Using Data To Personalize Ecommerce Shopping Experiences

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Over-personalizing in a way that feels intrusive or creepy 🙅🏼 Sending irrelevant recommendations due to inaccurate or outdated data 🙅🏼 Over-segmenting to the point where segments are too small to be effective 🙅🏼 Using templated, robotic language that sounds unnatural The key is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

As an analyst, I was intrigued to read an article about Instacart's innovative "Ask Instacart" feature integrating chatbots and chatgpt, allowing customers to create and refine shopping lists by asking questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on user's past buying habits and provides recipes and a shopping list once users have selected the option they want to try! This tool not only provides a personalized shopping experience but also offers a gold mine of customer insights that can inform various aspects of a business strategy. Here's what I inferred as an analyst : 1️⃣ Customer Preferences Uncovered: By analyzing the questions and options selected, we can understand what products, recipes, and meal ideas resonate with different customer segments, enabling better product assortment and personalized marketing. 2️⃣ Personalization Opportunities: The tool leverages past buying habits to make recommendations, presenting opportunities to tailor the shopping experience based on individual preferences. 3️⃣ Trend Identification: Tracking the types of questions and preferences expressed through the tool can help identify emerging trends in areas like healthy eating, dietary restrictions, or cuisine preferences, allowing businesses to stay ahead of the curve. 4️⃣ Shopping List Insights: Analyzing the generated shopping lists can reveal common item combinations, complementary products, and opportunities for bundle deals or cross-selling recommendations. 5️⃣ Recipe and Meal Planning: The tool's integration with recipes and meal planning provides valuable insights into customers' cooking habits, preferred ingredients, and meal types, informing content creation and potential partnerships. The "Ask Instacart" tool is a prime example of how innovative technologies can not only enhance the customer experience but also generate valuable data-driven insights that can drive strategic business decisions. A great way to extract meaningful insights from such data sources and translate them into actionable strategies that create value for customers and businesses alike. Article to refer : https://lnkd.in/gAW4A2db #DataAnalytics #CustomerInsights #Innovation #ECommerce #GroceryRetail

Here’s a common myth about personalization: All you need is a customer’s name to make it effective. True personalization goes much deeper, it’s about understanding behaviors, preferences, and needs to create meaningful experiences. Collecting the right data isn’t just about volume, it’s about relevance. You can’t offer genuine personalization without truly knowing your audience. Here’s how I’ve approached it: ➜ Identify key data points. Don’t collect data just for the sake of it. Focus on what will actually help you understand your customers better, things like purchase history, browsing behavior, and engagement patterns. ➜ Leverage tools wisely. Using the right tools is crucial. We’ve integrated platforms (like HubSpot) to ensure we’re gathering and utilizing data that matters, not just creating noise. ➜ Respect privacy. Personalization should never come at the cost of privacy. Being transparent with your audience about what data you collect and how you use it builds trust. ➜ Test and refine. Data isn’t static, and neither should your approach to personalization be. Continuously test what works and refine your strategy to meet your customers' evolving needs. ↳ By focusing on relevant data, not just more data, we’ve been able to create personalized experiences that resonate, leading to stronger customer relationships and better results. What’s been your biggest challenge in collecting data for personalization? How are you overcoming it? #data #personalization #hubspot

Let's build a Recommender for an E-Commerce clothing site from scratch. 🛍️📈 This notebook shows how to deliver personalized, scalable recommendations even in cold-start scenarios. 👉 Product details include: - Price, - Rating, - Category, - Description, - Number of reviews, - Product name with brand. We have two user types, defined by their initial product choice at registration or general preferences around price range and review requirements. We'll use the Superlinked Framework to combine product and user data to deliver personalized recommendations at scale. Let's dive in 🏗️: 1️⃣ Data Preparation ⇒ Load and preprocess product and user data. 2️⃣ Set up the Recommender System ⇒ Define schemas for products, users, and user-product interactions. ⇒ Create embedding spaces for different data types to enable similarity retrieval. ⇒ Create the index, combining embedding spaces with adjustable weights to prioritize desired characteristics. 3️⃣ Cold-Start Recommendations ⇒ For new users without behavior data, we'll base recommendations on their initial product choice or general preferences, ensuring they're never left in the cold. 4️⃣ Incorporate User Behavior Data ⇒ Introduce user behavior data such as clicked, purchased, and added to the cart with weights indicating interest level. ⇒ Update the index to capture the effects of user behavior on text similarity spaces. 5️⃣ Personalized Recommendations ⇒ Now it's time to tailor recommendations based on user preferences and behavior data. ⇒ Compare personalized recommendations to cold-start recommendations to highlight the impact of behavior data. Ant that's a wrap! 🔁 Adjusting weights allows you to control the importance assigned to each characteristic in the final index. This tailors recommendations to desired behavior while keeping them fresh and relevant... it's easier than chasing the latest fashion trends. ✨ Dig into the notebook to implement this approach 👉 https://lnkd.in/edeQW344 Why not show some support by starring our repo? ⭐️ We'd appreciate it more than a free fashion consultation! 😉

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

Advanced personalization work involves 'growth engineering' as a new 'role' to connect the dots and architect the data, from tools/data like: • CDPs • Data warehouses • Testing tools that enable adaptive approaches, e.g., Contextual Multi-Arm Bandits (or similar) • And advanced 'intent' or 'propensity' data and models. The last point is where Mr. David Mannheim comes in. He just pushed out a cool Ecom report on intent. (check it here, https://lnkd.in/gFcB-s7f) Whats in there are concepts, vocabulary, taxonomy that influences the last point of propensity data. Things like (from the top of the report): - 63% feel manipulated by ecommerce tactics (only 11% don’t) - 46% feel overwhelmed on ecommerce sites - 83% use discount codes when they would have bought at full price - 1 in 5 will stop shopping if they get an early pop-up The TRICK is to get this data accessible to the testing and engagement platform setup. Feature attribute data: > CDP defined User-level attributes: account tier, number of past upgrades purchased, engagement metrics (time on site, feature usage). > Session-level attributes: current time of day, day of the week, user’s device type, current navigation path or product page. > External attributes (optional): Geo-location, known seasonal promotions, pre-determined propensity model data All this sounds cool, but WHY/WHERE to apply this stuff? Here's my thinking: > Adaptive Learning: A dynamic personalization approach continuously updates the probability distribution of reward for (offer/product/promo) as new data is collected. Unlike a static A/B test, it doesn’t wait for a full experiment cycle to end before updating which offer to show next. (we don't care what wins, just push to what is working best now) > Context Utilization: This setup leverages user and environmental context (e.g., user account age, user’s current usage tier, time of week, location). This allows for personalized experiences rather than one-size-fits-all solutions. Add in explicit propensity and 'intent' data (h/t to David here) and you really get cooking. > Handling Concept Drift: If certain upgrades become more or less attractive over time, the testing/personalization algorithm automatically adjusts. This adaptability ensures that the system remains optimal in the face of changing market or user conditions. Yes this is where AI experimentation tools come into play, but the foundation of tooling and explicit data ontology (use case and model connections) needs to be there first. A personalization (also AB testing) recipe is only as good as it's ingredients. Bottom line? The right data, connected smartly, powers personalization that actually works—and keeps evolving. Want to dig deeper into David’s intent report, architect your own growth engineering setup, or just swap ideas on making this real for your team? I’m all ears—DM me or drop a comment below. Let’s cook up something impactful together!

Most brands drown in the process of personalizing too much. I recently worked with a brand that went super deep into this, making users create detailed customer profiles through their pop-up with specific interests. Their welcome series was completely segmented, if you clicked on "couches," you'd only see couch-related content throughout the entire sequence. Most people would think this hyper-personalized approach is “cutting edge”, and leave it alone. This left a TON of revenue on the table since it limited their brand discovery. After looking at the data, we tested a different approach right away. We featured best sellers of the brand, highlighting each of them with individual product categories underneath the existing segmentation. By keeping the personalized elements but introducing best-selling products across categories, we significantly lifted engagement and revenue metrics. It’s simple: - Customers don't always know your full product range - Limiting visibility to one category restricts discovery - Your best-sellers have proven market engagement regardless of initial interest - Site exploration leads to higher average order values The welcome series absolutely crushed it with this strategy. We also found that their original strategy worked better in the post-purchase flow. Customers are more inclined to accept other offers of the same category after they purchased a product, rather than getting bombarded with 100 different couches at the beginning. The key takeaway here is to test the balance between personalization and data. Testing will always be King. Don't always assume that extreme personalization is always the answer, sometimes a hybrid approach delivers the best results.

I talked to several DTC brands in the last 10 days that told me SMS marketing performance was significantly down this BFCM compared to last year... here is a fix guaranteed to increase performance and lower unsubscribes in 2025👇 Inject better customer data into your SMS platform 👉 Guided selling funnels SMS has become one of the most saturated marketing channels in just a matter of years in DTC eCommerce. I think its awesome — and I believe whole heartedly in the channel having spent years in SMS marketing. However, while brand and consumer of adoption of SMS has risen, true personalization has not. Enter guided selling funnels as the fix. Guided selling funnels, also known as "quizzes", are high converting eCommerce funnels that ask shoppers a series of questions about their interests and preferences and pair them with the with products for seamless check out. On average, they increase conversion by over 300%. Quiz funnels handle purchase objections, develops a customer relationship, and most importantly for SMS, gives the brand a VAST amount of zero party data to connect with their customer in future marketing experiences. It's quite literally "marketing funnel efficiency". 💡 In fact, a brand putting a first time shopper going through a Digioh experience can capture over 50 unique points of zero and first party data to use to send personalized texts. Any brand who is not pushing this into segmentation for their campaigns automations is missing out Here are a few examples of vanilla texts brands send 😒 Coffee brand: "Shop all coffees now" Athleisure brand: "Check out our best sellers" Men's Grooming Brand: "Shop grooming products" 🔑 And here's how it becomes highly relevant by using quiz funnels and pushing that data into SMS ☕️ A coffee brand learns a shopper likes dark roast, whole beans, and usually buys 5lb bags. They uses that data to say: "shop whole bean dark roast coffee". 🏀 An athleisure brand knows a shopper is shopping for their husband, prefers dry-fit clothing, and likes neutral colors. They can retarget the shopper with "Get your husband the all new performance wear". " 🪮A mens grooming brand knows a shopper likes sandalwood, has sensitive skin, and doesn't like multi-step routines. - They can then text the shopper with, "Try the all new sandalwood face wash for one step results". Instant meh to great — and this can all be done easily within any SMS platform. SMS lives and dies by right message right time and having better data ensures a message can actually connect. With how expensive it can be to acquire an SMS subscriber and then retarget them with campaigns... relevancy matters. SMS doesn't need to be vanilla when paired with zero party data! As someone who cut their teeth in sms marketing… I couldn’t be more pumped to help Digioh customers do this at scale within platforms like Klaviyo, Postscript, Yotpo, Attentive, Braze, etc... to make texts that consumers want in their inbox!