Your Genesys Blog Subscription has been confirmed!
Please add email@example.com to your safe sender list to ensure you receive the weekly blog notifications.
Subscribe to our free newsletter and get blog updates in your inbox
Don't Show This Again.
This blog post was co-authored by Genesys technology partner, Daisee.
Managing a team of agents has many moving parts; the new COVID-19 norm of working from home has only increased challenges. Oversight has become far more complex — it’s physically impossible to monitor and inspect every single interaction between your agents and your customers.
The shift toward working from home will be long-lasting. PwC reports an average of 54% of employees will continue to work remotely post-COVID-19, if their companies allow it. This lack of proximity to your agents might make it seem inevitable that you’ll have to make compromises. Yet, the risk of exposure is greater as the normal structure and process are disconnected.
For most organizations, it’s time-consuming to interpret the interaction data from your agents’ conversations — and even harder to act on it. When measuring this data manually, supervisors often feel like they’re trying to find a needle in a haystack. This means urgent issues might not receive the immediate attention they require, or they could be missed completely.
Managers and quality officers spend unnecessary time listening to call recordings and manually scoring against criteria that is important to them. Typically, this only happens for 2% of calls, and intervention is unlikely or inappropriate because the sample size is so small.
That was before COVID-19.
With these new conditions, companies have had to rapidly adapt; the exposure to risk and compliance is only more apparent. In addition, the uncertainty of agent conduct and performance are growing concerns.
Organizations rapidly tried turning to speech and text analytics. These platforms promised the world but were typically inaccurate and rudimentary in their analysis and insight capabilities. This was because of the use of a process called word spotting, which was a machine that simply looked for a word within a transcript and categorized it. The problem with this is that humans typically need to program every synonym or possible way of saying something into the system to locate the word or phrase. Identification of individual words is simplistic and doesn’t indicate meaning or context within a conversation.
Revolutionary interaction analysis technology can now provide machine-read comprehension, otherwise known as Natural Language Understanding (NLU). This uses a combination artificial intelligence (AI) and Natural Language Processing (NLP) to derive meaning within a conversation.
The software groups language based on meaning and is able to locate features within interactions where similar things are said in different ways. It also contains the unique ability, through unsupervised machine learning, to identify the core reason for a call and to ascribe a label for the reason. All calls are then categorized and compared over time to show emerging trends and new issues.
The model behind the software uses the mathematics of word context to increasingly learn language features. This technique is the key property that separates deep learning and AI from traditional machine learning.
Now there’s a way to provide certainty, clarity, insight and to demystify the unknown to make sense of what is going on — even when your agents are remote.
Daisee directly facilitates best practices in a world first. It analyzes and dynamically scores 100% of customer interactions automatically using NLU and identifies issues that require human intervention through an automated quality scorecard. This patent-pending digital scorecard dynamically changes based on the characteristics of the interaction to ensure the scoring is relevant. Additionally, the grading mechanism enacts a workflow for low-scoring, high-risk calls so managers can review quickly and comprehensively with evidence-based rationale detailing precise areas for improvement.
This makes the Daisee solution more accurate in comparison to legacy systems, saves managers time and provides more actionable insights. Providing feedback and responsive management that clarifies and prioritizes workflow is the best practice to ensure productivity for teams working remotely, as outlined by EY 2020: Managing People and Work Remotely. The applications are vast, and span from basic quality assurance through to sophisticated conduct risk management.
Customer service excellence is the expectation for organizations, regardless of COVID-19 conditions. Contact centers, whether on site or working remotely, can leverage AI to process huge volumes of unstructured data to transform insights into outcomes.
Managers can spend more time training agents who need it with confidence — not reactively guessing which agents need support. By enhancing the customer experience through automated quality management, Daisee can help contact centers increase customer satisfaction. And that translates into increased customer loyalty and revenue.
To learn more about navigating uncertainty using AI so you can deliver better results for your business, register for the upcoming webinar on October 1. And visit Daisee in the Genesys AppFoundry Marketplace.
Subscribe to our free newsletter and get blog updates in your inbox.