Retail KPI Tracking

Align your UX metrics to the business KPIs. We've been discussing what makes a KPI in our company. A Key Performance Indicator measures how well a person, team, or organization meets goals. It tracks performance so we can make smart decisions. But what’s a Design KPI? Let’s take an example of a design problem. Consider an initiative to launch a new user dashboard to improve user experience, increase product engagement, and drive business growth. Here might be a few Design KPIs with ways to test them: →  Achieve an average usability of 80% within the first three months post-launch. Measurement: Conduct user surveys and collect feedback through the dashboard's feedback feature using the User Satisfaction Score. →  Ensure 90% of users can complete key tasks (e.g., accessing reports, customizing the dashboard) without assistance. Measurement: Conduct usability testing sessions before and after the launch, analyzing task completion rates. →  Reduce the average time to complete key tasks by 20%. Measurement: Use analytics tools to track and compare time spent on tasks before and after implementing the new dashboard. We use Helio to get early signals into UX metrics before coding the dashboard. This helps us find good answers faster and reduces the risk of bad decisions. It's a mix of intuition and ongoing, data-informed processes. What’s a product and business KPI, then? Product KPI: →  Increase MAU (Monthly Active Users) by 15% within six months post-launch. Measurement: Track the number of unique users engaging with the new dashboard monthly through analytics platforms. →  Achieve a 50% feature adoption rate of new dashboard features (e.g., customizable widgets, real-time data updates) within the first quarter. Measurement: Monitor the usage of new features through in-app analytics. Business KPI: → Drive a 5% increase in revenue attributable to the new dashboard within six months. Measurement: Compare revenue figures before and after the dashboard launch, focusing on user subscription and upgrade changes. This isn't always straightforward! I'm curious how you think about these measurements. #uxresearch #productdiscovery #marketresearch #productdesign

Most DTC have no clue how to calculate LTV. Or blindly trust the accuracy of attribution tools. Here’s the guide: Step 1 - Clean data → remove all outliers, test orders, and missing data. Step 2 - Pick a time period for cohorts (last year, this year, last 3 months, etc.) Step 3 - Calculate Net AOV Gross AOV = Total Gross Sales / Total Orders Average Discount = Total Discounts / Total Orders Average Returns = Total Returns / Total Orders Average Shipping = Total Shipping Collected / Total Orders Net AOV = Gross AOV - AVG Discount - AVG Returns - AVG Shipping Collected Step 4 - Calculate Profit Contribution Margin Average Variable Cost = ( COGS + Last-Mile Delivery Fees + Fulfillment Fees + Payment Processing Fees + Packaging & Labels ) / Total Orders Profit Contribution Margin = ( Net AOV - AVG Variable Cost ) / Net AOV Step 5 - Calculate Churn Rate Retention Rate = Total Customers with more than 1 Orders / Total Customers Churn Rate = 1 - Retention Rate Step 6 - Calculate Average Customer Lifespan (ACL) ACL = 1 / Churn Rate Step 7 - Calculate Average Purchase Frequency (APF) APF = Total Orders / Total Customers Step 8 - Calculate Customer Lifetime Value (CLV or LTV) LTV = Net AOV * Gross Margin * ACL * APF Step 9 - Calculate Net Profit / Customer Net Profit = LTV - CAC NOTES: ✂️ Consider trimming your sorted order data by 2% (remove 1% of the top and 1% of the bottom orders). 📏 Consider longer time periods to capture a more reflective LTV (1 year is better than 1 month). 🧮 Include Gross AOV, AVG Discount, AVG Returns, and AVG Shipping in your analysis to understand levers that impact Net AOV. ⌛ Calculate LTV:CAC by cohort to see the dynamic of changes. ⚙️ Exclude operating/fixed costs from your calculations. LTV is the breakeven level for customer acquisition. How do you calculate LTV? P.S. Check out the limited version of my LTV:CAC model - link in the comments!

I always recommend a simple playbook for building and refining dashboards: 1. What's happening 2. Why is it happening 3. How do we fix it 4. Fix the problem, a bunch of times 5. Automate the problem away 6. Delete the dashboard Why not just skip to step 5 (automating stuff)? Because you really need to get step 1 correct, otherwise steps 2-5 will be a waste. Let's drill down into each step: ___ 1. What's happening Most dashboards start with a bar chart and a table. A bar chart of a key metric over time, and a table below it showing the raw data (to double click into stuff). The goal of this step is to identify either an output metric (like revenue, sign ups, etc) or an input metric (emails sent, candidates reached out to) and watch it move. 2. Why is it happening Don't skip to step 2 too early. First make sure that looking at step 1 (what's happening, is actually worth double clicking into). For some KPIs, all you need is a bar chart and a table. But when you need to understand the why, I tend to start with a drill down into a row of the table from row 1. This could be a single customer view, an employee view, etc. Get more details (tables, charts, summaries) and make them available to users to try and figure out why things are happening. Don't try and skip to a solution. Just throw a bunch of raw data into one place. 3. How do we fix it Over time, you'll add data points in step 2 and remove them. The layout of the dashboard will change and evolve. This is because you're iterating towards a clear path to fix the problem in a repeatable way. The goal of this step is to find a model that flows naturally and works in a repeatable way to fix the problem. 4. Fix the problem, a bunch of times Now that you have a working approach, start using the dashboard to solve the problem. Use it again. And again. Make tweaks any time the solution is not perfect. Add toggles, optimize the layout etc. Make sure it flows, and works for edge cases. 5. Automate the problem away Now you know you're solving a real problem, you found the main data points to identify and address the issue, you've created a step-by-step workflow to resolve the issue, and you've battle tested the solution. At this point, start figuring out if there's a way to automate the solution. It might involve engineering effort. It might involve an automation tool or RPA solution. But just imagine. Once you automate the solution, you can finally... 6. Delete the dashboard This is always the best part :) If you find a solution to the problem, it's time to move onto the next problem. ___ Everything in the business doesn't warrant all of these steps. I've built dashboards that get to step 1, we build a bar chart and a table, use it to measure progress for a few months, and delete the whole thing when our priorities change. Priorities always change. Make sure you're only going deep on the problems that are absolutely critical to your business RIGHT NOW.

Algorithmic merchandising was our catalyst for a 62% increase in revenue – with the same traffic. Here's our crazy experiment👇 We ran a crazy experiment over the last couple of weeks. While analyzing the data to find the next big growth lever for one of our longest-standing brands I’ve noticed something interesting. Over 32% of the site-wide traffic was hitting collection pages. Also, I identified some outperforming products (hidden champions) that were getting a lot of clicks even though they weren't in prime positions. On the other hand, some products that were getting the most impressions weren't performing as well. People stopped browsing more often when there were a lot of poor performing products in the visible space. So good products didn't even get a chance to be shown to many people. What if we could change the allocation of these products? – Give good products more visibility and bad products less. The challenge now was to find those outliers and position them accordingly. The real breakthrough came when I figured out how to use this data to improve product placement on collection pages. My approach went beyond just tracking clicks. I looked at several key metrics to get a full picture of how each product is doing: → CTR by position → Basket Rate → Purchase Rate: → 90-day Product LTV These 4 indicators were fed into RetentionX's machine learning process to generate a performance indicator that creates a score from 0-100. Products that weren’t performing as well in their current spots were moved to less prominent positions, freeing up space for the real stars — the products that were outperforming expectations. For the first time, our customer had a clear strategy for how to present their products, one that went beyond just gut feelings and good looks. They could now combine our automated insights with their own logic for sorting products—like aligning email campaigns with what customers would see on the site, push new arrivals and demote low stock items. The changes we made had a noticeable impact. Collection pages, which had been somewhat overlooked, suddenly became the go-to place to track what was happening with their customers and how their products were being perceived. The numbers told us we were on the right track, and remember this is a $40M+ brand: → 62% More Profit from the Same Traffic → 27% Additional Increase in Revenue → 23% Higher Conversion Rate → 12% Increase in AOV → 18% Increase in Basket Rates When we saw how well this approach worked, we knew we couldn't keep it to ourselves. So Merchandise Automation is now part of our RetentionX Core product. Read the full case study here: https://lnkd.in/dHh_Sbkp

I told a client to spend $200 to acquire a $60 customer and watched the marketing manager's face turn white. My reputation was on the line on a Zoom call with this pharmaceutical subscription company. Their ads weren't scaling, and I know suggesting a $200 CAC on a $60 purchase sounds insane. But I also knew something they (somehow) were missing: Their customers don't just buy once and disappear. They keep spending $60 every single month for an average of 7.5 years. I’ll math it out for you: $60 × 12 months × 7.5 years = $5,400 lifetime value 20% profit margin = $1,080 profit per customer Would you spend $200 to make $1,080? Every single time, right? But I see this constantly. Marketers have strong opinions about their customer acquisition costs without actually knowing their customer lifetime value. If you're making budget decisions based only on first purchase data, you're leaving serious money on the table.

A new CMO walked into a 1P brand and asked just the right question… "If we’re not profitable until the second or third purchase, then why are we judging success on the first?" For this consumables brand, about a third of revenue flowed through Subscribe & Save, effectively inflating topline sales and making ad efficiency look great. But while ACOS looked healthy, finance was seeing ongoing margin erosion. Working together, we connected Amazon Marketing Cloud data including Flexible Shopping Insights into a fresh dashboard in Velocity. Now every retail order (ad-attributed or not) feeds a cohort view of lifetime value. Here’s where this post is different from most AMC “case studies” that just celebrate dashboards... This one is about partnership, operations, and profitable growth. Here's how we worked together to transform the business: 1️⃣ Bring finance to the table Finance calculated customer acquisition costs across the product catalog and leadership agreed to an initial three-month payback rule. With those KPIs in place, we set out to make the Amazon channel profitable, a goal we accomplished in just 90 days. 2️⃣ Test new customer acquisition one product at a time The pilot focused on a single hero product, and used the AMC audience modifiers to push bids into a very competitive range, but only for true new-to-brand shoppers. Within four weeks, we beat the CAC target by 25% for the product while maintaining strong repeat purchase rates. 3️⃣ Trust in putting profits over volume  Once AMC’s Flexible Shopping Insights exposed that 25-35% coupons were wiping out margin (especially on SnS orders), leadership agreed to roll back the discounts that had fueled share growth. In our early tests, we proved that slimmer discounts in the 15-20% range still converted a healthy, and profitable customer base. 4️⃣ Rolling it out  With proof in hand, the CAC to LTV playbook expanded across every category over the next six weeks. A Velocity dashboard now tracks CAC, LTV, and payback period, with media budgets only increasing when the CAC:LTV ratios support it. 5️⃣ The Results By the end of Q1 2025 (just 90 days after replacing ACOS/TACOS with a CAC:LTV scorecard) the brand’s Amazon business had already matched its operating profit from all of 2024 without adding a dollar of incremental media spend. With a new north star for success on Amazon, the team is now raising the bar. They’re carving out category specific CAC targets and experimenting with longer, flexible pay-back windows. This will let them ramp ad spend for the highest value cohorts while still safeguarding profits. Imagine walking into your next leadership meeting with a slide that ties CAC:LTV to both profitability and category share gain. How would the conversation change for the better?

⏳ Waiting on the perfect software to track KPIs? Don’t. Start simple. 📊 Start with a spreadsheet. Here’s how we did it at Hill Manufacturing & Fabrication before we had our current systems in place: 📌 In Column A, list your key KPIs ↪️ In Column B, list Target then Actual and skip a row. 🔂 Rinse and repeat to put all your KPI's on the list. Start small. 📅 In the top row, put your starting week’s date. ➡️ Next cell: =previous cell+7. Now you’ve got your weekly timeline. Then just start logging: Target, Actual, Repeat for each KPI. 👉 You can also do them one line per KPI. I prefer tracking targets weekly because they can vary. I can adjust targets as I spot trends, but still see how we did against historical targets. You can always layer in: 🎯 Conditional formatting 📈 Trend graphs 🗣️ Weekly huddle reviews That first “dashboard” changed how we ran the shop. It wasn’t fancy...but it was real. And it helped us make better decisions, faster. Don’t wait for perfect. Use what you’ve got and just get started. #BuyTheNumbers #GrowingValue #DataDrivenDecisions #NumbersMatter #MFGLeader #MakingChips #SaveAShop MakingChips Buy the Numbers# Nick Goellner Paul Van Metre Jennifer Dubose Hill Manufacturing & Fabrication

Customer Lifetime Value 2.0 After analyzing 500+ customer accounts, I've discovered that traditional CLV calculations miss up to 60% of actual customer value. Here's an enhanced framework for 2025: 1. Direct Revenue + Referral Value 📈 Most companies track: - Base subscription revenue - Feature upgrades - Seat expansions - Service fees But they miss the hidden revenue multipliers: - Referred leads convert 3x better - Referred deals are 20% larger - Some customers generate 5+ referrals yearly - Case study & reference call impact For example, Acme Corp's (Wile E. Coyote, CEO) $100K ARR becomes $400K, including their referral impact. Traditional CLV misses 75% of its value. 2. Implementation Resource Investment 🎯 Innovative companies track both costs and value signals: - Technical onboarding hours - Integration complexity - Data migration scope - Training investment - Success planning effort Key finding: Higher initial investment often yields better retention. One enterprise client reduced time-to-value by 40% after we increased implementation support. 3. Support Ticket Investment 💡 Support interactions create measurable value: - Product feedback quality - Feature adoption correlation - Customer expertise growth - Expansion opportunities Data point: Customers engaging support 3-5 times in the first 90 days show 40% higher retention rates than non-engagers. 4. Product Feedback Impact 🔍 Value creators: - Beta testing participation - Feature request quality - Bug report impact - Advisory board input - API usage insights Case study: Mid-market customer feedback led to UI improvements, reducing overall churn by 15%. 5. Community Engagement ROI 🌟 Measuring network effects: - Knowledge base contributions - Forum participation value - User group leadership - Brand advocacy reach - Peer support impact Success metric: Top community contributors save our support team 200+ hours annually through documentation and peer assistance. New CLV Formula: CLV = (Direct Revenue + Referral Value) × Expected Lifetime - Implementation Investment - Support Investment + Product Feedback Value + Community Impact Value Results from companies using this framework: - 35% more accurate retention predictions - 25% higher expansion revenue - 40% increase in referrals - 50% more valuable product feedback - 30% growth in community engagement Implementation Tips: 1. Start small - Pick one new value dimension - Test with a pilot group - Gather baseline data - Scale what works 2. Cross-functional alignment - Connect Success, Product & Support data - Create shared value metrics - Build automated tracking - Set review cadence 3. Measure impact - Track prediction accuracy - Monitor retention correlation - Document value stories - Share learnings How does your organization measure hidden customer value? What metrics beyond direct revenue have you found most insightful?

If your metrics are a mess, your dashboard will be too. First get the metrics organized into the top KPIs, their drivers and the deeper metrics. Then build your dashboard with that organization. In this example, there are a lot of data points associated with email campaigns. And some messy dashboards will just be splattered with all those metrics. But how do the metrics fit together? Which ones are the true KPIs? No one can tell from a messy dash. A great first step in organization is aligning those metrics along the customer experience -- from send to sale. Then layer on how the metrics fit together, including the definitions of how certain ones are calculated. Prioritize with leaders the top three or so metrics as the true KPIs. Clarify which metrics are driving those KPIs. Minimize the metrics that aren't as important. This KPI diagram helps ensure the purpose of the dashboard is aligned with what leaders need to know. Then the dashboard design will fall into place. No more mess. See more of my dashboard and chart makeovers: https://lnkd.in/eBKuCJp6 #dataforexecs #datavisualization #dashboards #nomoremess

I recently explored a game-changing approach to sales performance—one that shifts the focus from just hitting numbers to actually understanding and improving the factors driving success. Sales teams often work under immense pressure, but what if we focused on leading indicators instead of just lagging results? Here are 10 Sales KPIs that go beyond revenue targets and help build sustainable success: 1️⃣ Lead Response Time ⏳ How fast does your team follow up with inbound leads? A delay of even 5 minutes can cut conversion rates in half. 2️⃣ Qualified Leads per Month 🔥 Not all leads are equal. Tracking qualified leads (instead of just total leads) ensures you focus on real opportunities. 3️⃣ Win Rate 🏆 How many deals are actually closing? A high volume of calls doesn’t mean much if conversions are low. 4️⃣ Average Deal Size 💰 Are you selling bigger solutions, or are deals shrinking? This metric reveals whether your value proposition is strengthening. 5️⃣ Sales Cycle Length 📅 How long does it take to close a deal? Shortening the cycle means optimizing the buyer journey and eliminating friction. 6️⃣ Customer Acquisition Cost (CAC) 💸 Are you spending more than necessary to acquire a customer? A sustainable sales process should lower CAC over time. 7️⃣ Customer Lifetime Value (CLV) 🤝 Are customers coming back? A high CLV means you’re selling long-term value, not just one-time deals. 8️⃣ Churn Rate ❌ Are you losing clients faster than you’re gaining them? A high churn rate signals deeper issues with customer experience or product fit. 9️⃣ Sales Productivity Metrics ⚙️ Track key activities like calls, emails, and demos per rep. Are they focusing on high-impact activities or just staying busy? 🔟 Forecast Accuracy 🔮 If projections are constantly off, there’s a disconnect between sales teams and market reality. Refining this improves strategic decision-making. 💡 Here’s the shift that caught my attention: Instead of measuring just outcomes, high-performing teams measure what drives the outcomes. Sales isn’t just about closing deals. It’s about optimizing the right behaviors, refining processes, and creating value—so revenue becomes the natural result. 🚀 Which of these KPIs do you track the most? Let’s discuss! #sales #marketing #business #sale #fashion #shopping #entrepreneur #realestate #digitalmarketing #onlineshopping #b #smallbusiness #deals #style #discount #love #branding #promo #success #forsale #cars #instagram #follow #salestips #instagood #lagos #property #motivation #socialmedia #salesalesale