Personalization Techniques For Online Stores

𝟔𝟔% 𝐨𝐟 𝐀𝐈 𝐮𝐬𝐞𝐫𝐬 𝐬𝐚𝐲 𝐝𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐬 𝐭𝐡𝐞𝐢𝐫 𝐭𝐨𝐩 𝐜𝐨𝐧𝐜𝐞𝐫𝐧. What does that tell us? Trust isn’t just a feature - it’s the foundation of AI’s future. When breaches happen, the cost isn’t measured in fines or headlines alone - it’s measured in lost trust. I recently spoke with a healthcare executive who shared a haunting story: after a data breach, patients stopped using their app - not because they didn’t need the service, but because they no longer felt safe. 𝐓𝐡𝐢𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐩𝐞𝐨𝐩𝐥𝐞’𝐬 𝐥𝐢𝐯𝐞𝐬 - 𝐭𝐫𝐮𝐬𝐭 𝐛𝐫𝐨𝐤𝐞𝐧, 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐬𝐡𝐚𝐭𝐭𝐞𝐫𝐞𝐝. Consider the October 2023 incident at 23andMe: unauthorized access exposed the genetic and personal information of 6.9 million users. Imagine seeing your most private data compromised. At Deloitte, we’ve helped organizations turn privacy challenges into opportunities by embedding trust into their AI strategies. For example, we recently partnered with a global financial institution to design a privacy-by-design framework that not only met regulatory requirements but also restored customer confidence. The result? A 15% increase in customer engagement within six months. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐫𝐞𝐛𝐮𝐢𝐥𝐝 𝐭𝐫𝐮𝐬𝐭 𝐰𝐡𝐞𝐧 𝐢𝐭’𝐬 𝐥𝐨𝐬𝐭? ✔️ 𝐓𝐮𝐫𝐧 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐧𝐭𝐨 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐦𝐞𝐧𝐭: Privacy isn’t just about compliance. It’s about empowering customers to own their data. When people feel in control, they trust more. ✔️ 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐏𝐫𝐨𝐭𝐞𝐜𝐭 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: AI can do more than process data, it can safeguard it. Predictive privacy models can spot risks before they become problems, demonstrating your commitment to trust and innovation. ✔️ 𝐋𝐞𝐚𝐝 𝐰𝐢𝐭𝐡 𝐄𝐭𝐡𝐢𝐜𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Collaborate with peers, regulators, and even competitors to set new privacy standards. Customers notice when you lead the charge for their protection. ✔️ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐀𝐧𝐨𝐧𝐲𝐦𝐢𝐭𝐲: Techniques like differential privacy ensure sensitive data remains safe while enabling innovation. Your customers shouldn’t have to trade their privacy for progress. Trust is fragile, but it’s also resilient when leaders take responsibility. AI without trust isn’t just limited - it’s destined to fail. 𝐇𝐨𝐰 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐫𝐞𝐠𝐚𝐢𝐧 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧? 𝐋𝐞𝐭’𝐬 𝐬𝐡𝐚𝐫𝐞 𝐚𝐧𝐝 𝐢𝐧𝐬𝐩𝐢𝐫𝐞 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫 👇 #AI #DataPrivacy #Leadership #CustomerTrust #Ethics

Real-time data analytics is transforming businesses across industries. From predicting equipment failures in manufacturing to detecting fraud in financial transactions, the ability to analyze data as it's generated is opening new frontiers of efficiency and innovation. But how exactly does a real-time analytics system work? Let's break down a typical architecture: 1. Data Sources: Everything starts with data. This could be from sensors, user interactions on websites, financial transactions, or any other real-time source. 2. Streaming: As data flows in, it's immediately captured by streaming platforms like Apache Kafka or Amazon Kinesis. Think of these as high-speed conveyor belts for data. 3. Processing: The streaming data is then analyzed on-the-fly by real-time processing engines such as Apache Flink or Spark Streaming. These can detect patterns, anomalies, or trigger alerts within milliseconds. 4. Storage: While some data is processed immediately, it's also stored for later analysis. Data lakes (like Hadoop) store raw data, while data warehouses (like Snowflake) store processed, queryable data. 5. Analytics & ML: Here's where the magic happens. Advanced analytics tools and machine learning models extract insights and make predictions based on both real-time and historical data. 6. Visualization: Finally, the insights are presented in real-time dashboards (using tools like Grafana or Tableau), allowing decision-makers to see what's happening right now. This architecture balances real-time processing capabilities with batch processing functionalities, enabling both immediate operational intelligence and strategic analytical insights. The design accommodates scalability, fault-tolerance, and low-latency processing - crucial factors in today's data-intensive environments. I'm interested in hearing about your experiences with similar architectures. What challenges have you encountered in implementing real-time analytics at scale?

As consumers seek more individual experiences and interactions, companies turn to #AI to deliver 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡𝙞𝙯𝙚𝙙 𝙥𝙧𝙤𝙢𝙤𝙩𝙞𝙤𝙣𝙨 𝙖𝙩 𝙨𝙘𝙖𝙡𝙚. For some time now, companies have been trying to address customer needs through #personalization, using data and analytics to craft more relevant consumer experiences. Using improved analytics models, brands and retailers can better provide valuable offers to micro-communities wherever they want to engage. Meanwhile, #genAI enables marketers to create tailored content that is relevant to those groups. According to McKinsey & Company, marketers should unlock personalization at scale, by upgrading five areas of their #martech stack and processes: 1. Data: by improving #data collection and analysis, marketers can gain deeper insights into customer behaviors and preferences. 2. Decisioning: to develop personalized promotions and content through more robust targeting, companies can also benefit from refreshing their #decision engines with new AI models. 3. Design: a sophisticated design layer that oversees offer management and #content production helps manage the process, fueling both operational excellence and agility. 4. Distribution: achieving true, real-time personalization requires a sophisticated #marketing architecture that delivers seamless and consistent messaging to the right audiences at the right time on the right channel. 5. Measurement: to validate the #ROI of personalization efforts, rigorous incrementality testing, standardized performance metrics, and measurement playbooks are essential. Are there other capabilities or technologies required for marketers to better target promotions and deliver individual content?

Zomato faced a big problem: How can we turn app browsers into loyal customers? The goal was clear, improve the user experience with personalized restaurant suggestions. But there were a few challenges too: 🔴 Understanding user preferences from massive data. 🔴 Combining multiple data sources for meaningful insights. 🔴 Developing accurate recommendation algorithms. 🔴 Processing data in real time to keep users engaged. 🔴 Building trust in the recommendations to ensure they felt helpful, not intrusive. To tackle this, Zomato used a structured approach: 🟢 Data Collection and Cleaning - They collected user behavior data (searches, clicks, abandoned carts). - They analyzed restaurant details (cuisine types, delivery times, ratings). - Past orders were also analyzed for trends. 🟢 User Segmentation - Users were grouped based on age, location, past orders, and browsing habits. - This helped them identify patterns and preferences. 🟢 Developing the Recommendation System - Combined collaborative filtering (what others like you prefer) and content-based filtering (what matches your past orders). - Fine-tuned algorithms with ongoing testing for better accuracy. 🟢 Implementation and Testing - They rolled out the recommendations and tested them through A/B experiments. - Adjusted based on user feedback and data performance. 🟢 Continuous Improvement - Introduced feedback loops for real-time adjustments. - Regular updates ensured the system stayed relevant to evolving user needs. And, the impact was impressive: ⬆️ 35% more time spent on the app by users receiving personalized suggestions. ⬆️ 28% higher click-through rates, showing better engagement. ⬆️ 22% increase in orders per user per month due to tailored suggestions. ⬆️ 18% boost in retention rates, turning occasional users into loyal customers. ⬆️ 12% higher average order value, leading to revenue growth. ⬆️ 15% jump in monthly revenue, proving personalization works! I see this as the perfect example of using data to deepen customer relationships. It's not just about the tech—it’s about understanding people and making their experience smoother and more personal. 📊 Data is the secret to building trust and loyalty. What do you think? Can other industries learn from Zomato’s success? How can personalization improve your industry? #zomato #deepindergoyal

You Saw That Ad on Your Phone… But Bought It on Your Laptop, Right? 💡 Welcome to the fascinating world of Cross-Device Targeting, one of the most quietly powerful tools in digital advertising. It’s not magic, and it’s not just retargeting, it’s about recognizing that people move between devices constantly, and designing smarter ad experiences around that behavior. Let’s break it down: 🔄 It’s 11 PM. You’re scrolling through Instagram on your phone. You spot a sleek coffee machine. Intriguing.. but not tonight. Next morning, you’re back at your desk, logged in on your work laptop. Same product. Different format. Right timing. This time? You click. You buy. ☕ That, in essence, is cross-device targeting done right, treating the customer journey as one continuous experience, not a series of disconnected screens. Why does this matter? • We don’t make decisions on the first click. • Attention spans are short. Devices are many. • Consistency across platforms = Trust + Recall. Pro Tips for Marketers? ▪️ Customize creatives based on device type. → Keep it swipeable on mobile, detailed on desktop. ▪️ Set up frequency caps across devices. → Avoid bombarding the user on every screen they own. ▪️ Use sequential messaging. → Storytelling that unfolds, not repeats, across screens. ▪️ Measure cross-device conversions, not just last-click. → Your attribution model should evolve with your customer behavior. Got a “cross-device moment” where you bought something after seeing it on multiple devices? Share your story below! 👇 #DigitalMarketing #ProgrammaticAdvertising #CrossDeviceTargeting #CustomerJourney #MarketingTips #AdTech

💡 Mapping user research techniques to levels of knowledge about users When doing user research, it's important to choose the right methods and tools to uncover valuable insights about user behavior. It's possible to identify 3 layers of user behavior, feelings, and thoughts: 1️⃣ Surface level - Say & Think This level captures what users say in conversations, interviews, or surveys and what they think about a product, feature, or experience. It reflects their stated opinions, thoughts, and intentions. Example: "I prefer simple products" or "I think this app is easy to use." Methods: Interviews, Questionnaires. These methods capture stated thoughts and opinions. However, insights may be influenced by social norms or biases. 2️⃣ Mid-level - Do & Use This level reflects what users actually do when interacting with a product or service. It emphasizes actions, usage patterns, and observed behaviors, revealing insights that may differ from what users say. Example: Users may claim they enjoy customizing app settings, but data shows they rarely change default options. Methods: Usability Testing, Observation. Observation helps to reveal gaps between what people say and what they actually do. 3️⃣ Deep level - Know, Feel and Dream This level uncovers deep motivations, emotions, desires, and aspirations that users may not be consciously aware of or may struggle to articulate. It also includes tacit knowledge—things people know intuitively but find hard to express. Example: A user might not realize that their preference for a minimalist design comes from the information overload of a current design. Methods: Probes (e.g., participatory design, diary studies). Insights collected using these methods will uncover implicit and emotional drivers influencing behavior. 📕 Practical recommendations for mapping ✅ Triangulate insights by using multiple methods. What people say (interviews/surveys) may differ from what they do (observations) and feel. That's why it's essential to interpret these results in context. For example, start with interviews to learn what users say. Follow up with usability testing to observe real behavior. Use probes for long-term or emotional insights. ✅ Align research with business goals. For product improvements, focus on usability testing to catch interaction issues. For innovation, use probes to generate new ideas from user insights. ✅ Practice iterative learning. Apply surface techniques (like surveys) early to refine assumptions and guide more in-depth research later. Use deep techniques (like probes) for strategic decisions and to foster innovation in long-term projects. 🖼️ UX Research methods by Maze #ux #uxresearch #design #productdesign #uxdesign #ui #uidesign

Before diving headfirst into AI, companies need to define what data privacy means to them in order to use GenAI safely. After decades of harvesting and storing data, many tech companies have created vast troves of the stuff - and not all of it is safe to use when training new GenAI models. Most companies can easily recognize obvious examples of Personally Identifying Information (PII) like Social Security numbers (SSNs) - but what about home addresses, phone numbers, or even information like how many kids a customer has? These details can be just as critical to ensure newly built GenAI products don’t compromise their users' privacy - or safety - but once this information has entered an LLM, it can be really difficult to excise it. To safely build the next generation of AI, companies need to consider some key issues: ⚠️Defining Sensitive Data: Companies need to decide what they consider sensitive beyond the obvious. Personally identifiable information (PII) covers more than just SSNs and contact information - it can include any data that paints a detailed picture of an individual and needs to be redacted to protect customers. 🔒Using Tools to Ensure Privacy: Ensuring privacy in AI requires a range of tools that can help tech companies process, redact, and safeguard sensitive information. Without these tools in place, they risk exposing critical data in their AI models. 🏗️ Building a Framework for Privacy: Redacting sensitive data isn’t just a one-time process; it needs to be a cornerstone of any company’s data management strategy as they continue to scale AI efforts. Since PII is so difficult to remove from an LLM once added, GenAI companies need to devote resources to making sure it doesn’t enter their databases in the first place. Ultimately, AI is only as safe as the data you feed into it. Companies need a clear, actionable plan to protect their customers - and the time to implement it is now.

Exciting Innovation in LLM-Based Recommendations! I just read a fascinating paper titled "Rethinking LLM-Based Recommendations: A Query Generation-Based, Training-Free Approach" from researchers at KAIST. This work addresses critical challenges in using Large Language Models for recommendation systems. Current LLM-based recommendation methods face several limitations: - Inefficiency with large candidate pools - Sensitivity to item positioning in prompts (the "lost in the middle" phenomenon) - Poor scalability - Unrealistic evaluation methods using random negative sampling The researchers propose an innovative solution called Query-to-Recommendation (QUEREC), which takes a fundamentally different approach: >> How QUEREC Works Instead of the traditional method of feeding candidate items into prompts for reranking, QUEREC leverages LLMs to generate personalized queries that directly retrieve relevant items from the entire candidate pool. This eliminates the need for candidate pre-selection entirely! The framework operates through several key components: 1. Item Query Generation: The LLM analyzes item metadata and user reviews to generate queries that capture the distinctive features of each item. 2. User Query Generation: The system creates personalized queries based on user history and preferences. 3. Similarity-based Retrieval: Using a pre-trained text encoder, the system computes similarity scores between user and item representations. 4. Divergent Perspective Reranking: QUEREC combines insights from both LLM-generated queries and traditional collaborative filtering models to produce the final recommendations. >> Technical Advantages What makes this approach particularly impressive: - Training-Free Implementation: QUEREC can be integrated into existing ID-based recommendation systems without additional training. - Parallel Architecture: Unlike traditional serialized pipelines where LLMs rerank pre-selected candidates, QUEREC operates in parallel with traditional recommendation models, allowing both to extract top-k items independently from the entire item pool. - Enhanced Diversity: Experiments showed QUEREC produces more balanced distribution of recommended items compared to conventional models that exhibit bias toward specific item groups. - Improved Performance for Minor Items: The approach significantly outperforms existing methods for items that appear less frequently in training sets. This approach represents a significant advancement in recommendation systems, offering a more efficient, scalable, and diverse approach to personalized recommendations. The training-free nature makes it particularly valuable for rapidly evolving recommendation environments.

"Real-Time Analytics for Mobile App Crashes using Apache Kafka, Flink, Spark and Pinot at Uber" In today’s fast-paced world of software development, new changes (both in code and infrastructure) are being released at breakneck pace. Uber rolls out ~11,000 changes every week and it’s important for us to have a way to quickly be able to identify and resolve issues caused by these changes. A delay in detecting issues can create a number of issues including impacts to: user experience, our ability to facilitate transactions on the platform, company revenue, and the overall trust and confidence of our users. Uber has built a system called “Healthline” to help with Mean Time To Detect (MTTD) and Mean Time To Resolve (MTTR) issues and to avoid potential outages and large-scale user impacts. Due to the ability to detect the issues in real time, this has become the go-to tool for release managers to observe the impact of canary release and decide whether to proceed further or to rollback. Learn more about the real-time analytics with #opensource technologies in the #cloud in Uber's engineering blog post: https://lnkd.in/eZ23hmMy #apachekafka #apacheflink #apachepinot #apachespark 

ChatGPT eCommerce drop: Part 3 (foundational Q&A) Q: Why should eCommerce leaders pay attention to ChatGPT’s shopping assistant? The way consumers discover and decide what to buy is fundamentally shifting, from keyword search to conversation. If your product content isn’t optimized for AI discovery, you're lagging. Q: How is this different from Google search or traditional marketplace discovery? Old-school search engines return a list of links or paid ads. ChatGPT returns curated, context-rich product suggestions with images, pricing, reviews, and direct buy links. Difference is that AI models understand intent, not just keywords. Instead of “best sneakers,” a user may ask, “What’s a comfortable walking shoe for traveling through Europe in the summer?” ChatGPT understands that nuance and recommends accordingly. Q: What powers ChatGPT’s product recommendations? It’s a mix of structured product data and contextual intent signals. Product metadata (titles, descriptions, tags, inventory) Real-world reviews with specific use cases or outcomes Signals of trust (brand credibility, availability, content quality) Integrations with platforms like Shopify and product feed partners The AI model then uses this data to recommend products that match the why, not just the what. Q: So what changes for brands now that AI is in the shopping flow? Discovery is an earned visibility game. You can’t just outbid, you have to out-relevance. Generic content doesn’t work; rich context wins. Volume of reviews matters less; specificity and clarity matter more. The brands showing up in ChatGPT’s results are the ones with deep, well-structured content and high-context product storytelling. Q: What are the key elements brands should focus on to stay visible in AI-driven shopping? Priorities: 1. Structured Data Implement schema markup across product pages. Use tools like Shopify’s native integrations to feed product info cleanly. 2. Contextual Product Descriptions Who is this for? What does it solve? What makes it different? 3. High-Context Reviews Prompt users to share how and why they used a product. 4. Review Accessibility Make reviews public, crawlable, and visible next to your products. 5. Feed Accuracy Keep product data synced: availability, pricing, variants, and descriptions. Outdated info will kill your ranking in AI. AI models favor reviews that mention specific use cases, emotions, and product outcomes. A single thoughtful review like “Perfect for marathon runners with flat feet” now outranks 50 vague 5-star ratings. I’m excited for this AI eCommerce era. More to come from The Other Group #ai #ecommerce #commerce