Understanding Ecommerce Analytics Tools

🚨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

At this stage, I believe most businesses are using metrics of some sort. So the biggest problems with metrics today is not that they are not used, it's that the wrong ones are used. Or there are just too many. Companies are often unaware they are using the wrong metrics. This usually happens when they are either copying what others are doing because it sounds like something they "should" be doing, or they lack clarity about what's really important to their growth. The other problem I mentioned was the use of too many metrics. It's really not necessary to measure everything! Collecting and analyzing huge amounts of data can create decision paralysis and make it difficult to focus on what really matters. Instead of helping, it can slow down decision-making. There IS a simple solution. It starts with focusing on identifying areas that matter most to your growth. 1️⃣ Begin by defining your top business goals. Ask, "What do we want to achieve?" Whether it’s increasing customer retention or improving operational efficiency, your metrics should directly support these goals. 2️⃣ Avoid overload by choosing only 3–5 core metrics that are critical to your goals. For example, track Net Promoter Score for customer satisfaction, or Cycle Time for operational efficiency. 3️⃣ Implement tools to automate the tracking of these metrics, so you can easily monitor progress without manually crunching numbers. This saves time and ensures real-time data. 4️⃣ Set up a routine to review the data—weekly or monthly. Look for trends and areas of improvement, and adjust your actions based on the insights gained. 5️⃣ Make sure your team understands the importance of these metrics and how they can contribute to improving them. This helps ensure accountability and alignment across the organization. Do you have any tips for effective metric management? What works in your organization? Leave your comments below 🙏 #measurewhatmatters #metrics #leadership #datamanagement #continuousimprovement

Meta says purchases are up 50%. Shopify says they're up 7%. Somebody's lying, and it's probably not Shopify. Just reviewed a client's dashboards that perfectly capture the attribution crisis plaguing performance marketing. Meta Ads Manager:  → 50% increase in purchases → ROAS improving to 2.43x → Spend up across all accounts → "Winning" campaigns everywhere Shopify Analytics (same period): → 7% increase in actual orders   → Revenue growth flat → AOV unchanged → Real business impact: minimal The uncomfortable truth? We're celebrating fake growth. This isn't about iOS changes or cookie deprecation. It's about platforms optimizing for credit, not results. When you run multiple accounts, retargeting campaigns, and cross-platform efforts, attribution becomes a hall of mirrors. Every platform claims victory for the same conversion. The fix isn't better attribution models. It's incrementality testing: → Geographic holdouts (run ads in some regions, not others) → Customer surveys asking "how did you actually find us?" → Marketing mix modeling that accounts for organic growth → Focus on net new customer acquisition, not total conversions I've seen brands "optimize" themselves into bankruptcy while their dashboards showed green arrows everywhere. Real performance marketing means measuring what matters: incremental revenue, not platform-reported conversions. The best campaigns often look terrible in ad dashboards because they're creating demand, not just harvesting credit. How big is the gap between your platform metrics and actual business growth?

You don’t need more dashboards. You need a proper routine health check. Most e-commerce teams are already overwhelmed by numbers. Revenue, traffic, ROAS, CR… dashboards show them all – but rarely show what actually matters. A real 🩺 health check is different. It looks at the whole system: – Are profits growing with revenue? – Are repeat customers buying faster or slower? – Is CAC payback still acceptable? – Which “stable” metrics are hiding dangerous shifts? We built a one-page checklist that turns this into a routine: + Core metrics you must review + Driver breakdowns for CR, AOV, LTV, Margin + Segmentation lenses to catch blind spots + Red flag indicators when numbers contradict each other + A simple framework to turn anomalies into testable hypotheses Run it monthly. Run it after every major campaign. And you’ll catch the leaks before they turn into losses. Save this and share with your team.

Attribution has never been perfect, but for DTC brands, it has become significantly harder in the past few years. Apple’s iOS14 updates, third-party cookie deprecation, and increased privacy regulations have disrupted traditional attribution models. Brands that once relied on last-click attribution, ad platform reporting, or rule-based LTV calculations now face major blind spots in understanding which marketing efforts drive long-term value. Even those investing in first-party data strategies, post-purchase surveys, and media mix modeling (MMM) struggle to fully connect the dots. The reality is that data is still fragmented across multiple platforms such as Shopify, Klaviyo, Google Analytics, ad networks, and third-party analytics tools. Most solutions focus on aggregating data, but aggregation alone doesn’t tell the full story of how customers move through the funnel and what actually drives retention. Rob Markey - In his article, "Are You Undervaluing Your Customers?" published in the Harvard Business Review, Markey emphasizes the significance of measuring and managing the value of a company's customer base. He advocates for creating systems that prioritize customer relationships to drive sustainable growth. Chip Bell - Recognized as a pioneer in customer journey mapping, Bell has contributed significantly to the field of customer experience. In an interview titled "The father of customer journey mapping, Chip Bell, talks driving innovation through customer partnership," he discusses how organizations can co-create with customers to drive innovation and enhance the customer journey. So how do brands solve this? 1. Shift from static LTV models to predictive insights - Traditional LTV calculations are backward-looking, often based on averages that don’t account for future behavior. Predictive analytics, using real-time behavioral and transactional data, can provide a more accurate forecast of customer lifetime value at an individual level. 2. Invest in first-party data strategies that go beyond acquisition - Many brands have adapted to privacy changes by collecting more first-party data, but few are fully leveraging it. Loyalty programs, surveys, and on-site behavioral tracking can provide valuable insights into retention and repeat purchase drivers, helping brands reallocate spend more effectively. 3. Adopt AI-driven segmentation and customer equity scoring - RFM segmentation and standard cohort analysis have limitations. AI-powered models can help identify high-value customers earlier in their lifecycle, predict churn risk, and optimize acquisition based on true long-term value, not just early spend. Markey and Bell have long emphasized that customer loyalty isn’t built on transactions alone, it’s about the entire journey. Brands that can better understand and predict customer value will be the ones that thrive in a world where third-party tracking is no longer a reliable option. #CustomerJourney #Attribution #CustomerEquity

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.

A client came to us frustrated. They had thousands of website visitors per day, yet their sales were flat. No matter how much they spent on ads or SEO, the revenue just wasn’t growing. The problem? Traffic isn’t the goal - conversions are. After diving into their analytics, we found several hidden conversion killers: A complicated checkout process – Too many steps and unnecessary fields were causing visitors to abandon their carts. Lack of trust signals – Customer reviews missing on cart page, unclear shipping and return policies, and missing security badges made potential buyers hesitate. Slow site speeds – A few-second delay was enough to make mobile users bounce before even seeing a product page. Weak calls to action – Generic "Buy Now" buttons weren’t compelling enough to drive action. Instead of just driving more traffic, we optimized their Conversion Rate Optimization (CRO) strategy: ✔ Simplified the checkout process - fewer clicks, faster transactions. ✔ Improved customer testimonials and trust badges for credibility. ✔ Improved page load speeds, cutting bounce rates by 30%. ✔ Revamped CTAs with urgency and clear value propositions. The result? A 28% increase in sales - without spending a dollar more on traffic. More visitors don’t mean more revenue. Better user experience and conversion-focused strategies do. Does your ecommerce site have a traffic problem - or a conversion problem? #EcommerceGrowth #CRO #DigitalMarketing #ConversionOptimization #WebsiteOptimization #AbsoluteWeb

Marketers are selling themselves short if they rely on pixel attribution alone for CTV. For one recent CTV campaign, we worked with our client’s CRM analytics partner, Fueled, to match users who were served ad impressions against those that had converted on the website. The point was to see how many purchases could be tied back to CTV impressions, so as to not solely rely on pixel based DSP reporting as the source of truth. Over the course of 30 days, the campaign recorded 2,482 attributed unique hompepage visitors via pixel tracking, but 8,777 verified visitors through CRM analysis...nearly a 4x difference! At checkout completion, pixels logged 109 conversions, while CRM-verified data identified 1,252 actual purchasers. That means over 90% of real sales were never credited in pixel-based attribution! Why the gap? Because CTV introduces a fundamental shift in how attribution works. People see an ad on a connected TV but complete their purchase later on a different device, their phone, tablet, or laptop. Pixels were originally designed to measure direct, same-device activity against which both the impression and conversion occurred. While most platforms now use cross-device graphs to bridge that gap, those graphs rely on probabilistic modeling and partial identifiers. Their accuracy is often overstated, and they can’t compensate for the scale of signal loss we’re seeing today. Compounding this are modern privacy dynamics: browsers like Brave and Firefox block tracking scripts, iOS strips campaign parameters off URLs, and many users exit before a “thank you” page fires a conversion event. Each of these weakens the connection between ad exposure and the eventual sale. As James Borow recently said "pixels are for targeting, not measurement". That’s why Conversion APIs (CAPIs) have become critical. Instead of depending on browser-side events, CAPIs send verified conversion data directly from the advertiser’s server to the media platform’s server, bypassing browsers entirely. Each transaction is transmitted with hashed identifiers, email, phone, or customer ID, enabling privacy-safe reconciliation between ad impressions and downstream purchases. Platforms like Meta, Shopify, Google, and The Trade Desk now treat CAPIs as the backbone of modern attribution. For CTV in particular, where conversions don’t happen on the same device, server-to-server data exchange restores visibility and gives marketers a true view of how their media performs across screens. Big thanks to Fueled and founder Sean Larkin for partnering with us on this initiative, and exciting to see Fueled’s new CAPI integration with The Trade Desk rolling out this week.

📌 eCommerce Dashboard KPIs Cheat Sheet (Save this for future reference!) Building a dashboard with the following metrics will give you a 360 overview of your eCommerce performances. 👉 Understanding and tracking these KPIs will put you ahead of your competition and drive more growth. 1️⃣ Acquisition Metrics Acquisition metrics help you understand how effectively you're attracting potential customers to your eCommerce site. It includes: ► Cost Per Acquisition (CPA): The average cost to acquire a single customer ► Traffic Sources: A breakdown of where your site visitors are coming from (e.g., organic search, paid ads, social media, direct). ► New vs. Returning Visitors: The ratio of first-time visitors to those who have visited before, indicating your ability to attract new customers and retain existing ones. ► Click-Through Rate (CTR) for Ads: The percentage of people who click on your ad after seeing it ► Cost Per Click (CPC): The amount you pay each time someone clicks on your ad, crucial for managing ad spend. 2️⃣ Engagement Metrics Engagement metrics show how well visitors are interacting with your eCommerce site and marketing efforts. It includes: ► Average Time on Site: The average duration of a session on your site. ► Pages per Session: The average number of pages viewed during a session. ► Bounce Rate: The percentage of visitors who leave your site after viewing only one page, potentially indicating issues with site relevance or user experience. ►Social Media Engagement Rate: The level of interaction (likes, comments, shares) your social media posts receive, relative to your follower count. 3️⃣ Conversion Metrics Conversion metrics indicate how effectively you're turning visitors into customers on your eCommerce platform. It includes: ►Conversion Rate: The percentage of visitors who complete a desired action (usually a purchase). ► Checkout Abandonment Rate: Similar to cart abandonment, but specifically for users who begin the checkout process but don't complete it. ► Average Order Value (AOV): The average amount spent each time a customer places an order. ► Revenue per Visitor: The average revenue generated per site visitor, combining conversion rate and average order value. 4️⃣ Retention & Growth Metrics These metrics demonstrate how well you're retaining customers and encouraging repeat purchases. ► Customer Lifetime Value (CLV): The total revenue you can expect from a single customer account throughout their relationship with your business. ► Repeat Purchase Rate: The percentage of customers who make more than one purchase, indicating customer loyalty. ► Customer Retention Rate: The percentage of customers you retain over a given period, crucial for sustainable growth. Remember: while these metrics are crucial, it's important to align them with your specific business goals and customer journey. Which KPIs do you like to track? Share your insights below! 👇 #DataVisualization #DataAnalytics #BusinessIntelligence

I've worked with more than 750+ eComm brands on their data connection between Shopify and Meta/Facebook. There's tons of problems I've found, but these are the top 5 data & tracking issues brands have (and don't even realize). ❌ Landing Pages drop tracking code - there's a ton of excellent third-party landing page platforms out there. Most people don't realize that they drop tracking code and lead to data gaps. (You need custom code that properly passes tracking code from the Landing page to the Shopify Checkout) ❌ Click data missing from Checkout - lots of customers need multiple web sessions to go from ad click to product purchases. Most people don't realize this leads to dropped click data and purchases that look like direct traffic (but should be attributed to ad clicks). (You need code that matches sessions and stitches the data together to ensure click data is included with all Purchase events, when available) ❌ Over-counting from non-web orders - A lot of brands have Shop orders, subscription renewals, and offline/draft orders get processed through the Shopify checkout. A basic CAPI connection will send Purchase events for these orders, which leads to misattribution and over-counting. (You need code that is smart enough to see the order source and re-route non-web orders to separate events) ❌ Light payloads with low EMQ - Most brands and most developers don't realize just how much data you can send in any given payload. If your data payloads are missing external id, FBP, and phone info, it leads to low EMQ scores and limits the performance of your ads. (You need an advanced CAPI connection that sends the upper limit of all data, ensuring maximum data coverage) ❌ Data volume too low - Many brands fail to hit the minimum volume of 50 conversions per ad set per week. Under this threshold, Meta simply isn't getting enough data to exit the learning phase and will optimize to clicks instead of conversions. (You need to either increase spend or consolidate your campaigns to ensure you have 50+ weekly conversions) --- If you're using the free/native Shopify CAPI connection, you likely have 3 or more of these issues. Even brands using paid CAPI solutions usually have 1 or more of these issues. If you need help assessing and/or fixing your data and tracking setup, comment below or shoot me a DM.