Retail KPI Tracking

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES, UEQ 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. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < 60s (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 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 > 80% (usage of a new feature per user) 10. Time to pricing quote < 2 weeks (for B2B systems) 11. Application processing time < 2 weeks (online banking) 12. Default settings correction < 10% (quality of defaults) 13. Search results quality > 80% (for top 100 most popular queries) 14. Service desk inquiries < 35/week (poor design → more inquiries) 15. Form input accuracy ≈ 100% (user input in forms) 16. Time to final price < 45s (for eCommerce) 17. Password recovery frequency < 5% per user (for auth) 18. Fake email frequency < 2% (for email newsletters) 19. First contact resolution < 85% (quality of service desk replies) 20. “Turn-around” score < 1 week (frustrated users → happy users) 21. Environmental impact < 0.3g/page request (sustainability) 22. Frustration score < 5% (AUS + SUS/SUPR-Q + Lighthouse) 23. System Usability Scale > 75 (overall usability) 24. Accessible Usability Scale (AUS) > 75 (accessibility) 25. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 local design KPIs that reflects the impact of their work, and 3–4 global design KPIs mapped against touchpoints in a customer journey. 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. [more in the comments ↓] #ux #metrics

What factors affect the CAC(Customer Acquisition Cost) and should be incorporated in its calculation? Many digital-only brands include just their media spends in the CAC computation. However, CAC varies a lot based on the following factors. 1. Discounts New user discounts play an important role in the first-time user conversion rate. So, media CAC goes down whenever we introduce a new user discount. Hence, adding the new user discount to the CAC(Customer Acquisition Cost) calculation is crucial to measure channel efficiency correctly. 2. Seasonality Seasonality increases the demand and also improves the conversion rate. So, the campaign tends to perform better during the high season weeks. And it goes up right after the event as the demand drops. Averaging the CAC over this period is essential for channel sanity. Tip: Never claim the credits for the CAC reduction leading the seasonal event. You will be in trouble. 3. Brand campaign boost Brand campaigns almost always improve the performance of campaigns CAC. It could be due to more brand-aware people coming to your audience pool or additional exposure to the audience closer to the purchase. It always does. Give credit where it is due. After a successful brand campaign, there will also be a baseline shift in the CAC. Make sure you are inculcating it in your scale-up plan. Of course, when there is genuine improvement in the CAC due to campaign optimization and creative, take the credit. These days, you get them less often when you are operating at scale.

Are You Spending Too Much to Acquire a Customer, Or Not Enough? E-commerce brands often focus on lowering their customer acquisition costs (CAC). But what if cutting CAC is actually hurting growth? The real question isn’t just how much does it cost to acquire a customer? It’s how much should you be spending? If you knew with certainty that a customer would generate $500 in long-term profit, would you hesitate to spend $100 to acquire them? Probably not. But many brands take a one-size-fits-all approach, capping CAC at an arbitrary percentage of their first purchase revenue. This can lead to underinvestment in acquiring high-value customers and overinvestment in customers who won’t stick around. A better approach is to align CAC with long-term customer equity, not just at a blended level, but dynamically across customer segments. Some customers have significantly greater revenue potential than others. The challenge is identifying which customers will create sustainable profitability over time. The chart illustrates that customer acquisition cost (CAC) and lifetime value (LTV) are not linear, spending more on acquisition can lead to higher-value customers, but only up to a certain point. Key Insights: There is an optimal CAC range. - Spending too little on CAC (left side of the chart) may result in acquiring lower-value customers, limiting long-term profitability. - Spending too much (right side of the chart) can lead to diminishing returns, where LTV does not justify the extra spend.   The breakeven threshold matters. - The red dashed line represents where CAC = LTV, meaning any spend above this line is unprofitable unless justified by strategic goals (e.g., market share growth). Smarter spending, not just lower spending, drives profitability. - Many brands mistakenly focus only on reducing CAC, but the real goal is to align CAC with future LTV dynamically across customer segments. What This Means for Retailers Instead of asking, “How much does it cost to acquire a customer?”, the real question is: - How much should we spend to acquire the right customers? - How long will it take to break even on acquisition costs? - Which acquisition channels and products lead to the highest-value customers? Retailers who leverage AI-driven insights to align CAC with future Customer Equity, not just at a blended level but dynamically across customer segments, can spend smarter, scale faster, and drive long-term profitability. If you want to go deeper on this topic, Professor Peter Fader has done extensive research on customer-centric growth strategies. Check out this fascinating podcast with Nick Hague on how businesses can take a more data-driven approach to optimizing CAC. https://lnkd.in/eGu5EM5g #CustomerAcquisition #EcommerceGrowth #MarketingStrategy #CustomerEquity #GrowthMarketing #CACvsLTV #RetailStrategy #Profitability #WGBTpodcast

Dashboards should deliver the insights to the right people at the right time. Here is how to build a dashboard for different stakeholders. 1. 𝗧𝗼𝗽 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀: Strategic, Big Picture     When designing dashboards for executives, think high-altitude view. They don’t need to know the granular details. They want clear, strategic insights that drive business decisions. Here’s what to focus on: • 𝗞𝗣𝗜𝘀, 𝗡𝗼𝘁 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Top managers care about key performance indicators connected to business goals (revenue, profit, market share). Focus on the top 3-5 metrics that have a significant impact on them.    • 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆: Avoid cluttered boards and use clean, intuitive visuals like summary cards or high-level bar charts.    • 𝗧𝗿𝗲𝗻𝗱𝘀 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝘀: They are interested in the big picture of past trends and future forecasts.    • 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Provide clear takeaways and recommendations. They want to know what steps to take next. 2. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗨𝘀𝗲𝗿𝘀: Actionable, In-Depth Insights     For operational teams, dashboards need to dig deeper. They’re in the weeds, and they need tools that help them drive tactical decisions and track day-to-day performance. • 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗨𝗽𝗱𝗮𝘁𝗲𝘀: Ensure your dashboard pulls from live data sources so operational teams can act quickly.    • 𝗗𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: They need to see everything like sales numbers, inventory levels, and customer response times. Drill-down options are required.    • 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Build custom filters to let users explore data by region, product line, or department.    • 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝗙𝗼𝗰𝘂𝘀: Highlight bottlenecks or inefficiencies in real-time so they can act fast.   How do you adjust your dashboards for different stakeholders? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #dashboard #stakeholder #careergrowht

Power BI for Sales Performance Analysis Boosting Sales with Power BI: A Real-Life Success Story   Scenario: Challenge: Our sales team struggled with tracking performance metrics across different regions and product lines. The data was scattered across various sources, making it difficult to get a unified view.   Solution: We implemented Power BI to consolidate sales data from CRM, ERP, and other systems into a single, interactive dashboard.   Steps: 1. Data Integration:    Used Power BI's built-in connectors to pull data from multiple sources.   Example Query:     let         SalesData = Sql.Database("ServerName", "DatabaseName", [Query="SELECT * FROM Sales"])     in         SalesData     2. Data Modeling:   Created relationships between tables to allow for comprehensive analysis.   Example: Linked sales data with regional data to analyze performance by region.   3. Interactive Dashboards:   Designed dashboards to track key metrics like total sales, sales growth, and regional performance.   Features: Drill-down capabilities, slicers for filtering by date, product, and region.   Impact: Improved Visibility: Sales managers now have a clear, real-time view of performance metrics. Faster Decisions: Quick access to data enabled faster decision-making and strategy adjustments. Increased Sales: Identified high-performing regions and focused efforts on underperforming areas, resulting in a 15% sales increase.     Include screenshots of the Power BI dashboard, before-and-after performance metrics, and user testimonials. Have you used Power BI to transform your sales performance? Share your story in the comments!   #PowerBI #Sales #DataVisualization #BusinessIntelligence #TechInnovation #DataDriven

“Dashboards are dead”? Only the context-free ones. Most teams start with definitions. They write a KPI dictionary, argue about formulas, then stack charts. Start with relationships. Map what drives what. Use metric maps and driver trees to sketch causality. ↳ Then define formulas. ↳ Then design screens. ↳ Then pick visuals. Here’s the 4-layer model we use: 1) Maps & Drivers: – metrics maps – driver trees 2) Definitions: – cohorts – formulas – granularity – attribution model – validation checks 3) Information Architecture – filters – page flow – drill paths – segments – comparisons 4) Visuals & UX – chart patterns – color semantics – legends & labels – responsive layout – conditional formatting Why this order? Because “what moved?” is useless without “why.” Common traps this avoids: ✕ Glossary-first thinking. Clean formulas ≠ causal logic. ✕ Chart sprawl. More graphs ≠ more clarity. ✕ Mixed levels. Result, diagnostic, actionable in one pot. If your dashboard doesn’t explain change, it’s reporting, not analytics. Build the logic first. Then display it. #dashboards

𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐊𝐏𝐈 𝐓𝐫𝐚𝐩: 𝐀𝐫𝐞 𝐘𝐨𝐮 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 𝐖𝐡𝐚𝐭 𝐓𝐫𝐮𝐥𝐲 𝐌𝐚𝐭𝐭𝐞𝐫𝐬? In the world of dashboards and data, it's easy to fall for the illusion of performance. We celebrate spikes in impressions, footfalls, or likes—without asking the harder question: “𝑰𝒔 𝒕𝒉𝒊𝒔 𝒎𝒆𝒕𝒓𝒊𝒄 𝒎𝒐𝒗𝒊𝒏𝒈 𝒕𝒉𝒆 𝒏𝒆𝒆𝒅𝒍𝒆 𝒇𝒐𝒓 𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔?” Welcome to the Vanity Metrics Trap—where numbers look good but don’t guide decisions. In my work across industries, I’ve seen teams obsess over what’s easy to measure rather than what’s essential to monitor. 🎯 So, how do we cut through the noise? I use a simple 3-layer framework to define Impact KPIs: 1️⃣ Objective-Centric – Does this KPI directly align with a strategic goal? 2️⃣ Actionable – Can the team act on this metric to change the outcome? 3️⃣ Outcome-Oriented – Is this tied to revenue, retention, efficiency, or experience? ✅ Examples of real Impact KPIs (by function): 💲 Sales: Instead of leads generated, track Lead-to-Close Conversion Rate ⚒️ Operations: Don’t just monitor machine uptime, track Downtime Impact on Fulfillment SLAs 🥖 F&B / Retail: Move beyond footfalls—focus on Spend per Transaction or Menu Item Profitability 🛃 Customer Experience: Rather than CSAT alone, track Repeat Purchase Rate or Churn-to-Recovery Ratio The goal isn’t to ignore surface metrics—but to trace them to the bottom line. 💬 Your Turn: 𝑾𝒉𝒂𝒕’𝒔 𝒐𝒏𝒆 𝑲𝑷𝑰 𝒚𝒐𝒖𝒓 𝒕𝒆𝒂𝒎 𝒓𝒆𝒑𝒐𝒓𝒕𝒔 𝒕𝒉𝒂𝒕 𝒔𝒐𝒖𝒏𝒅𝒔 𝒈𝒐𝒐𝒅—𝒃𝒖𝒕 𝒅𝒐𝒆𝒔𝒏’𝒕 𝒓𝒆𝒂𝒍𝒍𝒚 𝒅𝒓𝒊𝒗𝒆 𝒗𝒂𝒍𝒖𝒆? Or, what’s one under-the-radar KPI that changed the way you made decisions? #DataDrivenDecisionMaking #KPIs #BusinessIntelligence #ImpactMetrics #BusinessStrategy

A DTC fashion brand founder reached out to me, frustrated. "We’re spending lakhs on ads, but every new customer is costing us ₹1,200. How do we scale without burning money?" I checked their numbers: 📉 Customer Acquisition Cost (CAC): ₹1,200 📉 Repeat Purchase Rate: 12% (way below industry standards) 📉 Average Order Value (AOV): ₹1,800 (low margin for ad-heavy growth) 📉 ROAS: 2.1X (barely breaking even) They were stuck in the classic DTC trap: 🚨 Scaling cold traffic with direct sales ads 🚨 Over-relying on discounts to convert 🚨 No focus on repeat purchases or brand loyalty We flipped the strategy in 3 steps: 🔹 Built a Content-First Funnel → Instead of selling immediately, we warmed up cold traffic with: • UGC & influencer testimonials (trust-building) • "How to style" content (engagement) • Brand storytelling ads (higher click-through rates) 🔹 Reworked Retargeting → Instead of spamming discounts, we created: • Social proof ads (before & after styling looks) • Exclusive limited-edition drops for engaged audiences • Cart abandonment sequences with urgency-driven copy 🔹 Fixed Retention & LTV → Profits come from repeat customers, so we: • Introduced personalized post-purchase offers • Built a VIP program for early access & loyalty perks • Increased email + WhatsApp engagement (repeat buyers grew 2.3X) 💡 60 days later, here’s what changed: ✅ CAC dropped from ₹1,200 → ₹740 ✅ Repeat purchase rate jumped from 12% → 28% ✅ AOV increased from ₹1,800 → ₹2,300 ✅ Monthly revenue scaled from ₹15L → ₹24L 🚀 Scaling isn’t about cheaper ads. It’s about smarter customer journeys. If you’re struggling with CAC, ask yourself: ⚡ Are you educating cold audiences or just pushing sales? ⚡ Is your retargeting strategy fixing objections or just repeating the same ads? ⚡ Are you retaining customers or constantly chasing new ones? Fix your funnel, and you’ll scale profitably. What’s your biggest challenge in lowering CAC? Drop it below.👇 #DTCGrowth #ScalingStrategies #CACReduction #RetentionMarketing

Post 8: Deciphering Brick-and-Mortar Metrics - Essential KPIs for Fashion Store! 📊🏢 Hello everyone! 😊 Continuing our #RetailAnalyticsJourney, let's navigate the metrics labyrinth for brick-and-mortar fashion stores. We'll shed light on Key Performance Indicators (KPIs) that are vital for assessing performance, strategizing operations, and ensuring customer satisfaction in physical stores. ✏ Foot Traffic/Customer Entry👣 This KPI reflects the number of customers visiting the store within a certain period. It provides insight into the store's appeal and can help identify successful marketing efforts or store layouts that attract customers. For example, if 1000 people entered in store last month, the foot traffic/customer entry is 1000. ✏ Conversion Rate 🔁 While foot traffic measures attraction, the conversion rate quantifies action, indicating the percentage of store visitors who make a purchase. It can highlight the effectiveness of sales staff, pricing strategies, and the overall shopping experience. If 300 out of those 1000 visitors made a purchase, the conversion rate would be (300/1000)*100 = 30%. ✏ Sales per Square Foot 🏢 This KPI measures the average revenue a retailer generates for every square foot of sales space. It's a crucial metric for physical stores, demonstrating the productivity of the retail space and helping to optimize store layout and merchandise display. Suppose the store made 500,000 in sales last month and it's 2,000 square feet, sales per square foot would be 500,000/2,000 = 250. ✏ Shrinkage Rate 📉 Shrinkage is the percentage of inventory lost to theft, damage, or administrative errors. High shrinkage can significantly impact profitability. Tracking this rate can help identify operational areas needing attention to minimize losses. If store started with 500 items and only sold 400, but ended up with 80 at the end, the shrinkage is (500-400-80)/500 * 100 = 4%. ✏ Gross Margin Return on Investment (GMROI) 💰 GMROI offers a snapshot of the profitability on inventory investments. It's the gross profit made from selling merchandise compared to the cost of the inventory. High GMROI indicates a successful return on inventory investments, while low GMROI can signal pricing or purchasing strategy adjustments. Suppose company made 50000 in gross profit from an inventory costing you 20000, GMROI would be 50000/20000 = 2.5. Mastering these KPIs will help fashion retailers operate successful brick-and-mortar stores in an increasingly digital world. Remember, it's about harmonizing data and intuition, creativity and strategy. 🎭 Don't forget to share, like, and comment below! Let's keep growing together on this exciting #RetailAnalyticsJourney! 🌟 #retail #fashionretail  #analytics #DataDrivenDecisions

As a former growth operator who achieved 19x growth in two years, I've seen the significance of Lifetime Value. We reached a point where offering four free services per client and remaining profitable was feasible, thanks to a high LTV. However, calculating LTV can be a challenging but vital task for early-stage startups. Here’s how you can calculate it: LTV is the total expected revenue from a single customer account, combining the user's revenue value with their lifespan. For startups, predicting a user’s lifespan involves educated estimations based on available data. Rather than calculating LTV for each user, aim for an average LTV based on your typical user. This provides a reliable gauge of your sales effectiveness and customer retention. Start with your average purchase value. For example, if you have three different pricing plans, analyze a month's data to determine the total number of plan purchases and divide this by the total cost of these purchases. This gives you the average purchase value. Next, estimate the purchase frequency per user. Divide the total number of purchases by the number of unique purchasing users to get the average frequency rate. These steps will help you determine an average LTV, crucial for understanding and enhancing your startup's financial health and growth potential.