Generative AI for Customer Experience: 17 Cases from Global Brands

In the digital era, the rapid spread of customer opinions through social media and online reviews highlights the necessity of managing customer experiences with great care. Effective CX management is vital not just for resolving issues promptly but also for sustaining business growth and ensuring long-term profitability. While Artificial Intelligence has been around for decades, the ease of use and public exposure of ChatGPT has created heightened visibility with the average consumer and quickly gained momentum for business leaders. From a customer experience perspective, there is great excitement (but also fear) in how large language models, like ChatGPT, can be used in customer- facing settings. McKinsey fittingly describes this as “data liquidity.” Without the free flow of data from across the ecosystem, AI’s recommendations and predictions may be flawed or—even worse—wrong.

This would be possible whether the customer communicated to the business via social media, chat, email, text or phone. Pedro Andrade is vice president of AI at Talkdesk, where he oversees a suite of AI-driven products aimed at optimizing contact center operations and enhancing customer experience. Pedro is passionate about the influence of AI and digital technologies in the market and particularly keen on exploring the potential of generative AI as a source of innovative solutions to disrupt the contact center industry.

One example I am particularly excited about is the concept of proactive customer communications. Companies can use incoming customer service data to identify problems more quickly like product outages or downtime, and then immediately get messages out to their larger customer base…before most of them even knew there was an issue. Since generative AI tools share many of the same features as conversational AI solutions, they can also address many of the same use cases. We’re already seeing an increase in companies using generative AI to create intuitive chatbots and virtual assistants.

Historically, however, the extraction of information from volumes of data has been difficult—especially when it’s unstructured. AI changes the game by automating the process of pulling specific details from customer support tickets, chat transcripts, conversation generative ai for cx sentiment or surveys (to name a few) to surface vital but buried information. You can foun additiona information about ai customer service and artificial intelligence and NLP. It helps shoppers choose products based on budget, dietary needs, and dish ideas. The chatbot assists with meal planning and suggests anti-waste solutions, promoting sustainability.

Join CX Network’s All Access AI Revolution in CX webinar series and learn from leading experts and brands. A concern that frequently arises among customers is how their personal data is used by AI platforms. There have been incidents such as the case of ScatterLab in South Korea, where data from 10 billion conversation logs, originally collected for a dating counselling service, were used to develop an AI chatbot called Lee-Luda. As with all nascent technology, organizations must take concerns around data privacy, transparency and trust into consideration to avoid being on the wrong side of the law and alienating customers.

Customer Service

These changes highlight the necessity of generative AI within the customer service environment. Nevertheless, there are some pitfalls businesses need to avoid when implementing generative AI into their contact centers. Anyone who has worked in customer service understands the challenge of responding to the sheer volume of customer queries at a near-constant rate. As Arlia describes, generative AI’s ability to produce customer-facing copy is a godsend to teams who are already stretched to capacity. This includes contact center agents who will be able to use Q to quickly form responses to customer queries without needing to manually search knowledge bases and documents.

Pre-processing involves feeding the GenAI analytically robust quantitative aggregations, allowing the system to focus on generating qualitative insights, thereby minimizing its engagement in less reliable quantitative analysis. It is important to couple summarization with a robust data pre-processing pipeline to avoid GenAI hallucinating, which it really likes to do; more on that later, though. Technology and AI are developing at neck-breaking speed and the ways people and organizations work are now changing on, quite literally, a monthly basis. Your organisation is unique and so is the approach needed to grasp the true essence of generative AI and pinpoint areas where it can deliver significant value. Our commitment to continuously review and assess, drives an unwavering pursuit of competitive advantage.

Generative AI is transforming customer experience by providing dynamic, intelligent, and highly personalized interactions. This technology leverages sophisticated algorithms to understand and anticipate customer needs, making every customer interaction more engaging and effective. Integrated services like music streaming, eCommerce, and even payments streamline daily tasks. The company expands the boundaries of AI-driven customer interactions with this unique approach. The brand introduced call center AI to deliver superior assistance to their consumers. This empowers agents to better understand buyer needs and tailor their responses accordingly.

According to research by SurveyMonkey, 90 percent of the public prefer customer service from a human rather than an automated chatbot. While AI has clear benefits in terms of speed and efficiency, it cannot replace the emotional intelligence and empathy that are necessary to build customer loyalty. As consumer expectations evolve, organizations across industries are turning to cutting-edge technologies to stay ahead in the race for customer loyalty and market share. One of the most influential developments of recent years is generative artificial intelligence (AI), a powerful tool that is reshaping how businesses operate. With Generative AI for CX, we help organizations develop tuned foundation models and help them navigate the complexities smoothly.

It also generates improvement suggestions, summarizes conversations in bullet points, and uses data to identify conversations requiring urgent attention. Enhancing customer experience (CX) strategically involves implementing sophisticated methods that adapt to and anticipate customer needs across all touchpoints. It’s about moving beyond basic service delivery to create tailored, meaningful interactions that elevate the entire customer journey.

As executives begin to consider the commercial implications for Generative AI technology, many are prioritizing the opportunity for it to elevate customer experience and drive growth. According to a recent Gartner poll, 38% of executives indicated the primary focus of Generative AI- investment is customer experience. These scenarios contribute to increased churn rates, negative word-of-mouth, and ultimately, a decline in revenue and brand equity. Recognizing and addressing these challenges head-on is essential for businesses aiming to excel in today’s competitive environment. Understand the voice of your customers in realtime with Customer Feedback Analytics from Chattermill.

It’s also a common component in the chatbots and virtual assistants customers interact with through text and speech, for self-service interactions. Conversational AI has become the backbone of many advances in the customer experience and contact center landscapes. It forms part of the tech behind conversational intelligence tools, such as those offered by CallMiner, Calabrio, and Talkdesk. After processing input, conversational AI tools can generate responses based on their data. Some more advanced solutions can even enhance their responses by using additional forms of analysis, such as sentiment analysis. While the impact of advanced AI algorithms can be felt everywhere, it’s particularly prominent in the contact center.

Businesses should assess the potential benefits of generative AI along these dimensions and evaluate which will drive the most benefit at a use-case level and program level, and which will directly support delivery of CX goals. Generative AI offers an exciting possibility for CX to generate designs of entirely new experiences by creating novel combinations that use high volumes of experience data. CX leaders should be exploring VoC and CX applications to leverage what is available in production environments and understand what is on the roadmap for the solutions they already use.

In “Why consumers love generative AI”, we explore the potential of generative AI as well as its reception by consumers, and their hopes around it. Instead of their response being obfuscated behind a bar chart, GenAI can really bring their feedback to life through summaries and suggestions. Will this technology replace some insight analyst roles – yes, to be absolutely realistic, I think it probably will.

The bot led customers through a playful quiz, rewarding those who answered correctly with a free bouquet. Winners could then use the intelligent feature to create customized messages for their mothers. This innovative tactic deepened buyer connections with the brand and skyrocketed engagement metrics.

The CX AI Butterfly Effect: Smart self-service, big impact

They should understand how to collaborate effectively with the AI and step in when necessary. These are advancing rapidly now due to gen AI, which enables them to produce text-based responses in natural language. An LLM is a type of gen AI that uses deep learning techniques and vast data sets to understand, generate and predict new content.

Like most forms of AI, generative AI relies on access to large volumes of data, which needs to be protected for compliance purposes. It can cause issues with data governance, particularly when teams have limited transparency into how an LLM works. The question is, which of these two solutions do you need, and do you need to choose between one or the other?

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Generative AI has the potential to create a high impact across key customer-facing functions, including marketing, sales, commerce, and customer service. Manually creating and maintaining help center resources is a time-consuming process that hinders the ability to deliver effective client care. At Master of Code, we’ve built an AI-powered knowledge base automation solution for a top-tier enterprise. Explore the power of AI and learn about its potential in AI for CX For Dummies, NICE Special Edition. When artificial intelligence (AI) is integrated into business strategy, your business can provide outstanding customer experience (CX) seamlessly and effectively.

Accelerate and optimize the creation of knowledge articles while improving service request resolution speed, consistency, and customer experience. Predictive AI and machine learning uses individual performance pattern data to optimize field service scheduling and helps service teams maximize resource efficiency at scale and get more jobs completed per day. New tools that establish https://chat.openai.com/ generative AI guardrails, deepen our commitment to help our customers adopt AI in a way that’s simple, safe, and effective. This strategy is not just about mitigating risks; it’s about accelerating the value delivered to our customers. When applied across industries, generative AI’s focus and capabilities facilitate outcomes that seemed futuristic until recently.

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Companies that excel in delivering positive experiences not only build a reputation for being customer-centric but also significantly enhance their brand perception and influence consumer preferences. This differentiation is particularly crucial in industries where products and services are similar, making CX a decisive factor in competitive success. Customer Experience (CX) is far more than just a buzzword; it’s a pivotal element of successful business strategies across all sectors. In fact, 90 percent of businesses have declared CX as their primary focus, underscoring its significance in today’s competitive landscape. Additionally, it provides a framework for evaluating the impact of AI-enhanced CX using key performance indicators and metrics. Not only do customers value personalization, but they also want interactions to be fast and convenient.

Chatbots also have the bad habit of wandering off-topic or coming to a “dead end,” ruining CX. As Boere describes, any organisation engaging in AI should have clear policies to ensure its implementation is ethical. “For example, businesses must have diverse teams to avoid transferring human bias into the technical design of AI – as the AI is driven by human input. The programme can then be trained and calibrated with more information to produce responses at scale. Alfred Kahn is the founder and CEO at OvationCXM, a customer experience management company.

Conversational AI, for instance, can empower teams to deliver fantastic service across multiple channels 24/7. Conversational AI is a type of artificial intelligence that allows computer programs (bots) to simulate human conversations. It combines various AI techniques to ensure people can interact with computer systems just like talking to another human being. AI-generated email responses to service inquiries help improve service agent productivity and consistency while accelerating response times and time to resolution. Generative AI refers to a class of artificial intelligence (AI) systems that are designed to generate content including text, images and audio in formats that resemble those that a human would produce.

Although experts are of the opinion that Gen AI will be able to take on some tasks however, most believe that the technology is creating rather than replacing. When we examine our contact centers, employees who utilize the generative AI tools are clearly more content and are getting a boost in their productivity. It’s easier to be kind and empathetic when you’re not working with complicated systems and have repetitive tasks. Sometimes it’s wrong — but when it’s wrong, in every case, we’ve been able to trace the issue back to knowledge content or behavioral programming that we control. We fine-tune extensively in the first week, and eventually, so those errors happen less and less. That’s why we’re so confident about deploying production-ready solutions quickly.

People expect 24/7 availability, self-service options and seller-free experiences, not to mention personalization, convenience and speed. And although chatbots have gotten significantly better over the past several years, customers will still scream, “Speak with an agent! Leverage generative AI to enrich your customer understanding, create smarter automation in real time and boost employee performance. AI technologies can also be used to blend competitive intelligence, market trends and customer data at speeds that no human can achieve. While performance analysis isn’t simple, the more information a brand has at their fingertips, the better informed their decisions will be  – even more so if they have programs in place to act upon this intelligence.

Don’t get caught up in AI myths – embrace the future of customer service with VoiceOwl and watch your business soar. While having technical knowledge can be helpful, many low-code and no-code platforms and tools like VoiceOwl that are user-friendly help non-technical users to set up and use AI solutions. Low-code and no-code platforms are growing rapidly which allows non-tech users to access and implement AI functions without writing complicated code. This helps to make AI use even more accessible to those who have no technical background. The advent of Large Language Models (LLMs) and Generative AI might tempt some to believe that LLMs are for large enterprises.

Before deploying new generative AI tools, CX leaders should assess the current adoption of existing AI functionality in the organization’s VoC platform. These deployments are remarkable in terms of how much knowledge the system can remember. Imagine an AI that juggles massive amounts of info better than any person could. The Crescendo CX assistant is flexible—it can deliver great results with surprisingly little information. See for yourself on our homepage — that assistant only has a few pages to work with.

Duolingo’s responsive language learning

From the different applications of generative AI in CX to how to implement it along with real-world use cases, this guide is your compass for navigating this new world and its profound impact on customer experience. This strategy goes beyond meeting needs; it fosters a culture of continuous improvement and sets new benchmarks in CX excellence. CustomGPT.ai equips businesses to exceed evolving customer engagement standards, fostering enduring relationships and establishing new industry standards.

This is the use case that most people are familiar with, thanks to applications like ChatGPT. As AI models prove consistent performance, they can gradually take on some end-user interactions to free up human resources for more complex troubleshooting and support. AI holds unimaginable promise to turn human-powered service into a hybrid of man and machine that actually elevates the level of customer care.

• She’s creating a group-wide strategy for key data issues, such as architecture, tooling, governance, and value.
• Electricity and gas provider implemented AI-powered IVR systems and a flexible staffing model to meet increased caller surge demands.
• That said, moving from prototype to production deployment requires careful consideration.
• This journey represents not just technological enhancement but a complete reimagining of the customer experience.

We believe the generative AI is a tool that can not only enable efficiency and enhanced creativity, but it can significantly empower both customers and employees. Given everyone is going mad about it – we have decided to share our thoughts of how this new technology can be used to analyze customer feedback and find insights. Discover Lyra – the only AI for CX analytics that connects customer feedback to business goals. Read the latest Gartner report to discover the key impacts of AI in the contact center, and implementation recommendations for contact center leaders to optimize ROI and mitigate risks. Customers expect businesses to provide personalised, efficient, and interactive experiences that meet their needs.

Across the board from middle-management to executive leadership to board rooms, every business is trying to figure out how generative AI can transform their processes, enhance their customer experiences, and ultimately reduce costs. This development sparked a wave of excitement and innovation in the Customer Experience (CX) space, as businesses began to explore the ways in which generative AI could be used to improve their customer interactions. Tripadvisor’s latest new AI-powered generator turbocharges trip itinerary creation. The solution creates custom routes based on destination, dates, and traveler preferences. The brand’s vast database of reviews and opinions ensures reliable, community-driven recommendations. Overall, the use of Generative AI for personalization mediates a consolidated planning experience and deeper user engagement.

Generative AI is not a technology that warrants a “wait and see” approach; the time to act is now — not just for customer experience but for the organization as a whole. Our sweet spot is working with around a hundred well-maintained knowledge articles. It’s more of an observation based on working with customers that have well-maintained knowledge repositories. Of course the number will likely grow as generative AI makes it even easier to maintain clean and accurate knowledge repositories. Plus, we believe in continuous improvement, which is why our solution includes human-in-the-loop support.

The fun part is that this is not a high-minded dream of the future of customer service; it’s the likelihood. Being a part of this space, it will be incredibly exciting and fun to witness it unfold over the next few years. The ugly category makes the point that “ideal implementation isn’t easy.” It counsels organizations to start with an incremental approach to AI and to identify CX pain points for high-impact AI augmentation. Though conversational AI and generative AI have different strengths, they can both work in tandem to improve customer experience. Tools like Microsoft Copilot for Sales are considered generative AI models, but they actually use conversational AI, too.

They are also trained to iteratively adjust to minimize the difference between generated and desired outputs. AI refers to computational technologies that can perform tasks that typically require human input by mimicking aspects of human intelligence, including learning, reasoning and self-correction. Companies leverage AI to automate tedious tasks at scale so that employees can focus their time on innovation and problem solving.

But honestly, would you want your customer service AI spouting off random, potentially incorrect, information? To be clear, these implementations aren’t demos – these are live production deployments handling real customer conversations. We’ve had some pretty amazing results, and I’d like to share some of what we’ve learned. “Right now is a great moment for us because we have the necessary teams to test and the technical and data maturity to create products at scale once we’re ready,” she said. “You can’t manage what you can’t measure. And you can’t manage effectively if you can’t measure in real time. The key to success is summarizing information to help you make the next decision effectively — and that’s what Clari does,” he said. Lancelot said the sales team now benefits from a single view of customers and they got to that point quickly by creating a consolidated technology stack.

The GPT-enabled system searches the company’s databases and generates automatic responses to frequently asked questions. Eddie said an email that might have taken an agent 12 minutes to answer now takes about a minute and a half. Generative AI is a subset of both Machine Learning and Natural Language Processing that focuses on generating new content or outputs based on patterns from a given dataset. Generative AI models are trained using large datasets to capture underlying structures in the data, often using deep learning techniques to capture complex patterns and generate high-quality data that resembles the training data.

Businesses that leverage generative AI to deliver impeccable experiences will gain a competitive advantage and achieve higher customer retention rates, ultimately leading to increased revenue and success. The tech researcher says service agents will use AI-powered tools to ask natural language questions and receive answers to customer questions rather than searching databases for information. There’s lots of hype about the potential power of generative artificial intelligence (AI), but real-world implementations are harder to find. While the technology could change the world of work forever, its implementation right now is focused on a few key areas — and one of these is customer experience (CX). With NLP, IVR systems can provide more accurate responses and even draw insights from company databases and CRMs to personalize interactions. They can also be configured to route conversations based on various factors, such as customer sentiment or agent skill level.

The efficiencies created by generative AI are helping CX leaders demonstrate the value of investing in customer experience. This report answers a few of the most common questions our analysts have received from leading CX practitioners. It’s designed to help you understand the why, what, and how of generative AI (genAI) for customer experience (CX) transformation. They can pinpoint key action items and discussion trends, automatically classify and triage customer service tickets, and improve the routing process. They can even help organizations create more comprehensive training resources and onboarding tools for new contact center agents, boosting team performance. Most of these solutions build on the foundations of conversational AI, enhancing bot performance with access to large language models (LLMs).

AI-powered tools such as virtual assistants, sentiment analysis algorithms, and recommendation engines can enable companies to create differentiated offerings that address evolving customer needs and preferences. By continuously iterating and improving their products through AI-driven insights, CX management firms can stay ahead of the competition and capture market share, thereby driving revenue growth and profitability. 2023 was a year of great activity in the field of artificial intelligence as well as customer experience, AI and CX. In the meantime, expectations for customers increased as more and more businesses tried to push the limits of the kinds of experiences AI automation services could provide. The companies at the forefront were leaning towards the next generation of AI methods, which include generative AI as well as emotion AI as well as knowledge AI and many more. These technologies, which form the foundational elements for Enterprise AI, enabled companies to provide more effective, meaningful and, perhaps most importantly, more personal interaction with customers.

As a result, MetLife has seen a 3.5% increase in first-call resolutions and a 13% boost in consumer satisfaction. The focus on AI-driven empathy ensures customers feel heard and supported from their very initial interaction. An electronics manufacturer aimed to enhance CX and boost sales with a new direct-to-consumer channel. Master of Code Global (MOCG) developed an Apple Messages for Business chatbot with a Gen AI component for their website. This conversational shopping assistant helps customers find the perfect products. It also answers questions accurately and streamlines the purchase process through Shopify integration.

Sure, there’s the old saying that “the customer is always right,” but your business has objectives. Not all customer conversations are created equal – you have your ideal customer, after all. Wasting time on someone who’s a bad fit (like that too-small prospect for your B2B SaaS) is actually a bad customer experience for everyone. According to PwC, AI is set to be the key source of transformation, disruption and competitive advantage in today’s fast changing economy, with the potential to contribute up to $15.7 trillion to the global economy in 2030.

Access your copy to put your enterprise on the right path to adopting generative AI for CX. Counter your CX uncertainty with this analyst insight that addresses your core concerns around how it works, privacy challenges, benefits for CX, and more. Our community is about connecting people through open and thoughtful conversations.

In the last year alone, we’ve lost count of the number of contact center, CRM, and CX software vendors introducing new AI capabilities for customer service teams. Deflect common customer inquiries by letting AI-powered conversational bots help provide Chat GPT support, answer questions, capture details, and resolve issues without human interaction. Fast forward to today, and we’ve transitioned from elementary AI tools to sophisticated generative AI systems, revolutionizing the landscape of customer support.

By deciphering feedback across different channels, these businesses can zone in on trends and individual customer preferences, facilitating focused product enhancements and personalized marketing strategies. And it’s worth mentioning that people rarely fill out surveys unless they’re unusually happy or unhappy, while sentiment gives brands another data point to make decisions and understand each customer. In 2024, the significance of AI in contact centers is poised to increase substantially. Generative-AI-driven chatbots will take charge of initial customer inquiries, while advanced algorithms will forecast customer needs, leading to streamlined operations and personalized customer interactions. The synergy of automation with AI is anticipated to tackle intricate tasks, diminishing response times and amplifying overall efficiency.

• To navigate this transformative landscape, Forrester Research addresses eight key questions frequently posed by CX professionals in this report, aiming to shed light on the workings and implications of GenAI.
• Our customers are already reaping the benefits, seeing unprecedented improvements in customer experience, along with significant cost reductions and boosts in operational efficiency.
• The programme can then be trained and calibrated with more information to produce responses at scale.
• Ultimately, weaving conversational and generative AI together amplifies the strengths of both solutions.
• While large corporations can reap the benefits of deploying LLMs as can small-scale companies.
• Wasting time on someone who’s a bad fit (like that too-small prospect for your B2B SaaS) is actually a bad customer experience for everyone.

Businesses, regardless of size, need to understand their customers’ journeys, scale them up, automate tasks, empower employees to respond in real-time, and connect partners to these journeys to drive customers towards completion. Smart conversational assistants can analyze inbound ticket information and assign issues to specialized generative models to help with customer service. Conversational bots can even draw insights from FAQs and knowledge bases created by generative AI during discussions. Since 2018, we’ve been a pioneer in this space, and our integration of generative AI across the CX Cloud platform is revolutionizing the way we automate contact center operations. Our customers are already reaping the benefits, seeing unprecedented improvements in customer experience, along with significant cost reductions and boosts in operational efficiency.

With such changing customer demands, CX leaders are working to deploy generative AI. Zendesk found that emerging technology like generative AI is causing 70% of businesses to rethink the entire customer journey. Generative AI—AI that creates content of some sort—can be a game changer for customer service (CX). But it must be implemented carefully and gradually, according to a report from TechSee, a vendor of AI agents for customer service. There are various ways contact centers can connect generative AI and conversational AI. For instance, conversational AI bots can generate better answers to customer questions by calling on the insights of back-end generative models.

Discover the importance of CX and what digital tools are changing the status quo in the banking and finance industry. Electricity and gas provider implemented AI-powered IVR systems and a flexible staffing model to meet increased caller surge demands. Bedrock can be used to build generative AI-powered apps from pre-trained models by startups including Anthropic, AI21 Labs, and Stability AI. Jassy’s comments were supported by Gartner research, which – in October – found that 55 percent of organizations are either piloting or in production mode with generative AI. By extracting this Q&A, it can be used to populate a knowledge library that trains the AI to generate responses and eliminates the need to write help articles from scratch.