Chatbots for Customer Engagement

This year, I have conversed with a countless number of chatbots. But I have to say the bot The Estée Lauder Companies Inc. just rolled out at Jo Malone London is definitely one of the most fun. “Where would we like to journey today?” It asked me. “The freshness of an orchard? The warmth of a blooming flower garden? The windswept allure of the coastline?” This chatbot, built in collaboration with Google Cloud on Gemini, has a unique tone because it faces a uniquely difficult task: translating natural language into the intangible and totally nondigital experience of smelling. The goal is to convince online shoppers to spend north of $100 on a scent suggested by a chatbot conversation filled with ethereal adjectives and descriptions of the English countryside. And so far it seems to be working. Since a soft launch begun in October, online shoppers who used the tool made purchases at almost double the rate of those who didn’t, helping Estée Lauder achieve that rare unicorn in generative AI: a tool that actually drives top-line growth. I enjoyed it so much that I, a person who has never worn any kind of scent before, almost decided I had to walk around every day smelling like a bucket of summertime blackberries. Still debating that one. Estée Lauder's chief technology, data and analytics officer Brian Franz told me he's considering similar experiences for other ELC brands, including Kilian Paris, Frédéric Malle, Tom Ford Beauty and Le Labo, as the company, which has struggled in recent years, looks to the fragrance category to execute its turnaround. What do you think? A ton of retailers seem to be building their own chatbots these days, but curious whether it actually translates to sales or not. And what distinguishes the fun chatbots from the boring ones? Curious to hear your shopping experiences! Read the full story on Estee Lauder here: https://lnkd.in/edzxifkc

Buyers are tired of AI spam cannon personalization tools. Here are the two biggest problems with them from my perspective as a sales leader that’s been burned in the past: 1. AI doesn't have access to great data to make relevant copy (i.e. public info from a person's LinkedIn profile isn't usually helpful) 2. People don't know how to instruct the AI to build great messaging. You pretty much have to be a prompt engineer to get anywhere near decent copy. Excited that I believe we are finally starting to crack the code on this with our launch of RoomieAI today: Here’s how we are doing that: 1st, 2nd & 3rd party signals/intent are being considering in the model Unlike other AI personalization tools on the market, RoomieAI uses buying signals—web visits, LinkedIn engagement, job changes, dark funnel activity or ANY other signal captured in Common Room — to create the messaging. The difference in output is context. We aren’t an “AI spam cannon” trained on commoditized data. Instead, it uses 50+ signals that are meaningful to conversion and relevant. === Here are some prompts that we’ve built in collaboration with some pipe gen legends to get closer to a world where AI copy is actually relevant:

If your ecom funnel could talk, what would it say about your traffic? One QuickReply.ai customer, a beauty brand, thought things were fine. They had 80,000 visitors a month. Meta ads were performing well. Their bottom-of-the-funnel SEO was strong. They were even seeing some traffic from ChatGPT recommendations. Abandoned carts were a known problem. Like most brands, they had reminders in place, retargeting ads running, and even occasional discounts. These worked, but only to a point. But there was a gap they hadn’t noticed. Out of every 100 visitors who reached a product page, fewer than five added anything to the cart. The drop-off wasn’t happening at checkout. It was much earlier, right at the product view stage. So they tried the usual solutions. They tested discounts. No change. Adjusted product descriptions. No difference. Improved page speed. Smooth, but no spike in conversions. Running out of options, they decided to try something simple. A WhatsApp message sent from QuickReply, to anyone who viewed a product but didn’t take action. A polite, direct message - “Still thinking about the Vitamin C serum?” They expected a few clicks back to the site. What they didn’t expect was the flood of replies. “Will this work on sensitive skin?” “Can I use it with retinol?” “How long before I see results?” Turns out, this was their Achilles' Heel. The issue wasn’t interest. People were curious but had questions, and the product page couldn’t always answer them. Instead of just nudging them back to product pages, they started a conversation. The automation didn’t push sales. It connected visitors to agents who could answer questions in real time. Doubts turned into discussions. Questions turned into orders. ₹20,000 in ad spend brought in ₹1 million in revenue. Every brand knows how to chase abandoned carts. But how many are paying attention to those who never even make it that far? Most brands focus on chasing abandoned carts. But the bigger leak often starts earlier - with people who browse, hesitate, and leave. And sometimes, all it takes to bring them back is a chance to ask.

When you're deploying AI agents for a CX function, having a good Knowledge Base is a non-negotiable. Why? When optimized, it can empower your AI agents to deliver fast, accurate responses. When neglected, it can leave customers frustrated and agents underperforming. If you want to make sure your help center actually HELPS, here are 5 strategies you can deploy: 1. Structure your content in a Q&A format with clear headings and concise instructions to make it easy for both customers and AI to find relevant information. 2. Use precise keywords. If you have membership tiers, explicitly say which tier you're talking about. 3. Update content regularly with release dates for new features and remove outdated articles. 4. Use visuals (carefully). Reference images and annotations can improve usability—just make sure you have the bandwidth to keep them accurate. 5. Make agents accessible by providing a clear link to the AI agent channels for when customers need help beyond the answers available to them. A lot of companies view help centers as a nice-to-have but the truth is, the ROI is massive. And if you're thinking of using (or already use) AI agents for your customer support, you need to keep it well maintained so the agents can: → Identify knowledge gaps → Make suggestions to make your documentation easier to understand When your help center is optimized, AI agents can perform at their best, which translates to happier customers and less workload for your team. Read the full article for more strategies we recommend—link in the comments! 👇

Some of the biggest ecommerce wins come from understanding how shoppers actually behave, then using those insights to improve the experience. We’re seeing that pattern across brands using FERMÀT’s pixel on their sites. Three recent examples: For one brand, surfacing complementary products inline through our onsite AI assistant helped collapse multi-session discovery into a single session. In month one, that translated into ~7,500 search sessions and a 3.1x add-to-cart lift on chatbot sessions. Another retailer found that homepage entrants converted 55% better than shoppers landing directly on a PDP, changing how they thought about traffic quality and landing experience. And for a third, FERMÀT determined that two distinct shopper types were shown the same product page: one fluent in technical specs, one not. That insight led to a module translating raw specs into plain-language benefits, making the PDP work better for both audiences. Different brands, different fixes, same pattern. Seeing how your customers actually shop and acting on it is more impactful than following generic benchmarks.

Most teams obsess over the 5% who reply. But what about the other 95%? That’s where your investment lives. You spent real time (and real money) building that list: → Enrichment tools → Research → Personalization → Manual review And then… No reply? No interest? Off to the next batch? The best operators treat every reply—yes, no, or silence—as a chance to learn, improve, or close the loop. That’s where automation shines: → Out-of-office replies? Route them into a “Back to Work” campaign timed to their return → “Not a fit” replies? Offer a referral link with affiliate commission → Total silence? Trigger a 90-day re-engagement path with new angles or case studies → Ghosted after showing interest? Schedule soft nudges before archiving → Unsubscribes? Instantly tagged and blocked from future sends None of this needs to be manual. It just needs to be mapped—and automated—with intent. Because if your outbound system treats non-responders like throwaways… You’re burning time and money. The real cost isn’t in outreach. It’s in letting 95% of your effort go nowhere. — 🔔 Follow Nathan Weill for automation strategies that turn missed chances into second ones. #OutboundOps #SalesAutomation #NoCode #LeadFollowUp #Zapier #OperationalExcellence #RevenueOperations

For decades, businesses have built call centers, service teams, and help desks to fix issues faster. Yet speed alone never created loyalty. The real measure of service has always been how it makes people feel: heard, understood, and valued.   Now, with AI transforming how we engage with customers, that emotional foundation is being redefined. 62% of customers now say they prefer chatting with a bot over waiting for a human, as long as it provides faster, more accurate service, according to Salesforce.   This statistic shows that people still seek empathy and understanding, but they also want quick, smart responses. That’s where AI chatbots and virtual assistants come in.    So, what is the role of AI chatbots and virtual assistants in improving customer support? Here are a few key roles they play: ▪Immediate Understanding: 🔅 AI can analyze tone, sentiment, and keywords to understand the customer's state of mind instantly. This allows responses to feel timely and considerate, not robotic. ▪Faster Resolutions with Context: 🔅 Virtual assistants can resolve repetitive tasks instantly while passing complex cases to human agents with full context, so customers never need to repeat themselves. ▪Consistency Without Fatigue: 🔅 Unlike human agents, AI doesn’t get tired or lose patience. It brings calm, consistent support anytime, in any language, across any channel. ▪Empathetic Language Modeling: 🔅 The latest AI models are trained to respond with warmth and tact, saying things like “I understand how frustrating this must be” or “Let me take care of that for you,” just like a well-trained agent would. ▪ Boosting Human Support: 🔅 By handling the routine, AI allows human agents to focus on high-emotion, high-stakes moments where real connection is needed, creating a more powerful hybrid model. Are chatbots naturally empathetic? Not yet. But they can be designed to behave empathetically, and that’s a game-changer for CX. Support today focuses on meeting people where they are, not just directing them where the system wants. In regions like Saudi Arabia, where expectations for digital transformation and real-time service are rapidly growing, support becomes a strategic necessity. When technology understands people and people trust technology, customer support becomes more effective. #Customerexperience #CX #AI #Chatbots #Virtualassistants

Customer service is one of the most exciting areas where I see AI driving transformative change. I have been carefully watching industry trends, and it’s clear that banks are beginning to bring their behind-the-scenes AI innovations to the forefront of customer interactions. This does not mean AI will replace human interaction. ❌ AI can power customer service chatbots and automate responses to common questions. Customer service employees can dedicate their time to solving more complex issues that require human interaction using critical thinking and personalised attention. This shift towards AI-powered customer service has the potential to improve response times and provide 24/7 support to customers. Advanced Natural Language Processing and GPTs will accelerate and grow customer service capabilities especially on the frontline. The use of NLP and GPTs in customer service represents a significant leap forward in banks' ability to understand and respond to customer queries.

Context Engineering is the #1 Gen AI Skill in 2026 For 99% people, that means using 'Projects' properly LLMs like ChatGPT and Claude have this powerful feature called Projects However, most NEVER set up project correctly - leading to more AI slop First thing, remember every project has 3 components ↳ Memory ↳ Instructions ↳ Files 1. Separate Instructions from Knowledge (when setting up Project) ↳ Instructions: HOW to behave (tone, format) ↳ Files: WHAT to know (uploaded files) Example: Upload 'Style Guide' or 'Design System' as a file in Files, not as an Instruction 2. Be Explicit in Instructions ↳ Don't write vague instructions like 'Be helpful and organized' ↳ Use explicit structure such as 'Problem - Why It Matters - Solution - Example' In my usage and testing, I have found that Claude 4.x and GPT-5.x follow instructions quite literally 3. Upload Files, Don't Paste in conversations ↳ Stop copy-pasting brand guidelines into every chat ↳ Upload once: PDFs, Docs, spreadsheets, images and AI references automatically ↳ Then reference them, instead of re-explaining them every time 4. Use Markdown Structure (get help from ChatGPT) ↳ Headers, bullets, code blocks beat an unformatted blob of text ↳ Models read structured formatting more reliably 5. Name Files Descriptively ↳ Don't upload files with names like 'Document1.pdf' ↳ Name it appropriately such as 'Brand_Voice_Guidelines_2026.pdf' ↳ Filenames help AI retrieve the right context 6. Keep Knowledge Base Lean ↳ More files ≠ better performance. ↳ Too much context leads to performance degradation ↳ Only upload what's essential for THIS project 7. Isolate by Context Boundary ↳ Treat each project as a separate memory bubble ↳ Client A work stays separate from Client B ↳ Confidential work should not overlap into client-facing projects 8. Add Examples for Complex Tasks (few-shot) ↳ Simple tasks: skip examples ↳ Specific workflows: show 1-2 good quality sample exchanges 9. Audit Monthly ↳ Memory: ChatGPT default: viewable/editable overall for user ChatGPT project-only: BLACK BOX Claude: transparent, editable summaries for each Project ↳ Files: Delete outdated docs, old team members, expired info Stale context leads to degraded performance I, myself, break down projects like this: ↳ one Project per client ↳ one for PM work - experiments, prototypes ↳ one for Strategy ↳ one for LinkedIn posts ↳ one for the newsletter No matter what work you do - whether it is strategy, writing, research, media, content, documentation - your LLM is only as good as the context you give it. --------- I am Priyadeep Sinha and I help you level up with AI one strategy at a time For my best, most detailed resources, subscribe to my weekly newsletter Work in Beta where I share the AI strategies I used as a VP-Product building winning AI products: https://lnkd.in/gPqYEzaJ

To enhance customer service efficiency and satisfaction, implementing intelligent chatbots and automated response systems is key. These systems operate 24/7, reduce costs, and provide consistent, personalized interactions. Here's a short guide on the key aspects to consider: 👉 Types of Chatbots Traditional rule-based chatbots follow predefined rules to answer specific questions, offering limited interactions. AI-based chatbots use generative AI, machine learning, and natural language processing to understand and respond to a wide range of questions naturally and effectively. 👉 Automated Response Systems AI-powered Interactive Voice Response (IVR) systems, automated email replies, and instant messaging bots streamline customer support. These systems handle inquiries efficiently, routing them to the appropriate departments and ensuring quick, accurate responses across various communication channels. 👉 Security & Privacy Considerations To safeguard customer information, ensure that chatbots and automated systems comply with data protection regulations such as GDPR. Transparency is key; customers must be informed that they are interacting with a chatbot and offered options to connect with human operators when needed. 👉 Implementing Intelligent Chatbots Successful chatbot implementation starts with defining clear objectives to address specific customer service needs. Choose a platform that supports natural language processing and integrates with existing systems. Continuously train and optimize the chatbot using updated data for better performance. 👉 Enhancing Customer Service Personalize interactions using customer data to provide tailored responses and recommendations. Collect feedback to refine the chatbot's performance. Combine automated systems with human support to handle complex issues requiring a personal touch, ensuring comprehensive customer service. 👉 Measurement & Analysis Monitor performance metrics like resolution time, customer satisfaction, and chatbot usage to evaluate effectiveness. Use data analysis to identify areas for improvement, optimizing chatbot functionality and ensuring a continuously improving customer service experience. #CustomerService #AI #Chatbots Ring the bell to get notifications 🔔