Mobile Personalization Techniques

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

While working closely with hundreds of brands, one thing kept bothering me. We were all sending “personalized” campaigns. Same flow. Same timing. Same channels. Just a different name at the top. And we called that personalization. But it didn’t feel personal. Some customers only responded on WhatsApp, yet we kept emailing them. Some opened emails at night, but we sent them in the morning. Some were ready after one touchpoint, others needed three. Completely different people, treated exactly the same way. That’s where the idea of 1:1 personalization started for us. What if every customer had their own journey with different channels, different timing, different decisions? What you’re seeing here is not a campaign. It’s one customer, being understood in real time. Athena tracks behavior across touch points what they viewed, where they dropped, how they engage Then the Markopolo AI INC adapts the journey. 🔥 Push is skipped due to fatigue 🔥 Email is prioritized based on high engagement 🔥 SMS is timed based on past clicks 🔥 Calls happen when the user is most likely to respond Not a fixed workflow, but decisions made for that one individual. This is what 1:1 personalization actually looks like.

Using "Hey {first name}" in your marketing emails and calling it personalization is like picking up a rock and calling it a hammer. Technically, it works. But we have better tools now, and failing to take advantage of them is going to leave you choking on the dust of your competitors. Here's how to catch up with the times and use TRUE personalization to boost engagement, loyalty, and conversions: 1. Use dynamic content fields to customize emails based on customer attributes, behaviors, and preferences. Go beyond just {first name} – incorporate product views, past purchases, and customer lifecycle stage. Don't be creepy! Be conversational. You want the reader to feel like you understand their needs, not like you've been peeking through their blinds. 2. Set up behavior-triggered automations like browse abandonment and cart recovery flows. Make these highly relevant by including viewed products, social proof, and timely offers. Marketing is all about getting the right offer in front of the right person at the right time, and behavior-based emails are one of the best ways to do that on a consistent basis. 3. Implement Recency, Frequency, and Monetary Value (RFM) segmentation to deliver personalized messaging to different customer groups. Target VIPs, at-risk customers, and prospectives customers with specific messages to convert or retain them. 4. Create personalized journeys that adjust the user's experience based on customer data or actions. For example, if you're sending the exact same post purchase sequence to a repeat purchaser as you are for a first-time buyer, you're missing a huge opportunity. 5. Use replenishment flows for consumable products, reminding customers when it's time to reorder. Or, capture email addresses on PDPs for sold out products and notify them when the item in back in stock. Easy sales. Be careful to avoid these common personalization mistakes: 🙅🏼 Over-personalizing in a way that feels intrusive or creepy 🙅🏼 Sending irrelevant recommendations due to inaccurate or outdated data 🙅🏼 Over-segmenting to the point where segments are too small to be effective 🙅🏼 Using templated, robotic language that sounds unnatural The key is finding the right balance ––  personalized enough to be relevant and engaging, but not so specific that it becomes cringey or off-putting. When done well, personalization makes customers feel heard, understood and valued. This builds loyalty, increases engagement, and ultimately drives more conversions and revenue. Level up your personalization with one (or more!) of these strategies, and your KPIs are going to shoot up and to the right.

You’re not losing customers because of product. You’re losing them because the experience feels generic. AI-powered personalization fixes that. Here’s how to do it right: Step 1: Track user behavior, not just demographics. Behavior reveals intent—AI learns what they actually want. 📌 Netflix’s content suggestions led to 80% of watch hours coming from recommendations. Step 2: Customize in real-time across channels. Personalization should follow the user journey, not just one touchpoint. 📌 Amazon adjusts homepage product listings based on recent searches, boosting conversions. Step 3: Avoid the creep factor. Hyper-relevant can feel invasive—balance personalization with subtlety. 📌 Spotify Wrapped works because it surprises and delights without oversharing. If your message isn’t tailored, it’s invisible. P.S. What brand nails personalization for you? #Leadership #Sales #Marketing

“It’s not what users want. It’s what they want right now.” Relevance is the new differentiator. Context is the new currency. Old mindset: What new service should we add? New mindset: What existing service can we make more relevant—right now? Don’t make users search for value. Let value find them. This means: Stop filling the home screen. Start anticipating need. Stop offering everything. Start responding to context. Stop defining users by past segments. Start watching current signals. The smartest apps don’t just grow wider. They grow sharper. The Feature Fatigue Problem Feature bloat used to be impressive. Now it’s just friction. Most SuperApps today look like cluttered marketplaces: endless icons, banners, and buried microservices. Yet most users only touch 2–3 features regularly—and often can’t find what they actually need. We’ve spent years building catalogs. But users don’t want catalogs. They want companions. Why Relevance Wins In a saturated app world, being helpful in the moment beats being powerful in general. If a user’s mobile data is running low, offering a “Data Booster” pack right then—via a smart, timely popup—feels like magic. Offer the same pack a day later via a generic banner? White noise. Enter the Context Graph To power this shift, SuperApps need what we call a context graph—a real-time model of behavior, environment, and inferred need. It captures signals like: App usage (scrolls, taps, top-ups, sessions) Wallet balance and velocity Device state (battery, connectivity) Location and time patterns Offer response history Think of it as a dynamic, evolving graph where each edge carries weight: urgency, intent, recency. In PokeAI, our modular AI system: Seki segments users based on this context. Loki assigns evolving personas. Reki recommends the most relevant offer. Pingi delivers it via the best channel and moment. It’s not personalization. It’s predictive presence. Delivery is Part of the Intelligence Even the best offer fails if it lands the wrong way. Context includes the how, not just the what: During a transaction? Show a modal, not a banner. High-value product? Use a persuasive, time-sensitive overlay. What We Learned From Netflix & TikTok The platforms that win attention today aren’t necessarily the ones with the most content. They’re the ones that reduce cognitive load. Netflix doesn’t ask users to browse forever. TikTok doesn’t ask them to choose. These apps master session context: time of day, recent activity, repeat patterns, attention span.SuperApps should apply this mindset to lending, top-ups, shopping, food, and beyond. Conclusion: Build Context Engines, Not Catalogs Your SuperApp doesn’t need more services. It needs better timing, better instinct, better memory. Because in today’s digital economy, relevance isn’t a feature. It’s a competitive moat. full article here https://lnkd.in/gDBACJHH #AI #Personalisation #martech

💡How to use personalization to improve user experience The main goal of personalization is to deliver content and functionality that matches specific user needs or interests with no effort from the targeted users. Good personalization makes interaction with a product seamless. ❗ Customization and personalization are not the same Customization allows users to choose exactly what they want (e.g., visual theme, font size, etc.). Not many people customize their experience. Personalization anticipates what they want (e.g., tailor content based on user preferences and behaviors). It happens automatically without user effort. Personalization principles in product design:  ✔ Progressive profiling. Quite often, users don’t know what they need. Collect user information gradually over time rather than asking for extensive details upfront. ✔ Contextualization. Personalize experience based on contextual factors such as location, device type, and time of day. ✔ Behavioral triggers. Good personalization is deeply rooted in a user journey. Use triggers to initiate personalized interactions (e.g., abandoned cart scenario, special offers based on past purchases). ✔ Cross-channel consistency. Ensure a consistent personalized experience across different channels (e.g., website, mobile). Sync user data and preferences across channels to provide seamless interactions. ✔ User feedback. Implement feedback loops to refine personalization based on user responses. ✔ Transparency. Clearly communicate to users how their data is used for personalization.  ✔ User control. Provide options for users to control and adjust their personalized experiences (e.g., privacy settings, opt-out mechanisms). 📖 Guides: ✔ Personalization pyramid: a framework for designing with user data (by Colin A. Eagan M.S. and Jeffrey MacIntyre) https://lnkd.in/dakcjb9F ✔ How much personalization is enough in UX design? (by Mariia Kasym)  https://lnkd.in/dXYYB2qX ✔ 5 levels of product personalization (by Guillaume Galante)  https://lnkd.in/dywXRv-R ✔ 6 tips for successful personalization (by Amy Schade)  https://lnkd.in/dxfGKPKu 🖼 UX personalisation pyramid by Colin A. Eagan M.S. and Jeffrey MacIntyre #personalization #personalisation #ux #userexperience #uxdesign

Incorporating Artificial Intelligence (AI) and Machine Learning (ML) into mobile apps has revolutionized the way users interact with technology, offering personalized experiences, predictive capabilities, and automation of tasks. Here's a look at how AI and ML are transforming mobile app development: 1. **Enhanced Personalization**: - AI and ML algorithms analyze user data, preferences, and behaviors to deliver personalized recommendations, content, and experiences within mobile apps. Whether it's suggesting relevant products, curating personalized playlists, or offering tailored news articles, AI-powered personalization enhances user engagement and satisfaction. 2. **Predictive Analytics**: - AI and ML models leverage historical data and patterns to make predictive insights and recommendations. In mobile apps, predictive analytics can anticipate user actions, preferences, and needs, enabling proactive suggestions, reminders, and notifications. For example, predictive text input, smart email categorization, and personalized calendar scheduling streamline user interactions and enhance productivity. 3. **Natural Language Processing (NLP)**: - NLP algorithms enable mobile apps to understand and respond to natural language inputs, such as voice commands, text messages, and chat conversations. Virtual assistants like Siri, Google Assistant, and Alexa leverage NLP to interpret user queries, provide relevant information, and perform tasks, enhancing user convenience and accessibility. 4. **Image and Speech Recognition**: - AI-powered image and speech recognition capabilities enable mobile apps to analyze visual and auditory data. Image recognition facilitates features like visual search, augmented reality (AR) experiences, and automatic image tagging. Speech recognition powers voice-enabled interfaces, voice-controlled commands, and voice-to-text transcription, making apps more intuitive and user-friendly. 5. **Automated Customer Support**: - AI-driven chatbots and virtual assistants offer automated customer support and assistance within mobile apps. These virtual agents can answer user queries, provide product recommendations, resolve common issues, and guide users through complex processes, reducing the burden on human support agents and improving customer satisfaction. The integration of AI and ML technologies into mobile apps is reshaping the way users interact with digital products, offering personalized experiences, predictive capabilities, and intelligent automation. By leveraging AI and ML algorithms, mobile app developers can create smarter, more intuitive, and more valuable apps that meet the evolving needs and expectations of users in today's dynamic digital landscape. #MobileAppDevelopment #AppSuccessTips #UserExperience #AppFeatures #AppTesting #AppUpdates