The Power of AI and Experience Orchestration for Speed and Efficiency
AI is the engine behind experience orchestration — fueling a range of capabilities to help businesses deliver seamless end-to-end customer interactions, including:
Predictive AI: Using machine learning techniques, AI makes quantitative predictions based on trends, patterns and correlations. It helps you understand and anticipate customer pain points and needs; optimise your offering; and create a more efficient, personalised customer journey.
Automation AI: Intelligently automates repetitive tasks, functions and workflows to eliminate the need for human resources.
While these aren’t completely new forms of AI, their increasing sophistication and emerging functionality significantly extends their value to experience orchestration.
In this post, we’ll look at how these AI innovations help companies orchestrate the experiences that today’s customers demand.
Customer Experience in the Era of Virtual Assistants
Recent studies show that consumers still want access to human support agents — without complications. But they often want to start with self-service for faster problem resolution.
Navigating these complex workflows to provide the best user experience is the latest challenge for businesses. They need to scale support services without adding costs while giving customers the personalised service they want. For simple requests, virtual assistants have become preferred for their availability, speed and accuracy.
It was only about five years ago that Amazon offered only live chat for customer support. Today, the retail giant can resolve issues like “my order is missing” without involving a customer service team. It uses a virtual assistant to respond to questions and complete interactions.
Use cases like this are based on rules that determine whether a particular issue is worth a human agent’s time. The resolution is often a simple refund. For more expensive items, or a more complex request, customers can automatically transfer to a human.
Optimising the use of top agents by using virtual agents addresses both consumer preferences and the needs of businesses to control costs and boost revenue.
Top agents — those with the best skills and performance — are freed from many mundane, repeatable tasks that a virtual assistant can easily handle. As these agents focus on more complex and high-value tasks, you can continually update your understanding of customers, make improvements to products and services, and optimise workflows.
Major Benefits of Virtual Agent
While chatbots have become indispensable in providing information to customers, virtual agents are considered transactional. They can complete more complex tasks that don’t require a human agent.
While they can’t comprehend all the nuances of human language or make a true emotional connection with customers, new language processing models are closing that gap. This technology enables virtual assistants to interpret and respond with to requests with a deeper level of understanding. Rather than being smart tools, they become true assistants that can complete interactions from end to end.
Virtual assistants offer several AI-powered functions that “super charge” agent productivity. By using predictive capabilities, these assistants can build a base of satisfied customers. These are key components in achieving the efficiencies of experience orchestration.
Auto-Summarisation Goes Beyond the Bottom Line
During customer support calls, agents jot down notes throughout the conversations because they have to complete a wrap-up. In addition to taking time, there’s the risk of the agent not capturing specific points and responses. Or they might misunderstand a specific point.
This is stressful for agents as they try to capture details at the speed of the conversation. It can make customers feel like the agents aren’t paying attention. And that can diminish customer relationships.
At the end of interactions, auto-summarisation capabilities save time, often five minutes per agent. These summaries benefit call centres overall because they’re creating consistent, accurate summaries that are often better than human agents at providing objective, unbiased overviews. They also tend to be more thorough, as AI-created summaries lack human error and simple mistakes, such as an agent forgetting a detail after the call.
Keep in mind that AI can change context within metrics. For example, if an agent spends more time on high-value calls versus many more short calls, average handle time might increase. This is just one of the KPIs you’ll need to examine.
In addition to savings with staff and reduced distraction, the business now has notes from every customer and these are entirely reusable by anyone else who happens to talk to that customer.
Contact centre supervisors can take advantage of the summaries to see full and accurate interactions without having to discern who said what. This can be especially helpful in the case of a dispute, or to capture exceptionally good interactions.
As part of an AI for experience orchestration strategy, auto-summarisation removes a time-consuming task for agents while ensuring that an important source of insight is consistently captured. It keeps agents more focused, engaged and motivated while reducing stress.
Serving Up Centralised Knowledge
Agents searching for knowledge manually to solve a customer query is time-consuming. And it interrupts the flow of their conversations with customers.
To the customer, it can feel like the agent isn’t listening to them. It also requires agents to assess the relevance and accuracy of the information because individual customer goals, needs and journeys differ.
Centralised knowledge, integrated on a single cloud platform, uses AI to provide answers directly to agents or customers by serving up relevant information from the knowledge base proactively during interactions. It fosters efficient decision-making and problem solving.
This ensures that the correct information is available at the right time for the right people. And it’s continually optimised for the benefit of anyone’s query.
For example, agents might have a favorite knowledge base among several they can choose from to answer customer questions. Maybe this favorite seems more up-to-date, or it has good information on more technical topics.
But your chatbots might use a knowledge base specially developed for their needs. And that information covers the most common questions only. It’s also regularly updated. As separate sources, it’s a setup for confusion.
By identifying knowledge data and accessing it from a centralised source, you can take full advantage of data you already own about your existing customers. From this, you can implement any number of AI applications and access your knowledge from multiple points of view, including business, technical and finance. Deployed AI apps will all depend on the same knowledge that’s integrated and continuously updated.
As your AI maturity progresses and your expertise grows, so will the value of your centralised knowledge to support it. This means higher self-service rates that are satisfying, better first-call resolution and less frustration. Your customers receive a better overall experience from a single source of truth. And you likely earned a repeat customer.
Reducing the Slowdown of Multiple Screens
Multiple screens once seemed empowering for agents who could search widely for answers to queries. But having to navigate in multiple places for information is time-consuming and frustrating.
In a traditional contact centre, it’s common for agents to have two or three monitors as a way of getting to five or more knowledge repositories. Even the simplest service requests, such as “I’d like a refund because I didn’t receive my order,” requires a search.
On one monitor, the agent might have their chat screen or voice call screen. Another monitor has their order management system, such as Salesforce, Billify or Shopify. Each of these might include a distribution tracking window to locate products in transit.
Then there’s another window with customer history that comes directly from Service Cloud. Agents are in constant shifting and searching mode trying to come up with the best and fastest response.
As data and insights are consolidated onto a single platform, AI and experience orchestration can present responses in fewer windows. This reduces the risks of agents missing information because they no longer have multiple places to search for answers.
Maximising the Potential of Automation
Today, we can predict and automate processes through virtual agents. And we can provide the best customer experiences throughout their journeys in real time.
These capabilities help all areas of the contact centre, including agents and supervisors, resource managers and admins. AI improves almost everyone’s ability to be more efficient.
Most of the automation and optimisation technology discussed is already being used to some degree in growth-oriented businesses. But its capabilities are growing rapidly.
Next Best Action
As expectations for service continue to rise, the idea of being on a customer support call for five minutes to complete a straightforward task will be too long for any customer. They’ll leave your brand for one that’s more efficient and customer-centric.
In the near future, AI-powered tools will give agents all the information they need at their fingertips. They’ll be able to automatically pull data from the customer conversation to fill out forms, for example, when issuing refunds. Or, if a customer wants to schedule a product pickup, the next-best option will understand that task and automatically complete it.
Optimisation will include better tracking of virtual agents as they perform these tasks, giving businesses more data and insights into ways to improve. These fast and user-friendly capabilities deliver a great customer experience.
Let’s say an airline has a call from a frequent flyer who needs assistance and prefers an online solution. But the airline doesn’t have an automated process for his issue, so he’s escalated to an agent every time he has an issue. At that point, he might have to wait five minutes for the agent to read through the previous conversation, to see what the customer has been told and what he’s trying to do.
Next-best action capabilities take the predictive power of AI-powered experience orchestration and apply it to streamline the customer journey. Interactions are optimised based on the customer’s unique preferences, behaviours and historical data.
Understanding Segments
AI is also using its understanding of segments to identify customers in real time and adapt virtual agent conversations accordingly. Let’s say that one way you segment customers is by annual spend. If a “Silver” value customer calls, you can instantly adapt the virtual agent dialogue to make sure the answers are relevant to silver customers.
You won’t offer information that’s only available to higher-spend “Gold” customers. This is one of the most valuable forms of hyperpersonalisation, as it eliminates mistakes before they happen.
AI and experience orchestration continue to drive improvements in traditional KPIs, but much more effectively than in the past. Call deflections get customers to a resolution faster without long hold times. Auto-summarisation shaves minutes off interactions. And you’re creating a consistently positive experience with your brand.
Plan Now for the Future of Customer Experience
Experience orchestration is based on a customer-centric strategy that’s a game changer for business. Powered by AI, it enables businesses to seamlessly coordinate technology, interactions and touchpoints. They can predict customer actions, shape and optimise journeys — and drive personalisation at scale.
See how the Genesys Cloud™ platform enables you to harness valuable AI capabilities through the power of experience orchestration.
