Personalized Content Suggestions

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Over-personalizing in a way that feels intrusive or creepy 🙅🏼 Sending irrelevant recommendations due to inaccurate or outdated data 🙅🏼 Over-segmenting to the point where segments are too small to be effective 🙅🏼 Using templated, robotic language that sounds unnatural The key is finding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.

How many times have you logged on to Linkedin and found yet another email that starts with: "Hey [First Name]," followed by a generic pitch that does not concern your interests or needs. Sound familiar? We've all been there. And it's frustrating. As a fractional CMO/Consultant, I've seen this happen repeatedly. Businesses think they're doing personalization right but need to do better. It's not enough to use someone's name or company. 👉🏾 True personalization is about understanding their challenges, goals, and needs. For example, on LinkedIn, scroll through their feed and see what they post, talk about, like, and comment on. This helps as a starting ground on how to approach them and what to discuss. So, instead of sending a LinkedIn message that says: "I'd love to connect and learn more about your business," try something like: "I noticed you're working on [specific project]. I have some ideas on how you could [achieve a specific goal]. Would you be open to a quick chat?" See the difference? It's not just about being personal; it's about being relevant. And when you're relevant, you're not annoying — you're helpful. 👉🏾 So, think about this the next time you craft a personalized outreach campaign. →"Would I find this message valuable? →Does it address my specific needs and interests?" If the answer is no, it's time to return to the drawing board. 👉🏾 Also, tools like Crystal Knows help you fine-tune your message and tone when reaching out to maximize the impact of every conversation. Let's aim for genuinely helpful messages, not just another annoyance in their inbox. What do you think about personalized outreach? #b2bmarketing #demandgeneration #leadgeneration #ABM

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

In today's digital age, delivering personalized content is essential for media organizations looking to engage readers effectively. However, balancing algorithmic recommendations with editorial judgment presents a unique challenge: how can we ensure that recommendations are both relevant to readers and aligned with journalistic values? In this tech blog, data scientists at The New York Times share their approach to integrating editorial judgment into algorithmic recommendations. Their method follows three key steps, ensuring that human oversight is embedded at every stage of the recommendation process. The first step is pooling, where a set of eligible stories is created for a specific module. While the system automatically generates queries to populate this pool, editors also have the flexibility to manually curate or edit the selection when necessary. The second step is ranking, which involves sorting stories using a contextual bandit algorithm. To prioritize mission-driven and significant stories, the team quantifies editorial importance in multiple ways. One such approach allows editors to assign a rank to each story, with more recent and newsworthy articles generally receiving higher priority. Finally, before stories are shown to readers, the system applies editorial adjustments based on predefined newsroom rules. One key intervention is the Pinning function, which allows editors to override the algorithm and manually place critical stories at the top of the list. Beyond these core steps, the team has developed additional functionalities to enhance this integrated approach, ensuring The New York Times’ Home Screen Content strikes the right balance between automation and editorial oversight. Their work exemplifies how media organizations can effectively blend human judgment with machine learning—enhancing reader engagement while preserving the integrity of journalism. #DataScience #MachineLearning #Algorithm #Personalization #Journalism #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/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gDFTxxWQ

Personalization is not just about using a prospect's name. To provide a truly personalized experience, you should learn your prospect's language, communication style, and what's important to them right now. Sound complicated? It's really not. Everyone utilizes social media. Learning about your prospects has never been easier. The vast majority of decision-makers, executives, and C-suite members use some form of social media every day. They actively absorb content, watch trends, learn new skills, and find areas of opportunity. You can start right here on LinkedIn. Look at their profile and see what they're posting, commenting on, and liking. Is there a common thread to their messages? Maybe they like data, facts, and figures. They could discuss their vision, the future of their industry, and current concerns. Do they post content or write a blog? Read it. It's the best way to meet them. This is their language. Communicate with them the way they communicate with others. Note the topics they discuss or engage with; these are your lead-ins to having a conversation with them. Join groups related to their industry, learn the hot topics and their jargon and acronyms. Your research should extend beyond the person, the company, and their current role. It's about tailoring your message to them and engaging them like a coworker or trusted partner.

🚀 The Era of One-Size-Fits-All Events Is Over. Stop Doing It. Personalization isn't a single action, it's a series of intentional, strategic choices that come together to make every attendee feel genuinely valued. We’re not just organizing events anymore — we’re crafting journeys. 🧭 In today’s marketplace, attendees expect more than just a badge and a schedule. They want curated content, meaningful connections, and real-time relevance that makes them feel seen. That’s where hyper-personalization comes in. And no, it’s not just using someone’s name in an email. It’s about using data and technology to design experiences that feel custom-built for each person. 🧠📊 As an event marketer, I’m all in on data-driven strategy. This is where we move beyond logistics and design every touchpoint to be personal, memorable, and valuable. Here's some ways that can look like across the attendee journey: Before the Event: 🎯 Targeted Invitations & Content: Use behavioral data to send invites that speak directly to someone's interests. A marketer might get a blog post on campaign strategy, while a developer receives a product case study. 📝 Dynamic Registration: Ask tailored questions based on the attendee’s role or industry to build rich attendee profiles from the start. During the Event: 🤖 AI-Powered Agendas & Recommendations: Event apps can recommend sessions, speakers, and exhibitors based on real-time behavior, interests, and profiles — reducing decision fatigue and maximizing impact. 🤝 Smart Networking: Go beyond job titles. Use AI to match attendees with shared goals, values, or expertise for deeper, more meaningful conversations. 🎉 Personalized On-Site Experiences: Greet attendees by name on welcome screens, print session tracks on badges, or use RFID to tailor in-person interactions. 📽️ Customized Content Delivery: Make booth visits unforgettable. When someone scans their badge, show a video personalized to their company, role, or industry — turning a quick interaction into a memorable moment. 🧢 Personalized Swag: Skip the generic t-shirt. Offer attendees the ability to choose colors, styles, or even print their name on a water bottle or notebook. After the Event 📬 Tailored Follow-Up: Instead of a generic “thanks for coming,” send curated content based on sessions they attended, people they connected with, and their unique interests. 📚 Personalized Content Hubs: Create a portal where attendees can revisit the event — with homepages tailored to their track, interests, or role. 📊 Custom Surveys: Don’t ask vague questions. Personalize post-event feedback forms to reflect their specific journey. 🤔 What's one thing you're doing to add a touch of personalization to your events? Or, as an attendee, what's a personalization strategy that has truly impressed you? Let's share some ideas in the comments! #EventProfs #EventMarketing #HyperPersonalization #EventTech #ExperienceDesign #EventStrategy #PersonalizedExperiences

Unsolicited Google Discover Tip Ever wonder how Google Discover decides what to show you? 🤔 It's a fascinating mix of 1) personalization, 2) trending topics, and 3) location-based content. Let's break it down: 1. Personalized Content: This is tailored to your interests based on past searches, the content you've clicked and consumed, and even explicit signals you provide. This includes actions like actively following topics or sources in Google Discover, clicking on 'more' or 'less' buttons for certain types of content, or even directly interacting with the Google app to indicate your preferences. Google's AI is constantly learning what you like! 2. Impersonalized Content: This reflects broader trends and popular topics in your country. It might not always align with your personal interests, but it keeps you in the loop on what's capturing everyone's attention. 3. Local Content: This is where your location comes into play. Google Discover considers your home, work, and current location to surface relevant local news. The Impact of Trending Topics: But here's the kicker: major events and trending topics can dramatically influence the volume and type of content you see. Take the recent election, for example. Our new tool, NewzDash DiscoverPulse, tracks Google Discover data in real-time using a panel of real users. We observed a massive surge in articles classified by Google Discover as related to politics around election day, jumping from 400+ articles in the previous week to over 1200+ articles per day! 🤯 After the election, the volume gradually returned to normal levels (around 200+ articles). It's important to note that the impact of any given trend can vary significantly based on the supply of relevant content available from publishers and the level of audience demand for information on that topic. Why This Matters for Content Creators: Similar to the Politics example, you can see today financial content around retirement and pension is getting popular as we get closer to the end of the year. Understanding these dynamics is crucial for anyone creating content. By knowing what types of content are most visible in Google Discover at any given time, you can: - Optimize your content strategy: Create content that aligns with current trends and user interests. - Improve your chances of discovery: Increase your visibility in Google Discover and reach a wider audience. - Stay ahead of the curve: Anticipate shifts in content trends and adapt your approach accordingly. I hope this helpful. If you want to see how DiscoverPulse can help you analyze Google Discover trends and optimize your content strategy, try it out for yourself: https://lnkd.in/ewmJr9Ba #GoogleDiscover #ContentStrategy #SEO #DataDriven #DiscoverPulse

🚀 𝐏𝐚𝐫𝐭 3 𝐨𝐟 𝐭𝐡𝐞 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐫 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐒𝐞𝐫𝐢𝐞𝐬: 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐂𝐨𝐧𝐭𝐞𝐧𝐭-𝐁𝐚𝐬𝐞𝐝 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐫 𝐒𝐲𝐬𝐭𝐞𝐦 🚀 In this third installment of our "Building Your Own Recommender Systems" series, Arun Subramanian and I dive into creating a content-based recommender system from scratch! 🧩 𝐖𝐡𝐚𝐭’𝐬 𝐈𝐧𝐬𝐢𝐝𝐞? 🔹 Hands-On Implementation: A Python-based example using Pandas to recommend movies based on genres and release year. 🔹 Vectorization Techniques: An overview of text vectorizers like Count Vectorizer, TF-IDF, and Word Embeddings—and how we use them. 🔹 Practical Use Case: Step-by-step guidance on generating movie recommendations with cosine similarity. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬? Content-based recommender systems are the backbone of personalized user experiences across industries like e-commerce, streaming, and more. This post is for the ML enthusiasts and data scientists who want to learn about building robust and scalable recommendation engines. 🌟 𝐌𝐢𝐬𝐬𝐞𝐝 𝐭𝐡𝐞 𝐄𝐚𝐫𝐥𝐢𝐞𝐫 𝐏𝐚𝐫𝐭𝐬? 1️⃣ Part 1: Architecture of recommender systems https://lnkd.in/e5aAYww7 2️⃣ Part 2: Performance Evaluation metrics https://lnkd.in/eqh9-q35 Check out Part 3 here: https://lnkd.in/ebrdiFfs GitHub Repo: https://lnkd.in/gvgbeUqD Next: In Part 4, we’ll dive into collaborative filtering, exploring how user behavior drives recommendations. What would you like to learn more about recommender systems? Drop your questions below and we'll bring the industry insights! 👇

Do you cater to multiple customer personas? Guiding them to the right products from the get-go can significantly enhance their shopping experience. One effective strategy is to implement a "Choose Your Own Adventure" approach on your ecommerce homepage. Why This Approach Works: → Personalization: By allowing customers to select their persona or interests, you can tailor the shopping experience to their specific needs and preferences. → Improved Navigation: This method helps visitors quickly find the products that are most relevant to them, reducing the time they spend searching and increasing the likelihood of a purchase. → Enhanced Engagement: A personalized experience keeps customers engaged and encourages them to explore more of your catalog and return in the future. How to Implement It: → Identify Key Personas: Start by identifying the main customer personas you serve. For example, if you're a skincare brand, your personas might include "Teens," "Adults," and "Mature." → Create Clear Pathways: Design your homepage to feature clear, clickable options for each persona. For instance, you could have buttons or images labeled "Teen Skin," "Adult Skin," and "Mature Skin." → Tailor Content: Once a visitor selects their persona, direct them to a customized landing page that features products, testimonials, and content relevant to their needs. Show product recommendations tagged for each persona. Bonus points: Setup a personalization campaign that adapts each page of your site with language and imagery to match each persona. e.g. A teen would see imagery of other teens and copy on the page follows suite. By implementing a "Choose Your Own Adventure" approach, you can create a more personalized and joyful shopping experience for your customers, ultimately driving higher conversions and revenue.

People don’t want another blast email—they want to feel like you’re talking to them. Marketo’s personalization tools help make each interaction unique, genuine, and relevant. Tools within Marketo to Personalize Your Outreach: 1. Dynamic Content Blocks: Dynamic content lets you tailor emails with the right message, image, or offer for each group. It’s especially useful for customizing specific sections within a single email while keeping the rest consistent. 2. Tokens for Personalization: A little personal touch, like a name or company mention, goes a long way. Tokens can be added across all folders by setting them at the top level or customized at the program level for maximum flexibility. 3. Behavioral Triggers: Timing is everything. Set up triggers based on actions like page visits or clicks to ensure you’re reaching out when your audience is most engaged. 4. Lead Scoring: Lead scoring helps you prioritize and deliver the right content at the right time, tailored to each lead’s journey. You may also want to bring in data from your ABM tool for this. What You Can Personalize: 1. Name: Start with the basics—everyone loves seeing their own name. 2. Geolocation: Context matters. Personalize based on region or city to show you understand their specific needs or local interests. 3. Persona: Tailor messages to different buyer personas, ensuring each one feels like it’s made just for them (because a CFO and a VP of Sales aren't interested in the same thing). 4. Images and Visuals: Swap out images based on location, industry, or interest to make your content feel relevant to each recipient. 5. Content Recommendations: Use browsing history or past interactions to recommend the next best asset. 6. Product or Service Interests: Send personalized messaging around the particular products or services each lead has shown interest in, making it feel like you’re offering a solution just for them. 7. Engagement Stage: Adapt your content based on where they are in the buyer’s journey, from awareness to decision-making. This ensures each message aligns with their current needs and level of interest. Again, your ABM tool might be helpful here. 8. Company Name and Industry: Recognize the lead’s company or industry to show that you understand their business context and challenges, especially useful for B2B audiences. 9. Past Purchases or Transactions: Make returning customers feel valued by referencing past purchases or transactions. This can work wonders for upsells, cross-sells, and loyalty programs. And don’t forget—this customization can be extended to landing pages too! Consistent, seamless experiences make all the difference. In today’s world, personalization isn’t just a nice-to-have—it’s how you build real connections. With Marketo, you’re not just sending messages; you’re creating relationships that feel authentic and worth investing in. #marketingoperations #marketingops #personalization #emailmarketing #landingpages #marketo