Personalizing Customer Journeys Without Overstepping

Here’s a common myth about personalization: All you need is a customer’s name to make it effective. True personalization goes much deeper, it’s about understanding behaviors, preferences, and needs to create meaningful experiences. Collecting the right data isn’t just about volume, it’s about relevance. You can’t offer genuine personalization without truly knowing your audience. Here’s how I’ve approached it: ➜ Identify key data points. Don’t collect data just for the sake of it. Focus on what will actually help you understand your customers better, things like purchase history, browsing behavior, and engagement patterns. ➜ Leverage tools wisely. Using the right tools is crucial. We’ve integrated platforms (like HubSpot) to ensure we’re gathering and utilizing data that matters, not just creating noise. ➜ Respect privacy. Personalization should never come at the cost of privacy. Being transparent with your audience about what data you collect and how you use it builds trust. ➜ Test and refine. Data isn’t static, and neither should your approach to personalization be. Continuously test what works and refine your strategy to meet your customers' evolving needs. ↳ By focusing on relevant data, not just more data, we’ve been able to create personalized experiences that resonate, leading to stronger customer relationships and better results. What’s been your biggest challenge in collecting data for personalization? How are you overcoming it? #data #personalization #hubspot

Most brands drown in the process of personalizing too much. I recently worked with a brand that went super deep into this, making users create detailed customer profiles through their pop-up with specific interests. Their welcome series was completely segmented, if you clicked on "couches," you'd only see couch-related content throughout the entire sequence. Most people would think this hyper-personalized approach is “cutting edge”, and leave it alone. This left a TON of revenue on the table since it limited their brand discovery. After looking at the data, we tested a different approach right away. We featured best sellers of the brand, highlighting each of them with individual product categories underneath the existing segmentation. By keeping the personalized elements but introducing best-selling products across categories, we significantly lifted engagement and revenue metrics. It’s simple: - Customers don't always know your full product range - Limiting visibility to one category restricts discovery - Your best-sellers have proven market engagement regardless of initial interest - Site exploration leads to higher average order values The welcome series absolutely crushed it with this strategy. We also found that their original strategy worked better in the post-purchase flow. Customers are more inclined to accept other offers of the same category after they purchased a product, rather than getting bombarded with 100 different couches at the beginning. The key takeaway here is to test the balance between personalization and data. Testing will always be King. Don't always assume that extreme personalization is always the answer, sometimes a hybrid approach delivers the best results.

The Personalization-Privacy Paradox: AI in customer experience is most effective when it personalizes interactions based on vast amounts of data. It anticipates needs, tailors recommendations, and enhances satisfaction by learning individual preferences. The more data it has, the better it gets. But here’s the paradox: the same customers who crave personalized experiences can also be deeply concerned about their privacy. AI thrives on data, but customers resist sharing it. We want hyper-relevant interactions without feeling surveilled. As AI improves, this tension only increases. AI systems can offer deep personalization while simultaneously eroding the very trust needed for customers to willingly share their data. This paradox is particularly problematic because both extremes seem necessary: AI needs data for personalization, but excessive data collection can backfire, leading to customer distrust, dissatisfaction, or even churn. So how do we fix it? Be transparent. Tell people exactly what you’re using their data for—and why it benefits them. Let the customer choose. Give control over what’s personalized (and what’s not). Show the value. Make personalization a perk, not a tradeoff. Personalization shouldn’t feel like surveillance. It should feel like service. You can make this invisible too. Give the customer “nudges” to move them down the happy path through experience orchestration. Trust is the real unlock. Everything else is just prediction. #cx #ai #privacy #trust #personalization