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

🔥 The Bundesliga Breakthrough: Personalizing Context, Not Content At the Sports Forum at Amazon Web Services (AWS) re:Invent 2025, I learned something rare… something most business leaders never hear: Everyone talks about personalized content. Almost no one talks about personalized context. And that’s where the Bundesliga — One of the top football leagues — is quietly years ahead of the market. Here’s the insight most executives miss: AI doesn’t scale content. AI scales understanding. Bundesliga’s AI system works because it doesn’t start with the match. It starts with the fan. Before a single line of commentary is generated, the system builds a real-time “context graph”: - who the fan is - how long they’ve followed the league - what commentary style keeps them hooked - which players they track - what cultural cues resonate in their region - and what emotional tone they respond to This is the whole magic. Gen AI is just the surface. The real breakthrough is the context engine underneath. >> Why this matters for executives? Most companies try to personalize by tweaking the message. Bundesliga personalizes by changing the lens through which the customer sees the message. That shift is massive. Because when you personalize context: - Engagement stops being random - Marketing stops being guesswork - CX stops being generic - Loyalty stops being an accident >> The uncomfortable question Most leaders ask: “How do we create more personalized content?” The better question — the Bundesliga question — is: “Do we truly understand the context our customers live in?” Because here’s the uncomfortable truth: > You can’t personalize content at scale unless you personalize context first. Bundesliga shows the future. The next decade of CX belongs to companies that invest not only in storytelling… …but in systems that understand their customers better than customers understand themselves. Your turn: 👉 How could your customer experience improve if your systems learned their context the way Bundesliga’s does? #CustomerExperience #AILeadership #GenerativeAI #Bundesliga #DigitalTransformation #Personalization

Foundation models have transformed natural language processing, but their impact goes beyond text. In a recent tech blog, Netflix’s machine learning team shared how they are building foundation models for recommendations, designed to learn from sequences of user interactions — much like how LLMs learn from sequences of words. At the center of this approach are three major components: - First, the data. Sequences of user interactions undergo tokenization. These tokens capture richer context than isolated signals and become the training ground for the foundation model. - Second, the prediction objective and architecture. Unlike standard LLMs, where every token is treated equally, in the recommendation context different user interactions carry different weights. For example, a full movie watch is more meaningful than a quick trailer view. The team also extends the training objective to predict multiple future items rather than just the immediate next one, aligning recommendations with long-term satisfaction instead of short-term clicks. - Finally, the team highlights unique recommendation problems such as the cold-start issue for new content and incorporates solutions like weighted representations from dual embeddings, as well as incremental training to help the system warm start and evolve smoothly. There’s much more technical depth in the blog, and I highly recommend checking it out. In short, foundation models for recommendations can’t simply copy LLMs. They must be carefully adapted — aligning data, objectives, and architecture to achieve meaningful personalization at scale. #DataScience #MachineLearning #Analytics #Recommendation #Personalization #AI #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/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/g_33Tbfn

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

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

Every time you open Netflix or YouTube, a huge machine is running in the background. Most people know "recommendation systems" exist. Few know what's actually inside them. Over the past few months, I've been writing a full series on RecSys for ML engineers — from the basics all the way to production-grade ranking. Here's the full map: 1️⃣ RecSys Fundamentals — The three core approaches powering every modern recommender: https://buff.ly/47f06cx 2️⃣ How Recommendation Systems Learned to Think — From collaborative filtering to generative AI agents: https://buff.ly/MZQXSkC 3️⃣ The 3-Stage Funnel — Two-tower models, vector databases, cross-encoders and how they work together at scale: https://buff.ly/dlfsK7w 4️⃣ How YouTube Finds Your Next Video in Milliseconds — Two-tower retrieval, in-batch negatives, and the tricks that make it work: https://buff.ly/Y1fsKmG 5️⃣ Vector Search at Scale — IVF, PQ compression, and making 100M vector search actually possible: https://buff.ly/OlwpsNl 6️⃣ From Candidates to Clicks — How modern systems go from 1,000 candidates to the one item you actually tap: https://buff.ly/O2FbdkN Six posts. One complete picture. If you're an ML engineer who wants to actually understand what runs at Netflix, YouTube, and Instagram scale — this is the series.

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.

What if an interface could adapt to your world in real time? Imagine your car’s dashboard subtly shifting to shades of green as you drive through a forest, or an app adjusting to your personal accessibility needs without breaking. For the past few months, I've spoken with many of you and I’ve realized we’re all working toward the same ambitious goal: creating interfaces that offer a seamless blend of brand personalization, true adaptability, and accessibility. This is about building an experience that is not only true to a brand's perception but is also tailored to our individual needs as consumers. My exploration so far has revealed three foundational concepts that I feel are important to make this a reality. In the upcoming months, I’ll be sharing our journey as we explore these concepts. Some ideas will work, some will fail. I don’t know where this path will lead, but I want to bring you along in the process. 1.⁠ ⁠Contextual Awareness This is the idea that an element understands its environment. A button, for example, knows what surface it’s sitting on and adapts accordingly. While tools like Figma use variable collections to simulate this, the approach is often fragile because it lacks a scalable underlying logic. This very challenge was a driving force behind developing the graph engine. I’m excited to share that a solution for this is now possible directly in modern browsers with pure CSS, laying a powerful and scalable foundation for the future. 2.⁠ ⁠Content Awareness Imagine an interface that reflects the content it displays. We see a version of this in Spotify’s UI, which adapts to album art to create a more immersive experience. This principle allows the UI to react dynamically, personalizing the experience in real-time based on its content. 3.⁠ ⁠User Awareness This pillar brings it all together by focusing on the user’s specific needs. It means designing systems that can respond to a user with Parkinson’s who may need more forgiving interaction areas, or accommodating the universal reality that as we get older, we need larger fonts. The key is to make these adjustments without breaking the interface or compromising the brand experience. These three pillars form the blueprint for the next generation of user interfaces. By understanding where an element is, what it contains, and who is using it, we can create experiences that feel truly alive. I think there’s more to discover beyond our current methods. Let's explore what it means to build something truly adaptive, together.

𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐌𝐨𝐧𝐞𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐧 𝐅𝐢𝐧𝐭𝐞𝐜𝐡 In the fast-evolving fintech landscape, data monetization has become a crucial engine for growth. Harnessing data insights allows fintech companies to create personalized experiences, optimize financial products, and drive profitability. But with great power comes great responsibility - specifically, the responsibility to protect consumer privacy. Globally, privacy laws like GDPR, CCPA, DPDPA and others are setting new standards for data handling. Fintech companies must navigate this complex regulatory environment while exploring data monetization opportunities. As we stand at the cusp of 2025, the conversation around how we manage, monetize, and protect data in fintech is not just about compliance or innovation; it's about redefining trust in the digital age. In an era where data breaches are headline news, consumer trust is fragile. Balancing data use with robust privacy measures isn't just good practice; it's essential for maintaining customer loyalty and brand reputation. 𝐻𝑜𝑤 𝑐𝑎𝑛 𝑓𝑖𝑛𝑡𝑒𝑐ℎ 𝑛𝑎𝑣𝑖𝑔𝑎𝑡𝑒 𝑡ℎ𝑖𝑠 𝑑𝑒𝑙𝑖𝑐𝑎𝑡𝑒 𝑏𝑎𝑙𝑎𝑛𝑐𝑒? 𝟭. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗶𝘀 𝗞𝗲𝘆: Clearly communicate how data is collected, used, and protected. When users understand how their data benefits them, they are more likely to engage. 𝟮. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮-𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: Monetize insights, not individual identities. Aggregating and anonymizing data can provide value while protecting privacy. 𝟯. 𝗨𝘀𝗲𝗿 𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝗺𝗲𝗻𝘁: Give users control over their data. Options to manage consent and access their data foster trust and demonstrate respect for their privacy. 𝟰. 𝗣𝗿𝗶𝘃𝗮𝗰𝘆-𝗙𝗶𝗿𝘀𝘁 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀: Leverage advanced encryption, secure data-sharing methods, and privacy-enhancing technologies to build a robust data protection framework. 𝟱. 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Beyond compliance, investing in cybersecurity infrastructure is crucial. This includes not just technology but also training for employees and establishing a culture of security awareness. The future of fintech will be defined by those who can master this balance. It's about creating value from data while ensuring that privacy isn't just an afterthought but a core value proposition. As we move forward, the integration of advanced privacy technologies, ethical frameworks, and a commitment to transparency will not only protect but also empower users, setting new benchmarks for what it means to be a leader in fintech.   How do you see the future of data privacy shaping the fintech landscape? 𝘐𝘮𝘢𝘨𝘦 𝘚𝘰𝘶𝘳𝘤𝘦 : 𝘋𝘈𝘓𝘓-𝘌 #Fintech #DataPrivacy #DataMonetization #Trust #Innovation #Privacy #Leader #ConsumerCentricity #Innovation #Ethical

The search bar is dead. And most e-commerce platforms don’t even know it yet. After working closely with AI systems and recommendation engines, I’ve learned one thing: “Personalized shopping” was never truly personal. It was pattern matching. It was collaborative filtering. It was reactive logic pretending to be intelligence. Now we’re entering a different era. → From personalized to personal → From search-based discovery to proactive intelligence → From browsing endlessly to AI agents working for you This is agentic commerce. Traditional e-commerce makes you do the heavy lifting: Search → Filter → Scroll → Compare → Hope Agentic commerce flips the entire model: Describe what you want → AI delivers with context One of the most interesting examples I’ve seen is Glance. They are not building another shopping app. They’re building a contextual, agentic AI commerce layer powered by multiple specialised agents working together. Instead of one algorithm guessing what you like, Glance deploys multiple AI agents working for you in parallel: → Weather Agent analysing real-time climate and fabric suitability → Trends Agent tracking global shifts and micro-trends → Occasions Agent anticipating upcoming events → Physical Agent understanding your skin tone, undertones, and body type → Lifestyle Agent decoding your aesthetic preferences All coordinated by an orchestrator that synthesises everything into a unified styling strategy. That’s not basic personalization. That’s contextual intelligence. And the most powerful shift? You see yourself in the generated looks. Not stock visuals. Not generic models. You. Commerce becomes a conversation instead of a search box. From personalized to personal. AI agents working for you. Learning with every interaction. Refining your style instead of just tracking clicks. This is the rise of agentic commerce. #Glance #AICommerce #AgenticAI