Implementing Customer Experience Software

So you can make fun of the Old School Enterprise software vendors if you like, the Oracles, the SAPs, the IBMs, etc. Yes, they aren’t all growing like the Cloud leaders, although actually, most are doing pretty darn well right now. But they do know one thing: what’s important to enterprise buyers. Even if they don’t always have the most current state-of-the-art products. And one thing they do know is how complex it is to switch from one vendor to another. 98% of start-ups just don’t get this right. If your product is in a brand-new category, then maybe it’s not a lot of work to rip out an old system. But most of you are displacing an existing solution. If that’s paper, then there will still be onboarding costs. If that’s Excel, again, real costs too. But the all-in costs go up dramatically more when you are replacing an existing vendor and system with tons of structured data. How the heck do you get that structure, that exact data set, those custom objects, those workflows … to work in a new tool? It can be close to impossible for many prospects and customers. You might think this only matters for big enterprise vendors at scale, but you’re likely wrong. Buyers of any size that come in as prospects with existing systems will be thinking just as much about soft costs as hard costs. 1️⃣ What are you doing to automate or at least simplify 90% of the migration from another vendor for your customers? 2️⃣ If you can’t do it yourself, do you have partners that can do migrate from an old vendor for them? 3️⃣ Can you get it done during a pilot, so it’s less risky? 4️⃣ Can you get it done automatically, even, before the pilot? That’s amazing. 5️⃣ Be honest. Watch a complex, tough switch from a competitor to your solution. Be honest about the soft costs. Are you even sure it's worth it? You can make the customer do all the work to migrate to your solution. That’s what 95%+ of SaaS vendors do. But you’ll lose some. And you know who you’ll lose even more? The ones you >could< have stolen. The ones that are on the edge. Not so fed up to switch. But close. Imagine if you did the work for them. If nothing else, develop a network of partners that can do this for you. Even if you have to pay them, say, $20k of a $100k deal to do the migration for a customer, it’s worth it. It’s probably even worth $100k of a $100k deal for the right customer. Because imagine they stay for a decade. The best ones do. I’ll give you a personal example. We pay about $20,000 a year for a product we use that is a bit dated. We went and talked to the newer, slicker competitor. They wanted $40,000 a year, which was a lot more, but we were willing. Then, late in the process, they told us there would be a $20,000 migration fee. And … and … that we’d have to run both apps at the same time for up to a year, as they couldn’t migrate all our data.  That it was an “us issue”, not a “them issue” they said. See ya never.

🧑🏽💻 "I have no idea what I'm doing... but I'll figure it out." This is basically my everyday life working in a seed-stage startup, and I often rely on applying my data best practices to "non-data" business problems to unblock myself. 🚀 Most recently, I've been working on a migration from Hubspot to Salesforce, where I have limited experience in sales and these tools. But by reframing it into a data engineering problem, I all of a sudden have a wealth of knowledge to make this migration happen. 👇🏽 Here's how I approached it: 1. Determine what's the business use case and expectations from my business stakeholders? 2. Create a flow chart that logically maps out the process of going from "lead capture" to "discovery call" and how a lead's "status" changes throughout the workflow. 3. Map the workflow to an underlying architecture of our various tools and integrations to make this process happen, AND determine which data fields are being changed. 4. Determine all the data fields being used in our current system (Hubspot), then map them to the fields in the new system (Salesforce)-- it's unlikely these fields map 1:1, and thus, be sure to document all of your decisions as you are updating business logic. 5. Measure your baseline counts (e.g. lead counts by "lead stage") in your current (Hubspot) and new (Salesforce) systems. 6. Begin unhooking third-party integrations from the old system and move the integration to the new system so new "lead events" are not interrupted. 7. Test the updated integrations with known values-- for me, I went through the entire "sales journey" as if I were a "lead" by filling out our lead form with a test account, scheduling test meetings, etc., and ensuring the expected data shows up in Salesforce. 8. Begin backfilling data and iterating until your expected counts match in the old and new systems. Bonus: Create a doc that details the entire process and your decisions, as well as create a Slack channel to give real-time updates to ensure your business stakeholders are in the loop. 💯 With this reframe, I went from "How do I migrate from Hubspot to Salesforce!?" to instead, "I've done a database migration before, so let's apply it to Hubspot and Salesforce!" 👀 Check the comments below to see the impact already made to for one of my business stakeholders! #data #dataengineering #sales #salesforce

Nobody wants to hear this, but creating and landing a new design system in existing software is an 18-month project. At best. Most teams don’t make it that far. I’ve been through this three times, each with a different approach. The products were complex enterprise apps, in cybersecurity and dev tools. At one company, we went big. Clean-slate redesign. New system. Bold vision. But once we tried to land it in the product, the complexity crushed us. Dependencies stacked up. Scope ballooned. A year later, the project was dead and the product was worse off. At another, we tried slow, incremental replacement. It was working… until it wasn’t. Progress stalled. The company got acquired. The project froze, half-done. The one time it worked? We picked a single feature, landed the system there, and expanded from that foundation. The difference wasn’t the quality of the design. It was the implementation strategy. Replacing a system isn’t a visual exercise, it’s an architectural one. You’re not just swapping styles. You’re rewiring years of logic and layout. And unless the rollout is carefully scoped and sequenced, it will break down. So, if you’re starting a system overhaul: 1. Don’t blow up the current system 2. Land a pilot of the new system in one real feature 3. Expand intentionally from there That’s the only way I’ve seen it work.

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.

Stop buying CRM systems. Start building Customer Management capabilities. The technology was never the problem. Most organizations approach customer management backwards: -> They experience pain (lost deals, poor service, disconnected teams) -> They research CRM platforms -> They buy the one with the best demo -> Then nothing fundamentally changes -> Then they blame the tool. Or the vendor. Or IT. But here's the uncomfortable truth: Salesforce, HubSpot, Dynamics—they're all good systems. None of them will solve your customer management problems on their own. Because customer management isn't a technology. It's a capability. So what's the difference? Technology thinking: → "We need a CRM" → Focus on features, integrations, price → IT-led implementation → Success = go-live date Capability thinking: → "We need to understand and serve our customers better" → Focus on outcomes: retention, revenue, experience → Cross-functional transformation → Success = measurable business impact A true Customer Management capability includes: ->Strategy - Clear vision of what customer success looks like for your business ->Process - Defined workflows for how customer information flows across teams (Sales, Marketing, Support, Product) ->People - Teams with the skills, training, and incentives to actually use customer insights ->Data - Clean, accessible, governed data that people trust and act on ->Technology - Tools that enable the above (not drive it) ->Governance - Ownership, accountability, continuous improvement Here's what capability-first looks like: Before you evaluate a single platform, ask: - What customer outcomes are we trying to drive? - What does our ideal customer journey look like, end-to-end? - Which teams need to collaborate, and how? - What decisions will we make differently with better customer data? - Do we have the processes and skills to sustain this? - Who owns customer experience in our organization? Answer these first. Then choose technology that fits your capability strategy. Not the other way around. #CustomerManagement #CRM #BusinessStrategy #DigitalTransformation #CustomerExperience #TechnologyStrategy #ScaleUp

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.

When Coldplay concert get more attention than IT adoption… No wonder your “cutting-edge” tools sit idle. The organization is spending millions on IT tools (without anyone asking their opinion). And that's why at the end of the day, they’re still using STATIC SPREADSHEETS. Here’s why your shiny tech is gathering dust, and 7 ways to fix it. You were given “the best” software, market leader. You expected dashboards, live data, and instant insights. But you and the team are still using Excel, copying and pasting numbers like it’s 2005. This is not a tech problem. It’s a deployment nightmare. Complex rollouts kill adoption. When implementation drags on for months, people lose patience. If onboarding feels harder than learning a new language, your team will run back to what they know. Here’s what happens: • The new system needs endless configuration. • Training takes weeks, not hours. • Every update breaks something else. • Support tickets pile up. • No one knows who owns what. So, the team reverts to spreadsheets and tribal knowledge. They want to get work done, not fight with software. The result: Your investment sits idle. Your data is out of sync. Decisions are made on old numbers. Here are seven tips to break this cycle: 1. Ruthless simplicity ↳ Choose tools that work out of the box. ↳ Avoid endless customization. 2. Fast wins ↳ Roll out in phases. ↳ Show value in days, not months. 3. Embedded training ↳ Make learning part of the workflow. ↳ Use short, focused sessions. 4. Clear ownership ↳ Assign champions for each tool. ↳ Give them time and authority. 5. Real feedback loops ↳ Listen to the users. ↳ Fix pain points fast. 6. Celebrate progress ↳ Share wins. ↳ Show how the new system saves time. 7. Kill the spreadsheet ↳ Remove old templates. ↳ Make the new way the only way. The best tech is useless if no one uses it. Make adoption easy. Make change stick. Or watch your investment fade into the background, one spreadsheet at a time.

The Website Consent Problem: Too Many Tools, Too Little Harmony Websites rely on various third-party tools like analytics platforms, ad managers, and tag managers. While these tools are essential for functionality, each has unique privacy settings. The real challenge is ensuring they work together to honor user consent. When integration fails, consent flows break, leading to compliance risks and loss of trust. Websites often use over 20 different types of tools. Key categories of website tools: 1. Analytics tools Google Analytics and Adobe Analytics track user behavior and performance. They rely on settings like Google Consent Mode to operate compliantly. Without proper integration, they may collect data before consent. 2. Ad management platforms Prebid.js and Google Ad Manager manage ad delivery. They need frameworks like IAB TCF strings to serve personalized ads only with user consent. Misconfigurations can lead to tracking and legal risks. 3. Tag management systems (TMS) Google Tag Manager and Tealium control when other tools are deployed. The CMP (Consent Management Platform) must load first to capture consent preferences. Without proper setup, tools may fire prematurely. 4. Heatmaps and session recording tools Hotjar and FullStory track user interactions to improve experience. These tools collect sensitive data and should operate only with explicit consent. Poor configurations can result in privacy issues. Why honoring consent is a challenge? - Fragmented ecosystem Most tools operate in silos, making it hard to create a unified consent flow. Without integration, tools don’t respect shared consent signals. - Regulatory complexity Privacy laws vary across regions, requiring different approaches for compliance (e.g., opt-in vs. opt-out). Configuring tools to meet global regulations adds complexity. - Lack of real-time monitoring Consent flows change as tools are updated or replaced. Without regular monitoring, settings can become outdated, leading to unauthorized data collection. - Misaligned priorities Revenue goals often take precedence over compliance. This results in shortcuts like firing tracking scripts before consent is obtained, risking penalties and user trust. What should Privacy Teams do? 1. Audit your website List all third-party tools and document their data flows. 2. Understand privacy settings Review each tool’s privacy settings and integration with the CMP. 3. Fix tag management systems Ensure the CMP loads first to capture user consent before other tags fire. 4. Verify CMP integration Confirm the CMP communicates consent signals to all tools for consistency. 5. Automate, automate, automate Manual consent flow monitoring is time-consuming and prone to errors. Work with tech teams to automate consent checks or use vendors specializing in consent monitoring automation. This will help in catching issues early on. #Privacy pros, How are you auditing your website’s tools and #consent flows?

A lot of people think the toughest part about deploying AI agents in enterprise environments is to figure out the best model to use - OpenAI vs Claude vs DeepSeek. Completely wrong. We have worked with top enterprises and multiple public companies to deploy AI support agents, and here’s what we’ve learned: the real question isn’t whether AI can automate support, it’s how to make AI work effectively in the complex, human-centric world of enterprise operations. Yesterday, I was on a call with the Senior VP of Operations for a company handling 4 million annual support issues, and the top questions were: 1. How do we test and monitor the AI at scale? What will effective QA from humans look like? 2. What are the guardrails in the model? Will the AI self-QA before the humans have to QA? 3. What's the workflow to manage the knowledge - can the AI go and update our knowledgebase when it learns new topics? 4. How do we design a hybrid support model so that AI<>Humans can collaborate depending on who is best equipped to respond 6. Most importantly, how do you integrate AI agents into complex enterprise systems without disrupting workflows? - Zendesk + Confluence + Notion + Slack These aren’t just technical challenges, they’re operational and strategic challenges that require deep expertise in both AI and customer experience. The future of AI in customer support isn’t just about the models themselves. While foundational AI infrastructure will inevitably become commoditized (Welcome DeepSeek AI), the real value lies in application layer - the tools and systems that bring AI agents to life and deliver real value in the messy, hybrid environments of large enterprises, with minimal changes. At Fini, we’re building the future of AI-driven support by tackling these questions head-on and delivering real value for our enterprise customers. Out platform makes it dead easy for enterprises to self-deploy, and let their CX teams manage AI<>Human collaboration. The future of customer support is here, and it’s hybrid. Let’s build it together.

AI + HI = Improved CX In today’s digital world, businesses strive to deliver exceptional customer experiences (CX) to stand out. While artificial intelligence (AI) has revolutionized CX by enabling automation, personalization, and efficiency, it cannot fully replace the human touch. AI enhances CX by processing vast amounts of data in real time, predicting customer preferences, and providing instant responses through chatbots, recommendation engines, and self-service options. It reduces wait times, offers 24/7 support, and ensures consistency across interactions. However, AI alone has limitations—it lacks emotional intelligence, creativity, and the ability to handle complex, nuanced customer concerns. Human agents bring empathy, critical thinking, and problem-solving skills that AI cannot replicate. When combined with AI, human agents become more efficient, as AI handles routine tasks, provides insights, and allows them to focus on high-value interactions. Impact on BPO KPIs 1. First Call Resolution (FCR) Improvement: • AI-driven knowledge bases and predictive analytics equip human agents with real-time solutions, reducing repeat calls. • Virtual assistants handle routine inquiries, allowing human agents to focus on complex issues. 2. Reduction in Average Handling Time (AHT): • AI-powered tools like speech analytics and automated summaries minimize the time agents spend on after-call work (ACW). • Virtual assistants can gather customer information before handing over to a live agent, speeding up resolutions. 3. Increased Customer Satisfaction (CSAT): • AI ensures faster response times and personalized interactions based on past behavior. • Human agents, equipped with AI-driven insights, can provide more empathetic and accurate solutions, improving overall satisfaction. 4. Enhanced Agent Productivity and Utilization: • AI automates repetitive tasks such as data entry, ticket classification, and FAQs, freeing up agents for complex interactions. • Sentiment analysis tools help agents adjust their approach in real time for better engagement. 5. Lower Cost Per Contact: • AI-driven self-service options reduce the volume of inbound calls and chats, lowering operational costs. • Intelligent routing ensures the right agent handles the right query, optimizing workforce efficiency. 6. Improved Net Promoter Score (NPS): • Personalized AI-driven recommendations and proactive outreach enhance customer engagement. • The combination of AI efficiency and human empathy fosters long-term customer loyalty. The synergy of AI and HI leads to an improved CX by ensuring speed, accuracy, and emotional connection. AI-driven insights empower human agents to offer proactive solutions, while human empathy ensures customers feel valued. AI and HI are not competitors but collaborators. Businesses that successfully integrate both will deliver superior CX, optimize BPO performance, and achieve sustainable growth in an increasingly digital world.