Using AI to Understand Customer Behavior Patterns

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

"Learning to walk again, I believe I've waited long enough"  🎤 "Walk" by Foo Fighters Had a fascinating conversation with a group of CS leaders last week about AI. The dialogue reminded me of how we learn to ride a bike - wobbly at first, but gradually our brain forms new patterns until it becomes second nature. AI learns similarly, and it's transforming how we think about #CustomerSuccess. Here's what's blowing my mind: 🔎 Pattern Recognition: Just like how great CSMs spot customer health issues before they become problems, AI is identifying patterns humans miss. At Gainsight, we recently saw this firsthand when Staircase AI detected brewing sentiment issues in email threads that weren't even copied to our CS team. It caught subtle tone changes that signaled future churn risk. 🎯 Learning from Mistakes: Remember your first customer call? AI also improves through trial and error. One thing we've learned from implementing Staircase is that relationship patterns often hide in unexpected places - casual Slack messages sometimes reveal more about customer health than formal QBRs. 🌱 Unexpected Discoveries: The most exciting part? AI is finding patterns we never knew existed. Last week, our system identified a customer at risk not from negative sentiment, but from a sudden shift to overly formal communication - a pattern that often precedes vendor reevaluation. 🤝 Human + Machine Partnership: The future isn't about AI vs humans. It's about how we work together. Our best CSMs are using AI to analyze thousands of customer interactions instantly, freeing them to focus on building deeper relationships. One CSM told me last week: "AI handles the patterns, I handle the people."But here's what keeps me up at night: Are we moving fast enough? While we debate whether to embrace AI, our customers are already experiencing AI-powered experiences everywhere else. What unexpected patterns has AI helped you discover in your customer relationships?

Have you ever wondered when people are most irritated when calling customer service? I've been diving into Replicant's sentiment data to uncover when customers are most likely to express anger toward our AI agents (and our customers). The results reveal fascinating patterns that connect human behavior, seasonal shifts, and time-of-day preferences. 🌡️ Seasonal Impact: Autumn shows consistently higher anger rates in customer interactions (up to 25% higher than summer). Do people's moods change as winter approaches? ⏰ Time-of-Day Patterns: Early morning interactions (6-8 am) show notably higher frustration levels, suggesting that no one is really a morning person." 📈 Escalation Trajectory: The steady increase in negative sentiment from mid-morning to evening reveals how customer patience deteriorates throughout the day. 📱 Behavioral Shifts: Summer callers call earlier while winter callers cluster later in the day - a perfect example of how environmental factors directly impact customer interaction patterns. These insights aren't just interesting data points - they're actionable intelligence for designing more responsive AI systems that adapt to human behavioral patterns. By implementing time-sensitive response protocols, we can potentially reduce negative interactions by 15-20%. What patterns are you seeing in your customer interaction data? The answers might transform your approach to AI implementation.