In 30 years of working in contact centres, Ed Creasey, VP of Global Solutions Engineering at Calabrio, has seen the industry through many technology introductions. The advent of AI-driven analytics is no different; except that in some respects, it kind of is. Fear not, he is here to explain:
“AI can now cater to increasingly advanced use cases, but that’s also going to have unintended consequences – just like any other technology,” he says.
“That said, there’s also a specific set of known AI-related consequences that we can anticipate if we want to use AI-driven analytics in the contact centre.”
So, what’s changed, what hasn’t, and how can contact centres navigate the ocean of new possibilities? Let’s dive in.
What’s Changed
To see what changed for contact centre analytics, we must consider the following key aspects:
Observing the AI: Most contact centres use analytics to measure agent interaction and performance – but what about bots?
“Businesses are investing in customer-facing bots, but they’re not analysing that experience, which means these bots could be causing CX issues that slip under the radar,” Creasey explains.
“It’s essential to look at the customer journey through all touchpoints and ensure your analytics covers both agent and machines.”
Data-Powered Decisions: With modern-day contact centre analytics providing AI-powered summarisation, evaluation, categorisation, and root cause analysis at scale, contact centres can then use their output for generative Business Intelligence (BI). This allows them to ask questions and draw insights at the press of a button.
“By telling your BI tool which KPI you want to look at, you immediately get automatic visualisations of the data and relevant insights, making data-driven decisions easier than ever,” Creasey says.
Cornerstone KPIs: With AI able to tell contact centres anything about their data, Creasey and Calabrio believe it’s important to stop and think: What do we want to measure, and how? To do this, contact centres need to define Cornerstone KPIs to measure CX, each having its own signals.
“If it’s a bad customer experience from an agent, a negative signal to look out for would be a human agent’s unwillingness or inability to help,” he says.
“For a bot, it could be repetition, making a customer rephrase or repeat themselves too.”
What Hasn’t Changed
According to Creasey, what tends to stay the same when it comes to technological innovations – in this case, AI and analytical technology – is the appearance of underlying problems.
Here are two areas requiring special attention:
Agent Experience: When setting up automated quality measures to assess agent performance or suggesting coaching, contact centres must be extra careful of potential AI bias.
“We’ve seen cases where AI made decisions that put different groups at a disadvantage due to being trained on insufficiently diverse data,” Creasey notes.
“In the contact centre world, agents could be unfairly marked lower for empathy or professionalism due to such mistakes, which organisations must anticipate.”
Data Security: When analysing customer data, contact centres must be cautious about who will be using it, what they’ll be doing with it, and whether it will be used to train other AI models.
“It’s no secret that AI was built on everyone’s data (with permission), and without the right kind of responsibility, this can have consequences,” Creasey says.
“Organisations should ask about encryption, storage, and use of their data for model training.”
Navigating AI-Driven Analytics: Practical Advice
What can businesses do to use AI-powered analytics right and avoid risks? Here are two important questions to ask when doing it.
[1] How Do I Think About My Data?
First things first: define the problem.
Think about the type of data you need: is it trends, root cause analysis, or a KPI? Once established, define what you’re going to do with it to change the CX.
“Sometimes, especially with AI, we’re so excited that we get totally carried away, forgetting what we wanted the information for. So, focus is key,” he clarifies.
[2] How Do I Get Trained to Use Analytics?
Training is mostly relevant for companies with internal BI and analytics experts. For businesses that don’t specialise in these fields, managed services can be a more suitable option:
“Businesses who don’t do analytics in-house can significantly benefit from hiring a team of experts to do analytics for them, then present quality data back to them regularly,” Creasey notes.
How Can Calabrio Help?
As a leading provider of contact centre and analytics solutions, Calabrio offers various services to help businesses make optimal use of their data: bot and agent analytics, automated categorisation & quality management, AI-powered interaction summaries, Generative BI, workforce management, and much more.
“We do this globally, across all channels, and in the cloud – all to make our customers successful and help them improve their agent and customer experiences,” Creasey concludes.
To learn more about Calabrio’s services, visit their website here.
