How Generative AI Improves Customer Experience

🧠 Is Generative AI Just Cool, or Does It Really Have an Impact? That's the big debate in tech circles these days. A study led by researchers from Stanford University, MIT, and the National Bureau of Economic Research (NBER) sheds light on this question by examining the real-world impact of deploying generative AI in a customer support environment. Their analysis offers empirical evidence on how AI tools, specifically those based on OpenAI's GPT models, are transforming customer service operations at a Fortune 500 software company. The researchers employed a mix of methodologies: a randomized control trial (RCT) and a staggered rollout, encompassing around 5,000 agents over several months. By analyzing 3 million customer-agent interactions, the study assessed metrics such as resolutions per hour, handle time, resolution rates, and customer satisfaction (Net Promoter Score). To understand the AI's impact over time, dynamic difference-in-differences regression models were used. Here is what they found: 1. 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐁𝐨𝐨𝐬𝐭 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲: The AI tool led to a 13.8% increase in the number of customer queries resolved per hour, particularly benefiting less experienced agents. 2. 𝐍𝐚𝐫𝐫𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐆𝐚𝐩: AI tools accelerated the learning curve for newer agents, allowing them to reach the performance levels of seasoned employees more quickly. 3. 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐚𝐭𝐢𝐬𝐟𝐚𝐜𝐭𝐢𝐨𝐧: The AI deployment resulted in higher customer satisfaction scores (as shown by improved Net Promoter Scores) while maintaining stable employee sentiment. 4. 𝐋𝐨𝐰𝐞𝐫 𝐀𝐭𝐭𝐫𝐢𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐞𝐬: Interestingly, the AI support led to reduced attrition rates, especially among new hires with less than six months of experience. 5. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: The AI system reduced the need for escalations to managers, improving vertical efficiency. However, its impact on horizontal workflows, like transfers between agents, showed mixed results, suggesting more refinement is needed in AI integration. 6. 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐞𝐝 𝐀𝐈 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: The software wasn’t off-the-shelf; it was a custom-built solution tailored to the company’s needs using the GPT family of language models. This emphasizes the importance of context-specific AI applications for effective outcomes. For leaders, managers, and AI practitioners, these insights are invaluable—highlighting not just the potential of AI, but also the nuanced ways it reshapes workflows, impacts employee dynamics, and transforms customer experiences.So, does generative AI really make a difference? According to this study, the answer is a resounding yes—but it depends on how thoughtfully it is deployed. Link 🔗 to the paper: https://lnkd.in/ejhUfufz

It will happen slowly, then all of a sudden. Your customers will shift how they search for information about your products. They will use: 1) Decision engines like Google, designed to help them compare products, confirm product details and make purchases. 2) Information engines like ChatGPT and Google’s AI Overviews that feel more like a conversation with a trusted expert or knowledgable friend. Traditional search engines hand you a research project — many pages to sift through to find the information you seek. Generative AI search engines give you direct answers — with a chance of hallucination and inaccuracies. Here's what marketers need to understand: 🔹 Acknowledge the shift: Your customers are learning how/when to use two different types of search engines. There's the traditional "decision engine" like Google, and the "information engine" like chatGPT. 🔹 Accept that humans are lazy: Humans will choose the most convenient option. It’s human nature. Your customers prefer speed and convenience over absolute precision. 🔹 Information queries are moving to AI: When your customers want to learn about their problems, they’ll have conversations with AI instead of reading your blog posts. If your brand isn't appearing in these AI responses, you're becoming invisible to a growing audience. 🔹 Prepare for reduced website traffic: Expect fewer visits from basic informational queries as AI handles these directly. However, the traffic you do receive will be higher-intent visitors, closer to making a decisions, that should convert better. 🔹 Update your content strategy: Create different content for different search engines — intent-targeted informational content for generative AI search, and conversion-focused content for traditional search. 🔹 Build content AI can't summarize: Create interactive content, like calculators and data-driven content that requires user input. This ensures your brand stays visible even as AI handles informational queries. 🔹 Focus on intent, not keywords: The old approach of targeting high-volume keywords is outdated. Instead, understand and align with your customers' search intentions. The key takeaway? Humans are lazy. Your customers will consistently choose the convenience of direct answers from generative AI, even if those answers are sometimes inaccurate. They want to avoid sifting through pages of search results. As marketers, we need to adapt to this new reality. We must create content that caters to both types of searches: (1) content that helps your brand appear in generative AI responses for informational queries and (2) content that attracts and converts for decision searches on traditional search engines. How are you starting to search differently with generative AI?

Generative AI has been transforming customer service by enabling chatbots and human-like interactions with customers. However, there is still significant potential for further advancements. In a recent tech blog post, data scientists from Razorpay detailed their journey to develop a more effective customer ticket categorization system using a generative AI solution. The existing categorization system depends on customers selecting an issue category to route their tickets. Despite additional information provided by customers, the team found that this approach had an accuracy of around 60%. The new generative AI solution removes the reliance on customer-selected categories, instead using a generative AI model to interpret customer inputs and determine the relevant categories. Tickets are pre-processed to extract key information (e.g., removing PII) and then analyzed by a large language model (LLM) to identify key issue categories. The team designed prompts with clear instructions, defined tasks, and expected outputs. Additionally, they enhanced the system’s accuracy by incorporating a knowledge base for the LLM using Retrieval-Augmented Generation (RAG) technology. This generative AI-powered solution increased ticket categorization accuracy from 60% to 85%. It also reduced the need for manual customer input, making the overall experience more seamless. This is a nice resource for anyone exploring how to leverage generative AI in tailored use cases. #machinelearning #datascience #generativeai #llm #categorization #customer #automation – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gY7smWYF

Verizon has implemented several generative AI (GenAI) initiatives to enhance its customer experience and streamline operations. Here's a summary of key GenAI tools and applications: Segment of Me: This tool uses customer data to provide personalized product offers and tailored online experiences, including customized merchandise, copy, and images. Personal Shopper: This GenAI tool analyzes customer profiles to help employees quickly understand customer needs and preferences. It has reduced transaction times by 2-4 minutes and has an 85% accuracy rate in predicting customer preferences. Personal Research Assistant: Developed in partnership with Google, this tool helps customer service representatives quickly access information from Verizon's knowledge base. It's currently used by over 40,000 Verizon reps and is expected to save 20-30% of time on normal tasks. Fast Pass: This tool matches customers with the most suitable customer care representative based on their specific needs, ensuring they speak with someone knowledgeable about their particular issue. Verizon has been implementing these GenAI tools across various customer touchpoints, including retail stores, customer care, and digital platforms. The company aims to simplify customer interactions, reduce cognitive load on employees, and provide more personalized experiences. Brian Higgins, Verizon's chief customer experience officer, noted that the company has a strong workforce focused on GenAI development and numerous partnerships providing new services. Verizon's long-standing work with customer data has positioned them well for these AI-driven innovations. #verizon #5g

How SAP is Using AI to Enhance Customer Experience SAP leverages AI to significantly improve customer experience through a variety of innovative approaches and tools. Here are the key ways SAP is enhancing customer interactions: 1. Personalized Interactions - Customer Profiles: AI-generated customer profiles powered by real-time data from the SAP Customer Data Platform enable businesses to deliver tailored and relevant experiences. This includes personalized recommendations and targeted marketing content. - Predictive Engagement: AI's predictive analysis allows businesses to anticipate customer needs and offer proactive solutions, enhancing engagement and satisfaction. 2. Automation of Repetitive Tasks - Role-Based AI Tools: SAP provides job-specific AI tools to automate time-consuming tasks for service, sales, and commerce teams. This includes generating content, summarizing customer issues, and suggesting solutions, which frees up teams to focus on more value-adding activities. - Catalog Management: AI assists in product discovery by automatically extracting and enriching product attributes from images and text, generating product descriptions, and improving search capabilities, which enhances the shopping experience 3. Enhanced Customer Support - Proactive AI Responses: AI models in SAP's Customer Experience portfolio detect questions and suggest responses in natural language, derived from business data. This proactive approach helps resolve customer queries faster and more accurately. - Self-Service Options: AI-powered self-service tools and chatbots provide 24/7 customer support, reducing response times and improving availability. 4. Integration with Business Processes - Embedded AI Features: SAP integrates AI capabilities directly into its products, such as SAP Sales Cloud, SAP Service Cloud, and SAP Commerce Cloud. These embedded features help in generating personalized content, automating responses, and providing real-time insights. - Holistic Data Utilization: SAP's AI solutions leverage data from various sources, including ERP systems, to provide comprehensive insights and enable more informed decision-making, leading to better customer experiences. 5. Generative AI Innovations - Joule AI Assistant: SAP introduced Joule, a generative AI assistant that helps streamline customer service and marketing tasks by providing contextual insights and automating routine processes. This enhances productivity and ensures more personalized customer interactions. 6. Predictive and Preventative Support - Predictive Analytics: AI-driven predictive analytics in SAP solutions help businesses forecast demand, optimize inventory, and plan more effectively. This ensures that customer needs are met promptly and efficiently. By embedding AI across its customer experience solutions, SAP aims to deliver more personalized, efficient, and proactive customer interactions, ultimately driving higher satisfaction and loyalty. #SAP #AI #ZaranTech

AI is not hype. Let's talk about AI productivity gains. Walmart CEO on using AI in their latest earnings: "We've used multiple LLMs to accurately create or improve over 850,000,000 pieces of data in the catalog. Without the use of generative AI, this work would have required nearly 100X the current headcount to complete in the same amount of time" These are some of the use cases he mentioned: 1. Improvement of Product Catalog: Using generative AI to accurately create or improve over 850 million product catalog data pieces. 2. Order Picking: AI assists associates in picking online orders by showing high-quality product packaging images to help them quickly find what they're looking for. 3. AI-Powered Search: Customers and members benefit from AI-powered search on Walmart's app and site. 4. Shopping Assistant: A new AI shopping assistant provides advice and ideas, answering customer questions like "Which TV is best for watching sports?" 5. Follow-up Questions: The AI assistant is being developed to respond to more specific follow-up questions, such as "How's the lighting in the room where you'll place the TV?" 6. Supporting Sellers on Marketplace: AI helps sellers on Walmart’s marketplace by improving their experience and helping them grow their businesses. 7. Testing New Experience for Sellers: A new experience is being tested for U.S.-based sellers that allows them to ask AI anything, focusing on making the selling experience seamless. 8. Summarizing and Answering Queries: The AI assistant provides concise answers to sellers without requiring them to sort through long articles or other materials. The sooner you begin moving quickly, learning, and iterating, the sooner you'll start transforming your business and integrating AI across all operations. Companies that fail to do this will inevitably face disruption.