Just a few years ago, managers in traditional brick-and-mortar contact centers could listen to just a tiny fraction of their agents’ conversations from an agent sitting across the site. Today, artificial intelligence (AI) transforms how contact center manage their communications, making it possible to not only gather insights from multichannel conversations between remote agents and customers on every interaction, but also to use the insights to understand how to improve CX.
“Today, we’re capturing 100% of conversations across a multiplicity of channels,” says Dan Miller, lead analyst and founder of Opus Research. “This concept was economically nuts five years ago.”
Conversational intelligence is finally making companies customer-centric. While the demand for companies to be customer-centric has existed for years, conversational intelligence is making it a reality.
“In the past, that term ‘customer-centric’ was just lip service,” says Miller. “It used to be impossible to conduct deep analytics in real time, culling automated insights to share across the organization in a consistency across all channels. Now, consistent answers can be shared regardless of where the queries come from. We are now able to instantiate solutions at pace and scale.”
In a recent webinar, “AI Made Easy: Effortlessly Resolve Customer Needs at Pace & Scale,”
Miller highlights recent research related to conversational data and how industry leaders are using it today in the contact center. Here’s what you need to know to use conversations to improve self-service.
- The most impactful use cases for conversational data: Miller cites a recent survey by Opus Research of 400 contact center and customer care leaders across North America. Nearly 80% of respondents said they use conversational data to improve customer experience (CX), with chatbots close behind at 64%. Another 48% use conversational data to enable agent-assisted support.
“This tells me that the lightbulb has gone off that self-service will be consumed by an automated entity and a live agent will not always be needed for certain calls,” Miller says.
- How conversational intelligence is enabling businesses: By and large, companies are leveraging conversational intelligence to improve productivity—fully 87% of respondents in a survey by Opus Research. They’re using AI to track traditional contact center measurements, such as average hold time and agent productivity.
One of the key benefits of AI lies in its ability to automatically find patterns that guide CX. AI has made it possible to discover both operational and CX opportunities. Since customers and agents are spread out everywhere, this is not something that humans could do manually. We needed a more robust piece of technology to locate these patterns, and now we have it.
Other common ways businesses are currently using conversational intelligence include:
- Identifying opportunities that can be shared with departments across the organization: 77%
- Increasing revenue: 62%
- Increasing operational efficiencies: 60%
- Collected data is still underutilized: “One thing that comes through loud and clear in recent research,” Miller says, “is that while conversational data is plentiful, it’s often underutilized.” While 80% of respondents in one Opus survey said they are actively using active speech recognition (ASR) to transcribe speech data, two-thirds of them said they significantly underutilizing this information.
Voice calls aren’t going anywhere. We can use those voice calls in a different way to help the rest of our channels—to support agents, to support customers and give them and give customers a better experience. Being able to find the right tools to leverage this data is important.
How to Make the Most of Data
A few key steps can help contact center and CX leaders make the most of conversational data.
- Bring together the various components of the conversational cloud.
When considering what technology to leverage or evaluating how to make the most of data, it can be helpful to consider the different types of assets that make up what Miller calls “the conversational cloud.” Over the last decade, companies are increasingly housing various types of conversational data in a single place:
- Call processing and task routing
- Automatic speech recognition, natural language processing and analytics
- Application logic, including algorithms, workflows and robotic process automation
- Big data, or customer records and the sum of knowledge about them from all sources
Conversational intelligence really comes into fruition when companies can access all of these resources in one place. Another challenge is not just using the data but getting the data to all interact together to be able to find the patterns.
- Build the right self-service and agent-assisted journeys faster.
Technology on the market today enables companies to harness conversational data from voice and chat interactions across all channels to:
- Identify needs across journey from self-service through agent-assist
- Connect the customer with the most suitable agent
- Guide agents in real-time with soft-skills and resolution steps
- Measure every experience for coaching and process improvement
This allows companies to close the gap quickly on underutilized data and meet customers’ needs.
- Power self-service with data to improve CX.
AI powers smart, effective chatbots and virtual agents by learning from optimal interactions with top-performing agents. Insights gained from conversations in voice and chat unlock the keys to self-service CX that works. This data improves self-service by:
- Identifying the best automation opportunities
- Optimizing resolution paths
- Answering more customers’ questions in digital channels
A purpose-built AI algorithm for CX quickly identifies self-service opportunities using conversational data from multiple channels. Instantly, with AI the valuable, but otherwise underutilized data powers smart self-service.
For example, many customers use voice calls to make appointments. From conversational intelligence, AI derives the interaction pattern that shows how these types of calls can be automated with chatbots and virtual agents.
In appointment calls to doctors’ offices, AI uncovers high sentiment and few conversational turns. In this case, a bot would offer automated resolution that could easily help efficiently schedule appointments. A bot would reduce the need for live agent calls by 30%. This is a good example where self-service is not decreasing the workload; it’s the best way to provide better customer experiences.
It’s important to take the customer’s perspective, Miller says. “Recognizing sentiment and the words we choose has become very valuable because those are signals as to how to best serve that individual,” says Miller. “When you leverage the ‘first party data’, the customer’s actual utterances, you can create better CX.”
“The more conversations between customers and the brand are measured and collected over several years, the better the CX will become. Whether it’s automated self-service or agent-assisted digital channels, these technologies should be embedded across the board to ensure consistency,” he adds.
While AI improves many day-to-day activities, bots are stuck in the dark ages. Learn more about how you can leverage AI to differentiate customer experience (CX) on your self-service channels in our on-demand webinar, “AI Made Easy: Effortlessly Resolve Customer Needs at Pace & Scale,” and move your digital CX into the fast lane with Enlighten XO to build smart self-service – fast.