Call centers are the backbone of customer support.
Earlier call centers used to be typical in-house, manned by company personnel who were well-versed in the product or service but today, call centers are outsourced and managed by a professional team.
These days call centers are generally run without any direct relation to the product or the service department of the company.
It’s no surprise that customer insight matters tremendously in this scenario. Without knowing what the customer wants, call centers can goof up things by acting on unrelated scripts alone.
Since 2020, the traditional working of call centers has shifted dramatically. AI-powered technology is getting introduced in many industries. And interestingly, an aggressive AI trend is getting momentum for contact centers as well.
Though personnel is still involved, AI has been seen as the core advantage in processing many of the first-level customer needs. AI has proven to be a better solution than the hated phone answering tree.
Here are some of the AI trends you should watch out for.
Managing the Call Flow
AI has been successfully applied in large call centers to be the gatekeeper. It can manage the call volume, assign the next incoming calls, track call performance, and signal where pre-defined weaknesses are appearing for follow-up attention.
This type of workload management is ideal for the call center. Instead of spending long hours on monitoring and taking metrics, the management simply has to respond to the flagged issues.
Employee Retention Improvement
Much of the negative perspective of call centers involves the grinding hours. Responding to calls without a break or recovery, and moving from one ticket to the next can be draining.
No surprise, much of this work has moved overseas as it’s not appealing in the domestic labor market. That said, good employees make a huge difference in productivity.
AI is now being applied to manage on-time versus off-time, rerouting workload to ready staff when current staff is about to go on a break.
Consistent expectations of breaks and making work life easier tends to improve retention. It can significantly reduce the training costs a company has to bear for hiring new staff frequently.
AI can easily be used to reward call center staff based on performance metrics. As the AI tracks and identifies worthy candidates, they recognize the efforts of the awardee as well as set visual examples for other staff to follow.
No surprise, productivity goes up knowing the management system is watching the performance, especially beyond just the number of calls handled.
AI Network Management
Previously, network load-balancing had to be managed by network administrators. The pandemic in 2020 forced everyone to rethink this approach.
Now systems rely on AI for managing the network and providing work tools to staff in-person or remotely.
AI is also being applied for handling network reliability consistently across the Internet.
Based on predefined strategies, AI can identify which performance areas need improvement.
AI can also suggest where workload shifts should be applied while measuring performance and contact center activities.
The results can also be tailored to the times when performance is high and low, as well as correlated with different factors such as shift changes, time of day, the skill level of staff, etc.
Managers can observe and address not only the obvious performance factors but also the hidden ones.
Tonal versus Context Customer Analysis
Customers give off a tremendous amount of information about their experience both with voice tone and context.
Call center staff generally focus on context, but tone also triggers lots of signals if listened carefully.
AI systems can now measure the customer’s enthusiasm or apathy towards various points of discussion in calls, flagging potential risks to accounts about to be lost versus those that are “in the green” and doing well.
There’s no doubt that the AI approach will continue for call centers. What is on everyone’s radar is how much of a change it will make in productivity and revenue output.
As companies build better strategies with their AI experience, we can only expect positive results in the future.