How Technology Supports Customer Success

For SaaS companies, customer churn is closely tied to growth. From an industry standpoint, the average churn rate for mid-market companies is between 12% and 13%. With renewal-based revenue models, churn directly affects both topline and bottom line. At Egnyte, AI and Machine Learning have been pivotal in our journey to improving customer retention and reducing churn. We have noted a 2.5 to 3 points reduction in churn rate by deploying AI programs that are actionable for both our customers and CSM teams. AI can offer powerful capabilities to help SaaS companies significantly reduce churn by enabling proactive and data-driven customer retention strategies. Some of these strategies are: 1. Predictive Churn Analytics Machine Learning models analyze vast amounts of customer data (usage patterns, support interactions, billing history, feature adoption, login frequency, etc.) to identify subtle patterns that precede churn. They can flag customers as "at-risk" before they can explicitly signal dissatisfaction, allowing for proactive intervention. It can further assign a "churn risk score" to each customer/ user, enabling customer success teams to prioritize their efforts on the most vulnerable and valuable accounts. The actionable operational data that we received by employing ML is the essence of churn analytics. 2. Hyper-Personalized Customer Experiences AI allows SaaS companies to move beyond generic communication to highly tailored interactions based on user behavior and feature adoption. AI can suggest relevant features, integrations, or workflows that the user might find valuable but hasn't yet discovered. AI can also determine the optimal timing and channel of customer-focused content, such as help desk articles, feature awareness videos, and case studies. 3. Automated Customer Support and Engagement AI can enhance customer support, making it more efficient and impactful. AI-powered chatbots can handle common customer queries 24/7, reducing wait times and providing instant solutions. Advanced chatbots use Natural Language Processing (NLP) to understand complex queries and provide personalized responses. It also helps in online enablement, reducing onboarding costs. While these strategies are already redefining the way CSM and enablement teams service customers, their significance in the cadence of customer retention strategies is going to increase hereon. Enterprises need to use AI intelligently and efficiently and focus on gleaning actionable insights from their AI strategies. #B2BSaaS #Churn #CustomerRetention

Great customer experiences don’t happen by accident. The top CX teams are doing a few things differently. 👇 The strongest teams I’ve seen are staying close to what their customers need and keeping a close eye on what their systems might be missing. They’re making smart, focused changes that support both the customer and the agent experience. Here are 3 things I’m seeing top CX teams lean into right now: ✅ Tapping into voice data: Customer conversations are full of insight. Leading teams are using voice data to spot patterns, flag common issues, and make meaningful improvements. It’s also helping them coach more effectively and tighten up workflows. ✅ Using AI to stay consistent and responsive: AI is helping teams manage FAQs, handle routine requests, and keep up with demand, without losing that personal touch. It also frees agents up to focus on the conversations that need more attention. ✅ Coaching for confidence: The best coaching is tailored. High-performing teams are using real examples from calls and real-time insights to help agents grow, improve, and stay in sync. If you’re looking to evolve your CX strategy, these are 3 great places to start. #CustomerExperience #AI #ContactCenter

For years, companies have been leveraging artificial intelligence (AI) and machine learning to provide personalized customer experiences. One widespread use case is showing product recommendations based on previous data. But there's so much more potential in AI that we're just scratching the surface. One of the most important things for any company is anticipating each customer's needs and delivering predictive personalization. Understanding customer intent is critical to shaping predictive personalization strategies. This involves interpreting signals from customers’ current and past behaviors to infer what they are likely to need or do next, and then dynamically surfacing that through a platform of their choice. Here’s how: 1. Customer Journey Mapping: Understanding the various stages a customer goes through, from awareness to purchase and beyond. This helps in identifying key moments where personalization can have the most impact. This doesn't have to be an exercise on a whiteboard; in fact, I would counsel against that. Journey analytics software can get you there quickly and keep journeys "alive" in real time, changing dynamically as customer needs evolve. 2. Behavioral Analysis: Examining how customers interact with your brand, including what they click on, how long they spend on certain pages, and what they search for. You will need analytical resources here, and hopefully you have them on your team. If not, find them in your organization; my experience has been that they find this type of exercise interesting and will want to help. 3. Sentiment Analysis: Using natural language processing to understand customer sentiment expressed in feedback, reviews, social media, or even case notes. This provides insights into how customers feel about your brand or products. As in journey analytics, technology and analytical resources will be important here. 4. Predictive Analytics: Employing advanced analytics to forecast future customer behavior based on current data. This can involve machine learning models that evolve and improve over time. 5. Feedback Loops: Continuously incorporate customer signals (not just survey feedback) to refine and enhance personalization strategies. Set these up through your analytics team. Predictive personalization is not just about selling more; it’s about enhancing the customer experience by making interactions more relevant, timely, and personalized. This customer-led approach leads to increased revenue and reduced cost-to-serve. How is your organization thinking about personalization in 2024? DM me if you want to talk it through. #customerexperience #artificialintelligence #ai #personalization #technology #ceo

As I’ve shared our vision about Customer Success 5.0 (CS5.0), people often ask whether I believe AI will replace CSMs. My answer? NO. AI will amplify the impact of great CSMs, empowering them to deliver deeper, more personalized value to each customer. This is how I think about the impact of AI in customer success. Legacy CS = (EQ + IQ) Before AI, we relied heavily on the relationship skills (EQ) and smarts (IQ) of CSMs to build deep relationships with customers and ensure we delivered the outcomes they expected. Often, CSMs had to dedicate extraordinary time and effort juggling a wide range of tasks just to keep up – prepping for and summarizing meetings, finding updates, analyzing data, writing emails, answering product questions, and more. Yes, these efforts worked in most cases but often demanded heroic contributions from CSMs just to sustain customer relationships, let alone grow them. Now the CS5.0 equation redefines our approach: CS5.0 = (EQ + IQ) * AI With AI as a significant accelerator, collaborator, and multiplier, we're able to reimagine customer success across: 1. The Heart (EQ) ❤️ Emotional Intelligence (EQ) remains at the core of customer success. It’s about understanding, empathizing, and connecting with our customers on a human level. Building lasting relationships and anticipating customer needs requires empathy, active listening, and authenticity. 2. The Brain (IQ) 🧠 Human Intelligence (IQ) emphasizes data-driven decision-making and strategic thinking. Leveraging our ability to interpret complex data and solve critical challenges in the context of each customer relationship is essential—what I call “contextual intelligence.” IQ drives operational excellence and execution, ensuring our solutions are thoughtful, contextual, and aligned with customer needs. 3. The Multiplier 🤖 AI is the multiplier that elevates our efforts. In customer success, AI is not here to replace human interactions but to enhance them. AI-driven tools (like our SmartCS AI) assist CSMs by managing a significant share of repetitive tasks—automatically summarizing meetings, surfacing key updates, analyzing customer data, and drafting responses. By offloading these tasks to AI, CSMs can focus on high-value activities—solving strategic customer challenges, anticipating needs, and providing tailored solutions. They become much more consultative vs. transactional. AI enables CSMs to focus on what they do best—building relationships (EQ), driving smart solutions (IQ), and delivering impactful results. Where do you see the greatest opportunity for AI to empower CSMs in delivering exceptional customer experiences?

I've got a real-world example to share. I noticed that our CSMs and Insight Managers spent the majority of some client calls explaining the same confusing product feature over and over. These were hard-won meetings with key stakeholders, but we wasted precious time on repetitive, low-value conversations. I decided to share recordings of these calls with our executive team, and the response was eye-opening. DISQO rallied to invest in better training, tools, and product enhancements, making that feature more intuitive for our customers. Without AI surfacing these insights from hundreds of hours of calls, we might never have connected the dots. It wasn't a skill issue on the CSM side but a systemic opportunity. That's why we use AI to listen to every single customer conversation. That's how AI elevates customer experiences. #AI #CustomerExperience #CX #Listening #Learning #Value #Opportunity

A coffee shop is utilizing AI-powered cameras, not merely for efficiency but as a tool for transformative customer service. AI technology analyzes customer flow, drink preparation times, and customer dwell time. This data isn't just numbers - it's an asset that empowers managers to refine operations and enables baristas to offer exceptional service. 𝐖𝐡𝐚𝐭 𝐬𝐞𝐭𝐬 𝐭𝐡𝐢𝐬 𝐢𝐧𝐢𝐭𝐢𝐚𝐭𝐢𝐯𝐞 𝐚𝐩𝐚𝐫𝐭 𝐢𝐬 𝐢𝐭𝐬 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 This fusion of AI with traditional customer service models demonstrates that the future of work isn't about replacing humans with machines. Instead, it's about leveraging technology to amplify human capabilities and enrich customer experiences. By alerting staff to long queues or potential customer issues, it facilitates proactive service management, ensuring a consistently positive experience for every visitor. 𝐒𝐦𝐚𝐫𝐭 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 Integrating real-time sales data, this system could aid in intelligent scheduling and inventory management. This is how traditional customer service merges with futuristic technology to create a seamless service experience. 𝐖𝐡𝐢𝐥𝐞 𝐭𝐡𝐞 𝐛𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐚𝐫𝐞 𝐜𝐥𝐞𝐚𝐫, 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 𝐢𝐧𝐭𝐨 𝐞𝐯𝐞𝐫𝐲𝐝𝐚𝐲 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐬𝐧'𝐭 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: >> Privacy Concerns: Implementing surveillance and data analysis must be handled with strict adherence to privacy laws and ethical standards. >> Training and Adaptation: Staff must be adequately trained to work alongside AI tools, which requires time and investment. What are your thoughts on integrating AI in customer service? How can we address the challenges to ensure technology respects and elevates the human element? #innovation #technology #future #management #startups