Let’s say your support center is getting hammered with repeat calls about a new product feature. Historically, the team would escalate, create a task force, and maybe update a knowledge base weeks later. With the tech available today, you should be able to unify signals from tickets, chat logs, and social mentions instead. This helps you quickly interpret the root cause. Perhaps in this case it's a confusing update screen that’s triggering the same questions. Instead of just sharing the feedback with the task force that'll take weeks to deliver something, galvanize leaders and use your tech stack to orchestrate a fix in real time. Don't have orchestration in that stack? Start looking into this asap. An orchestration engine canauto-suggest a targeted in-app message for affected users, trigger a proactive email campaign with step-by-step guidance, and update your chatbot’s responses that same day. Reps get nudges on how to resolve the issue faster, and managers can watch repeat contacts drop by a measurable percentage in real time. But the impact isn’t limited to operations. You energize the business by sharing these results in a company-wide standup and spotlighting how different teams contributed to the OUTCOME. Marketing sees reduced churn, operations sees lower cost-to-serve, and leadership sees a team aligned around outcomes instead of activities. If you want your AI investments to move the needle, focus on unified signals, real-time orchestration, and getting the whole business excited about customer outcomes....not just actions. Remember: Outcomes > Actions #customerexperience #ai #cxleaders #outcomesoveraction
Role of Technology in Customer Touchpoints
Explore top LinkedIn content from expert professionals.
Summary
Technology plays a transformative role in enhancing customer touchpoints by streamlining interactions, understanding customer needs, and delivering tailored experiences. It bridges the gap between businesses and consumers, ensuring smoother, more personalized, and proactive communication at every stage of the customer journey.
- Unify data signals: Combine information from various sources like chat logs, social media, and customer inquiries to identify pain points and respond swiftly to their needs.
- Personalize interactions: Use AI to deliver customized recommendations and solutions based on customer behavior and preferences to foster stronger relationships.
- Proactively address issues: Leverage predictive technology to anticipate and resolve customer problems before they arise, improving satisfaction and reducing support costs.
-
-
What CTOs in Banking Should Do with AI for Customer Experience A few months ago, I sat with the CTO of a major bank who shared a familiar frustration: “We’ve invested millions in AI, but our customer experience hasn’t improved the way we expected.” I asked a simple question: “Are you using AI to solve real customer pain points, or are you using it because it’s expected?” That conversation led us down a path that many banking leaders are navigating today—leveraging AI not just for efficiency, but to truly enhance customer relationships. AI and the Future of Banking Customer Experience The global AI in banking market is expected to reach $130 billion by 2030, growing at a CAGR of 32% (Allied Market Research). This isn’t just about chatbots or fraud detection anymore; AI is redefining how banks engage with customers at every touchpoint. McKinsey reports that banks effectively using AI can increase customer satisfaction by 35% while reducing operational costs by up to 25%. The challenge, however, is execution—CTOs must ensure AI is seamlessly integrated into both digital and human interactions. How Leading CTOs Use AI for Customer Experience 1- Hyper-Personalization Example: JPMorgan Chase uses AI to analyze customer behavior and provide real-time loan and investment suggestions, increasing engagement by 40%. 2- AI-Powered Virtual Assistants Example: Bank of America’s Erica, an AI-powered assistant, has handled over 1.5 billion interactions, offering personalized financial insights. 3- Predictive Analytics for Proactive Engagement Example: A European bank using AI-driven insights reduced customer churn by 22% by proactively addressing financial concerns. 4- AI-Enhanced Fraud Detection Example: Mastercard’s AI-based fraud prevention has reduced false declines by 50%, improving trust and security. A Real-World Impact: AI in Action One of our banking clients struggled with high customer complaints about slow loan approvals. By integrating AI-driven document verification and risk assessment, approval times dropped from 5 days to 5 minutes. The result? A 30% increase in loan applications and a significant boost in customer satisfaction. The Human-AI Balance in Banking Despite AI’s capabilities, customers still value human interaction. 88% of banking customers want a mix of AI-powered convenience and human support when dealing with financial decisions (PwC). The key for CTOs is to balance automation with empathy—ensuring AI enhances, rather than replaces, the personal touch. The Road Ahead AI is no longer a futuristic concept in banking—it’s a strategic necessity. CTOs who embrace AI for customer experience, not just efficiency, will lead the industry forward. At Devsinc, we believe the future of banking isn’t just digital—it’s intelligent, personalized, and deeply customer-centric. The question is, are we using AI to replace transactions, or to build trust? Because in banking, trust isn’t just a feature—it’s the foundation.
-
🚀 AI can transform customer experience — but only when it's applied with purpose. From customer journey mapping to predictive support, enterprises are turning to their digital consulting partners to embed AI where it drives real business outcomes. Here’s how we are making it happen 👇 🔍 1. Customer Journey Mapping + Use Case Identification We decode friction points and map “moments that matter,” then identify where AI can add the most value — from churn prediction to next-best-action models. 🎯 2. AI-Powered Personalization & Recommendations Using deep learning, behavioral segmentation, and recommendation engines, we help enterprises deliver personalized content, offers, and experiences — at scale. 🛠️ 3. Proactive Support with Predictive AI Predictive models and AI assistants anticipate issues before customers even notice — driving loyalty, reducing support costs, and boosting satisfaction. 💡The real power of AI isn’t just in the algorithms — it’s in applying them where human experience and business goals intersect. 👉 Are you seeing success with AI in your CX journey? Would love to hear your experiences. #AI #CX #DigitalTransformation #CustomerExperience #VPspeak