Choosing Ecommerce Customer Service Tools

If you didn't see the news, California just finalized its California Privacy Rights Act (CPRA) regulations on ADMT (automated-decision-making tools...think routing, scoring, profiling). Europe already has the AI Act. Singapore, Brazil, and Canada are next in line with similar AI-oversight bills. The takeaway is simple: If an algorithm is going to nudge a customer or rate an employee, regulators now want to know how, why, and with what data. Oh, and they also now expect an auditable paper trail. If you haven't started designing for these regulations, here are a few things to start doing. Like, today: First, whether you like it or not, dual jurisdiction is now the new normal, and U.S. rules no longer lag behind Europe. An “EU Compliance” badge won't cut it when California or the FTC asks for your ADMT impact assessment. Design for the regulatory extremes, and partner with your Risk and Legal teams to see if that takes care of the middle part of the regulatory curve. But make sure you’re ticking all the boxes. Second, explainability should now be a service level to be defined and to meet. This means that risk assessments, opt-outs, human-override flows, and data-provenance logs have to be part of every release. Treat them just like uptime and latency. Third, employee experience is officially in scope. Tools that allocate work shifts or score performance need the same transparency you’d give to customer-facing models. This is a really big deal. It will improve employee trust but creates extra work that needs to be planned for, prioritized, and resourced. Last but not least, and my "always-on" advice: start small. Just map one high-impact workflow (e.g., complaint escalation, agent performance dashboards, etc.). Document the data used, the decision logic, and the path to human appeal. And if you can’t explain it to a regulator in under 5 PPT slides, refactor before you scale it. It's way better to audit yourself now than to have a regulator do it later. They're not bad people, but you also don't want them in your cubicles either. #customerexperience #employeeexperience #privacy #ai #automation #regtech

🛎️ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐢𝐧 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐮𝐩𝐩𝐨𝐫𝐭: 𝐓𝐡𝐞 𝐄𝐧𝐝 𝐨𝐟 𝐋𝐨𝐧𝐠 𝐖𝐚𝐢𝐭 𝐓𝐢𝐦𝐞𝐬? “Your call is important to us. Please stay on the line…” We’ve all heard that line—and rolled our eyes. But now, something is changing. And fast. Welcome to the era of AI-powered customer support. Companies are no longer relying only on traditional ticketing systems. They’re deploying smart AI agents that don’t just answer queries—they understand them. Here’s how: ✅ Intercom Fin — A GPT-4 powered support assistant that resolves 50%+ queries without human handoff. ✅ Forethought — Predicts intent and delivers relevant answers before customers even finish typing. ✅ ChatGPT APIs — Powering custom chatbots that respond with context, tone, and accuracy. The benefits? ⚡ Instant replies ⏱️ 24/7 support 🎯 Personalized responses 💸 Lower operational costs But here's the kicker: AI isn’t replacing human support—it’s amplifying it. → Complex cases still go to real agents. → Bots handle the repetitive stuff. → Everyone wins. As expectations rise and patience drops, the real question isn’t if companies will adopt AI in support—it’s how fast. Would you be comfortable being helped by a bot, if it resolved your issue in 60 seconds? Do let me know in the comments! 👇 #CustomerExperience #GenerativeAI #CustomerSupport #Automation #ChatGPT #AITrends #TechInnovation

60-sec tutorial on Privacy in AI. Here's what you need to know: Every day, AI tools you use can create risks if you expose private data to them. If that data leaks, trust is lost and organizational reputation breaks. Laws like GDPR, CCPA, and HIPAA now demand strict privacy. Here’s how to keep privacy strong when working with AI: 1. Data Minimisation → Only collect what you need. Nothing more. A good AI system asks for just enough data to do its job. Not five years of history when one will do. Not every detail about a customer when only a few are needed. If an AI tool wants more, pause. Check if it’s really needed. Less data means less risk. 2. Security → Protect every bit of data. AI tools can store what you type. Public tools like ChatGPT may keep your input for training. Never share customer or sensitive data with public AI. Stick to approved tools (by your organization). Follow your company’s privacy rules. Strong passwords, encryption, and access controls are a must. 3. Consent → People own their data. If you collected data to improve a product, you can’t use it for something else without asking. Want to train a new AI model? Get new consent. Always respect the limits of the original agreement. If in doubt, ask for clear permission. Privacy in AI is about more than rules. It’s about trust. It’s about doing the right thing for people and for your company. Collect only what’s needed. Protect it. Get consent. That’s how you build trust and stay on the right side of the law. Follow for more 60-second AI tutorials. Next up: Accountability - keeping humans in charge of AI outcomes. Image source: Pinterest, DigitalSwift LLC

AI in customer service isn’t the future. It’s already delivering ROI — at scale. 📍Kapiva, a health D2C brand in India, just showed how it’s done 👇 Their goal? Turn purchase into loyalty — not just a one-time transaction. The problem? Customer support tickets piling up, delayed responses, and low retention. Enter: AI Chatbots (powered by LimeChat) Here’s what changed: ✅ 70K+ tickets/month handled by AI ✅ 90%+ efficiency boost via automation ✅ 4+ CSAT maintained (without burning out agents) ✅ Seamless escalation to humans when needed ✅ Query resolution that feels human, not robotic This isn’t just cost-saving. It’s experience-enhancing. Because let’s be real — if your CX sucks, your LTV will too. AI isn’t here to replace support teams. It’s here to free them up for the stuff that actually needs a human touch. And D2C brands that get this? They’re winning. Have you seen similar CX automation in action? #D2C #CustomerExperience #AI #MarketingAutomation #Growth

Four years ago, I worked on a technical automation project for a client via Fiverr. This week, he reached out again, same client, new challenge. He needed a system where AI-powered agents could make personalized inbound & outbound calls to leads and then automatically handle all the follow-up tasks without human intervention. So, I built a connected automation using Retell AI, GoHighLevel (GHL), Make.com, and Chatdash that: ➡️ For inbound calls: Looks up the lead in GHL in real-time, sends the details back to the agent, and allows live appointment booking while on the call. ➡️ For outbound calls: Triggers from actions in GHL, sends lead info to the agent for a personalised approach, waits for the call to finish, gathers transcript + sentiment, and stores it in GHL. ➡️ Across both: Retell AI checks calendar availability, and Make.com books meetings based on the lead’s preferred time, no manual follow-up needed. Impact: ✅ Personalized conversations every time ✅ Automated note-taking and sentiment logging ✅ Faster appointment scheduling with zero back-and-forth Automation isn’t just about replacing task; it’s about enhancing human interactions so agents can focus on building relationships, not juggling tabs and CRMs. If your sales or support team still spends time searching for client info during calls or manually scheduling follow-ups, this is the type of automation that changes the game. PS: What’s one repetitive client interaction in your business you’d love to automate?

🎯 Enhancing Customer Service with AI in Microsoft Dynamics 365 CRM 🚀 Customer expectations are evolving, and businesses must adapt to provide faster, more personalized, and efficient support. Microsoft Dynamics 365 Customer Service is leading this shift with AI-driven innovations and automation-first experiences. Here’s what’s new in 2025: 🛠️ Key Enhancements in Dynamics 365 Customer Service 🔹 AI-Powered Case Summarization • Copilot now generates automatic case summaries from past interactions, enabling agents to get up to speed quickly. • Reduces manual effort and improves first-contact resolution rates. 🔹 Intelligent Routing with AI • Uses machine learning and natural language processing (NLP) to classify and route cases to the right agent or bot. • Ensures faster resolution times and improved customer satisfaction. 🔹 Proactive Issue Resolution with IoT & AI • Integrated IoT monitoring detects potential issues before customers even notice them. • Triggers automated case creation and assigns tasks to service teams before a failure occurs. 🔹 Virtual Agent Improvements • The built-in Copilot chatbot now offers more natural conversations and automated workflows for common issues. • Seamlessly integrates with Omnichannel for Customer Service, reducing agent workload. 🔹 Omnichannel Customer Engagement • Deeper integration with WhatsApp, SMS, and social messaging apps to enable real-time, AI-assisted interactions. • Supports live sentiment analysis to help agents tailor responses effectively. 🔹 Enhanced Knowledge Management • AI-driven knowledge article recommendations ensure agents always have the most relevant information. • Automated content updates keep documentation accurate without manual intervention. 💡 Business Impact ✔️ Faster response times and reduced case resolution effort. ✔️ More personalized customer experiences with AI-driven insights. ✔️ Improved agent productivity through automation and knowledge suggestions. ✔️ Cost savings by deflecting simple queries to AI-powered chatbots. 📌 Want to learn more? Check out Microsoft’s official release notes on Customer Service: https://lnkd.in/gvCtGZFd What’s your experience with AI in customer service? Have you implemented these features yet? Let’s discuss in the comments! 👇 #MicrosoftDynamics365 #CustomerService #AI #Automation #DigitalTransformation #CRM

AI agents and Automations are totally different Here's the truth about agents beyond marketing: Definition of an AI Agent (from Microsoft): “AI agents are autonomous, intelligent applications powered by generative AI that automate complex workflows, make context-aware decisions, and interact with systems or users to achieve specific goals.” Here are the main characteristics of an agent: 1️⃣ Smart microservices that combine generative AI models with tools to access and interact with real-world data. 2️⃣ Autonomous problem-solvers capable of perceiving environments, reasoning through tasks, and executing actions without (or with) constant human oversight. 3️⃣ Enterprise-grade workflow automators designed to handle everything from simple queries (via RAG) to end-to-end business process automation. Agent vs Automations/Workflows: Automations follow preset rules and are ideal for repetitive, structured tasks across different apps. AI agents offer more sophisticated, adaptive, and intelligent solutions for dynamic scenarios that require continuous learning and decision-making. 3 Examples: 1️⃣ Customer Support Email and Return Policy Check − Automation Behavior: A preset rule scans incoming customer support emails for keywords like "return" or "refund" and automatically sends a standard response with the company’s return policy from a template. − Agent Behavior: An AI agent reads the email, understands the customer’s specific question (e.g., "Can I return a damaged item after 30 days?"), checks the latest return policy, interprets exceptions, and drafts a personalized reply based on the context. 2️⃣ Web Scraping − Automation Behavior: A script runs daily to scrape a website for product prices based on fixed HTML tags, saving the data into a spreadsheet without any adjustments if the site’s structure changes. − Agent Behavior: An AI agent monitors the website, adapts to layout changes, identifies new pricing patterns, decides which data is relevant (e.g., discounts vs. regular prices), and summarizes trends for a report. 3️⃣ Appointment Scheduling − Automation Behavior: A tool checks a calendar for open slots and books appointments based on predefined rules (e.g., "no bookings before 9 AM") when a request comes in. − Agent Behavior: An AI agent analyzes past scheduling patterns, prioritizes urgent requests, negotiates with clients via natural language (e.g., "Would 10 AM work instead?"), and adjusts the calendar dynamically to optimize the day. Don't be fooled by those who present automations as AI Agents!

🫶 What do the 3 New Autonomous Agents offer for Dynamics 365 Contact Center & Customer Service? As the public preview for the 3 autonomous AI agents progresses, recently I've been discussing their impact with clients and wanted to pull together some key features and benefits into a single slide, so here you go. ⚙️ Case Management Agent This agent revolutionises the case lifecycle by automating the creation, updating, and resolution of cases across chat, email, and other service interactions. It reduces average handling time, minimises errors, and frees up customer service representatives to focus on more complex and value-driven tasks. 🧠 Customer Intent Agent Leveraging generative AI, this agent continuously learns from historical interactions to understand customer intent in real-time. It builds an evolving intent library that powers smarter self-service and delivers contextual support to human agents. The result? A more intuitive, responsive, and scalable service experience for customers. 📚 Customer Knowledge Management Agent Maintaining an accurate and useful knowledge base has always been a challenge. This agent simplifies the process by autonomously generating and updating knowledge articles from real-world cases, conversations, and notes. It ensures your content remains relevant and actionable while reducing the burden on service teams. As I mentioned, you can begin using all 3 agents now as part of the paid public preview (Copilot Studio message capacity required). If you've been using the agents, would love to hear your feedback in the comments. Learn more and get started: https://lnkd.in/erNDSkWg #autonomousagents #contactcentre #customerservice #d365

𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝘂𝘀𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 The paper presents an advanced approach to automating customer service using LangChain, an open-source framework designed for building custom LLM-powered chatbots. 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: -Integration of LLMs: The chatbot named Sahaay everages Google’s Flan-T5-XXL 😊 model for intelligent responses. -Data Collection & Web Scraping: The chatbot retrieves real-time data using BeautifulSoup for web scraping and FAISS for fast similarity searches. -Custom Knowledge Embeddings: The model uses Instructor-Large Embeddings to improve contextual understanding and retrieval of relevant information. -Fine-tuning & Deployment: The chatbot is fine-tuned and integrated into Gradio API for seamless deployment on websites. -Performance Benchmarking: A comparison between different Flan-T5 versions (XXL, Base, Small) showed that the XXL model provides the best accuracy and user experience. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 & 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 -Enhanced User Experience: The chatbot understands complex queries and retains context. -24/7 Support: Automated customer service operates round the clock reducing human workload. - Scalability: The framework is industry-agnostic and can be adapted for educational institutions, healthcare and e-commerce. Keep learning and keep growing 😊 !!

“Should we add more CSMs, or add more CS Ops?” It’s the allocation question every CS leader faces as budgets tighten and expectations rise. The wrong choice can damage customer retention, blow the budget, or both. The best CS leaders are following a simple formula: Make tech investments where they create efficiency. Make human investments where they generate retention and growth. The Clear Division of Labor Technology excels at tasks requiring consistency, speed, and scale where human judgment isn’t critical: • Administrative work and data processing • Routine communications and follow-ups • Process orchestration and workflow management Humans excel at tasks requiring judgment, creativity, and strategic thinking: • Strategic guidance and complex problem-solving • Relationship building and value creation conversations • Turning satisfied customers into advocates But here’s where segmentation changes everything. Segmentation Drives Everything What works for enterprise accounts doesn’t work for SMBs: High-value segments require human investment. The impact on retention and growth justifies the cost. High-volume segments require tech investment. They value speed and reliability, and unit economics demand efficient delivery. Scaling Isn’t Just Automation — It’s Trust Many CS leaders assume scaling means automating everything. But trust - the foundation of customer success - scales through a strategic blend of tech and human touch: Trust scales through consistency- Reliable delivery of promises, whether automated or human Trust scales through competence- AI-powered insights helping CSMs provide better guidance Trust scales through transparency- Proactive updates that keep customers informed Trust scales through personalization - Understanding unique needs at scale The Resource Allocation Framework Your segmentation strategy drives your resource allocation decisions. Map your customer journey by segment and classify touchpoints as either: • Efficiency-focused (perfect for tech) • Growth-focused (requiring human investment) Then audit where you’re using expensive human resources on automatable tasks, and where you’re using automation for interactions that demand human judgment. CS organizations that execute this principle operate with fundamentally better unit economics. They deliver personalized, strategic value to high-value customers while serving high-volume customers efficiently. They aren’t choosing between efficiency and growth - they’re achieving both. The framework is simple: tech for efficiency, humans for growth. But applying it requires knowing your customers well enough to understand which approach builds the most trust with each segment. Where are you misallocating resources between tech and human investments?