Customer satisfaction is largely driven by how agents respond to customers, and not always by the severity of the customer’s issue. While this seems counter-intuitive, it actually makes a lot of sense. Contact center agents can’t always control the customer’s situation, such as a service outage or a billing error. What agents can control is how they act when helping that customer.
Research shows that how agents behave — specifically, an agent’s soft skills, like empathy, rapport, active listening, and ownership, among others — can mitigate difficult situations and turn an unhappy customer into a satisfied one.
The key is being able to identify which behaviors affect customer sentiment and then assess these skills efficiently and accurately so agents can have a positive impact on customer satisfaction. Contact centers have traditionally approached this problem by hiring a lot of people to listen to a random sample of customer interactions and interpret how well agents are demonstrating these soft skills. The interactions are then scored based on their interpretations, and supervisors coach to targeted behaviors that have been identified as needing improvement.
The problem is that human listening is neither cost-effective nor consistent — two people evaluating the same call can disagree whether the agent demonstrated ownership of the problem, for example. Evaluators bring their own biases with them, making the process an inherently subjective one. In the end, agents don’t trust the process because they’re being measured based on a handful of calls each month, and supervisors don’t trust the process because it puts them in the difficult situation of justifying their assessment of the calls.
Artificial intelligence (AI) contact center technology can deliver behavioral insights that reside in the so-called “big data,” where pre-built models have been developed based on millions of hours of stored customer interactions. A comprehensive AI framework for customer engagement provides a consistent, accurate, and unbiased score of agent soft skill behaviors proven to drive customer satisfaction — on every single interaction.
By adding real-time interaction guidance to the customer satisfaction models, agents get immediate feedback on how to change the conversation when one of the AI behavioral models predicts the possibility of a negative outcome. Agents receive desktop prompts and specific recommendations, such as how to listen actively or speak more slowly, to have a more engaging conversation. Real-time interaction guidance is a good way to encourage agents to self-correct in the moment and reinforce skills they have already learned in a coaching session.
NICE ENLIGHTEN for Customer Satisfaction with Real-time Interaction Guidance is the first comprehensive AI framework for customer engagement. It not only uses AI to interpret and measure human behaviors both post-interaction and in real-time but also gives agents the guidance needed to proactively steer customer conversations into extraordinary experiences.
Learn more about how NICE ENLIGHTEN is helping organizations across industries turn good customer conversations into extraordinary ones.
