AI must enhance the customer experience.
Ideally, an AI-driven customer experience quickly assesses and resolves a customer concern and keeps the customer engaged with proper next steps. Consequently, it’s becoming clearer that an excellent customer experience can begin and possibly end by interacting with AI-driven technology, which includes chatbots, Conversational Agents (CA), Interactive Voice Recognition (IVR), and Intelligent Virtual Agents (IVA).
In this blog, we’ll examine three points:
- How AI drives improved customer satisfaction in the contact center
- Measuring AI’s effectiveness in the process
- How AI needs to improve in the contact center environment
We conclude that AI in the contact center space has an important and ever-expanding role in shaping the customer experience, but it’s not yet ready to supplant all human agents.
One Percent of Customers Answered “Absolutely”
Customers expect a seamless, friendly, frictionless, and brief experience when they interact with a contact center. “Resolving a complex situation with an enterprise-sized contact center operation” is not on their bucket lists, so it’s already an inconvenience for customers to embark on a journey through the contact center matrix.
In fact, according to Talkdesk, 43 percent of customers answered “No” to the question: “Do customer support contact centers always provide excellent service?” And 11 percent said “Absolutely…not.” Only 18 percent answered “Yes.”
And a mere one percent answered “absolutely.”
The Talkdesk study clarifies: “The majority of customers surveyed are dissatisfied with the service they receive from contact centers.”
So, it’s clear that customers very likely anticipate an unsatisfactory experience before they even initiate contact with a contact center.
And yet, the same study also concludes that customers’ top-three priorities are these:
- Problem solved quickly
- Personal interaction with an agent
- Speak with a skilled agent
This conflict between what customers expect and what they experience poses a significant challenge for businesses to overcome.
What is the Optimal AI-Driven Contact Center Customer Experience?
Customers get quick resolutions to their problems at less expense to contact centers, while human agents are available to solve more complex customer concerns. Everyone benefits from this type of AI-enhanced customer service.
When AI does the heavy lifting, it’s called Self-Help Level 0 resolution. This best-case scenario resolves a customer issue without involving a service agent.
This outcome is easier said than done. When it happens, it’s called “containment”, as in the customer was “contained” within AI technology. In customers’ minds, the intrinsic nature of contact center automation sets the expectation of quick, favorable resolution. Immediate technology; immediate results. No detours, delays, or distractions. A straight, continuous line from the problem to the solution.
More like a customer dash than a journey.
After all, according to Statista, 42 percent of contact center customers prefer the phone as the main method of communication. However, that does not mean customers necessarily always want to talk to a live agent. Self Help Level 0 is the best case for businesses, since, according to HDI, the cost of transferring to Level 1, which is usually the service desk, costs a business 10 times more than Level 0.
Even when it’s necessary to involve a human agent, customers expect quick, seamless handoffs with agents who are informed of their situations with the right balance of personalization.
Like we said—easier said than done. Yet, it’s what contact centers everywhere should strive to achieve, and many are gaining effectiveness at resolving basic and even more involved customer concerns with AI.
Deliver the Optimal AI-Driven Contact Center Customer Experience
With the present sophistication of AI technology (or relative lack thereof) the ability to provide a pristine data set is critical to help ensure the effectiveness of AI. The next step ensures that AI is fed the necessary data that will “train” it to respond correctly to customer requests. Aceyus can provide such data in large quantities in real time that continually improves the intuitiveness and insight of AI technology for the contact center.
And these are some of the Key Performance Indicators (KPIs) that are critical to measure the effectiveness of AI technology:
- Self-Service Engagement—percentage of users who attempt to engage with AI, as opposed to those who disconnect or immediately “zero out” to speak to an agent
- Task Completion Rate—percentage of tasks successfully completed within AI channels
- Cost Per Transaction—Cost of all contacts handled in AI channels
- Containment—percentage of interactions that begin and end in the AI system and did not require any agent intervention
Aceyus can integrate all these metrics, and many others, into one data source and visualize them on dashboards for review and analysis. This enables contact center managers to make real-time adjustments to their processes as needed to increase CX and CSAT.
IVAs and Chatbots Need to be “Human” in Nature
We’ve offered content worth reviewing in past blogs about chatbots, conversational agents as well as IVR and IVA. Maybe what we haven’t discussed to date is the present state of contact center AI technology. Ultimately, IVA and chatbots will take on a larger share of traffic that human agents currently absorb.
Realistically, though, most of this AI-based technology is, in many ways, still in the “toddler” stage. While some organizations have developed AI that handles complex tasks, a majority of AI today efficiently completes simple tasks, including call routing, bank balances, accepting payments, password resets—tasks that prevent agents from solving more complex customer scenarios.
However, AI can create a new portrait that looks like Rembrandt himself painted it. Why can’t it do more than answer a few basic questions? Can it even conduct a simple conversation?
Not yet. However, that technology is emerging. The conversations are not quite organic yet, but they are productive, as AI “learns” to “behave” and “respond” more like a human being.
AI Lacks Social Presence
Studies suggest that people inject a social element into a conversation with a CA, the “typing” version of an IVA. Customers know they interact with computer software, yet they expect a reciprocal “human” reaction from AI.
However, studies like this one from March 2020, conclude that CAs are not mature enough to reciprocate on a “human-enough” level. They lack the social presence, aka “skills”, of customers and agents. As a result, these positive-goal-seeking mechanisms guide can customers down blind-alley outcomes rather than prompt, satisfactory solutions.
To quote this study: “Though cost- and time-saving opportunities triggered a widespread implementation of AI-based chatbots, they still frequently fail to meet customer expectations (italics ours), potentially resulting in users being less inclined to comply with requests made by the chatbot.”
The Key to Machine Learning: Data
A long customer journey risks the exact opposite outcome that customers and businesses desire. But there’s a conundrum at play here: AI must “gain experience” to “learn” and become more “human” through machine learning.
And the key to machine learning is data—and lots of it. Machine learning shapes and adjusts critical algorithms that refine predictable patterns and responses to keep customers solve their specific challenges.
So, as AI technology matures, contact centers still need to keep pace with rising customer expectations.
Aceyus Connections: The Solution that Clarifies the AI-Driven Customer Experience
Aceyus Connections links customer data from AI sources and other technologies to deliver a true omnichannel encounter that supplies an end-to-end, 360-degree visualization of the customer journey. Schedule a demonstration today.
