3 Ways AI Can Improve Agent Engagement
Agent engagement might not sound that important, especially when contact center leaders gauge performance on customer satisfaction (CSAT). Agent engagement has been an afterthought for many, but there is a growing need to address agent experience as a key success factor and CSAT driver.
The more engaged agents are, the better the customer experience (CX) outcomes. This goes beyond answering calls quickly and first contact resolution (FCR). While these will remain important KPIs, agent engagement is also about having the right tools to empower them to provide meaningful and personalized customer service. There are many reasons why agent engagement is not successful, including low pay, being overworked and undervalued, and not having access to the tools they need to effectively resolve issues. Add to this the isolation of working from home, and it’s easy to understand why turnover is so high.
Raising pay is an easy response to this milieu, but it has greater impact on retention than engagement. If that’s the only response to improving contact center performance, the net result may be an increase in operating costs with nominal improvement in performance. Another approach would be to focus on team building, and for supervisors to be more supportive. This is mainly about soft skills, and while it can help drive morale, the impact on agent engagement will likely be limited.
For agents to really feel engaged, they need to know that the contact center is enabling them to do meaningful work with customers, so they feel they’re making a difference and not just providing transactional forms of service. As contact centers struggle to find the right technologies, they should view this as a prime use case for artificial intelligence (AI). To better understand why, here are three ways that AI, when deployed effectively, will improve agent engagement.
1. Service Automation
At a high level, service automation has always been the primary driver for AI in the contact center, but it’s important to recognize that there are many ways to automate service. In terms of agent engagement, the objective is for AI-based applications to handle as many routine and transactional inquiries as possible with chatbots and emailbots, for example. As the technologies around AI keep improving, they will become more trusted and capable of service automation.
The key capability to watch is conversational AI (CAI), as it elevates chatbot capabilities well-beyond IVR, where it can manage open-ended inquiries and unstructured data sets. ChatGPT is an even more recent advance in this area, where customer interactions are sufficiently human-like so live agents don’t need to be involved. As agents come to trust these forms of automation, agent engagement will improve as they’ll be spending more time and effort dealing with inquiries where the human touch makes all the difference.
2. Predictive Analytics
The applications for AI in contact centers are very diverse. While chatbots are customer-facing, analytics use cases are primarily for internal support. AI’s true power comes from being able to process massive amounts of data at wire speed. As the use of digital channels increases, so do the data sets that provide insight about customer behaviors, preferences, sentiment, and more.
AI-powered predictive analytics is still an emerging space, and the predictive accuracy will keep improving. AI can identify patterns and relationships in data that humans cannot see and can do it fast enough to support agents in real-time during customer interactions.
The analytics may not be 100% accurate, but they will be good enough to help agents do a better job of providing personalized service in ways they couldn’t do otherwise. Not only will service quality improve by having more complete and accurate information on hand, but analytics can also anticipate how customers will respond to different situations, and then “coach” agents to respond more effectively in the moment.
3. Intelligent Contact Allocation
Various approaches have long been in use to improve contact routing and allocation, but AI brings new capabilities that align well with how contact centers must adapt to the hybrid work model. With a distributed, largely home-based workforce, contact centers can draw agents from a much broader pool, but they must also be able to manage them efficiently.
Many agents are amenable to hybrid work, especially for the flexibility to work from home and choose their work hours. This new model can make agent engagement richer, as they’ll be working more on their own terms. However, to make this model work, contact allocation must be effective by only sending along inquiries that play to an agent’s strengths. That could be product knowledge, expertise about the market, or language. To some extent, contacts can be allocated on this basis without AI, but not to the scale needed for hybrid work.
More importantly, AI can map to these strengths on a more granular level and in real-time. As with all things AI, the key is tapping into numerous data streams that are not likely integrated to get a composite picture of each agent and which types of customers or situations line up best for them. With so many agents now working in isolation, quality agent engagement is critical to keep them motivated and performing at a high level. Contact allocation clearly has a role to play here and should be viewed as a leading use case for AI.