Using Data To Assess Ecommerce Market Viability

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

What happens when you align product performance with sessions, conversion rate, advertising spend, stock on hand and sell-through date? You stop guessing and start making commercial decisions with real clarity. The best merchandise planners and marketers already know this: no metric in isolation tells the full story. The strongest teams are combining traditional planning metrics with ecommerce performance data to understand not just what is happening, but why. For DTC brands, bringing these data points together turns a messy performance picture into a simple set of actions: 🔍 1. Decide what to advertise more When a product has strong conversion, healthy margins and enough stock to support demand, but low sessions, it’s usually a sign that it needs more visibility. This is the sweet spot for scaling paid spend: the product already proves it can sell — it just needs more traffic. 💸 2. Identify what to mark down If you’re holding too much stock and the sell-through date is creeping up, yet conversion is weak even with steady sessions, discounting becomes a strategic lever. Markdowns help clear inventory without wasting ad spend on products the customer clearly isn’t choosing at full price. ✋ 3. Know when to pull back advertising High ad spend + plenty of sessions but poor conversion = a red flag. This is where you pause or reduce spend, diagnose the issue (price, positioning, creative, customer reviews), and redirect budget to products with stronger unit economics. Sometimes the best ROI comes from simply stopping the leak. When metrics live in silos, teams argue. When metrics connect, teams act. This is how modern DTC brands protect margin, improve cash flow and scale the right products at the right time.

One of the hardest conversations I have with clients is telling them to kill a product. Nobody wants to hear it. Especially when they've invested $30K in inventory, $15K in development, and 6 months of their life. But keeping a losing product alive is the most expensive mistake in ecommerce. Here's the kill criteria we use at AMZ Optimized: Metric 1: Conversion rate below 50% of category average after 90 days of optimized content. If your listing has been professionally optimized — images, A+, copy, backend — and you're still converting at half the rate of competitors, the market is telling you something. The product doesn't fit. Metric 2: ACOS above break-even for 3+ consecutive months with no improvement trend. If you can't profitably drive traffic to the product despite optimization, the unit economics don't work at the price the market demands. Metric 3: Review velocity stuck below 2 per week after 6 months. If customers aren't reviewing, they're not excited about the product. Low review velocity signals weak product-market fit. Metric 4: Return rate 2x or higher than category average. The product has a fundamental issue — quality, expectation mismatch, or design — that optimization can't fix. If you hit 3 out of 4, the product is a drain on your business. A home goods brand we work with had a 12-product catalog. Profitable overall. But 3 products were chronic underperformers. Those 3 products: → Consumed 35% of total ad spend → Generated 11% of total revenue → Had return rates double the catalog average → Required disproportionate customer service time We killed all three. Result: Total revenue dropped 11%. Total profit increased 28%. The dead weight was subsidized by the winners. Removing it freed budget, attention, and inventory capital for products that actually worked. Holding onto a bad product is emotional, not rational. The math never lies. If the data says kill it, kill it. Redirect the resources to something the market actually wants.

Investors always talk about TAM (total addressable market). How do you guesstimate it for an e-commerce brand? Euro Monitor/Nielsen reports are too expensive and inaccessible. Yet, answering this question is crucial for every deck and also for internal teams to understand how much scale is possible for a product. Here’s a quick framework I've developed that should work for e-commerce: >Pick your category's top 10 products on Amazon. >These typically contribute ~50% of category revenue (based on my general assumption – you can take a different one if you like). >Multiply each product's review count by 30-40. (Industry data shows only 2.5-3% of customers leave reviews.) >Multiply the result by the product's selling price. Double the final number for total market size – assuming the top 10 products contribute 50%. >PS: Amazon now gives range estimates of product sales for high-selling SKUs. Let me break this down with a hypothetical example. Take the protein powder category: -Top product: 5,000 reviews, Avg price: ₹2,000 -Quick math: 5,000 x 35 x 2,000 = ₹35 Cr -Assume top 10 have similar reviews: ₹35 x 10 = ₹350 Cr -Market Size = ₹350 x 2 = ₹700 Cr Some additional pointers: -This should work across marketplaces with authentic reviews. -It is a guesstimation with assumptions – tweak them based on the category (e.g., top 10 product share or percentage of customers who review). -This assumes all reviews are from the past year – you could temper it by considering only half the reviews for the last year. I've seen founders spend months waiting for perfect data, losing valuable time to competition. But in the early stages, directional accuracy beats precision. I think this toolkit gives you enough available data to start and iterate. Thoughts? Have any other ways? Pro tip: Cross-reference this with Google Trends and keyword volumes. The intersection of these data points usually gives you a solid starting point. #business #startup #market #valuation #founder

I spent 6 months perfecting this one AI prompt for market research. It'll save you $10K in research fees and weeks of wasted time. [BEGIN PROMPT] I'm building [product] for [audience] to solve [problem]. Please analyze: MARKET SIZE AND GROWTH - Assess current market size and potential for growth - Evaluate key trends indicating increasing or declining demand - Provide evidence from search volumes, surveys, industry data - Include supporting evidence from Reddit, Amazon, and forums COMPETITIVE LANDSCAPE - Identify primary competitors with strengths and weaknesses - Highlight clear opportunities to differentiate meaningfully - Assess how sustainable my competitive advantage will be - Show what makes my solution truly different and better PRICING AND MARGINS - Suggest realistic pricing strategies and benchmarks - Analyze customer willingness to pay based on real data - Evaluate potential for recurring or expanded revenue - Find opportunities for complimentary products or upsells OPERATIONAL FEASIBILITY - Outline key resources needed to launch successfully - Evaluate unique operational challenges for this business - Identify opportunities or limitations in scaling efficiently - Assess talent, technology, and supplier requirements MARKETING STRATEGY - Identify most effective channels for my target market - Evaluate typical customer acquisition costs vs. lifetime value - Highlight tactics that will resonate with this audience - Provide specific marketing approaches with proof they work RISKS AND REGULATIONS - Outline significant market, operational, and regulatory risks - Identify potential barriers to entry or execution - Assess competitive threats and potential market shifts - Highlight any intellectual property or legal concerns LONG-TERM POTENTIAL - Analyze whether this idea has longevity beyond trends - Suggest realistic growth paths and potential expansions - Identify possible pivot options if needed - Evaluate long-term competitive landscape VALIDATION APPROACH - Recommend concrete next steps for testing this idea - Identify early warning signs that would indicate problems - Suggest minimum viable test approach before full investment - Outline key metrics that would indicate success - Provide a clear conclusion on overall viability with specific next steps. [END PROMPT] The secret isn't the prompt itself. It's how you use it: 1. NEVER one-shot this. I upload documents with detailed context first. 2. NEVER settle for the first draft. 3. ALWAYS update my documentation rather than trust AI memory. The only difference between exceptional results and mediocre ones is how well you direct it. But you need to know which questions to ask and how to interpret the answers. Come to my free AI masterclass and I'll show you how to leverage AI to start your business FAST - even if you're not a "techie": https://lnkd.in/g38q_WpV

📌📊 eCommerce Google Analytics 4 Dashboard I've recently created a Looker Studio Report for eCommerce brands. The main goal is to analyze website traffic performances using GA4 Data. Having a custom-built solution like this will give marketers an edge over their competition and understand performances from different traffic sources. ➡️ Where do your users come from? The dashboard breaks down traffic by channel, showing the distribution across direct, organic search, referral, and other sources. It also provides geographical data to understand your primary markets. ➡️ Which campaigns/traffic sources bring the most revenue? Revenue is broken down by channel, allowing you to compare the performance of different traffic sources over time. This helps identify which channels are most effective for driving sales. ➡️ What are the key performance indicators? The dashboard tracks crucial eCommerce KPIs including Total Revenue, Purchases, Conversion Rate, and Average Order Value. It also monitors user engagement metrics like sessions, bounce rate, and average session duration. ➡️ How does device type impact user behavior? Device type data shows the distribution of sessions across desktop, mobile, and tablet. This information can help optimize the user experience across different platforms. This level of insight helps marketers make informed decisions to drive better results for their advertising efforts. As a marketer, it has never been easier to manage your marketing data and turn it into actionable insights. ⚙️ Technical note: In this example, I've used Looker Studio native GA4 data connector to import the data 🔍 Demo Version: https://lnkd.in/e4YWQBGv #DataAnalytics #DataVisualization #BusinessIntelligence

Most eCom founders overcomplicate forecasting or avoid it entirely. Here's the simplest framework that actually works: You need 5 numbers: 1. Ad spend 2. CAC (customer acquisition cost) 3. AOV (average order value) 4. Total orders (new + returning) 5. Repeat purchase rate Once you have them, run this math: Ad spend ÷ CAC = new customer orders Last month's total orders × repeat rate = returning customer orders New + returning = total orders Total orders × AOV = gross revenue Real example: Month 1: - $50k ad spend - $50 CAC - 1,000 new customers - 1,000 returning customers (50% repeat rate from previous month) - 2,000 total orders × $100 AOV = $200k revenue Month 2 (scaling to $75k ad spend): - 1,500 new customers ($75k ÷ $50) - 1,000 returning customers (2,000 × 50%) - 2,500 total orders × $100 AOV = $250k revenue Month 3 (same $75k spend): - 1,500 new customers - 1,250 returning customers (2,500 × 50%) - 2,750 total orders × $100 AOV = $275k revenue Notice the compounding effect from repeat customers. This is why CAC and repeat rate matter more than ROAS. You can model your entire year in 20 minutes with these numbers. Pull the last 3 months of data, calculate your averages, then build forward month by month. All you need is a spreadsheet and these 5 metrics.

Early in my career, my CEO stopped by my office while reviewing some of my reporting and said, "Jeff, I don't understand why you have so many website sessions when you're spending almost nothing and we don't have an ecommerce site. Should we do something with these sessions?" That question changed my entire career trajectory. I was an analyst at the time; a numbers guy who lived in SQL and loved finding patterns in consumer data. Working as an analyst had its highs and lows, but it felt safe and predictable. But using the behavioral data, I could see the huge potential at Pier 1 Imports as it relates to digital transformation. We had thousands of people visiting our website with minimal marketing spend. They were browsing, engaging, spending time with our products online. The behavioral data showed clear purchase intent, but we had no way to capture it. We were essentially watching potential revenue walk away every single day. The numbers weren't just showing website traffic; they were revealing an entirely new customer journey that we weren't supporting. That's when I realized something: being an analyst wasn't just about reporting what happened: It was about translating data into strategic opportunities. The pivot from "numbers guy" to "digital strategist" wasn't about abandoning analytics but rather using those insights to shape business decisions. The CEO's question led us to build Pier 1's entire digital commerce platform from scratch. What started as curious behavioral metrics became a $500 million revenue channel that represented a third of the company's business. I learned that the best digital strategies aren't built on hunches or trends; they're built on what the data is actually telling us about customer behavior. The numbers guide the strategy, and the strategy amplifies the numbers. Sometimes the best career moves happen when we're paying attention to what the data is telling us. What signals are you seeing in your work right now? #DataDriven #DigitalStrategy #CareerPivot #Analytics #Ecommerce

I’m going to break down a classic framework here. Here’s how I look at estimating your total number of paying customers. If you have an e-commerce business you should pay attention. Learn how to sharpen these numbers to light the path to profitability and growth opportunities. Size up your total market potential. Zoom in on realistic demand. Converting interest into revenue. Market sizing - it's an integral part of the strategic conversation whenever we're assessing a business concept or mapping growth. I want to dig deeper into the classic breakdown - TAM, SAM, and SOM. These acronyms may seem abstract but really nailing down estimates guides decisions and sets expectations. First, TAM - the total addressable market. This bird's-eye view represents the absolute ceiling of potential customers. For a behemoth like Facebook, TAM encompasses essentially everyone on the planet. Less universal ideas have more contained TAMs, but it's still an expansive perspective. Then we have SAM - the serviceable addressable market. Here we narrow down to the segments we can readily capture based on geography, demographics, our business model and capabilities. SAM analysis spotlights untapped pockets within TAM to inform expansion decisions. Finally, SOM brings it to the bottom line - the serviceable obtainable market of likely buyers who'll actually convert. We size this through data analysis and projections of consumer behavior. Realistic SOM forecasting is critical for financial modeling and carving an achievable path to profitability. These market lenses enable some key strategic planning: First and foremost, gap analysis between SOM and higher levels grounds viability. A wide gulf exposes flawed assumptions or a non-starter idea. Secondly, SOM drives revenue planning through pricing optimization and customer LTV modeling based on purchase cycles. We expand transaction volume and wallet share over time. And thirdly, the distance between SOM, SAM and TAM informs go-to-market prioritization and growth capital allocation as we stair-step to scale. Of course, market estimation isn't an exact science. But refined TAM, SAM and SOM analysis, updated iteratively as data flows, provides essential guidance for startups through established players alike. I’d love to hear your thoughts and experiences leveraging sizing estimates to steer strategy. What challenges have you run into?