The Most Important Aspects of the Agent Experience
Brands are built on the great experiences that employees deliver to customers, starting with the contact center. As the core of customer experience, it’s crucial that organizations prioritize the engagement their agents need for effectively serving customers. But what does this look like? What top factors make for a superior agent experience that then results in an incredible customer experience?
A recent study from Customer Contact Week shows the following to have the biggest impact on the agent experience:
- Empowerment to offer custom/unique resolutions to customers (23%)
- Frustration with contact center tools like CRM, knowledge base and dashboards (21%)
- Training/coaching (13%)
- Opportunity for advancement (12%)
Arguably the best way to overcome these obstacles is by effectively implementing artificial intelligence (AI) in the contact center. New research from Vanson Bourne shows that the overwhelming majority (99%) of organizations are now using some form of AI in the contact center, with 33% citing “employee turnover” as a key driver of their investment.
Here’s how AI technologies like machine learning (ML), process automation and natural language processing (NLP) help drastically improve the agent experience.
The incorporation of AI into the desktop interface can provide agents with quick access to information so that they don’t have to leave their main screen or search through multiple systems of data to find an answer or make a change. This can be facilitated by real-time conversational analysis (more on this below) or ML predictive capabilities. In this way, certain words that are automatically sensed in conversation can help agents access the right information more quickly and efficiently.
For example, a customer might explain a situation in which the phrase, “I was wondering about your return policy” is mentioned. Before the thought is finished, a pop-up application could appear on the desktop explaining the return policy in full so that the agent doesn’t have to waste time looking it up or put the customer on hold. In this way, agents can feel more empowered to deliver custom or unique resolutions to customers.
For instance, if a customer says something to the effect of “I’m frustrated that my delivery is past due,” the words “frustrated” “delivery” and “past due” could trigger a special discount for the agent to offer to retain the customer. A solution like Avaya IX Contact Center achieves this with a consolidated desktop view of customer information and streamlined management of digital channels and devices. The result: more satisfied agents who feel empowered to solve customer issues.
Intelligent learning algorithms can be leveraged for NPL to perform tasks more optimally and accurately, improving the customer and agent experience. Real-time conversational analysis, for example, can decipher what a customer is saying before an agent is even connected to keep engagements fast—lowering operational costs—yet meaningful and efficient. This is especially beneficial for voice considering real-time constraints of the channel like comprehension and disambiguation of words. Agents can feel more immersed and fulfilled in their roles with less frustration of having to ask customers to repeat information.
Just as certain words can be analyzed or transcribed to improve the customer experience, supervisors can do so to improve the agent experience. For example, an agent might say something along the lines of, “I’m having some trouble opening an application, one moment please.” The words “trouble” and “application” could indicate to a supervisor a limitation in the application or a lack of understanding. This helps alleviate frustration agents have with contact center tools like CRM, knowledge base and dashboards while improving agent training and coaching. At the same time, it can help supervisors narrow in on agents best suited for advancement opportunities.
Conversational Topic Modeling
This form of unsupervised machine learning allows organizations to detect changes in the theme or topic of a conversation, both historically and in real-time, to improve agent assist over time. For example, an ML algorithm can detect “pay a bill” or “technical issue” as a recurring theme or topic shift to help supervisors provide agents with faster access to the right information or resources for handling the transition in conversation. This could be everything from clearer user manual information to more simplified explanations to block diagrams and visuals. This can be used not only in real-time conversations to improve the contact center experience but also for improved agent training.
The possibilities here are seemingly endless. Leveraged alongside predictive capabilities, organizations can use conversational topic modeling to intelligently predict when a customer might change the topic of a conversation to stay a few steps ahead. The technology can be used to help guide chatbot solutions toward more coherent, natural dialogue. Overall, conversational topic modeling is an important first step toward deeper understanding of human conversations, improving the customer and agent experience.
Other applications of AI for improving the agent experience include:
- Advanced workforce engagement: AI can be leveraged to forecast and schedule agent workdays, even allowing agents to bid on shifts, job share and flex schedules to accommodate for things like childcare and work-at-home.
- Sentiment analysis: Gain insight into employee sentiment and concerns in the same way you benefit from understanding that of the customer (even create an Employee Net Promoter Score).
- Virtual assistants: Just like any smart assistant solution used at home (Amazon Alexa, Google Home), a virtual assistant can help agents reduce handle time, increase accuracy, maintain compliance and stay more engaged in their roles.
The contact center is the cornerstone of meaningful human connection, and agents are at the heart of it all. To learn more about effectively adopting AI in the contact center, check out Vanson Bourne’s new research report, “AI: The De Facto for Contact Center Experience,” commissioned by Avaya.