Using AI scoring in contact centers to tailor agent training programs

Have you ever had a frustrating experience when calling a contact center, where the agent seemed untrained or unprepared to help you? It can be frustrating and even make you want to take your business elsewhere.

In today's digital age, networking and information sharing are made easy, allowing customers to share their experiences quickly. With customers being more vocal than ever, it's important for businesses to provide exceptional customer service.

Contact center agents are the first point of contact for customers, and their interactions with customers can have a significant impact on customer satisfaction{{What is customer satisfaction?}}. That’s why to provide a positive customer experience, it is vital for contact centers to prioritize the training and development of their agents, ensuring that they have the necessary skills to meet customer needs effectively.

But how do contact centers identify the areas where their agents need training and development? This is where AI scoring and evaluation forms come in. AI scoring is a process that evaluates conversations based on predefined performance metrics, and evaluation forms provide a standardized way to capture feedback on agent performance. 

This evaluation and scoring process is essential for identifying training needs and skill gaps, which can then be addressed through targeted training programs. Coaching that happens immediately after interactions, sometimes called integrated coaching, can improve team performance by 12%.

By upskilling contact center agents{{Customer service agents training}} and improving their performance, businesses can leave customers satisfied and eager to share their positive experiences, increasing the company's credibility and online presence. Companies that offered excellent customer service saw a 60% increase in business from U.S. customers in 2020.

In this blog post, we will explore how contact center AI scoring and evaluation forms can help identify training needs and skill gaps.

What is AI-powered conversation scoring and evaluation forms?

AI-powered scoring uses machine learning algorithms to analyze conversation data, including the language used, tone, grammar, attitude, and sentiment. By analyzing these factors, the AI can identify patterns and provide an objective score for each conversation based on specific performance metrics. This method is more efficient and consistent than traditional manual scoring, which can be time-consuming and subjective.

One of the key advantages of AI-powered scoring is that it provides coverage for 100% of customer interactions, whereas traditional manual scoring methods can only cover a small sample of interactions. With AI, every conversation can be analyzed and scored, providing a more comprehensive picture of overall performance.

This increased coverage can help identify patterns and trends in customer interactions, allowing companies to make more informed decisions about training, process improvements, and customer experience strategies. It can also help to identify and address potential issues before they become major problems.

Another benefit of AI-powered scoring is that it can provide real-time feedback to agents right after their interactions with customers. This immediate feedback helps agents to adjust their communication style and approach to better meet customer needs.

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Although customer satisfaction can vary among different contact centers, certain components are essential for a top-notch customer service conversation when using AI scoring.

• Greeting with "welcome, namaskar, good morning/evening/afternoon"
• A proper tone of voice
• Attentive listening skills
• Speaking at a natural pace
• Using appropriate language
• No silence or dead air
• No cross-talk or overlapping speech
• Demonstrating emotional intelligence
• Properly concluding the call or chat
• Using scripts appropriately
• Following hold procedure

AI-powered scoring can also assist the manual evaluation of agents and expand the coverage of the evaluation process. While manual evaluation is still necessary for identifying nuanced aspects of customer interactions, AI can help prioritize which conversations require closer scrutiny. This can include conversations with low sentiment scores or those that deviate from standard protocols. Read more about sentiment analysis{{What is sentiment analysis?}} to know how it enhances customer service.

Managers can then prioritize these conversations and use evaluation forms that include a set of criteria for assessing agent performance. These criteria can vary depending on the type of contact center, and the nature of the conversations agents handle. However, some common criteria include:

• Business-related benchmarks to ensure agents are meeting business standards and delivering high-quality customer service. These benchmarks should address the following questions:
  
  Did the agent conduct proper research on the customer's account and review prior notes?
  
  Was the agent assigned to the case/ticket based on their relevant skills, knowledge, and experience?
  
  Did the agent comply with all company procedures during the customer interaction?
  
  Was the conversation handled in a timely and efficient manner?
• Customer-related benchmarks to evaluate the extent to which the agent comprehended the customer's issue and provided a satisfactory resolution. The following are some examples of questions that can be used to evaluate an agent's performance in this category:
  
  Did the agent effectively prioritize the customer's needs and avoid using negative or tentative statements during the conversation?
  
  Was the customer provided with the necessary knowledge or resources to help them resolve similar issues on their own in the future?
  
  Did the agent maintain a positive attitude and demeanor, even when faced with challenging customers or situations?
  
  Was the agent able to demonstrate empathy and emotional intelligence by understanding the customer's perspective and emotions?
  
  Did the agent identify opportunities to offer additional value to the customer through products and services, potentially leading to sales or upselling?
• Contact center compliance benchmarks{{Contact center compliances}} are crucial to ensure that businesses operate legally and ethically. These benchmarks address questions such as:
  
  Did the agent follow all relevant regulations and guidelines?
  
  Did the agent handle sensitive customer information appropriately?
  
  Did the agent avoid discriminatory language or behavior?
• Process-related benchmarks should be reviewed to assess what happened after every customer interaction. Questions to answer include:
  
  Did the agent follow up with the customer as needed after the interaction?
  
  Were any escalation procedures followed appropriately?
  
  Was the call recorded and stored properly for future reference?
  
  Were any technical issues or system errors reported and addressed promptly?
  
  Were sufficient notes included by the agent, and was the issue marked appropriately?
• Outcome-based benchmarks are key performance indicators (KPIs) that are used to evaluate the agent's overall effectiveness in providing customer service.
  • Average Handle Time (AHT){{What is average handle time?}} - AHT measures the average amount of time agents spend resolving support or service requests. It includes hold time, talk time, and after-call work time.
  • Net Promoter Score (NPS){{What is a net promoter score?}} - This benchmark measures how likely customers are to recommend the brand to others.
  • First Call Resolution Rate (FCR){{What is First Contact Resolution?}} - FCR measures the rate at which agents are able to answer customer questions or resolve issues on the first contact.
    

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Identifying skill gaps in customer support agents

According to Gartner, a significant percentage of surveyed employees, around 70%, feel that they lack the necessary skills required to perform their job effectively.

Skill gaps exist in every business, and bridging them is essential for success and increased sales. Identifying and overcoming these gaps allows for the reevaluation of strategies and reorganization of the workforce to eliminate weak spots. It can also improve recruitment efforts and ensure suitable and qualified employees are hired, preventing losses that unsuitable employees could cause.

AI scoring and evaluation forms can help contact centers identify skill gaps by analyzing patterns and trends in agent performance. Contact center managers can identify common errors or areas where agents struggle, which can then be addressed through targeted training programs.

For example, if a particular agent consistently scores low in the areas of empathy and emotional intelligence, it may indicate a need for additional training in these soft skills. Similarly, if multiple agents struggle with handling complex customer issues or following company procedures, it may indicate a need for more comprehensive training in these areas.

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Identifying training needs in customer support agents

Once skill gaps have been identified, contact centers can develop targeted strategies to address them, such as coaching or additional resources.

One approach is to create a training program specifically designed to address the identified skill gaps. For example, if a significant number of agents are struggling with empathy and emotional intelligence, a training program could be developed to help agents better understand and connect with customers on an emotional level. This could include training on active listening, tone of voice, and even role-playing scenarios to help agents develop their skills.

A survey conducted by Gallup and Amazon revealed that almost half, 48%, of American workers who participated, would consider changing their current job if they were provided with opportunities for skills training. This demonstrates that investing in the development of employees' skills is a vital factor in retaining top talent and promoting employee satisfaction. 

Another approach is to provide ongoing coaching and feedback to agents based on their performance evaluations. Coaching sessions can be used to help agents understand where they need to improve and provide them with the support and resources they need to succeed. For example, an agent who struggles with script adherence may benefit from regular coaching sessions to help them understand how to use the script effectively.

In addition to coaching and training, contact centers can also use performance evaluations to identify opportunities for improvement in their processes and systems. For example, if multiple agents are struggling with the same issue, it may be a sign that there is a problem with the customer request routing system or the training program itself.

Conclusion

Learning from mistakes and building on successes is a fundamental principle of every business. To improve operations, it's essential to minimize negative factors, enhance positive aspects, and amplify effective strategies. AI scoring and evaluation forms are effective tools in a contact center business that enable them to evaluate agent performance by analyzing their conversations with customers. This approach helps identify areas of strength and opportunities for improvement, leading to better client satisfaction, exceeding expectations, and delivering top-notch customer service.