How to Analyze Consumer Behavior in E-Commerce

Certainly, while wishlists have emerged as a valuable tool for gauging consumer interest, there are several other methods and metrics that e-commerce platforms can use to measure consumer interest: 1. Cart Abandonment Rate: Observing how many customers add products to their carts but don't complete the purchase can provide insights into potential hesitations or barriers. 2. Product Views: The number of times a product is viewed can indicate its popularity or interest level. 3. Time Spent on Page: Monitoring the average time consumers spend on product pages can hint at their level of interest. 4. Product Reviews and Ratings: A high number of reviews or ratings, even if mixed, can signify strong interest or engagement with a product. 5. Search Query Analysis: Observing which products or categories users are searching for on the platform can indicate trending interests. 6. Social Media Engagement: Shares, likes, comments, and mentions related to products can provide insights into consumer preferences. 7. Referral Traffic: Analyzing traffic from external sites or social media can show where the interest is coming from and which products are driving it. 8. Customer Surveys and Feedback: Directly asking customers about their preferences or interests can yield detailed insights. 9. Sales Data: A straightforward metric, but analyzing which products are selling the most can clearly indicate consumer interest. 10. Click-Through Rate (CTR): Observing how often people click on a product after seeing it in a recommendation or advertisement can be a strong indicator. 11. User-Generated Content: If consumers are posting pictures, videos, or blogs about a product, it showcases genuine interest and engagement. 12. Repeat Purchases: Products that are frequently repurchased can indicate high levels of satisfaction and interest. 13. Customer Service Inquiries: The number and nature of questions related to a product can offer insights into areas of curiosity or concern. 14. Heatmaps: Tools that show where users most frequently click, move, or hover on a page can help in understanding which products or sections grab their attention. 15. Newsletter and Email Open Rates: If consumers are frequently opening emails about specific products or categories, it can be an indication of their interest areas. 16. Retargeting Campaign Success: The conversion rate of retargeting campaigns can provide insights into the residual interest of consumers after their initial interaction. By leveraging a combination of these methods, brands can gain a comprehensive understanding of consumer interest, helping them to tailor their offerings and marketing strategies more effectively. #ecommerce #LinkedInNewsIndia

🚨The greatest drop-off is from Product Details Page To Cart Page, so we must improve our Product Details Page! Not so fast ✋ In today's age of data obsession, almost every company has an analytics infrastructure that pumps out a tonne of numbers. But rarely do teams invest time, discipline & curiosity to interpret numbers meaningfully. I will illustrate with an example. Let's take a simple e-commerce funnel. Home Page ~ 100 users List Page ~ 90 users Product Display Page ~ 70 users Cart Page ~ 20 users Address Page ~ 15 users Payments Page ~12 users Order Confirmation Page ~ 9 users A team that just "looks" at data will immediately conclude that the drop-off is most steep between Product Details Page & Cart Page. As a consequence they will start putting in a lot of fire power into solving user problems on Product Display Page. But if the team were data "curious", would frame hypothesis such as "do certain types of users reach cart page more effectively than others?" and go on to look at users by purchase buckets, geography, category etc and look at the entire funnel end to end to observe patterns. In the above scenario, it's likely that the 20 cart users were power users whilst new & early purchasers don't make it to this stage. The reason could be poor recommendations on the list page or customers are only visiting the product display page to see a larger close up of the product. So how should one go about looking at data ? Do ✅ Start with an open & curious mind ✅ Start with hypothesis ✅ Identify metrics & counter metrics that will help prove/disprove hypothesis ✅ Identify the various dimensions that could influence behaviours - user type, geography, category, device type, gender, price point, day, time etc. The dimensions will be specific to your line of business. ✅ Check for data quality and consistency ✅ Look at upstream and downstream behaviour to see how the behaviour is influenced upstream and what happens to the behaviour downstream. ✅ Check for historical evidence of causality Dont ❌ Look at data to satisfy your bias ❌ Rush to conclude your interpretation ❌ Look at data in isolation - - - TLDR - Be curious. Not confirmed. #metrics #analytics #productmanagement #productmanager #productcraft #deepdiveswithdsk

Crowning a New Term: “Iceberg Metrics” 🧊 ✨ I’m calling it: Iceberg Metrics represent KPIs that only reveal the tip of what’s really happening below the surface. Metrics like abandoned carts seem simple but often mask much more—checkout friction, hidden costs, trust issues, and more. To truly understand and optimize, we need to dig deeper. Here’s how to dive into the “iceberg” of abandoned cart rates: 1. Establish Baseline Metrics: Start by gathering data on current abandoned cart rates, session times, and bounce rates using heat maps and session recordings to see where users drop off. 2. Segment the Audience: Analyze users by behavior (first-time vs. repeat visitors, mobile vs. desktop) and traffic source (organic, paid, email). 3. Experiment Hypotheses: Develop hypotheses for abandonment reasons—shipping costs, checkout friction, distractions, or lack of trust signals—and test them. 4. Run A/B Tests: Test variations like simplifying the checkout process, showing shipping costs earlier, adding trust badges, or retargeting abandoned cart emails. 5. Use Heat Maps & Session Recordings: Examine user behavior in real time. Look for confusion or hesitation, where users hover, and whether they engage with key information. 6. Contextualize Results: Analyze how changes impact overall user flow. Did simplifying checkout help, or did other metrics like bounce rate increase? 7. Ecosystem Approach: Examine how tweaks affect the full journey—from product discovery to checkout—balancing short-term improvements with long-term goals like lifetime value. 8. Iterate: Refine solutions based on experiment findings and continuously optimize the customer journey. This one’s mine, folks! #IcebergMetrics #OwnIt #DataDriven #EcommerceOptimization #NewMetricAlert Cheers, Your cross-legged CAC and CLV buddy 🤗

🛒 How Basket Analysis Can Drive eCommerce Growth: A Bangladeshi Scenario As eCommerce continues to grow rapidly in Bangladesh, businesses are dealing with more and more customer data. One of the most valuable and often overlooked ways to make sense of that data is through basket analysis. Whether you’re working at a platform like Daraz, Chaldal, Pickaboo, or even running your own online shop, basket analysis can help uncover what products people are buying together. These insights can help you make smarter decisions when it comes to marketing, product placement, bundling, and personalized offers. 🔍 What is Basket Analysis? Basket analysis (also known as market basket analysis) is a method used to find associations between products based on customer purchase history. For example: - What do people usually buy with rice? - Are customers who buy smartphones also buying covers or screen protectors? - Are snack items more popular during weekends? By identifying patterns like these, eCommerce platforms can: - Increase average order value - Run more effective cross-sell campaigns - Deliver personalized recommendations - Make better inventory decisions 🧺 Real-Life Example: A Case Based on Chaldal While analyzing data from Chaldal, one of Bangladesh’s largest online grocery platforms, we noticed something interesting. Many customers in areas like Dhanmondi and Mirpur were buying instant noodles and tomato ketchup together, especially during the evening. This pattern suggested a common need: quick dinner solutions, likely for students or working professionals. Based on this insight, we tested a few simple strategies: - Introduced a combo offer with noodles and ketchup - Showed both products in the “Frequently Bought Together” section - Ran targeted push notifications in the evening with a message like “Need a quick dinner? Grab our Noodles + Ketchup combo now!” The early results were promising: - Better product visibility - More engagement during evening hours - A small bump in basket size for repeat users We’re still monitoring the data, but it’s a great example of how even small insights can be turned into smart decisions. 💡 Final Thoughts You don’t need AI or complex tools to start using basket analysis. A simple SQL query or spreadsheet analysis can help you uncover product relationships that lead to real business value. #eCommerce #BasketAnalysis #DataAnalytics #DigitalBangladesh #CustomerInsights #BusinessGrowth #SQLforBusiness #OnlineGrocery #MarketingStrategy #StartupBangladesh

you're a pm at nykaa. repeat purchase rate dropped from 42% to 28% in the last quarter. what do you do? step 1: understand what "repeat purchase dropped" means → are customers buying once and never coming back? → or are loyal customers buying less frequently? → or are you acquiring a flood of low-intent new users who were never going to repeat? → repeat rate = users with 2+ orders / total users. a spike in one-time buyers tanks this number even if loyal customers didn't change. step 2: segment the drop → by cohort: did jan signups repeat less than oct signups? or is every cohort declining? → by category: is it makeup? skincare? haircare? if skincare held but makeup dropped, it's category-specific, not platform-wide. → by acquisition source: did users from instagram ads repeat less than organic users? → by first purchase: users whose first order was a Rs 200 lipstick vs. Rs 2000 skincare set have very different repeat behavior. step 3: check what changed recently → did you run a massive sale last quarter? heavy discounting attracts deal hunters who buy once at 60% off and never return at full price. your "growth" killed your repeat. → did you change the post-purchase email flow? → did subscription/auto-replenish options break or get removed? → did loyalty points expire or devalue? if 1000 points used to = Rs 100 off and now it's Rs 50 - repeat motivation drops. step 4: dig into the post-purchase journey → what happens after someone's first order? → check: do they get a personalized recommendation within 7 days? or silence until the next sale blast? → check: what % of first-time buyers opened the app again within 30 days? → check: of those who came back, what % added to cart but didn't purchase? price sensitivity or just browsing? → the gap between "came back to browse" and "came back to buy" is where the answer is. step 5: form a hypothesis example: if repeat purchase dropped specifically among users acquired during the big billion sale whose first order was under Rs 300 using a 70% off coupon → hypothesis: discount-led acquisition brought users who anchored to sale pricing. at full price, nykaa feels expensive compared to meesho or amazon beauty. → test: for first-time sale buyers, send a personalized "second purchase" offer within 14 days - not a flat discount but a bundle: "complete your skincare routine: cleanser + moisturizer at 20% off together." → measure: 60-day repeat rate for this cohort vs. control over 8 weeks. the answer is not to run another sale. the answer is understanding who you attracted and why they didn't stay. what would you fix first? #productmanagement #productsense #nykaa #d2c #retention #pminterview

I have spent years analyzing hundreds of eCommerce launches, and one pattern always emerges: Most purchase decisions are irrational, but predictable. Customers don't compare specs like a spreadsheet. They’re influenced by cognitive biases, i.e., mental shortcuts that steer attention, value perception, and urgency. Here are 9 biases I see shaping buying behavior every day: 1) Category Heuristics: → Customers focus on a few key specs to compare quickly. → Highlight top attributes to guide decisions instantly. 2) Power of Now: → Immediate offers drive faster action. → Delays reduce perceived value and urgency. 3) Social Proof: → Reviews and ratings boost trust. → Recommendations from others validate purchase decisions. 4) Scarcity Bias: → Limited availability creates urgency. → “Only a few left” nudges faster buying. 5) Authority Bias: → Expert endorsements reduce hesitation. → Recognizable brands or figures build instant credibility. 6) Power of Free: → Small freebies increase perceived value. → Free add-ons motivate purchase without extra cost. 7) Anchoring Bias: → First price sets the mental reference point. → Subsequent options feel more valuable or affordable. 8) Loss Aversion: → Fear of missing out drives immediate action. → People avoid losses faster than they seek gains. 9) Decoy Effect: → Middle option nudges buyers toward higher-margin choice. → Position options to shift perception without force. Here’s the truth: Cognitive biases allow you to design buying experiences that feel intuitive, effortless, and even inevitable. When applied to pricing, offers, bundles, and landing pages, these biases: → Increase conversion rates → Strengthen perceived value → Accelerate buying decisions → Reduce hesitation and cart abandonment Next time your conversion lags or launches underperform, Ask: Are you designing the experience, or leaving it to chance? Save & share this to help others in your network. Follow Asim Khaliq for more applied growth strategies.

To improve ecommerce product performance: don’t ignore customer reviews. Most brands do next-to-nothing with this valuable feedback. Yet, they are a goldmine because: 1. Customer reviews are generally more honest than surveys. 2. Which means the information in these reviews can effectively inform improvements for headlines, testimonials, content, or even sales pitches. At Enavi we utilize this information through our Human-Obsessed approach, based on the following set of questions: Identifying Pain Points 1 - What issues were customers trying to solve with the product? 2 - Is there a common thread that led users to shop for the products? Recognizing Recurring Features: 3 - Which aspects of the product are repeatedly mentioned, positively or negatively? 4 - How does that compare to what we “thought” was important for users? Noticing Benefits: 5 - Are there any benefits in the customer reviews that we didn’t consider previously? Identifying Outcomes: 6 - Which specific outcomes have customers highlighted? Acknowledging Concerns: 7 - Were there any hesitations before the purchase? Use Cases: 8 - What frequent uses or applications of the product are mentioned? 9 - Do the use cases align with what is mentioned in the product description and key messages? 10 - Could these reviews be harnessed for testimonials? By following this 10-step process, we've effectively enhanced product-specific conversion rates and overall performance. Why does it work so well? Because review mining with a Human-Obsessed focus isn’t just about making adjustments. It’s about building better products and growing your business. Where data ends, human insight begins.

What sets you apart from ALL the other products in your category?? It’s more than just your product. It’s about understanding your customer better than anyone else. With Enavi’s Customer Canvas, we don’t rely on assumptions.  We help you build detailed, human-first insights into: → What truly motivates your customers: Are they driven by convenience, prestige, or solving a problem? → What’s causing friction in their decision-making process: Is it confusion around pricing, uncertainty about product benefits, or trust issues with your brand? → How to create a seamless customer journey across every touchpoint: Are there gaps in their journey? Do they struggle with navigation, or lose momentum before checkout? We’re driving improvements across your entire business — from marketing to product development. How? We’re not just tracking clicks. We’re uncovering their motivations. Understanding their pain points. Delivering insights that transform every aspect of your marketing strategy. Not just your on-site experience. Be honest… do you REALLY know: — Motivations: What brings customers to your store?  Is it emotional, practical, or social factors that drive them? — Anxieties: Where are they getting frustrated or confused?  Why do they hesitate before making a purchase? — Behavioural Triggers: What’s the final nudge that pushes them to buy?  Is it a discount, a sense of urgency, or something else entirely? My guess is no. To gather this depth of insight, we use qualitative research tools like: 1. Post-Purchase surveys: Asking questions like: “What made you choose this product?” “What almost made you leave without purchasing?” 2. Customer interviews: Delving into their decision-making process with open-ended questions like: “When did you realise you needed this product?” “What would make you feel 100% confident in your purchase?” 3. Review mining: We analyse what customers are already saying, the praises and complaints. We use these to identify recurring themes in their desires and frustrations. 4. Support ticket analysis: We look at common complaints and issues that arise in customer support.  These often reveal hidden blockers in the customer journey that might not be obvious from the data alone. 5. Competitor benchmarking: What are your competitors doing right or wrong? And how can we leverage that insight to give you a competitive edge? And here’s what makes this approach so powerful: the Customer Canvas is not a static report. It’s a living, breathing document that evolves as your business — and your customers — change. Every update, every new product launch, every marketing campaign feeds into this evolving understanding of who your customers are and how best to serve them. Now, ask yourself: Are your current CRO tactics producing the real results you deserve? If not, it’s time to try something different. The Enavi Human Obsessed CRO is your answer.

Here’s how a customer we work withincreased ROAS 99% with a data-led approach And how you can do the same for your brand by cutting fluff & focusing on the metrics that move the needle. These are the exact 5 steps they used: ↳ Track the right metrics They used PenPath’s Purchase Intent Rate (PIR) dashboard as a guiding metric. Instead of relying solely on ROAS or CVR, they analyzed customer buying signals: - Adding to cart - Begin Checkout - Site searches - Email signups ↳ Clean up campaign data Set up clean campaign naming conventions to make data analysis easy & actionable. Specifically making things segmented by prospecting, retargeting, and by product category. ↳ Optimize by funnel stage Measured PIR by source, medium, and campaign to understand baselines for each stage of the funnel to measure interest for each traffic source and by product categories. ↳ Focus on what’s working For TOF effort with high PIR, they scaled or kept them even when ROAS was not performing and cut the rest. For BOF, they cut any campaign with low ROAS or PIR. This is an over simplification but that was the general approach. ↳ Scale high-intent audiences Lastly, they used purchase intent data to created improved retargeting audiences on Google and Meta. The Results? ✅️ ROAS skyrocketed from 1.35x to 2.69x (+99.555) in three months ✅️ Ad spend increased by 243% --- with no wasted dollars Pro Tip: Map your customer journey with intent-driven metrics. Focus on actions that align with each stage of your funnel (TOF, MOF, BOF) to uncover where customers drop off—and where to double down on winning strategies. If you’re an ecommerce decision maker, what data have you used to scale ROAS as quickly as possible? #Dataanalysis #Ecommercetips #Adspend #Ecommercesolutions

I've spent 10 years figuring out how to predict repeat customer purchases online. Here’s how to do it right and get to 95%+ accuracy: If you want to understand your repeat customers and predict their behavior, it all starts with cohort analysis. This sounds fancy, but it’s just grouping customers based on the date they made their first purchase. From there, you can build a clear picture of what’s happening in your business. Here’s the step-by-step process: 1. Assign customers to cohorts. Start by grouping customers by the month (or week, depending on your volume) of their first purchase. This will be the starting point for tracking retention and repeat purchase behavior. 2. Establish a baseline retention curve. Most customer behavior follows a predictable pattern: orders gradually taper off over time. Plot this out to create a baseline curve—a starting point to measure future cohorts against. 3. Weight for recent behavior. Here’s the thing: the customers you acquired last month are much more relevant to forecasting than the ones you acquired three years ago. Weight your analysis to focus on recent cohorts to get a more accurate picture of what’s next. 4. Segment by customer type. Not all customers behave the same way. You might notice early customers were all over the place—some subscribing, some buying once. Breaking this down by type (e.g., subscribers vs. one-time buyers) makes the data a lot more actionable. 5. Adjust for seasonality. Timing matters. A customer you acquire in October is probably going to shop again in November because… Black Friday. That doesn’t mean they’re inherently “better,” but you need to account for these factors when predicting future behavior. 6. Predict orders, not people. Instead of predicting how many customers will come back, focus on the total number of orders a cohort will generate. Then multiply that by your average order value to get to revenue. Trying to count subscribers, then adjust for churn, reschedules, or payment failure will create lots of inputs to manage and ultimately leads to precision without accuracy. 7. Keep it fresh. The most accurate forecasts come from constantly updating your data. Monthly refreshes are usually the sweet spot—they let you capture new trends without bogging you down with constant updates. Sounds like a lot of work? It doesn’t have to be. Drivepoint does all of this out of the box. Want to see how it works? We can ingest your Shopify and Amazon data into actionable retention and revenue forecasts and show you the results. Link in the comments to book time if you want to learn more. 🚀 #CohortAnalysis #Forecasting #Shopify #Amazon #DTC