How AI Can Help Improve Team Morale & Reduce Agent Turnover

John Ortiz at MiaRec explains how contact centre AI can help you achieve better team morale, improved employee experience, decreased occurrences of agent burnout, and, ultimately, minimal agent turnover.

Improve the Working Conditions of Your Agents

In my past life, I was a QA manager in a large contact centre. One thing that frustrated me the most was the high agent turnover.

Not only do you constantly have to train new agents, which often leads to less-than-ideal customer experiences, but you also see the personal toll it takes on an agent. The constant stress and lack of work-life balance can result in agent burnout.

That is one of the reasons I get so fired up when talking about MiaRec’s AI-powered solutions and conversation intelligence or contact centre AI solutions in general.

They can truly improve the working conditions of your agents, resulting in better team morale, improved employee experience, decreased occurrences of agent burnout, and, ultimately, minimal agent turnover.

What Is Agent Turnover & How Does It Impact Your Bottom Line?

Agent turnover refers to the rate at which your employees are leaving the contact centre to find another job or another contact centre to work for.

In my experience, agents will often jump from job to job every three to six months, but the exact time frames often depend on the type and size of the contact centre and the working conditions.

Agent turnover is one of the biggest problems for contact centres as it can significantly hurt a centre in multiple ways:

1.  Onboarding takes time and effort: Every new agent joining the contact centre will need to undergo some training. Depending on the contact centre, this usually involves one week of in-house training, followed by another week of shadowing and training. This requires additional effort, and the new agent won’t take calls for two weeks.
2. Turnover is expensive. It requires constant hiring, onboarding, and training of new employees. If you have to do this repeatedly, your labor costs will skyrocket, ultimately hurting overall profitability.
3. Reduced Service Quality: New agents take time to learn the ropes. They might initially be intimidated or struggle to navigate complex issues, leading to frustrated customers and unresolved problems.
4. Damaged Customer Experience: Inconsistent service due to a revolving door of agents hurts customer experience. Long wait times, repeated explanations, and a lack of familiarity with customer history all contribute to a negative experience.

This cycle can be very frustrating if you train a new agent every 12 weeks and spend two weeks training them.

How Contact Centre AI Can Help Reduce Agent Burnout & Turnover

There will always be some turnover in contact centres. It’s a matter of fact. However, there are a lot of things a contact centre can do to proactively improve its employees’ experience and working conditions to minimize the number of agents leaving and to increase the time of employment. Here are nine ways contact centre AI can help.

1. It Removes Bias and Increases Visibility by Automating Call Scoring

As much as you want to be unbiased as a supervisor, we are all humans. Therefore, we tend to score the performance of agents we personally like or get along with differently than those that we might not know or don’t get along with.

In addition, if we only have to rely on manual evaluation processes, we might only get to 2-5% of the calls, so chances are that we will catch an otherwise high-performing agent on a really bad day. This can be very frustrating for an agent.

By using Generative AI, you can now automatically score 100% of your calls. This gives you not only a much more accurate picture of your overall performance but also reflects each agent’s individual performance trends better.

Since Auto QA uses standardized scorecards across all agents all the time, you also remove human bias, making evaluations fairer.

2. It Automatically Identifies Agents in Need of Coaching/Support

Once you score all of your interactions with Auto QA, you can identify specific agents needing additional follow-up.

AI might flag particular calls for a more in-depth evaluation by a human supervisor or notify a supervisor that an agent might need extra help or coaching.

AI can also analyze live and recorded calls to identify areas where agents may need additional training or support.

For example, AI can automatically assign targeted training materials if an agent struggles with certain types of queries or procedures.

This helps agents improve their skills and provides them with the support they need to feel confident and competent in their roles.

3. It Automates and Helps Facilitate Agent Coaching

Effective coaching is one of the most useful ways to help your agents improve their jobs and feel more fulfilled.

However, supervisors cannot give feedback after every call and are often too busy to design individualized coaching plans. Using AI, you can now automate much of your agents’ coaching.

Other solutions will offer real-time coaching suggestions, although these capabilities aren’t fully mature yet. Another neat example of agent coaching is Virtual Sapiens, which analyzes body language in virtual/Zoom sessions and gives coaching advice in the live call.

4. It Can Monitor the Agent’s Well-Being Through Sentiment Analysis

One very innovative and impactful way to utilize AI to provide your agents with a healthier and more fulfilling work environment is through sentiment analysis.

Most people think of sentiment analysis as a way to measure customer satisfaction, improve service quality, prevent customer attrition, and so on, but if your solution tracks both agent and customer sentiment, agent sentiment can be a powerful tool in your arsenal to measure the agent’s well-being.

Depending on the particular approach to sentiment analysis, your conversation intelligence solution might use the context of the conversation (particularly the agent’s reactions) and the tonality of an agent’s voice to monitor their sentiment and stress levels during customer interactions.

If the system detects signs of stress or negative sentiment, it can trigger interventions such as suggesting a break, providing stress-relief tips, or alerting supervisors to provide additional support. This focus on mental health can prevent burnout and improve overall job satisfaction.

5. AI Can Identify Highly Successful Calls You Can Use to Recognize Employees

Topic analysis is another great way to help reduce agent turnover. For example, topic analysis can categorize all calls in which an agent was able to help a customer successfully.

As a contact centre leader, you can use this call data to give your agents public praise or recognition. A study by OC Tanner Institute found that employees recognized for their work are six times more likely to stay with their employer than those not.

6. AI Can Help Provide Real-Time Guidance and Assistance

Tackling complex scenarios can be very intimidating, especially for newer agents. Generative AI can offer real-time assistance to agents during calls by suggesting responses or providing additional information by scouring internal knowledge bases and help articles.

This reduces the cognitive load on agents, allowing them to focus more on engaging with the customer and less on searching for information or filling out forms.

7. AI Reduces Post-Call Work & Streamlines Administrative Tasks

Contact centre agents have to be great communicators and think quickly on their feet. They also have to capture the main points of the conversation, enter them into the CRM, and kick off the next steps. That is a huge load while taking calls six to seven hours a day.

AI can automate routine administrative tasks such as call logging, ticket creation, and after-call work by automatically summarizing the call or extracting key facts.

This reduces agents’ non-customer-facing workload, allowing them to focus more on interactions and less on paperwork, which can enhance job satisfaction and efficiency.

8. AI Can Help Create Personalized Development Plans

Using data from various interactions and performance metrics, contact centre AI can help you create personalized development plans for each agent.

These plans can focus on strengthening areas of weakness and further developing areas of strength, providing a clear path for career development and growth.

9. Predictive Analytics for Workload Management

By analyzing call patterns, customer feedback, and agent performance, AI can help forecast periods of high demand and adjust work schedules accordingly.

This ensures that agents are not overwhelmed during peak times, which can improve job satisfaction and reduce turnover.

Conclusion

In conclusion, proactively managing agent wellness and burnout is crucial for the success of any contact centre.

Contact centre AI offers innovative solutions to help reduce turnover, improve service quality, and enhance the overall agent experience.

From automating call scoring and providing personalized coaching to monitoring agent sentiment and offering real-time guidance, AI can revolutionize the way contact centres operate.

Agent Turnover Related FAQs

What Are Signs That My Agents Are About to Leave?

Leading indicators are factors that appear before an event and can be used for preventive measures. In this case, they signal potential employee dissatisfaction that could lead to agents leaving. Some examples that indicate your agents are about to quit are:

• High Number of Absences: Unplanned absences can indicate low morale, disengagement, or health issues stemming from stress.
• Low Employee Satisfaction Scores: Regular surveys that gauge satisfaction with work-life balance, compensation, workload, and management style can highlight areas needing improvement.
• Increased Customer Complaints: If agents are struggling or disengaged, it can lead to frustrated customers and more complaints. This can further add to agent stress.
• Decreased Quality Scores: A drop in call-quality metrics like resolution time or customer satisfaction scores might indicate a lack of motivation or training issues.
• Increased Coaching Needs: If agents require more coaching than usual, it could suggest difficulty handling complex situations or a knowledge gap, leading to frustration.

This blog post has been re-published by kind permission of MiaRec – View the Original Article

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About MiaRec

MiaRec is a global provider of Conversation Intelligence and Auto QA solutions, helping contact centers save time and cost through AI-based automation and customer-driven business intelligence.

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Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: MiaRec