Anticipating Customer Needs Effectively

I’m not asking my CSMs to resolve support tickets. I’m asking them to leverage them. Support tickets aren’t just a backlog of problems; they’re customer truth bombs waiting to explode. If you’re not mining them for insights, you’re flying blind—and that’s exactly how churn sneaks up on you. Every Customer Success team I’ve ever led has been trained to use Support tickets strategically. Why? Because they’re packed with insights that make us better at our jobs. ✅ We learn more about the product. ✅ We spot trends before they become problems. ✅ We understand our customers’ use cases more deeply. If you’re not tapping into support data, here’s what you’re missing: 🔥 Emerging Pain Points Recurring issues expose friction in the customer journey. Ignore them, and those minor frustrations turn into churn-worthy headaches. 🔥 Product Gaps Customers vote with their tickets. If the same feature requests or usability complaints keep surfacing, your roadmap is practically writing itself. 🔥 Engagement Risks A spike in tickets isn’t just noise—it’s a flare. Users don’t submit tickets when they’re thriving; they do it when they’re stuck, frustrated, or in need of more enablement. Here are a few ways my team and I are using these insights: ✅ Spot & Engage Struggling Users A surge in ticket volume? Proactively reach out before frustration turns into a cancellation. ✅ Create Targeted Content If the same questions keep coming up, turn those insights into help docs, webinars, or office hours. ✅ Surface Expansion Opportunities Seeing frequent feature requests? Build them—or better yet, use them to tee up expansion conversations. ✅ Map Out User Behavior Support tickets tell you who’s onboarding, who’s adopting new features, and who’s stuck. Use that data to drive deeper engagement. ✅ Collaborate with Product Your product team needs this intel. Share support trends regularly to influence meaningful fixes and features. High ticket volume isn’t necessarily a bad thing—but you need to know how to use it to your advantage. Bottom line? CSMs don’t need to fix support tickets. But the best ones know how to use them to drive retention, expansion, and adoption. _____________________________ 📣 If you liked my post, you’ll love my newsletter. Every week I share learnings, advice and strategies from my experience going from CSM to CCO. Join 12k+ subscribers of The Journey and turn insights into action. Sign up on my profile.

As founders, we're bombarded with advice: "Know your customer!" "Listen to your audience!" But amidst the buzzwords, a crucial question lingers: how do we truly understand what matters to our customers, beyond the surface-level preferences and fleeting opinions? My journey as a founder has been a constant dance between chasing "customer feedback" and uncovering the deeper desires fueling that feedback. I've learned that listening isn't enough; we need to actively decode and prioritize what truly resonates with our users. Enter the Customer Value Compass: Step 1: Chart the Terrain: 1. Gather diverse data: Collect feedback through surveys, interviews, user observations, social media sentiment analysis, and support tickets. 2. Identify recurring themes: Analyze the data for common threads, challenges, and desires expressed by your customers. Don't get bogged down in individual details; look for patterns. 3. Categorize by impact: Segment your identified themes into two categories: "surface-level preferences" and "core value drivers." Surface-level preferences: These are fleeting opinions, often influenced by trends or personal experiences. They can provide valuable insights for specific features or campaigns, but shouldn't define your core offering. Core value drivers: These are deeply held needs, desires, and motivations that underpin customer behavior. These are the true north stars you need to align with. Step 2: Calibrate the Compass: 1. Dig deeper into core value drivers: Conduct in-depth interviews, focus groups, or user testing to truly understand the "why" behind these themes. 2. Prioritize based on impact: Not all core value drivers hold equal weight. Assess their prevalence, intensity, and alignment with your business goals to determine which ones deserve the most attention. 3. Validate with data: Look for quantitative evidence to support your qualitative findings. Analyze usage data, conversion rates, and customer satisfaction metrics to ensure your understanding aligns with actual behavior. Step 3: Navigate with Confidence: 1. Align your product and strategy: Use your Customer Value Compass to inform product development, marketing messages, and customer support initiatives. 2. Communicate with clarity: When making changes or introducing new features, explain how they address the core value drivers you've identified. 3. Continuously iterate: The Customer Value Compass is a living document. Gather new data, conduct regular reviews, and be prepared to adjust your understanding as your customer base and market evolve. Remember, the Customer Value Compass is not a destination, but a journey. By prioritizing what truly matters to your users, you build a foundation for sustainable growth, loyalty, and success. So, silence the buzzwords, listen deeply, and let your customers guide your voyage. #FoundersJourney #CustomerInsights #DecodingValue #ValueCompass #CustomerCentricity #BuildingForUsers

As an analyst, I was intrigued to read an article about Instacart's innovative "Ask Instacart" feature integrating chatbots and chatgpt, allowing customers to create and refine shopping lists by asking questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on user's past buying habits and provides recipes and a shopping list once users have selected the option they want to try! This tool not only provides a personalized shopping experience but also offers a gold mine of customer insights that can inform various aspects of a business strategy. Here's what I inferred as an analyst : 1️⃣ Customer Preferences Uncovered: By analyzing the questions and options selected, we can understand what products, recipes, and meal ideas resonate with different customer segments, enabling better product assortment and personalized marketing. 2️⃣ Personalization Opportunities: The tool leverages past buying habits to make recommendations, presenting opportunities to tailor the shopping experience based on individual preferences. 3️⃣ Trend Identification: Tracking the types of questions and preferences expressed through the tool can help identify emerging trends in areas like healthy eating, dietary restrictions, or cuisine preferences, allowing businesses to stay ahead of the curve. 4️⃣ Shopping List Insights: Analyzing the generated shopping lists can reveal common item combinations, complementary products, and opportunities for bundle deals or cross-selling recommendations. 5️⃣ Recipe and Meal Planning: The tool's integration with recipes and meal planning provides valuable insights into customers' cooking habits, preferred ingredients, and meal types, informing content creation and potential partnerships. The "Ask Instacart" tool is a prime example of how innovative technologies can not only enhance the customer experience but also generate valuable data-driven insights that can drive strategic business decisions. A great way to extract meaningful insights from such data sources and translate them into actionable strategies that create value for customers and businesses alike. Article to refer : https://lnkd.in/gAW4A2db #DataAnalytics #CustomerInsights #Innovation #ECommerce #GroceryRetail

“She blinded me with science!” 🎤 “She Blinded Me With Science” -Thomas Dolby If a #CustomerSuccess leader found a genie that gave them 3 wishes, after rightly asking for “more wishes,” I’m guessing they would ask for a way to predict churn. [OK maybe they’d ask for world peace, a raise, … but go with me!] Our brilliant data science, Pau Ortí Codina, helped us understand which product features at Gainsight were the most predictive of retention or churn. For context, for years, we had done analysis to look at how usage of specific Gainsight Customer Success features correlate with retention. The challenge is that this approach can end up outputting many features that align to retention. But some of these features may themselves be correlated to each other. So the question is which few features we should focus on? Luckily, we had Pau. I asked him about his methodology and here’s how he approached it: 1: We started with hypotheses - which features could be indicators of retention. 2: We pulled renewal data from a year ago. 3: We made sure to avoid “survivorship bias” by looking at data 9-12 months before the renewal. The logic is that if you look at usage data near the renewal, it could be misleading. A customer’s usage could have dropped BECAUSE they are leaving. 4: We used several statistical methods to see how each feature correlated to renewal outcomes; we removed those without strong correlations. 5: We employed a decision tree classifier (see below) to understand how the variables relate to each other. 6: Pau then evaluated the model. If the model predicted a renewal, it was correct 96% of the time. By contrast, if the model predicted a churn, 50% of the time the client renewed. This isn’t great (ideally, churn predictions would have no false positives), but it’s better in CS to be more cautious rather than less. At the end of the day, we determined that clients with usage of our Journey Orchestrator digital automation feature were much more likely to renew. Have you run any data science-based model to predict renewal and churn for your business? If so, what did you learn? [The red boxes are confidential data that I blanked out]

The most important competence for building a sustainable DTC strategy: Data-Driven Customer Insights. Over the last decade direct-to-consumer marketers have suffered a 15% CAGR in CPM inflation for digital #advertising, according to research by Frederic Fernandez & Associates, dramatically increasing cost per acquisition. #DTC companies hence need to much better understand their target consumers, their path-to-purchase metrics, barriers/ drivers/ triggers & 4Ps preferences, and design a new omnichannel acquisition strategy. In my view, its time for DTC companies to build truly immersive and personalized customer acquisition strategies based on data driven customer insights. Data-driven customer insights are essential in the following 5 marketing areas: 🙋 Understanding Customer Behavior: To create personalized experiences, brands need to understand their customers' behaviors, preferences, and pain points. #Data analytics enables companies to track and analyze customer interactions across all touchpoints, providing deep insights into their journey and decision-making processes. 🎯 Personalization at Scale: Leveraging customer data allows brands to segment their audience and deliver tailored content, offers, and recommendations. This level of #personalization can significantly enhance customer satisfaction and loyalty, as consumers are more likely to engage with content that is relevant to their needs and interests. 📢 Optimizing Marketing Efforts: Data insights help brands to optimize their #marketing strategies and campaigns. By analyzing which tactics are most effective, companies can allocate resources more efficiently and improve their return on investment. ❤️ Enhancing Customer Engagement: Real-time data analysis enables brands to engage with customers at the right moment with the right message. This timely #engagement can drive higher conversion rates and foster a stronger emotional connection with the brand. 📈 Continuous Improvement: Data-driven #insights provide a feedback loop that allows brands to continuously refine their products, services, and customer interactions. This iterative process helps in adapting to changing customer expectations and market trends. By investing in data collection, advanced analytics, and skilled personnel, #DTC companies can create truly immersive and personalized customer experiences that drive engagement and loyalty.

The recommendation is a powerful tool for e-commerce sites to boost sales by helping customers discover relevant products and encouraging additional purchases. By offering well-curated product bundles and personalized suggestions, these platforms can improve the customer experience and drive higher conversion rates. In a recent blog post, the CVS Health data science team shares how they explore advanced machine learning capabilities to develop new recommendation prototypes. Their objective is to create high-quality product bundles, making it easier for customers to select complementary products to purchase together. For instance, bundles like a “Travel Kit” with a neck pillow, travel adapter, and toiletries can simplify purchasing decisions. The implementation includes several components, with a key part being the creation of product embeddings using a Graph Neural Network (GNN) to represent product similarity. Notably, rather than using traditional co-view or co-purchase data, the team leveraged GPT-4 to directly identify the top complementary segments as labels for the GNN model. This approach has proven effective in improving recommendation accuracy. With these product embeddings in place, the bundle recommendations are further refined by incorporating user-specific data based on recent purchase patterns, resulting in more personalized suggestions. As large language models (LLMs) become increasingly adept at mimicking human decision-making, using them to enhance labeling quality and streamline insights in machine learning workflows is becoming more popular. For those interested, this is an excellent case study to explore. #machinelearning #datascience #ChatGPT #LLMs #recommendation #personalization #SnacksWeeklyOnDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gb6UPaFA

How often do we receive a notification or an alert from a company about an issue before we even realize there’s a problem? Whether it’s a bank flagging suspicious activity, a delivery service notifying us of a delay, or a telecom provider offering compensation for downtime, proactive engagement is reshaping the customer experience landscape. Here’s an interesting fact: 67% of customers globally have a more favorable view of brands that offer or contact them with proactive customer service notifications. Yet, many businesses still focus solely on reactive support, missing the opportunity to elevate customer loyalty through preemptive action. In my opinion, the most impactful customer experiences don’t happen when customers reach out for help. They happen when businesses anticipate their needs and address them before they even ask. How, then, can businesses transform their CX strategies to embrace proactive engagement? Here are three essential strategies to lead the way: 1. Anticipate Customer Needs with Data and Insights The first step in proactive engagement is understanding your customers on a deeper level. Businesses can predict potential issues by analyzing behavioral patterns, feedback, and usage trends and offer solutions in advance. For example, monitoring a subscription service’s usage data could reveal customers at risk of disengagement, prompting a personalized offer to re-engage them. According to the 2024 Edelman Trust Institute Barometer, Saudi Arabia ranks first globally in trust in government leadership at 86%. The Kingdom is a clear example of how data-driven policies can foster trust. Businesses can follow this model by leveraging data insights to predict and address customer needs proactively. 2. Personalization: Beyond Generic Engagement Proactive engagement is most effective when tailored to individual preferences. Personalization goes beyond addressing customers by name; it involves delivering messages that resonate with their unique journeys. For instance, an e-commerce platform could recommend products based on browsing history or alert customers about restocks of their favorite items. 3. Solve Problems Before They Arise The ultimate goal of proactive engagement is to reduce friction. Offering solutions before customers encounter issues—like sending reminders for payments or proactively addressing service disruptions—can turn potential frustrations into positive experiences. At X-Shift, we’re committed to proactive engagement strategies that mirror these principles. While technology like AI is opening doors to automation, the human element—listening, anticipating, and personalizing—remains irreplaceable. The future of CX is proactive. Let’s lead the way! #Vision2030 #CustomerExperience #CX #Personalization #DigitalTransformation #SaudiArabia #CXTrends #CustomerLoyalty

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

Remember those classic moments where you walk into a bank, only to have the teller nonchalantly say, "Abhi lunch time hai...Lunch ke baad aana" (Come back after lunch timings)?  While this might raise a chuckle today,  it highlights a customer service gap that can seriously affect any business, especially in the BFSI sector. In today's customer-centric world, understanding how your customers feel about your brand and services is paramount. Customer behavioral traits provide a window into their likes, dislikes, and pain points. Businesses that can successfully interpret and address these sentiments hold a significant advantage when it comes to growth, customer retention, and overall success within the industry. Banks and financial institutions need to tap into a wealth of data to understand their customers better. Here are the top 5 data points crucial for analyzing customer sentiment: 👉 Social Media Activity: Monitor conversations, comments, and shares on social media platforms to get a real-time pulse on how people perceive your brand. 👉 Customer Feedback Surveys: Utilize targeted surveys to gather direct feedback on specific products, services, or overall experience. 👉 Call Center Transcripts: Analyze call center conversations to identify common issues, complaints, or praise. 👉 Website and App Analytics: Track how customers interact with your digital assets, including their navigation patterns and areas where they may be facing difficulties. 👉 Branch Interactions: Collect data from in-person interactions to assess customer satisfaction and areas for potential improvement. Analyzing massive amounts of customer data can be daunting. Google BigQuery offers BFSI institutions a powerful solution. This unified data engine helps banks: ℹ Consolidate Data: Store customer data from various sources into a single place. ⏰ Real-time Insights: BigQuery analyzes data as it flows in, giving you up-to-the-minute insights into customer sentiment. ⚡ Scalability: Handle increasing amounts of data with ease. 📊 Machine Learning Tools: Use predictive analytics to anticipate customer needs and identify potential dissatisfaction early on. So, you've gathered this rich customer sentiment data—how do you leverage it effectively? Here's where it gets exciting: 😎 Personalized Product Pitches: Tailor your marketing efforts and recommend products/services based on individual customer preferences and past behavior. 👨🏫 Service Improvements: Pinpoint areas where customer experience falls short, and focus your resources on enhancements. 👨🏭 Proactive Problem Solving: Pre-empt customer churn by addressing potential issues before they escalate. At Google, we understand the unique data challenges faced by the BFSI sector. We're committed to help you turn data into a driving force for growth and customer-centricity. Let's connect! Follow Omkar Sawant and (VJ) Vijaykumar Jangamashetti ☁️ for more such golden insights!

Predict, Personalize & Perform : From Leads to Loyalty Let’s be honest—customer lifecycle marketing (CLM) in B2B used to be a fancy word for “email nurture” and “CRM segmentation. But today, with AI, machine learning, and predictive data models, CLM is becoming something much more powerful: ➡️ A living, learning ecosystem that adapts to each buyer journey in real time. Here’s how we’re seeing AI and ML revolutionize CLM in B2B: 🔍 1. Predictive Journey Mapping Machine learning algorithms are helping identify where an account or contact actually is in the funnel—not just where your CRM says they are. ✅ No more generic MQL > SQL flows ✅ Dynamic scoring based on behavior, content engagement, and intent signals ✅ Real-time stage shifts based on predictive fit and readiness — 📈 2. Hyper-Personalized Nurturing (at Scale) AI models now create content clusters matched to personas, industries, and even buying committee behavior. 🎯 Email sequences, LinkedIn ads, and landing pages are personalized based on: Buyer role Past touchpoints Predicted product interest ICP match + firmographic data It’s not just segmentation—it’s micro-personalization powered by behavioral AI. — 🔁 3. Intelligent Retargeting & Re-Engagement Using ML-powered intent data and anomaly detection, you can now: Spot churn risks before they happen Trigger re-engagement sequences based on drop-off patterns Retarget accounts that show subtle buying signals across web, search, and social Retention is no longer reactive. It's predictive. — 📊 4. Revenue Forecasting + Attribution Modeling Thanks to data science, we can model: Which touchpoints actually move pipeline Which leads are likely to convert within a time window How to attribute revenue across full-funnel programs—not just the last touch This gives marketing the credibility and confidence we’ve needed for years. — 💡 The CLM Stack of a Modern B2B Org Should Include: ✔️ Customer Data Platform (CDP) ✔️ AI-powered segmentation + scoring ✔️ Predictive content engines (LLMs + RAG) ✔️ Lifecycle orchestration tools (e.g. Ortto, HubSpot, Marketo w/ ML layers) ✔️ Analytics + BI layer for optimization 🧠 Final Thought: In 2025, CLM isn’t just “marketing automation” with better templates. It’s about building an AI-powered engine that understands, anticipates, and activates each step of the buyer journey. You don’t need more content. You need smarter orchestration. 💬 Curious to hear from other B2B leaders: How are you bringing AI into your lifecycle marketing stack?