To say that manually evaluating calls is costly and time-consuming is an understatement. It’s a process rife with subjectivity, which can create an atmosphere of animosity and cause organizations to miss opportunities to improve the customer experience.
Two companies that moved to NICE Enlighten AI spoke recently at their Interactions Live conference about their experiences with the solution’s behaviour models, which enable contact centre agents to proactively self-correct in the moment.
At first glance, the companies have little in common. One is a large telecom that provides advanced fleet management software solutions; the other provides business enterprise solutions.
Despite their differences, however, their experiences moving to Enlighten AI had much in common. Here’s what they shared in separate breakout sessions at Interactions Live.
- Removing Subjectivity Reduces Animosity
The first comprehensive AI framework for customer engagement, it interprets and predicts human behaviours by objectively measuring all interactions consistently and efficiently to drive business outcomes.
The 100% coverage is what sold the telecom – they had been listening to just a couple of customer calls per agent per week. The company uses the solution to identify five agent behaviours that were driving positive customer sentiment and expedite time to insight.
When agents are evaluated on just a handful of calls per week, month or quarter, contact centres miss key insights, and coaches have to spend valuable time sifting through interactions to find a coachable moment.
Agents feel that the process isn’t fair, and that can create animosity between agents and the supervisors scoring their interactions.
By automating objective scoring on 100% of interactions, Enlighten AI empowers agents with the opportunity to proactively improve their performance while removing animosity from the process.
As the telecom found, it also uncovers the good as well as the bad, enabling the organization to give positive feedback about what agents are doing exceptionally well in addition to addressing areas that need improvement.
- AI and Quality Automation Upskills Rather Than Replaces Quality Teams
AI and quality automation can be frightening words; we hear a lot of predictions about what it will do to the workers whose tasks it is replacing.
Both the telecom and the enterprise solution provider found that rather than replacing the quality team, the implementation had the unanticipated effect of upskilling them.
At the enterprise solution provider, quality evaluators have now been upskilled as analysts in scorecard development, system administration and more. They’re focused on mining insights that would otherwise go unnoticed.
When they do listen to calls now, they have a new purpose, asking things like: “What do you want to know? What are your obstacles, and how can we help you overcome them?”
The telecom was also able to upskill the quality team. Rather than checking the box on a scorecard, QA teams now build queries and proactively identify issues. They look for large-scale trends and things that can drive further benefit to the business.
- A Pilot Team Helps Gain Stakeholder Buy-in
The old adage that the proof is in the pudding holds true today. Starting small with a tech implementation enables a smaller group of users to experience the benefits themselves, then serve as ambassadors of the solution across the organization.
There’s one thing the telecom would have done differently with its rollout – it would have increased the size of its pilot.
The teams who took part in the pilot loved how much time the solution saved supervisors and championed Enlighten AI as it was implemented group by group across the organization, improving acceptance across the board.
A larger group of champions would have enabled the organization to fuel adoption more quickly.
The enterprise solution provider, for its part, found that the success of its implementation hinged on being able to communicate one key message to each employee as it was rolled out – what, specifically, was in it for them.
This blog post has been re-published by kind permission of NICE – View the original post
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