Understanding Ecommerce KPIs

🚨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

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES to eliminate bias and gather statistically reliable results — with useful templates and resources. By Roman Videnov. Measuring UX is mostly about showing cause and effect. Of course, management wants to do more of what has already worked — and it typically wants to see ROI > 5%. But the return is more than just increased revenue. It’s also reduced costs, expenses and mitigated risk. And UX is an incredibly affordable yet impactful way to achieve it. Good design decisions are intentional. They aren’t guesses or personal preferences. They are deliberate and measurable. Over the last years, I’ve been setting ups design KPIs in teams to inform and guide design decisions (fully explained in videos → https://measure-ux.com). Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < Xs (for critical tasks) 3. Time to first success < 90s (for onboarding) 4. Time to candidates < 120s (nav + filtering in eCommerce) 5. Time to top candidate < 120s (for feature comparison) 6. Time to hit the limit of a free tier < 7d (for upgrades) 7. Presets/templates usage > 80% per user (to boost efficiency) 8. Filters used per session > 5 per user (quality of filtering) 9. Feature adoption rate > 30% (usage of a new feature per user) 10. Feature retention rate > 40% (after 90 days) 11. Time to pricing quote < 2 weeks (for B2B systems) 12. Application processing time < 2 weeks (online banking) 13. Default settings correction < 10% (quality of defaults) 14. Relevance of top 100 search requests > 80% (for top 5 results) 15. Service desk inquiries < 35/week (poor design → more inquiries) 16. Form input accuracy ≈ 100% (user input in forms) 17. Frequency of errors < 3/visit (mistaps, double-clicks) 18. Password recovery frequency < 5% per user (for auth) 19. Fake email addresses < 5% (newsletters) 20. Helpdesk follow-up rate < 4% (quality of service desk replies) 21. “Turn-around” score < 1 week (frustrated users -> happy users) 22. Environmental impact < 0.3g/page request (sustainability) 23. Frustration score < 10% (AUS + SUS/SUPR-Q) 24. System Usability Scale > 75 (usability) 25. Accessible Usability Scale (AUS) > 75 (accessibility) 26. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 design KPIs that reflect the impact of their work. Search team works with search quality score, onboarding team works with time to success, authentication team works with password recovery rate. What gets measured, gets better. And it gives you the data you need to monitor and visualize the impact of your design work. Once it becomes a second nature of your process, not only will you have an easier time for getting buy-in, but also build enough trust to boost UX in a company with low UX maturity. [Useful tools in comments ↓]

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 🤗

On the 10 year journey to Chubbies’ IPO, the realization that changed how we invest marketing resources was this --> Increasing ROAS * decreased * our growth. btw, I was the world’s largest ROAS (AKA Return on Ad Spend) fanboy for embarrassingly too long, but hey, my loss is your gain, so here's: 1. Three counterintuitive things I learned about ROAS 2. Two new ways to think about it 3. Three things you can do about this right now let's do it. ** Three counterintuitive things I learned about ROAS ** 1. “ROAS has been presented as a growth metric, when it’s actually anything but. In fact, ROAS is precision-engineered to keep brands small,” says Tom Roach. Chasing ROAS chases easy sales, not growth. Brand growth comes from light buyers, but focusing on high ROAS can lead to you targeting heavy buyers, therefore limiting growth. 2. ROAS is not actually a measure of *effectiveness* but how *efficiently* you achieved it. As Les Binet says: “Effectiveness first, efficiency second.” 3. Simply put, ROAS is the opposite of incrementality. ** Two new ways to think about it ** 1. It's like hiring an employee to stand just inside the entrance of your shop and tap shoppers on the back as they enter. A week later, the employee demand a raise, claiming credit for all the customers they’ve “enticed” to come in. 2. Imagine a soccer coach believing their forward is entirely responsible for every goal. As a result, in their infinite wisdom, they ditch their defense and midfield, only keeping their center forward. They end up losing every future game, but their “Goals Per Player” (the ROAS of this example) is higher than ever! ** Three things you can do about it right now ** 1. Vanity VS Value: Understand the negative externalities of the metrics we goal our teams on. For example, because many of us are seeing headwinds, brands either cut marketing spend or increase the ‘accountability’ of the dollars spent. The negative externality is that we're over-harvesting our existing customers in order to hit our numbers. ROAS and revenue from returning customers may be up (vanity metrics), but contribution dollars, share of search, and new customer revenue from unpaid sources (real business metrics) are likely down. 2. Party & Ponder: Spend half a day with your team and deeply consider the metrics you want to optimize your team’s efforts around in 2024. The whole team needs to take ownership of the metrics that matter AND have a deep understanding of the negative externalities of vanity metrics like ROAS. This is a super high-leverage use of time 3. Cultivate Creativity Completely (the 3C's of winning): Since marketing works by influencing future buyers, think about developing creative that gets noticed and gets remembered. Give your team permission to be bold, put on a show and have a little fun. As John Dawes of the Ehrenberg-Bass Institute says, “The brand that gets remembered is the brand that gets bought." Enjoy

Most eCommerce brands obsess over revenue and ROAS. But the real game is in the metrics no one talks about. Here are 10 overlooked KPIs that actually drive growth (and how to optimize them): ~~ 1. LTV:CAC Ratio (The Ultimate Health Check) LTV:CAC = Customer Lifetime Value ÷ Customer Acquisition Cost 1:1 = You’re bleeding money 3:1 = Healthy 5:1+ = Printing cash If you’re below 3:1, either: ✅ Lower CAC (better targeting, UGC ads, referrals) ✅ Increase LTV (subscriptions, upsells, memberships) == 2. 90-Day Repurchase Rate If a customer doesn’t buy again within 90 days, they probably won’t. Fix it by: • Winback campaigns with targeted incentives • Selling bundles that create habits • Building a loyalty program that rewards repeat buyers == 3. Contribution Margin (What’s Actually Left?) CM = Revenue – (COGS + Shipping + Discounts + Ad Spend) If your CM is under 30%, you’re scaling a business that won’t survive. Get margins up by: • Cutting discount dependency • Negotiating lower fulfillment costs • Adding Onward shipping protection == 4. Subscription Churn Rate (The Silent Killer) High churn = your brand is a leaky bucket Fix it by: • Adding pause & skip options via SMS (Skio for example) • Add more delivery options and product variety • Sending an email 7 days before renewal reminding them potential lost perks == 5. Time to Second Purchase (T2P) Track how long it takes for a customer to place their second order—then cut that time in half. Tactics to speed it up: • AI-based Email/SMS flows with hyper-targeted recommendations • Exclusive discounts for second-time buyers • Reorder reminders based on average usage time == 6. Gross Margin per Order (The Scaling Checkpoint) At scale, 40%+ gross margins keep you profitable. If you're below that: • Increase prices (test 10% bumps) • Reduce discounting, do Cashback instead (@ Onward) • Negotiate better supplier terms (carrier rates, 3pl, etc) == 7. Refund & Return Rate A high return rate = a CAC multiplier. Fix it by: • Charging for returns (but offering free exchanges) • Clearer product descriptions & sizing charts • Post-purchase emails on how to use the product == 8. Organic vs. Paid Revenue Ratio If 60%+ of your sales come from paid ads, you’re in trouble. Brands with real staying power win on organic channels. The fix? • SEO & content marketing • Affiliate & referral programs • Retention tactics (VIP, loyalty, subscriptions) == 8. SKU Concentration Risk If 80%+ of your revenue comes from one product, you’re vulnerable. Great brands expand without overextending. Turn one-time buyers into multi-SKU customers with: • Bundles • Exclusive add-ons • Subscription perks == 9. % of Revenue from Returning Customers A healthy DTC brand makes 40%+ of revenue from repeat buyers. If you’re below that, focus on LTV levers: • VIP memberships • Personalized email/SMS offers • Post-purchase nurture flows Follow Josh Payne for deep dives on DTC, SaaS, and investing.

🚀 Building a Dashboard KPI with Advanced Technologies: Insights from Our Journey at AQe Digital (Formerly AQe Group) & Ace Infoway Pvt. Ltd. 🚀 In today's fast-paced digital landscape, the demand for real-time data insights and actionable intelligence has never been more critical. Organizations across the globe rely on KPIs to monitor progress, evaluate success, and recalibrate strategies. At AQe Digital & Ace Infoway Ltd, we embarked on a journey to develop a next-gen Dashboard KPI system that integrates KPI Control Towers, automation, predictive and prescriptive analytics, and AI. In this article, I dive into our approach, the technologies we used, challenges we faced, and the impactful takeaways that have helped us redefine traditional dashboards and set a new benchmark in data-driven decision-making. Here’s a sneak peek into what the article covers: 1️⃣ Vision Behind the KPI Dashboard – Moving from traditional metrics to intelligent, actionable insights. 2️⃣ Key Technologies & Architecture – A powerful technology stack for real-time data processing and advanced visualization. 3️⃣ KPI Control Towers – Centralized data and intelligent monitoring with real-time insights. 4️⃣ Automation – Enhancing efficiency and reducing manual tasks. 5️⃣ Predictive & Prescriptive Analytics – Enabling proactive, data-driven decision-making. 6️⃣ AI-Driven Insights – Adding intelligence with NLP and anomaly detection. 🔗 Dive into the full article to see how we built this transformative platform and how it’s helping clients unlock the full potential of their data for strategic decision-making. If you’re interested in building a similar solution or exploring how advanced technologies can boost your KPIs, feel free to connect. Let’s discuss how we can take your business intelligence to the next level! Amit Mehta Nirav Oza, PMP, Hitesh V., Dr. Anavaratham PM , Jay Vaishnav Priyanka Wadhwani, Cheta Pandya, Jayvardhan Malviya , Abhilash Koshti, Vidhi Dhrangadhariya, Nupur Patel #KPI #Dashboard #DataAnalytics #AI #PredictiveAnalytics #PrescriptiveAnalytics #Automation #AQeDigital #AceInfoway #DataDriven #BusinessIntelligence #Innovation

Many FP&A teams have forecast accuracy as a KPI so let's check out six ways to improve your forecast accuracy... First, let's highlight the six ways and share some insights into each of them: ✅ Data quality ✅ Rolling forecasts ✅ Advanced analytics ✅ Scenario planning ✅ Collaboration ✅ Monitoring ---------- 1️⃣ Data quality Establish robust data management processes to collect, cleanse, and validate financial and operational data. Implement systems and tools that enable efficient data integration and analysis. Regularly review and improve data collection methods. 2️⃣ Rolling forecasts Instead of creating forecasts once a year, update them regularly throughout the year to incorporate the latest information. Rolling forecasts provide more agility and enable the FP&A team to react quickly to market dynamics and internal shifts. 3️⃣ Advanced analytics Utilize statistical methods, trend analysis, regression analysis, and predictive modeling to identify patterns and forecast future outcomes. Incorporate external factors, industry trends, and macroeconomic indicators into the forecasting models. 4️⃣ Scenario planning Develop scenario planning capabilities to forecast multiple potential outcomes. Create multiple scenarios, such as best-case or worst-case. Assess the impact of different scenarios on financial performance and evaluate risk mitigation strategies. 5️⃣ Collaboration Collaborate with business units, department heads, and other key stakeholders to gather input and insights for the forecasting process. Collaborative input enhances the accuracy of the forecast by incorporating diverse perspectives and domain expertise. 6️⃣ Monitoring Monitor actual performance against forecasts and conduct variance analysis. Identify and analyze the root causes of deviations between forecasted and actual results. Regularly review and update forecasts based on the insights gained from variance analysis. ---------- Personally, I don't think forecast accuracy is a goal in itself. But we should indeed measure it, understand the variances, and improve our models. This exercise will be highly insightful, however, let's not penalize people for not hitting a number we know will be wrong at the time of forecasting. Do you agree? What are other techniques to use to improve forecast accuracy? #finance #cfo #accountingandaccountants #careers ---------- 🎧 Listen to our #FinanceMaster Podcast here: https://bit.ly/3NLSt73 📰 Sign up for our newsletter here: https://bit.ly/TrendsInFnA 🧑🎓 Learn how we can help your finance team here: https://bit.ly/3prsWXH 🤝 Book a discovery call with me here: https://lnkd.in/eJWAub9r

Want to use machine learning for time series forecasting? The best models will identify the drivers of trends. I once worked with a KPI like the image below. My ML model identified a serious problem. First, let's establish a working definition of "trend" when it comes to time series forecasting: The tendency of the KPI to increase/decrease over time. Like the image above, my real-world KPI exhibited a strong upward trend. Additionally, as shown in the image above, the trend was linear (i.e., a straight line). Finance loved this KPI because it could be easily forecasted with high accuracy. Executives loved this KPI because it kept going up and up. I didn't like it all. The problem was that traditional forecasting techniques rely only on the historical KPI values. These forecasting techniques may implement additional calculations (e.g., moving averages) to enhance accuracy. However, these calculations are based solely on historical KPI values. So, it's no wonder that Finance was able to easily forecast the KPI. However, I wanted to know what the drivers of the KPI were. Enter machine learning forecasting models. Machine learning forecasting models can not only use historical KPI values, but can also include any other data that might impact KPI values: Month of the year Day of the week Economic data Promotions Weather Etc. In the case of my KPI, I was examining activities originating from the marketing team (e.g., promotions and digital ads). That's when my ML forecasting model uncovered a serious problem. The ML model identified that the primary external driver of KPI values was the marketing team's digital advertising spend. I dove into the data and found that digital ad spend increased over the same time period as the KPI. However, the digital ad spend was increasing at a higher rate. The digital ads were experiencing diminishing returns. We were burning budget to prop up the KPI. That's the power of ML forecasting models. BTW - Millions of professionals now have access to the tools to craft powerful ML forecasting models. Python in Excel is included with M365 subscriptions and provides access to libraries such as scikit-learn and statsmodels. Everything you need to go far beyond Microsoft Excel's forecast worksheet.

What if your hospital could predict a crisis… before it happens? Here’s how one mid-sized hospital turned used our predictive analytics model in their system. 📍Background: A 200 bed multi specialty hospital in Tier 2 India was constantly under pressure. Stockouts of critical medicines Sudden patient surges with no staff planning Equipment lying idle in one department while another faced shortages Finance team always firefighting Revenue was falling. Patient care was inconsistent. Staff was burning out. They implemented a Predictive Analytics System linked to: Patient admission history OPD trends Seasonal disease patterns Staff rosters Inventory data Billing + discharge cycles Within 3 months, the dashboard could show: 1) Which departments will have a spike next week 2) Which medicine stocks will run out in 10 days 3) How long each patient stays, on average, for each treatment 4) Where staffing gaps will occur in coming shifts 5) Where revenue leakages were happening due to idle assets The Impact: - Improvement in inventory efficiency - 31% drop in emergency stock orders - Higher staff availability during peak hours - Reduced patient wait time by 26% - Cost savings of ₹1.8 crore/year Predictive Analytics helps hospital leaders move from reactive mode to proactive control. It’s how hospitals stop surviving and start scaling. Whether you're managing a single unit or a hospital chain, Start by asking: "What patterns am I missing in my daily operations?" Because in healthcare, even a 1% smarter decision can save a life. Agree? #HealthcareInnovation #Predictiveanalytics #Hospital #tech