Using Data To Inform Ecommerce Brand Strategy

🌐 Behind Every Click is a Story I Let the Data Tell It. 📊✨ In a world where e-commerce brands pour thousands into campaigns and still struggle with cart abandonment, product returns, and low retention, the real question isn’t “What happened?” , it’s “Why did it happen?” and “How do we fix it?” 🔎 That’s where data comes in. 📈 And this is where Power BI becomes more than just a dashboard, it becomes a lens for clarity. Over the past few weeks, I built a full-scale, interactive e-commerce performance dashboard, touching every point from marketing campaigns to customer satisfaction. The goal? Make sense of the chaos. Turn complexity into simplicity. Drive action. 🧠 Here’s What I Discovered: ✅ Marketing Channels Instagram drove the most engagement, but Email had the best ROI. Billboard Ads, though expensive, performed poorly — proof that visibility ≠ value. ✅ Cart Abandonment Patterns Over 15% of carts were abandoned. The biggest culprit? Cash on Delivery (COD) users. Fashion orders also had the highest failure and return rates — a clear sign to revisit fulfillment strategies. ✅ Customer Insights That Matter Females aged 35–44 were power buyers across categories Credit Card and PayPal users had smoother journeys. ✅ Returns & Dissatisfaction Top reasons for returns: 📦 “Item Not As Described” 💔 “Arrived Damaged” These aren’t just logistics issues — they’re missed chances to improve product listings and supply chain quality. 🚀 What This Dashboard Achieved: Instead of just dropping charts, I focused on building a narrative: 📌 A story of behavioral trends 📌 A story of missed revenue opportunities 📌 A story that guides business decisions with confidence Power BI didn’t just help me visualize — it helped me strategize. 💡 Final Takeaway Your data is always talking. But without the right tools and the right mindset, it just looks like noise. 📣 This project reminded me why I love data analysis — not just for the numbers, but for the stories they unlock and the decisions they inspire. Let’s connect if you’re building something cool in the analytics space — I’m always open to swapping insights and perspectives. Thanks to Jude Raji for your Help #Datafam #PowerBI #EcommerceAnalytics #MarketingROI #CustomerExperience #DataStorytelling #BusinessIntelligence #DashboardDesign #DataDrivenDecisions #DataStrategy #DataVIZ

Let's consider a real-world example of how connecting KPIs can lead to valuable insights and informed decision-making: Imagine you're managing an e-commerce business, and you're keen to boost sales. You have several KPIs, including: 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐑𝐚𝐭𝐞 (𝐂𝐑): The percentage of website visitors who make a purchase. 𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐎𝐫𝐝𝐞𝐫 𝐕𝐚𝐥𝐮𝐞 (𝐀𝐎𝐕): The average amount spent by a customer in a single order. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐂𝐨𝐬𝐭 (𝐂𝐀𝐂): The cost of acquiring a new customer. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐋𝐢𝐟𝐞𝐭𝐢𝐦𝐞 𝐕𝐚𝐥𝐮𝐞 (𝐂𝐋𝐕): The predicted revenue a customer will generate during their relationship with your business. Here's how you might relate these KPIs: 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: You notice a positive correlation between CR and AOV. As the average order value increases, the conversion rate also goes up. This suggests that strategies aimed at increasing AOV, like offering bundled products or discounts for higher cart values, could lead to improved conversion rates. 𝐂𝐨𝐡𝐨𝐫𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: You group customers by their acquisition channel and analyze their behavior over time. You find that customers acquired through social media have a higher CLV compared to those acquired through paid search. This insight allows you to allocate more resources to social media marketing. 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠: You compare your AOV to competitors in the same niche. If your AOV is significantly lower, it might indicate an opportunity to increase prices or implement cross-selling and upselling strategies. 𝐂𝐚𝐮𝐬𝐞-𝐚𝐧𝐝-𝐄𝐟𝐟𝐞𝐜𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: You discover that a spike in CAC is associated with a drop in CLV. Upon investigation, you realize that a recent advertising campaign increased acquisition costs without proportionally increasing customer value. You decide to optimize your marketing strategy to maintain a healthy balance. 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: You create scenarios to test the impact of different strategies on your KPIs. For instance, you simulate the results of offering free shipping for orders above a certain value. This could lead to higher AOV and potentially increased CR, but it will also affect CAC and, in turn, CLV. By connecting these KPIs and analyzing their relationships, you gain a comprehensive view of your e-commerce performance. This empowers you to make data-driven decisions to optimize your sales strategy, allocate resources effectively, and ultimately grow your business. Remember, the key is not just to collect KPIs but to understand how they influence one another and how you can leverage this knowledge to drive business success

Using Data to Drive Strategy: To lead with confidence and achieve sustainable growth, businesses must lean into data-driven decision-making. When harnessed correctly, data illuminates what’s working, uncovers untapped opportunities, and de-risks strategic choices. But using data to drive strategy isn’t about collecting every data point — it’s about asking the right questions and translating insights into action. Here’s how to make informed decisions using data as your strategic compass. 1. Start with Strategic Questions, Not Just Data: Too many teams gather data without a clear purpose. Flip the script. Begin with your business goals: What are we trying to achieve? What’s blocking growth? What do we need to understand to move forward? Align your data efforts around key decisions, not the other way around. 2. Define the Right KPIs: Key Performance Indicators (KPIs) should reflect both your objectives and your customer's journey. Well-defined KPIs serve as the dashboard for strategic navigation, ensuring you're not just busy but moving in the right direction. 3. Bring Together the Right Data Sources Strategic insights often live at the intersection of multiple data sets: Website analytics reveal user behavior. CRM data shows pipeline health and customer trends. Social listening exposes brand sentiment. Financial data validates profitability and ROI. Connecting these sources creates a full-funnel view that supports smarter, cross-functional decision-making. 4. Use Data to Pressure-Test Assumptions Even seasoned leaders can fall into the trap of confirmation bias. Let data challenge your assumptions. Think a campaign is performing? Dive into attribution metrics. Believe one channel drives more qualified leads? A/B test it. Feel your product positioning is clear? Review bounce rates and session times. Letting data “speak truth to power” leads to more objective, resilient strategies. 5. Visualize and Socialize Insights Data only becomes powerful when it drives alignment. Use dashboards, heatmaps, and story-driven visuals to communicate insights clearly and inspire action. Make data accessible across departments so strategy becomes a shared mission, not a siloed exercise. 6. Balance Data with Human Judgment Data informs. Leaders decide. While metrics provide clarity, real-world experience, context, and intuition still matter. Use data to sharpen instincts, not replace them. The best strategic decisions blend insight with empathy, analytics with agility. 7. Build a Culture of Curiosity Making data-driven decisions isn’t a one-time event — it’s a mindset. Encourage teams to ask questions, test hypotheses, and treat failure as learning. When curiosity is rewarded and insight is valued, strategy becomes dynamic and future-forward. Informed decisions aren't just more accurate — they’re more powerful. By embedding data into the fabric of your strategy, you empower your organization to move faster, think smarter, and grow with greater confidence.

If you’re an e-commerce brand hitting 5x ROAS every day but you don’t know your LTV, you’re not growing. That’s the uncomfortable truth most e-commerce brands ignore. You’re scaling ad spend, ROAS looks good, sales are ticking up… but if you don’t know how much a customer is worth to you over time, you’re flying blind. Because your LTV isn’t just a vanity metric, it’s the foundation of your growth strategy. You can easily calculate your LTV. If your average order value is $60, your returning customer rate is 25%, and your repeat purchase gap is 4 months, you already have the data to estimate your LTV: LTV = (Average order value × Purchase frequency × Retention rate) Now here’s where most brands mess up: They stop at ROAS. They brag about 3x or 4x returns, without realizing they’re just re-targeting the same old customers and that’s not growth, but recycling. When you actually know your LTV, you can back-calculate your nCAC, and make better and informed decisions: → How much can you spend to acquire a new customer? → When you break even. → How fast you can scale profitably. Because growth starts when you know how many new customers you can afford to acquire every month. So if you’re an e-commerce brand doing decent volume, start here: → Pull your 6-month data, AOV, repeat rate, and time gap between orders. → Calculate your LTV using that formula. → Compare it to your CAC. If your LTV:CAC ratio is under 3:1, you’re leaving money on the table. Don’t just chase ROAS. Know your numbers, know your limits, and scale with intention. So, what is your LTV? #ecommercegrowth #digitalmarketing #mediabuying #founderinsights

Drowning in dashboards? You're not alone. Ecommerce teams usually aren't short on data. What's missing is a clear picture of what that data actually means. In other words, knowing what KIND of data you're sitting on. That's what drives better targeting and scalable growth. I've worked with dozens of ecommerce teams who were data-rich but insight-poor. But once we broke the data down into four clear types, performance started compounding. Here's how each type works and how they fit together: 1️⃣ First-party data ↳ The backbone of lifecycle marketing - Behavior you observe directly - site activity, purchases, email engagement. - Most accurate, privacy-compliant and foundational for retention. - Works for abandoned cart flows, custom segments, triggered emails. 2️⃣ Zero-party data ↳ Gold for personalization - Info customers intentionally share (quizzes, surveys, preference centers). - Reveals intent and helps tailor experiences. - Works for dynamic product recs, personalized SMS, on-site experiences. 3️⃣ Second-party data ↳ An underutilized growth lever - Trusted data shared from partners, like list swaps or co-marketing insights. - Adds reach without sacrificing context or quality. - Works for cross-promos, joint launches, collaborative campaigns. 4️⃣ Third-party data ↳ A fading legacy tactic - Aggregated info from data brokers (usually cookie-based). - Broad but increasingly limited in precision and shelf-life. - Works for paid ads (while they still work). When you know the data types,  You stop guessing and start layering. Layer them well (and connect customer identity across them), and you'll unlock high-quality personalization. That's when performance starts to compound. Where are you in this process currently? ♻️ Share this to help someone who's swimming in data but seeing no results. Follow me, Francesco Gatti, for more ecommerce data insights.

If I were a brand prioritizing New-to-Brand growth, here’s where I’d start. You can’t scale without continually bringing new shoppers into your ecosystem. Retention matters, but acquisition is what keeps the brand moving forward. And with around 57% of consumers beginning their product searches on Amazon, it’s a powerful place to reach new audiences right at the point of intent. That’s where AMC’s NTB data really proves its value. When you take advantage of the full suite of AMC, you can see exactly which campaigns and ASINs are driving first-time buyers, and use that knowledge to refine your strategy, retarget more effectively, and build long-term growth. Here are four ways I’d recommend using NTB data to drive meaningful impact for your brand: 1. Evaluate ASIN-level performance with context. Compare NTB and repurchase rates side by side to see which products attract first-time shoppers and which ones keep them loyal. If a product shows early NTB success (especially a higher-priced item), that’s your signal to double down and scale visibility. 2. Spot seasonality and peak buying periods. ASIN-level NTB data over a 1yr+ lookback window can reveal hidden trends showing what time of year your product drives the strongest NTB acquisition. Use this data to plan your campaigns around key shopping windows and seasons, so you’re meeting fresh audiences right when their intent is highest. 3. Layer NTB with Path-to-Purchase insights. By combining NTB data with your AMC Path-to-Purchase metrics, you can see exactly which combinations of ad types are turning browsers into first-time buyers and fine-tune your full-funnel strategy to mirror that success. You may find that the secret to turning your Sponsored Products campaigns into conversion machines is reaching those shoppers with a DSP impression earlier in their journey. 4. Differentiate strategies across your catalog. Every ASIN plays a different role in attracting and retaining customers. Use NTB and repurchase data to tailor strategies by product line. Hero products may focus on acquisition, while higher pack-size items are the key to driving retention and loyalty. Our Product Manager, Kadence L., said it best: “NTB data gives me a seasonal, ASIN-level view of what’s driving new-to-brand shoppers, helping me understand not just where growth is coming from, but how to strategically attract and convert new customers. For any ad manager, that insight is critical to building long-term brand momentum.” The impact? One of our clients, Greenworks, saw a 12.3% increase in new-to-brand customers during Prime Day using AMC. By pinpointing which top-of-funnel campaigns were driving the strongest new customer growth, they were able to optimize budget allocation and invest where it mattered most.

4 stats that shifted my view on ecommerce, brand building, and retail expansion: 1. Ecom is under 16% of retail sales 2. Apparel is the largest ecom category at 18% of retail sales 3. The top 10 ecom companies (Amazon, Walmart, eBay, etc) control 60% of the market 4. Selling only on Shopify means your marketing/ad spend accesses just 6.4% of total purchase opportunities (15.9% x 40% = 6.36%). For a long time, I didn't know these stats. I assumed e-commerce was a much larger share of total retail sales than it really is. I opposed channel expansion, thinking Shopify provided all the scale we needed Boy, was I wrong Side note: I also thought retail expansion was blasphemous due to loss of transaction control and brand ownership...wrong there too This belief severely limited our growth, profitability, and the revenue impact of our brand-building efforts. Learning the actual data changed our strategic views on channel expansion... ** What I learned ** Staying Shopify-only makes it harder to reach new audiences who would love our brand and product, but simply don't shop on brand sites, or online at all (in your category) ** How this ties to Brand ** Even the strongest brand can't fully monetize if only available in 6.4% of purchase opportunities ** Action to take ** If you're looking to combat increasing acquisition costs and are trying to rationalize brand investments, effective channel expansion (effective being the operative term that will comprise another bunch of posts) is a strategic option to consider. ** How my playbook would be different if I were to do it again ** I wish I knew this info far earlier, and if I were to do it again, I'd massively increase brand investments early on to create inbound from retail buyers to have the optionality and leverage to enter new wholesale/retail channels in the best way possible. ** Caveats ** 1. % of total retail sales from younger, more affluent buyers likely skews more toward ecom, but I could not find that number. I still wouldn't throw out the above data because of this assumption 2. Huge businesses can still be built solely on ecom—there are many examples 3. Your Shopify store is vital for testing products, gaining early revenue, and driving loyalty. I'm sure there are other details I'm missing, but hopefully we can put that aside and focus on the main takeaways: 1. Multichannel (particularly brick n mortar retail, but AMZ too) expansion will open up incremental TAM (and potentially at similar or accretive margin). 2. And, for all the brand builders out there looking to see their brand investments drive the maximum revenue & profit increases, your brand dollars will go so much further if you have a broad, high-quality retail presence. Don't make the same mistake I did Don't unnecessarily limit your growth potential Build a strong brand lots of people love, then put your product in more places Let's go!

Forget conversion rate and platform ROAS. After analyzing 4,000+ ecommerce brands, I found the 3 metrics that predict success better than anything else. 1. Brand Power This is a combination of direct traffic + organic traffic + branded search. Pro tip: To calculate this in Polar, you can create a custom metric with this advanced formula in 3 clicks. It proves your compounding marketing value. If people are actively seeking out your brand (vs. stumbling on it through ads), you're building a real asset. It’s a number you should see increase as your brand grows. 2. CAC by Product Most brands look at CAC as a single number. But tracking it by product reveals hidden opportunities. You can map the spend based on the landing page and the campaign name to figure out your hero product — the one that drives a significant portion of sales, customer acquisition, and brand awareness. Think of it like this: A beauty brand discovered their $10 moisturizer had a $2 CAC while their expensive products were much costlier to convert. That "low AOV" moisturizer became their best acquisition tool. 3. 180-Day LTV by Product Track the 6-month value of customers based on their first purchase to figure out which products create loyal customers. Sometimes your best entry point isn't your highest AOV product. It might be a cheaper product that will get people to come back. The problem with most platforms is they make these metrics hard to track. That's why we built them directly into Polar Analytics - just a few clicks and you can see exactly which products are driving your business forward. I’m curious: What other unconventional metrics do you track?

Amazon just released a guide for the Customer Loyalty Dashboard. Summary is below but and the entire guide is attached. Overview The Customer Loyalty Analytics Dashboard is a tool available in Amazon’s Seller Central under the Brand Analytics tab. It provides insights into customer shopping behaviors, helping brands increase customer lifetime value (CLV) through data-driven engagement strategies. Key Benefits • Increase Customer Lifetime Value: Loyal customers (top 10%) spend 3x more per order than others. A second-time shopper has a 45% chance of buying again. • Customer Retention vs. Acquisition: A 5% increase in retention can boost profits by 60%. • Optimized Marketing & Ad Spend: Target the right customers at the right time to improve engagement and return on investment (ROI). • Reduction in Customer Acquisition Cost: Engage customers who already show interest in your brand. Dashboard Features Customer Segmentation Customers are categorized into four loyalty segments: • Top Tier: Frequent buyers who spend the most. • Promising: Occasional buyers with above-average spending. • At-Risk: Customers who haven't bought recently. • Hibernating: Inactive customers with infrequent purchases. Two Dashboard Views: • Brand View: Overall customer segmentation, sales trends, and targeted promotions. • Segment View: In-depth data on each customer segment, including repeat purchase trends and predicted lifetime value. Brand Tailored Promotions: • New Audiences Feature: Identifies customers whose spending is expected to decline. • Cart Abandoners Audience: Re-engages shoppers who left items in their cart. Metrics Available: • Total Sales • Average Sales per Customer • Total Orders • Repeat Customers & Orders • Repeat Purchase Rate & Interval How It Works • Segmentation is based on RFM (Recency, Frequency, Monetary Value) analysis. • Machine Learning Predicts Future CLV: Uses customer history, purchase behavior, Prime status, reviews, and browsing activity. • Actionable Insights:  - Identify and engage high-value customers.  - Target at-risk customers before they stop buying.  - Personalize promotions based on customer segments. Eligibility • Available to registered brands in North America, Europe, and Japan. • Must be an internal brand owner with Brand Analytics access. How to Access Navigate to Seller Central > Brand Analytics > Customer Loyalty Analytics