The Importance Of Real-Time Data In Customer Analysis

Surveys can serve an important purpose. We should use them to fill holes in our understanding of the customer experience or build better models with the customer data we have. As surveys tell you what customers explicitly choose to share, you should not be using them to measure the experience. Surveys are also inherently reactive, surface level, and increasingly ignored by customers who are overwhelmed by feedback requests. This is fact. There’s a different way. Some CX leaders understand that the most critical insights come from sources customers don’t even realize they’re providing from the “exhaust” of every day life with your brand. Real-time digital behavior, social listening, conversational analytics, and predictive modeling deliver insights that surveys alone never will. Voice and sentiment analytics, for example, go beyond simply reading customer comments. They reveal how customers genuinely feel by analyzing tone, frustration, or intent embedded within interactions. Behavioral analytics, meanwhile, uncover friction points by tracking real customer actions across websites or apps, highlighting issues users might never explicitly complain about. Predictive analytics are also becoming essential for modern CX strategies. They anticipate customer needs, allowing businesses to proactively address potential churn, rather than merely reacting after the fact. The capability can also help you maximize revenue in the experiences you are delivering (a use case not discussed often enough). The most forward-looking CX teams today are blending traditional feedback with these deeper, proactive techniques, creating a comprehensive view of their customers. If you’re just beginning to move beyond a survey-only approach, prioritizing these more advanced methods will help ensure your insights are not only deeper but actionable in real time. Surveys aren’t dead (much to my chagrin), but relying solely on them means leaving crucial insights behind. While many enterprises have moved beyond surveys, the majority are still overly reliant on them. And when you get to mid-market or small businesses? The survey slapping gets exponentially worse. Now is the time to start looking beyond the questionnaire and your Likert scales. The email survey is slowly becoming digital dust. And the capabilities to get you there are readily available. How are you evolving your customer listening strategy beyond traditional surveys? #customerexperience #cxstrategy #customerinsights #surveys

When I interviewed Stephan Waldeis, VP of eCommerce Europe at Husqvarna Group, he said this about tracking real-time data and retailer partnerships. “We track customer behavior, we track inventory levels at our partners, we track sales performance — and of course, we possibly... we track all of that in real time. Imagine, our robots — at least the ones from the last 10+ years — are all connected. So, we have a lot of insights in which gardens they are driving, when they are operating, etc. And that is data that we are leveraging, but also data that we are sharing with our channel partners. That’s great even for the channel partners who are not really interested in operating an eCom site. We provide them with a lot of insights… what kind of products are interesting in your area, because we know exactly from visits on our site, which products in a particular region are more relevant — in Amsterdam versus in Berlin versus in Munich.” 𝗛𝗼𝘄 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗲 𝘁𝗵𝗶𝘀 𝗳𝗼𝗿 𝗖𝗣𝗚 𝗯𝗿𝗮𝗻𝗱𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝘁𝗼 𝗳𝘂𝗲𝗹 𝗴𝗿𝗼𝘄𝘁𝗵? 1️⃣ Activate Real-Time Retailer Collaboration Track and share real-time consumer behavior, inventory, and sales data with retail partners — even those with limited digital capabilities — to strengthen joint decision-making, optimize local assortments, and drive smarter sell-through at the shelf. 2️⃣ Localize Product Strategies with Regional Demand Signals Use geo-specific browsing and purchase data to tailor product recommendations, promotions, and stock levels at the city or neighborhood level — what sells in Amsterdam might flop in Berlin if you don’t read the digital shelf signals correctly. 3️⃣ Turn Connected Product Data into a Competitive Advantage Leverage connected device insights (where available) not only for product innovation but as a marketing and retail sales weapon, identifying usage patterns, seasonal trends, and regional preferences that can feed back into supply chain, DTC, and retail media strategies. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝟭𝟰,𝟬𝟬𝟬+ 𝗖𝗣𝗚, 𝗿𝗲𝘁𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗠𝗮𝗿𝗧𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗱 𝘁𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝘁® : 𝗖𝗣𝗚 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. About ecommert We partner with CPG businesses and leading technology companies of all sizes to accelerate growth through AI-driven digital commerce solutions. Our expertise spans e-channel strategy, retail media optimization, and digital shelf analytics, ensuring more intelligent and efficient operations across B2C, eB2B, and DTC channels. #ecommerce #dataanalytics #CPG #FMCG #data Milwaukee Tool Bosch Makita U.S.A., Inc. STIHL Mondelēz International Nestlé Mars Ferrero General Mills L'Oréal Henkel Beiersdorf Colgate-Palmolive The Coca-Cola Company Unilever L'Oréal Coty Kao Corporation adidas Nike New Balance PUMA Group the LEGO Group Sony Panasonic North America Bose Corporation

I just heard myself say out loud 'it takes 90 days' to build a solid user understanding and corresponding journey map. Afterwards I had a moment of realization . . . WHY?! WHY am I willing to accept it takes that long? I don't accept that timeline anywhere else in my business. For many CX leaders, client-in journey mapping is the foundation that intuitive, and simple E2E customer experiences are built upon. The big problem? Simple takes time. I guess I've always equated #qualitativeinsights = taking the time to do properly. But what if I'm restricting progress by only considering antiquated methodologies to gather those insights? For fast, flawless execution - time is THE scarce resource. One of the beautiful ways that AI is delivering value is by breaking the old adage: "You can have it FAST ⏩ , CHEAP 💰 , or high QUALITY 💫 . Pick two." 🫣 If we rethink qualitative to not mean high-touch conversations - but to focus on the quality of the range, depth and frequency of client input we can analyze from everywhere - I wonder how much more deeply informed at speed and scale CX leaders could be? 🤨 AI-driven analysis in real time can give businesses profound insights into customer behavior, preferences, and pain points across every touchpoint a business has. Now - imagine the power of AI's ability to identify emerging trends PLUS➕ the ability to real-time adjust your #CustomerExperience. Imagine if you could rapidly deploy #AIAgents that are custom built in real time to identify issues PLUS ➕ experiment quickly PLUS ➕ identify a statistically significant improvement PLUS ➕ execute at scale - all independently. So - maybe AI will eradicate journey mapping (as we know it), and instead usher in a new era of Pro-active AI #ExperienceManagement. That's a PLUS ➕ if you ask me. 👍

Business intelligence has always been about evaluating the past. Now, AI analytics are giving us a look into the future. For years, reporting was static and retrospective. It helped leaders understand what happened last month or last quarter, but offered little support for acting in the moment or anticipating what might come next. AI is changing that. By analyzing live data streams, surfacing patterns in real-time, and taking meaningful action, AI gives leaders a clearer lens on the present and a sharper view of the future. I’ve seen the impact across industries: • Healthcare: Identifying top call drivers and adjusting self-service flows immediately to reduce patient wait times. • Logistics: Spotting delays in agent response times and redistributing resources before service levels slip. • Retail: Tracking sentiment by product line and adapting campaigns to reflect what customers are actually saying. The benefits extend well beyond efficiency. With AI analytics, teams become more responsive, customer experiences improve, and decisions are made with greater clarity. How do you see real-time analytics reshaping the way your teams work? #BusinessIntelligence #AIAnalytics #DataAnalysis #CustomerExperience

This concept is the reason you can track your Uber ride in real time, detect credit card fraud within milliseconds, and get instant stock price updates.  At the heart of these modern distributed systems is stream processing—a framework built to handle continuous flows of data and process it as it arrives.     Stream processing is a method for analyzing and acting on real-time data streams. Instead of waiting for data to be stored in batches, it processes data as soon as it’s generated making distributed systems faster, more adaptive, and responsive.  Think of it as running analytics on data in motion rather than data at rest.  ► How Does It Work?  Imagine you’re building a system to detect unusual traffic spikes for a ride-sharing app:  1. Ingest Data: Events like user logins, driver locations, and ride requests continuously flow in.   2. Process Events: Real-time rules (e.g., surge pricing triggers) analyze incoming data.   3. React: Notifications or updates are sent instantly—before the data ever lands in storage.  Example Tools:   - Kafka Streams for distributed data pipelines.   - Apache Flink for stateful computations like aggregations or pattern detection.   - Google Cloud Dataflow for real-time streaming analytics on the cloud.  ► Key Applications of Stream Processing  - Fraud Detection: Credit card transactions flagged in milliseconds based on suspicious patterns.   - IoT Monitoring: Sensor data processed continuously for alerts on machinery failures.   - Real-Time Recommendations: E-commerce suggestions based on live customer actions.   - Financial Analytics: Algorithmic trading decisions based on real-time market conditions.   - Log Monitoring: IT systems detecting anomalies and failures as logs stream in.  ► Stream vs. Batch Processing: Why Choose Stream?   - Batch Processing: Processes data in chunks—useful for reporting and historical analysis.   - Stream Processing: Processes data continuously—critical for real-time actions and time-sensitive decisions.  Example:   - Batch: Generating monthly sales reports.   - Stream: Detecting fraud within seconds during an online payment.  ► The Tradeoffs of Real-Time Processing   - Consistency vs. Availability: Real-time systems often prioritize availability and low latency over strict consistency (CAP theorem).  - State Management Challenges: Systems like Flink offer tools for stateful processing, ensuring accurate results despite failures or delays.  - Scaling Complexity: Distributed systems must handle varying loads without sacrificing speed, requiring robust partitioning strategies.  As systems become more interconnected and data-driven, you can no longer afford to wait for insights. Stream processing powers everything from self-driving cars to predictive maintenance turning raw data into action in milliseconds.  It’s all about making smarter decisions in real-time.

As enterprises accelerate their deployment of GenAI agents and applications, data leaders must ensure their data pipelines are ready to meet the demands of real-time AI. When your chatbot needs to provide personalized responses or your recommendation engine needs to adapt to current user behavior, traditional batch processing simply isn't enough. We’re seeing three critical requirements emerge for AI-ready data infrastructure. We call them the 3 Rs: 1️⃣ Real-time: The era of batch processing is ending. When a customer interacts with your AI agent, it needs immediate access to their current context. Knowing what products they browsed six hours ago isn't good enough. AI applications need to understand and respond to customer behavior as it happens. 2️⃣ Reliable: Pipeline reliability has taken on new urgency. While a delayed BI dashboard update might have been inconvenient, AI application downtime directly impacts revenue and customer experience. When your website chatbot can't access customer data, it's not just an engineering problem. It's a business crisis. 3️⃣ Regulatory compliance: AI applications have raised the stakes for data compliance. Your chatbot might be capable of delivering highly personalized recommendations, but what if the customer has opted out of tracking? Privacy regulations aren't just about data collection anymore—they're about how AI systems use that data in real-time. Leading companies are already adapting their data infrastructure to meet these requirements. They're moving beyond traditional ETL to streaming architectures, implementing robust monitoring and failover systems, and building compliance checks directly into their data pipelines. The question for data leaders isn't whether to make these changes, but how quickly they can implement them. As AI becomes central to customer experience, the competitive advantage will go to companies with AI-ready data infrastructure. What challenges are you facing in preparing your data pipelines for AI? Share your experiences in the comments 👇 #DataEngineering #ArtificialIntelligence #DataInfrastructure #Innovation #Tech #RudderStack

Real-time data is no longer a luxury, but a necessity for hotels looking to thrive. We all know that in this industry, a moment can make or break a guest's experience. So, what if hotels could truly operate in that moment, armed with insights as they happen? That's the magic of real-time data. It's not just about dashboards and reports (though those are important too).  It's about allowing your team to anticipate needs, personalize interactions, and run a smoother operation, all in the blink of an eye. I genuinely believe that hotels still struggling with disconnected systems and sluggish data are missing out on a massive opportunity. Imagine a world where your tech works together, giving you a crystal-clear, instant view of what's happening across your property. That's the promise of smart integration, and it's a game-changer. As I often say (and truly believe), in today's competitive landscape, the hotels that can harness the speed of real-time data have a significant edge. It's about being proactive, not reactive. Curious to hear your thoughts. How are you leveraging real-time insights in your hotel? What are some of the biggest hurdles you're facing?