{"id":519597,"date":"2024-05-10T23:03:39","date_gmt":"2024-05-11T06:03:39","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/meet-the-future-with-ai-powered-experience-orchestration"},"modified":"2024-05-14T16:42:11","modified_gmt":"2024-05-14T23:42:11","slug":"meet-the-future-with-ai-powered-experience-orchestration","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-sg\/blog\/post\/meet-the-future-with-ai-powered-experience-orchestration","title":{"rendered":"Meet the Future with AI-Powered Experience Orchestration"},"content":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text]Artificial intelligence (AI) technologies promise to improve nearly all aspects of the customer experience (CX) while driving business growth. So it\u2019s only natural that business leaders have put \u201cAI adoption\u201d at the top of their to-do lists. The catch: Many businesses still face major challenges and uncertainties in realising AI\u2019s potential.<\/p>\n

Today, customers engage with brands through multiple touchpoints, including websites, social media, mobile apps and more. As a result, businesses need to ensure that each interaction is consistent, personalised and relevant to their customers\u2019 needs<\/a> and expectations \u2014 no matter where they are in their journeys or which part of your business engages with them. Without this visibility into buyer or customer behaviour, AI isn\u2019t doing its job for you.<\/p>\n

AI and experience orchestration<\/a> work together to automate and optimise the end-to-end customer journey, giving businesses a holistic view of customers and insight into how they\u2019re engaging. By leveraging the power of data, analytics and automation, businesses can deliver the right information, at the right time, to the right customer, across all touchpoints.<\/p>\n

Happy customers build customer loyalty. And having loyal customers means you\u2019ll need to spend fewer resources acquiring new ones. That\u2019s leading companies to realise the deep value of AI. And that makes it an indispensable component of your customer experience strategy.<\/p>\n

Understanding the Role of AI in Experience Orchestration<\/h2>\n

AI-Powered Experience Orchestration helps companies achieve more personalised and seamless customer experiences. It identifies key behaviour patterns, creating smart triggers for automated actions. It collects data to understand customer sentiment and optimise customer interactions throughout their journeys.<\/p>\n

It does this by coordinating and managing the deployment, integration and interaction of components within the orchestration engine. This includes orchestrating and optimising all these resources<\/a> to streamline and automate data flow for optimal outcomes across entire customer journeys.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_video link=”https:\/\/www.youtube.com\/embed\/ZUzNd8aZVcM?si=1W0Lr2yBviHjUDSx” src=”“https:\/\/www.youtube.com\/embed\/ZUzNd8aZVcM?si=UHpmClUN-ErppJTG“” width=”“560“” height=”“315“” frameborder=”“0“”][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=””]<\/p>\n

The Power of AI and Experience Orchestration for Speed and Efficiency<\/h3>\n

AI is the engine behind experience orchestration \u2014 fueling a range of capabilities to help businesses deliver seamless end-to-end customer interactions<\/a>, including:<\/p>\n

Predictive AI:<\/strong> Using machine learning techniques, AI makes quantitative predictions<\/a> 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.<\/p>\n

Automation<\/a> AI:<\/strong> Intelligently automates repetitive tasks, functions and workflows to eliminate the need for human resources.<\/p>\n

While these aren\u2019t completely new forms of AI, their increasing sophistication and emerging functionality significantly extends their value to experience orchestration.<\/p>\n

In this post, we\u2019ll look at how these AI innovations<\/a> help companies orchestrate the experiences that today\u2019s customers demand.<\/p>\n

Customer Experience in the Era of Virtual Assistants<\/h3>\n

Recent studies<\/a> show that consumers still want access to human support agents \u2014 without complications. But they often want to start with self-service for faster problem resolution.<\/p>\n

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.<\/p>\n

It was only about five years ago that Amazon offered only live chat for customer support. Today, the retail giant can resolve issues like \u201cmy order is missing\u201d without involving a customer service team. It uses a virtual assistant to respond to questions and complete interactions.<\/p>\n

Use cases like this are based on rules that determine whether a particular issue is worth a human agent\u2019s time. The resolution is often a simple refund. For more expensive items, or a more complex request, customers can automatically transfer to a human.<\/p>\n

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.<\/p>\n

Top agents<\/a> \u2014 those with the best skills and performance \u2014 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.<\/p>\n

Major Benefits of Virtual Agent<\/h3>\n

While chatbots have become indispensable in providing information to customers, virtual agents are considered transactional. They can complete more complex tasks that don\u2019t require a human agent.<\/p>\n

While they can\u2019t comprehend all the nuances of human language or make a true emotional connection with customers<\/a>, 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.<\/p>\n

Virtual assistants offer several AI-powered functions that \u201csuper charge\u201d 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.<\/p>\n

Auto-Summarisation Goes Beyond the Bottom Line<\/h4>\n

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\u2019s the risk of the agent not capturing specific points and responses. Or they might misunderstand a specific point.<\/p>\n

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\u2019t paying attention. And that can diminish customer relationships.<\/p>\n

At the end of interactions, auto-summarisation capabilities save time, often five minutes per agent. These summaries benefit call centres overall because they\u2019re 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.<\/p>\n

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\u2019ll need to examine<\/a>.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Serving Up Centralised Knowledge<\/h4>\n

Agents searching for knowledge manually to solve a customer query is time-consuming.\u00a0 And it interrupts the flow of their conversations with customers.<\/p>\n

To the customer, it can feel like the agent isn\u2019t listening to them. It also requires agents to assess the relevance and accuracy of the information because individual customer goals, needs and journeys differ.<\/p>\n

Centralised knowledge<\/a>, 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.<\/p>\n

This ensures that the correct information is available at the right time for the right people. And it\u2019s continually optimised for the benefit of anyone\u2019s query.<\/p>\n

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.<\/p>\n

But your chatbots might use a knowledge base specially developed for their needs. And that information covers the most common questions only. It\u2019s also regularly updated. As separate sources, it\u2019s a setup for confusion.<\/p>\n

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\u2019s integrated and continuously updated.<\/p>\n

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.<\/p>\n

Reducing the Slowdown of Multiple Screens<\/h4>\n

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.<\/p>\n

In a traditional contact centre, it\u2019s 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 \u201cI\u2019d like a refund because I didn\u2019t receive my order,\u201d requires a search.<\/p>\n

On one monitor, the agent might have their chat screen or voice call screen. Another monitor has their order management system, such as Salesforce<\/a>, Billify or Shopify. Each of these might include a distribution tracking window to locate products in transit.<\/p>\n

Then there\u2019s 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.<\/p>\n

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.<\/p>\n

Maximising the Potential of Automation<\/h3>\n

Today, we can predict and automate processes through virtual agents. And we can provide the best customer experiences throughout their journeys in real time<\/a>.<\/p>\n

These capabilities help all areas of the contact centre, including agents and supervisors, resource managers and admins. AI improves almost everyone\u2019s ability to be more efficient.<\/p>\n

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.<\/p>\n

Next Best Action<\/h4>\n

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\u2019ll leave your brand for one that\u2019s more efficient and customer-centric.<\/p>\n

In the near future, AI-powered tools will give agents all the information they need at their fingertips. They\u2019ll 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.<\/p>\n

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.<\/p>\n

Let\u2019s say an airline has a call from a frequent flyer who needs assistance and prefers an online solution. But the airline doesn\u2019t have an automated process for his issue, so he\u2019s 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\u2019s trying to do.<\/p>\n

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\u2019s unique preferences, behaviours and historical data.<\/p>\n

Understanding Segments<\/h4>\n

AI is also using its understanding of segments to identify customers in real time and adapt virtual agent conversations accordingly. Let\u2019s say that one way you segment customers is by annual spend. If a \u201cSilver\u201d value customer calls, you can instantly adapt the virtual agent dialogue to make sure the answers are relevant to silver customers.<\/p>\n

You won\u2019t offer information that\u2019s only available to higher-spend \u201cGold\u201d customers. This is one of the most valuable forms of hyperpersonalisation, as it eliminates mistakes before they happen.<\/p>\n

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\u2019re creating a consistently positive experience with your brand<\/a>.<\/p>\n

Plan Now for the Future of Customer Experience<\/h3>\n

Experience orchestration is based on a customer-centric strategy that\u2019s 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 \u2014 and drive personalisation at scale.<\/p>\n

See how the Genesys Cloud\u2122 platform enables you to harness valuable AI capabilities through the power of experience orchestration<\/a>.[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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