Anticipating Customer Needs Effectively

“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 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

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

Almost 10 years ago, I stepped away from my Head of Marketing role. Not because I didn’t love marketing, I did. A lot in fact. But because I wanted to solve the problem that I, and lots of my marketing peers were being tripped up by ↓ The disconnect between campaign and core. Companies often prioritise the performance customers see, but overlook the experience they feel. Brands craft powerful marketing messages promising simplicity, customer-centricity, or innovation, only for customers to experience the exact opposite once they interact with the business. 👎 A “customer-first” company with an impossible-to-reach support team. 👎 A “seamless” experience riddled with friction. 👎 A personalised campaign that leads to a generic, frustrating journey. And it's why I became a service designer; to bridge the gap between the customer experience and how teams show up, interact and deliver it every day. It’s not enough to talk about customer-centricity, because your customers are gonna see right through that. It has to be seen, actioned and felt in how teams work, make decisions, and design experiences - with your customers need at the core. Because this is the production behind your performance. At The Marketing Meetup last night, I shared my journey of building customer-centric cultures, and the three key steps that make it happen (OK, caveat here, this is a massively over-simplified version): ✅ Understand Customer insight isn’t just a marketing function. Every team should be plugged into real customer conversations. Dive into the data then push it further; spend time in their shoes, immerse yourselves in their worlds and bring those experiences into your daily team interactions. ✅ Embed Align your values and ways of working with your brand promises; map the experience gap by comparing brand messaging with real customer experiences. Train teams to think customer-first, ensuring CX is part of daily decision-making, and recognise and reward employees who bridge the gap, turning customer-centricity into action. ✅ Operate Customer-centricity must be a business-wide way of working, we're talking about moving from slogans to systems; Design cross-functional engagement strategies that span the 5Es: entice, enter, engage, exit and extend and develop customer journey ownership models - set up squads that are clear on who is responsible for each stage, and how teams work together to improve the end-to-end experience. Great brands don’t just tell great stories. They live them, from campaign to core. What companies do you think are doing this well? I would love to crowd-source a list of these examples, let me know in the comments below 👇 #CustomerCentricity #BrandExperience #ServiceDesign 

Despite heavy investments in digital tools, many organizations still struggle to deliver seamless customer journeys. Too often, brands assume that having a chatbot, a responsive website, or a few digital touchpoints means they’ve mastered omnichannel. But customers think otherwise, and they’re not shy about voicing their frustrations. But each one of the complaints highlights a missed opportunity to connect, resolve, and build trust. The good news, however, is that we’ve entered the era of Agentic AI, where intelligent systems go beyond just reacting. They think, plan, and act on their own. Unlike traditional AI, they’re aware of the context, goal-oriented, and capable of handling real-time interactions across different channels. These systems learn from behavior, anticipate needs, and continuously improve experiences, bringing us closer than ever to truly seamless, human-like customer journeys. But technology alone isn’t the answer. Transformation occurs when you combine Agentic AI, customer intent, and data within a unified, intelligent framework. So, how can organizations close the omnichannel gap and elevate customer experience? 1. Start by listening. Most companies overestimate how “connected” their channels are. Use real customer feedback and journey mapping to uncover friction points and blind spots. 2. Use Agentic AI to unify, not just automate. The new generation of AI can understand context, remember customer history, and act across channels, delivering personalized, human-like support without starting from scratch every time. 3. Think experience, not channels. Omnichannel isn’t about being everywhere; it’s about being seamless everywhere. Agentic AI allows you to break silos between sales, service, and support in real-time. 4. Invest in ecosystem intelligence. From product availability to delivery to CX, every part of your system must speak the same language. That’s when AI goes from reactive to proactive. At X-Shift we help organizations across sectors harness Agentic AI and next-gen digital tools to: ■ Deliver real-time, context-aware support that feels human because it’s built to understand. ■ Connect online and offline journeys so your customer never feels like they’re starting over. ■ Design predictive experiences, using AI to solve problems before they’re voiced. ■ Create adaptive strategies, powered by data and feedback loops, to keep evolving with the customer. ■ Build scalable digital frameworks that integrate legacy systems with new-age tech. With Saudi Arabia emerging as a regional leader in AI readiness and digital infrastructure, there’s never been a better time to go beyond surface-level automation and build intelligent, frictionless customer experiences that actually work. #AI #AgenticAI #Omnichannels #CX #Customer

Bridging the Gap: The CEO’s Growth Illusion Sales were dropping. The pipeline was drying up. The CEO didn’t hesitate. “We need a Bridge the Gap campaign—now.” What followed was a flurry of silver bullets: ✔ Marketing launched an awareness blitz. ✔ Sales ramped up outbound calls. ✔ Product rushed out a new feature. ✔ Customer success threw out discounts. It looked like action. It felt like a plan. But weeks in, nothing changed. • Traffic surged, but conversions stalled. • Sales calls increased, but deals didn’t close. • The new feature launched, but customers didn’t care. • Discounts drove short-term spikes, but loyalty declined. The gap wasn’t closing. It was widening. The Hard Truth About Growth Frustrated, the CEO turned to the Head of Marketing , “We’ve tried everything. Why isn’t this working?” She didn’t sugarcoat it. “Because you don’t bridge the gap by running faster. You do it by understanding why the gap exists in the first place.” Instead of reactionary tactics, she shifted the focus: ✔ Talk to lost deals. What changed? Why didn’t they buy? ✔ Analyze loyal customers. What kept them? What nearly pushed them away? ✔ Align sales & marketing. Focus on the right buyers, not just more leads. ✔ Reposition the offering. Were they still selling what the market actually needed? No big splash. No quick wins. Just thoughtful, data-driven adjustments. Three months later, lead flow was steady. Sales cycles shortened. Retention improved. What Every CEO Must Learn The biggest mistake in growth? Confusing movement with progress. Throwing tactics at a problem isn’t strategy. It’s panic. Real growth isn’t about doing more, faster. It’s about doing the right things, better. Because the companies that win? They don’t chase silver bullets. They build bridges that last. #Growth #Marketing #Strategy #CustomerCentricity

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?

Let's understand the importance of Gap Analysis and how and why a Business Analyst perform Gap Analysis. Let's imagine you want to learn how to play the guitar. You're a total beginner. 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐆𝐚𝐩 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: ➡️ Where are you now? You don't know how to hold a guitar, read music, or play a single chord. This is your current state. ➡️ Where do you want to be? You want to be able to play your favorite songs confidently. This is your desired state. ➡️ What's the gap? The gap is everything you need to learn and do to get from not knowing anything to playing your favorite songs. This might include getting a guitar, finding a teacher, practicing regularly, learning chords, and understanding rhythm. ➡️ Bridging the gap: You might take lessons, watch online tutorials, and practice every day. These are the actions you take to close the gap between where you are and where you want to be. 𝐍𝐨𝐰, 𝐥𝐞𝐭'𝐬 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐡𝐨𝐰 𝐚 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐮𝐬𝐞𝐬 𝐆𝐚𝐩 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐧 𝐚 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭: Imagine a company that sells clothes online. They want to improve their website to increase sales. A Business Analyst might do the following Gap Analysis: 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐒𝐭𝐚𝐭𝐞: Analyze the existing website – how user-friendly is it? How easy is it to find products? What are the current sales figures? They might look at website traffic data, customer reviews, and sales reports. 𝐃𝐞𝐬𝐢𝐫𝐞𝐝 𝐒𝐭𝐚𝐭𝐞: The company wants to double their online sales in the next year. They want a website that is easy to navigate, has a smooth checkout process, and encourages customers to buy more. 𝐓𝐡𝐞 𝐆𝐚𝐩: The BA identifies the gaps – maybe the website is slow, the search function is poor, or the checkout process is confusing. 𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐆𝐚𝐩:The BA might recommend solutions like redesigning the website, improving the search functionality, offering discounts, and streamlining the checkout process. They'll create a detailed plan with timelines and resources needed to implement these solutions. 𝐖𝐡𝐲 𝐢𝐬 𝐆𝐚𝐩 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐚 𝐁𝐀? ➡️ It helps BAs understand the difference between what a business has and what it needs to achieve its goals. ➡️ It provides a clear and structured way to communicate problems and solutions to stakeholders. ➡️ It helps prioritize the most critical areas for improvement. ➡️ It helps define measurable goals and track progress towards achieving them. ➡️ It's a core tool for problem-solving and driving successful business change. Essentially, Gap Analysis helps BAs to identify, understand, and bridge the gap between a business's current reality and its desired future. BA Helpline #businessanalysis #businessanalyst #businessanalysts #ba

Small service gaps lead to big customer churn. Here’s how we helped fix them for a top entertainment brand. Consistency Builds Brands, But Inconsistency Breaks Them. Whether it’s retail, hospitality, or entertainment, customers don’t just remember a great experience—they expect it every single time they visit. But when you’re managing multiple locations, maintaining that same high-quality service across the board becomes a real challenge. That’s exactly what we tackled in a recent mystery shopping audit for a leading entertainment brand with 66 arenas across 30+ cities. Our goal? Spot the gaps, ensure consistency, and turn good experiences into unforgettable ones. Key challenges we discovered: ❌ Service inconsistency during peak vs. off-peak hours ❌ Technical glitches in VR gaming & other equipment ❌ Staff unavailability during late evening hours ❌ Delays in food & beverage service ❌ Missed opportunities in customer engagement & sales No matter how great a business is, without real-time insights, these gaps can go unnoticed—until they impact customer loyalty. Our approach to fixing these issues: ✅ Mystery Audits at Scale: Month-on-month audits at each centre, covering different time slots ✅ Specialized Auditors: Experts in gaming & entertainment assessed staff interactions ✅ Real-Time Monitoring: Issues flagged instantly for quicker resolution ✅ Scenario-Based Testing: Simulated difficult customer situations to test staff preparedness ✅ Targeted Training Programs: Data-driven coaching for frontline staff The results? 1) Faster Response Time: Reduced delays in handling customer queries 2) Smoother Gaming Experience: Fewer technical disruptions, leading to higher customer satisfaction 3) Better Trained Staff: Enhanced communication & problem-solving skills 4) Improved F&B Services: Reduced wait times, leading to a better overall experience In just two months, we saw measurable improvements across key service parameters, reinforcing a powerful lesson: You can’t improve what you don’t measure. Brands invest heavily in infrastructure, marketing, and technology—but if the on-ground experience fails, everything else crumbles. Mystery shopping isn’t just about finding gaps and creating a roadmap for excellence. To business leaders running multi-location brands: How are you ensuring consistency in customer experience? Let’s discuss this in the comments! #CustomerExperience #MysteryShopping #RetailAudits #ServiceExcellence #BusinessGrowth