Choosing Ecommerce Customer Service Tools

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data.  ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose representative tasks and see how successful users are. It won’t be right the first time, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/d6yvKS6G 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush Charts + Tables: https://lnkd.in/dnDRtG32 👍 UI Charts: https://lnkd.in/eJkyB6zS UKO: https://lnkd.in/ehvcSnuV 👍 Wireframes: https://lnkd.in/e-m3VQqs 👍 [continues in comments]

As SaaS vendors scale, integration requirements shift from “nice to have” to mission-critical. But in parallel, the demands of enterprise IT - data residency, compliance, performance, and cost predictability, only become more stringent. At Integration App, we’re addressing this tension head-on by delivering a universal integration layer that runs directly within your infrastructure. Unlike hosted integration solutions or embedded iPaaS platforms that introduce new data flows, latency layers, and vendor-side operational dependencies, our model prioritizes infrastructure sovereignty. You retain full control over how and where integrations execute while benefiting from a platform that automates and abstracts the complexity of connecting to thousands of third-party systems. Here's what that unlocks: 1. Data Sovereignty by Default No proxies. No data egress. Customer data never leaves your environment. Whether you’re in a private VPC, on-prem, or operating under industry-specific compliance regimes (HIPAA, SOC 2, GDPR, FedRAMP), our deployment model ensures your security posture isn’t compromised by integration complexity. 2. Security and Compliance-First Architecture Deploy integrations in line with your own IAM policies, access control frameworks, and encryption standards. All executions occur in your trusted compute environment, enabling full auditability and adherence to internal and external governance requirements. 3. Infrastructure-Native Deployment The integration layer is designed to be deployed alongside your core application stack, whether containerized via Kubernetes or integrated into a custom CI/CD pipeline. 4. Performance Without Penalties Since integration flows run at the edge of your application stack, you avoid the latency and variability introduced by centralized middleware or external orchestration layers. 5. Predictable, Scalable Economics No usage-based throttling. No per-flow billing. With a flat pricing model and no API call metering, you can scale integration volume without introducing infrastructure cost uncertainty. This predictability becomes critical as integration use cases grow across customers, tenants, and third-party systems. AI-Augmented, API-Agnostic By decoupling Integration App logic from specific APIs, and using AI to generate contextual, app-specific execution paths, we eliminate the bottlenecks of manual, one-off integrations.

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

🔗 How to Integrate Dynamics 365 with Third-Party APIs Using Azure Functions In many businesses, Dynamics 365 sits at the core of operations. But success often depends on how well it connects with other platforms—billing systems, shipping providers, marketing tools, and more. When these systems don’t communicate: ❌ Teams waste time on manual data entry ❌ Customers face delays ❌ Leaders work with outdated or incomplete information 👉 The fix? Azure Functions. Azure Functions provides a serverless, cost-efficient way to integrate Dynamics 365 with third-party APIs. Instead of building heavy integrations, you can deploy lightweight code snippets that run only when triggered—keeping costs low and workflows smooth. 💡 Why this matters: Real-time data sharing between systems Accurate and connected business operations Faster insights for decision-making Scalable without expensive custom development In my latest guide, I walk through the practical steps to set up this integration, starting with: ✅ Registering an application for secure authentication in Azure AD …and continuing through the full process. 📘 Full step-by-step guide here: 👉 https://lnkd.in/g_jHakDM #Dynamics365 #Azure #AzureFunctions #APIIntegration #DigitalTransformation #Microsoft

🚀 From Data to Decisions: Technical Support Insights with Power BI One of the things I love about data analytics is its ability to transform raw numbers into actionable insights. Over the years, I’ve worked on multiple IT ticketing dashboards, helping teams track support tickets, monitor agent performance, and improve resolution times. In my previous organization, I worked extensively with BigQuery and various visualization tools to automate ticketing dashboards, eliminating manual reporting efforts and ensuring teams had real-time insights at their fingertips. That experience played a key role in shaping my latest project: developing a Power BI dashboard to analyze IT support ticket trends. 🔑 Key Insights from This Project: 📌 Ticket Volume Trends – Analyzed peak hours and ticket distribution across different sources and countries, providing clarity on workload patterns. 📌 Resolution Efficiency – Measured resolution times across ticket sources, identifying opportunities to enhance response speed and optimize workflows. 📌 Agent Workload Balance – Assessed ticket distribution among agents to ensure an even workload and improve overall efficiency in handling support requests. A huge thank you to Anh Leimer and Hien Tran for your invaluable feedback and support throughout this project. It made a world of difference! 🙌 And a special shoutout to Injae Park for sharing the amazing IT Service Ticket Overview dashboard. I loved the design and took inspiration from it for my Resolved Summary Page. ✨ Every project, every tool, and every challenge has been a stepping stone. Whether it was working with BigQuery and Looker Studio in my previous role or diving deep into Power BI now, the goal remains the same --> turning data into insights that drive real change. Have you worked on a dashboard or automation project that made a big impact? Let’s share insights! Interactive Dashboard Link: https://lnkd.in/gpAKgCec #DataAnalytics #PowerBI #DashboardDesign #Automation #BusinessIntelligence #ProblemSolving #Efficiency #DataVisualization

OpenAI dropped something big: retailers can now embed AI shopping agents into their Shopify stores - in just a few clicks. Through a new integration between the Storefront Managed Compute Platform (MCP) (Shopify’s tool for powering custom storefront features) and the OpenAI Responses API (controls how AI agents understand and respond to users), building a shopping assistant no longer requires authentication, custom code, or complex setup. By adding a store URL to the OpenAI Playground (an easy-to-use web interface for testing and deploying AI agents), a fully functional assistant can be deployed almost instantly. Once live, the AI assistant is capable of: - Searching the store’s live product catalog - Adding selected items to a shopper’s cart - Generating checkout links in real time The interaction is seamless. A shopper might type, “I’m looking for a lightweight men’s button-up shirt for a vacation,” and the agent responds with curated options. Upon selection, the item is added to cart - autonomously and without delay. The launch marks more than a product update - it’s a strategic step toward agentic commerce, where AI doesn't just inform but acts on behalf of the shopper. While OpenAI provides the intelligence and interface, Shopify is laying the groundwork for retailers to operationalize it at scale through tools like the Storefront Managed Compute Platform (MCP). And it’s not alone. - Perplexity offers one-click purchasing via Buy with Pro and is onboarding merchants through a free product data program. - Google is enhancing Search and Bard with shopping intelligence, making results more shoppable — though still not fully agentic. - Amazon is using generative AI in listings, reviews, and its Rufus assistant to improve discovery and streamline decisions. - Startups like Cocoon and Cartwheel are building white-label AI agents for brands, turning chat into personalized storefronts. We are clearly moving from search engines to shopping agents. Opinions: my own, Video source: Shopify Developers 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

🤖 𝐇𝐨𝐰 𝐭𝐨 𝐛𝐫𝐢𝐧𝐠 𝐀𝐈 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬 𝐭𝐨 𝐥𝐢𝐟𝐞: 𝐟𝐫𝐨𝐦 𝐏𝐎𝐂 𝐭𝐨 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 I often guide businesses through implementing AI solutions. Here's a breakdown of the typical journey from concept to production. 👉 Remember: AI implementation is a step-by-step process. This enables us to learn fast and iterate. 1️⃣ 𝐏𝐫𝐨𝐨𝐟 𝐨𝐟 𝐂𝐨𝐧𝐜𝐞𝐩𝐭 (𝐏𝐨𝐂) / 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐞 ▶ Validate the AI use case ▶ Test core functionality ▶ Gather initial feedback ❓ Key question: Is this AI solution feasible and valuable? 🖍 Example: A chatbot that can answer 5-10 basic customer queries, implemented with a few API calls and prompt engineering 2️⃣ 𝐌𝐢𝐧𝐢𝐦𝐮𝐦 𝐕𝐢𝐚𝐛𝐥𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 (𝐌𝐕𝐏)/ 𝐁𝐞𝐭𝐚 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 ▶ Develop core features ▶ Test with real users ▶ Gather comprehensive feedback ❓ Key question: Does this solve the problem effectively? 🖍Example: A chatbot integrated into a test website, handling 20+ common queries and basic conversations, used by a small group of beta testers. 3️⃣ 𝐆𝐨 𝐋𝐢𝐯𝐞 ▶ Scale the solution ▶ Integrate with existing systems ▶ Address security and compliance ❓ Key question: How can make the solution ready for real-world deployment? 🖍 Example: A fully integrated chatbot on the company's live website, handling thousands of queries daily, with secure data handling and 24/7 availability. 4️⃣ 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 ▶ Monitor performance metrics ▶ Gather user feedback ▶ Implement improvements ❓ Key question: How can we continually enhance value? 🖍 Example: Regular updates to the chatbot, adding new features like multi-language support, integrating with CRM systems, or implementing more advanced NLP capabilities like fine-tuning of the underlying LLM. 💬 Which stage do you find most challenging? Comment below! #AIImplementation #MachineLearning #ProductDevelopment #AI --- 👩💻 I am Verena. I enable businesses to achieve AI-driven success.

The last few weeks have been intense. I’ve been deep-diving into how AI and LLMs can transform the way we interact with Shopify data—not just for automation, but for smarter decision-making. So I built something small MVP. A chatbot that pulls real-time product, customer, and order data from Shopify, pushes it to vector DBs like Chroma, Pinecone, Milvus, and makes it searchable with OpenAI embeddings. You ask: “Where is my order?” → It checks login and gives you a contextual reply. You say: “Show me a red t-shirt under $30” → It fetches product data semantically. It’s not just for customer support—imagine CXOs chatting with their business data to get instant answers like: “What’s the best-selling product in California last month?” I wrote a deep-dive blog on how I built it, with all the tech breakdowns: - Shopify API - OpenAI embeddings - Vector DB - LLM orchestration Would love to hear your thoughts on similar use cases or how you’re approaching AI in eCommerce. #Shopify #AI #LLM #OpenAI #eCommerce #CustomerSupport #TechForBusiness #GenerativeAI #CRO #CXO

The customer support industry is experiencing a seismic shift, and at the heart of this transformation is the integration of Generative AI (Gen AI) capabilities. What's remarkable about this technology is that it empowers customer service agents, rather than replacing them, to provide personalized, efficient, and impactful support. Gen AI can generate human-like responses swiftly and accurately, reducing the need for human agents and supercharging efficiency. 🔍 Let's delve into how Gen AI is reshaping the customer support landscape, creating a league of super agents who harness AI to elevate customer service. Agile Case Resolution: Gen AI streamlines support by facilitating agile case resolution, helping service agents resolve customer issues with lightning speed. Personalized Experiences: By analyzing customer interactions, Gen AI crafts personalized responses, curates relevant knowledge articles, and delivers instant solutions, enhancing the capabilities of human agents. Boosted Agent Creativity and Proficiency: Gen AI propels customer support agents' productivity by 14%, freeing them to focus on complex tasks and deliver an exceptional customer experience. 24/7 Customer Service and Support: Businesses can now offer round-the-clock customer service and support, trimming the need for human agents and boosting operational efficiency. Deeper Customer Insights: Gen AI delves into customers' needs and behaviors, enabling businesses to anticipate future requirements and make data-driven improvements. Financial Benefits for Companies: Revenue Growth: Personalized and efficient support builds customer loyalty, leading to increased revenue. Cost Savings: Gen AI automates repetitive tasks, reducing operational costs and providing 24/7 support, saving on labor expenses. Improved Efficiency: Faster and more accurate responses trim resolution times and customer effort, enhancing overall efficiency. Real-World Examples: Email Response Automation: A Gen AI-based customer service application now handles a third of all customer inquiry emails, freeing agents for more complex tasks. Call Center Transformation: Retail giants like Walmart and Walgreens, financial institutions like Capital One, and airlines like Cathay Pacific use Gen AI to revolutionize their call center operations, delivering hyper-personalized customer experiences and driving revenue growth. Self-Service Support: Gen AI enhances self-service support, empowering customers to find answers quickly without human assistance As Gen AI continues to evolve, we can anticipate even more innovative applications in the customer support industry, further enhancing the customer experience and driving business success. The future of customer support has arrived, and it's incredibly promising! #CustomerSupport #AIRevolution #GenAI #Innovation #CustomerExperience 🌐🤝📈

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 🔔