analytics categories (42%)
- Manually selected random samples (30%)
Despite the lack of a statistically significant or holistic view, 85% of stakeholders use this data to make critical business decisions.
Teams don’t trust the process: Agents dispute performance feedback due to unrepresentative samples
The goal of any quality management program is to assess agent performance and provide feedback, but programs that rely on evaluators listening to a small random sample of calls and interpreting the results are inherently biased. This erodes confidence in the process. Left feeling that their evaluations are unfair, agents are often resistant to the feedback provided. Incorporating agent performance metrics into the evaluation process can provide a more accurate and fair assessment of agent performance, addressing agents' concerns about the validity of feedback.
In fact, 41% of contact center leaders say one of their top challenges in quality management is that agents don’t buy into their current feedback. Other top quality management challenges, according to our survey, are that evaluators are using a small sample size that is not representative of overall agent performance (38%) and that random sampling is not representative of agent performance (38%).
When feedback is inconsistent and the sample size is too small, it’s no surprise that agents will not want to accept the results and therefore won’t buy into the program.
AI in the Customer Experience Ecosystem
The pervasive integration of artificial intelligence (AI) in customer service is transforming the traditional paradigms of customer interaction and support. As organizations increasingly adopt AI solutions, the emphasis shifts towards enhancing customer experience (CX) and operational efficiency. This transformation is facilitated by the various capabilities of AI, which include advanced analytics, machine learning, natural language processing, and automation.
Deep Analytics and Data Utilization
AI's most profound impact in contact centers is perhaps in its ability to harness and analyze vast amounts of data. Unlike traditional methods that rely on limited datasets, AI can process and interpret complex data from various sources, including voice interactions, chat transcripts, email communications, and social media. This capability allows for a more nuanced understanding of customer behaviors, preferences, and needs. By applying predictive analytics, AI systems can anticipate customer issues and needs before they escalate, enabling proactive customer service that significantly enhances customer satisfaction.
Real-time Decision Making
The real-time processing capabilities of AI transform how decisions are made within the contact center environment. AI systems can provide immediate feedback to agents, suggest solutions, and even automate responses in real-time. This not only speeds up the resolution process but also helps in maintaining a consistent level of service quality across all customer interactions. For instance, AI-driven tools can analyze a customer’s tone and sentiment during interactions, providing agents with real-time insights and recommendations on how to tailor their approach to improve the conversation’s outcome.
Enhanced Personalization
Personalization is key in modern customer service, and AI elevates this to new heights. By analyzing individual customer data, AI tools can create personalized interaction experiences. This includes offering tailored recommendations, personalized solutions, and even anticipating future queries based on past interactions. Such a level of personalization not only increases customer satisfaction but also fosters loyalty and trust towards the brand.
Automation and Efficiency
AI significantly contributes to operational efficiency by automating routine tasks and processes. This includes tasks like ticket classification, routing inquiries to the appropriate department or agent, and even handling standard queries through chatbots and virtual assistants. Automation reduces the workload on human agents, allowing them to focus on more complex and sensitive issues that require human intervention, thereby optimizing the allocation of resources within the contact center.
Agent Training and Support
AI technologies also play a crucial role in training and supporting agents. Through detailed analytics, AI can identify gaps in an agent’s performance and provide customized training programs. Additionally, AI-powered tools can assist agents during live interactions by suggesting next steps, providing relevant information, or even automating parts of the interaction. This not only improves the agents’ performance but also enhances their job satisfaction by reducing stress and equipping them better to handle challenging interactions.
Challenges and Considerations
While the benefits of integrating AI in contact centers are significant, there are challenges and ethical considerations that need to be addressed. Privacy concerns, data security, and the potential for bias in AI algorithms are critical issues that organizations need to consider. Additionally, there is the challenge of seamlessly integrating AI technologies with existing systems and processes without disrupting service delivery.
Organizations must also consider the impact of AI on employment and the skills required for future agents. As AI takes over routine tasks, the role of the human agent will evolve, requiring higher-level skills such as emotional intelligence, problem-solving, and advanced communication skills.
The Future of AI in Customer Service
Looking ahead, the role of AI in customer service is set to expand further. Innovations in AI technology will continue to enhance its capabilities, making it an indispensable tool in the quest for exceptional customer service. As AI becomes more sophisticated, it could lead to more personalized and conversational interactions, driven by deeper insights and real-time data processing.
In conclusion, the integration of AI in contact centers is not just a trend but a fundamental shift in how customer service is delivered. It offers a pathway to not only streamline operations and improve efficiency but also to make customer interactions more meaningful, personalized, and satisfying. As organizations continue to navigate this digital transformation, the focus should always remain on leveraging AI responsibly and ethically to enhance both customer and agent experiences.
A path forward
AI technology stands as a pivotal tool in resolving customer inquiries effectively, enhancing customer retention, analyzing customer sentiment for superior service, ensuring high-quality customer service, and focusing on improving agent performance. By leveraging AI, call centers can track and improve customer retention rates, offer real-time guidance to agents based on customer sentiment analysis, and ensure that customer inquiries are resolved during the initial call, thereby improving the overall customer experience and reducing churn.
The survey results clearly illustrate that stakeholders are struggling to improve quality management. AI can easily solve this problem by analyzing 100% of all interactions to improve operational efficiencies and deliver more positive experiences.
Learn more about what we uncovered in our survey about the current state of quality management and why a growing number of contact center leaders are turning to AI to modernize their processes.
