Lauren Maschio at NICE outlines how companies can create and manage an automated quality process.
A growing number of organizations are turning to AI analytics-enabled quality to deliver increased speed to insight and fully align quality programs with their most critical business initiatives.
At our recent Interactions Live conference, we talked with current NICE customers to hear how they were able to successfully implement an automated quality program – and what other organizations should consider if they do the same.
The panel featured quality and analytics leaders from a third-party employee healthcare administrator and a financial services leader in addition to Silvia Verrone-Newton, a senior business consultant for NICE’s Value Realization Services team, the change-making arm of NICE’s contact centre transformation technology.
Both organizations have implemented Quality Central and Nexidia Analytics, which work seamlessly together on a common platform with a single user interface that enables users to more quickly operationalize data to support their quality assurance initiatives.
Here’s what they had to say about the process – and their recommendations for a successful implementation:
- Help teams understand how sentiment analysis drives CSAT, captures voice of customer (VOC) and more:
Many organizations are moving to the use of sentiment to not only gain insight into the customer but also serve as a meaningful KPI for agents.
The key, according to quality and analytics teams at the employee healthcare administrator, is contextualizing and customizing the sentiment score for your own environment.
You may find that the score that represents a negative call for your organization is higher than other organizations based on how your calls are distributed among ranges of sentiment scores.
Enabling supervisors to listen to calls to better understand what a given score sounds like – for example, how a call with a -2 sentiment score compares to one with a +3 score – builds trust and confidence in the scores.
- Make change management a central focus:
The bank tested out the use of sentiment scores with a pilot that included about 10% of its teams. Leaders asked for feedback from the pilot participants on what they liked about the dashboards.
Because the bank leveraged proven change management techniques during the rollout, the pilot participants felt like they were part of the change and that they had a voice in the change.
Verrone-Newton shared some insights from her experience of working with customers on many technology transformation projects.
She said, “It’s important to make a case that the current state is unacceptable for continued success while enticing employees with games, performance dashboards to track their progress, and other enhancements.”
“I think the number 1 reason why projects fail is that … people are not brought along on the ride soon enough and often enough. You want to talk through and be transparent about the areas that can be a struggle, but also give them a way to overcome those struggles,” Verrone-Newton added.
“Communicate, communicate, communicate. And remember, it’s not just about the technology. It’s just as much about the people.”
- Embrace the evolving role of quality
As analytics and quality teams increasingly partner, a new role is emerging for the quality analyst – a multifaceted role that does a little bit of everything, helping the company not only understand the customer experience, but also gain insight into what’s critical to the business and what may be a blind spot that needs to be addressed.
“I think the future of quality really is through analytics and AI,” Verrone-Newton said. “It’s a key part of elevating the quality program maturity.”
This blog post has been re-published by kind permission of NICE – View the original post
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