DISCUSS THE INSIGHTS INTO AGENT PERFORMANCE GARNERED FROM LISTENING TO CUSTOMERS FOR QUALITY ASSURANCE ( QA ). WHAT TYPES OF INSIGHTS CAN BE GAINED , IS THIS CHANGING , WHAT CHANNELS AND MEANS ARE BEST TO OBTAIN THEM , AND WHY ?
A : All customer engagement channels are equally valid , but the interactions are different and therefore the customer behavior must be measured differently as well .
Customers often share feedback differently when speaking versus emailing customer service . For the customer service agent , the tools and protocols are all the same too ; automated quality and post-interaction feedback , for instance , all work for the same end goal : to improve quality and CX .
Yet , to be effective , different standards must be applied depending on the engagement channel to ensure quality .
For example , there are two kinds of insights you can look at . These are : ( 1 ), agents following the performance standards " script ” and ( 2 ), agents listening to the customers and tweaking the scripts slightly to be more customer-friendly in the moment .
In the first scenario , Agent A scores 100 % by internal performance standards for following policy and procedure perfectly . Whereas in the second scenario , Agent B scores 92 % on quality because they changed the script yet is more aligned with customers ’ expectations . By looking at both , organizations can guide overall agent performance better to be more aligned with customer expectations .
CX professionals used to believe that people would talk about the same thing in the same way regardless of the channel . This is no longer true .
Today , consumers express themselves differently and will have different types of conversations based on channel , so you need to measure accordingly . You want to know if your customers call the contact center for specific issues versus using a chatbot for others .
The concept that companies are more likely to get high-emotion interactions on voice channels and more urgent requests on digital chat or messaging channels must be applied to measuring quality assurance too .
THERE IS ANECDOTAL EVIDENCE OF CUSTOMERS BECOMING MORE AGITATED AND STRESSED , TAKING IT OUT ON AGENTS OVER AND ABOVE THE ACTUAL SUBSTANCE OF THEIR COMMENTS OR SERVICE OR SUPPORT ISSUES , AND YELLING AT THEM AT A VOLUME THAT ISN ' T WARRANTED . IS THIS THE CASE , WHY IT IS OCCURRING , WHAT ARE THE CONSEQUENCES , AND HOW CAN CONTACT CENTERS SORT THE PROVERBIAL " WHEAT FROM THE CHAFF "?
A : Today ’ s expectation for immediate customer service has lowered the threshold to be disappointed .
This , compounded by the COVID-19 pandemic and political and economic uncertainty , means that the general public is rightfully living in a heightened emotional state . Unfortunately , the contact center agent is an easy outlet for escalated rage on occasion .
AI-powered analytics can help companies isolate the " noise " for better complaint management and agent confidence to provide a resolution .
Using deep learning models , AI can look through large volumes of engagement data and apply analytics to find patterns when spikes in similar complaints occur . Like (“ I can ’ t pay my bill online ”) versus one-off customer issues (“ My package is lost ”) and help provide suggestions accordingly .
That way , agents have more confidence in their road to resolution , whether solving for spiky or one-off problem behavior .
AI and analytics shine when used to identify the issue , determine how customers ' responses differ from the average behaviors and further suggest meaningful resolutions .
ADVANCED AI , INCLUDING LARGE LANGUAGE MODELS ( LLMS ) HOLDS PROMISES AND PITFALLS IN CUSTOMER SERVICE . CAN AI HELP WITH UNDERSTANDING CUSTOMERS , INCLUDING GAINING MEANINGFUL QUALITY INSIGHT ON AGENT PERFORMANCE ? AND WHAT ARE THE CAVEATS ?
A : Yes , AI can help in these areas . The two biggest challenges are a need for more relevant training on AI-powered solutions and a lack of prompt engineering and ongoing monitoring to ensure your AI is delivering correct , timely , and relevant responses . The caveat is that your AI must be trained on current data relevant to your customer engagements to deliver intelligent analysis .
One challenge with using generic ChatGPT to identify contact center problems is that it ' s trained on historic internet data and is unable to answer questions that require more current , or specialized , training data .
26 CONTACT CENTER PIPELINE