Conversion Rate Optimization Metrics

Are Open Banking providers misleading merchants with claims of 99.8%+ conversion? No — but merchants may be asking the *wrong question*. #Conversion in Open Banking isn't standardized like card authorization rates. Providers often use a range of definitions for the conversion metric. To get the full picture and ensure comparability, I recommend merchants request the following metrics in RFPs: 🏦 Technical Completion Rate 🏦 This measures how many payments are successfully completed after a customer finalises the initiation process (SCA in the bank app). It reflects bank API performance more than the provider's, but since it's commonly used, it's a useful starting point. 👆Conversion from Bank Selection👆 A critical point in Open Banking payments is selecting the bank for authentication. This metric shows how well the provider technically redirects customers to their bank for SCA. Furthermore, if the provider doesn't explain the process effectively you may see higher abandonment rates. 🛒 Conversion from Checkout 🛒 This is the most important, though hardest to measure. It tracks how many users complete a payment after choosing Open Banking as their method. It is most effective for providers who redirect customers to their own hosted payment page to select a bank and complete the payment. High abandonment at this stage can indicate poor communication or user confusion. Data splits you may want to request: New vs. Returning Users: Returning users typically convert better, but most merchants focus on new users. (If they can't provide this data then it gives insight into their data analytics) Bank-Specific Conversion: A provider’s performance varies because of their underlying bank mix. If one has more HSBC than Barclays the aggregate number is not comparable Value Bands: conversion of payments in the following bands 0-100,101-5000 and 5001+ can offer insight into a provider’s ability to handle different transaction sizes. Device & Channel: Providers must perform well in both mobile browsers and apps, particularly if you have an app integration. Finally, you may want to specify a customer conversion session - i.e. 20 minutes, 2 hours, 1 day. My experience is this is harder for providers to show consistently but could be valuable. If you’d like to discuss these metrics further, feel free to reach out! #Openbanking #openfinance #fintech #payments #checkout

Your conversion rate went up 40%. But your revenue is down. More common than you think. And it’s because CVR and AOV aren't telling you the full picture. Once you understand the reasoning, it’s hard to go back. Revenue per visitor (RPV) is a more holistic performance metric. It captures the full economic output of your traffic. Conversion rate (CVR) and average order value (AOV) are important… but in isolation can be misleading. Or worse. They can lead to optimization blind spots. Here are some examples to show why RPV is superior. RPV prevents false optimization. → You can increase AOV by raising prices, or removing lower-priced items, but tank your CVR → You can boost CVR by heavily discounting, but that tanks your AOV → RPV immediately tells you if these tradeoffs are worth it. RPV accounts for the relationship between AOV and CVR. → AOV and CVR don’t exist in a vacuum. As the above examples show, they influence each other. → RPV naturally weights these interactions, because RPV = Visitors x CVR x AOV → When you optimize for RPV, you’re forced to consider CVR and AOV simultaneously RPV has better alignment to business outcomes. → Your P&L doesn’t care about CVR or AOV. It cares about total revenue and profit generated from traffic. → RPV is basically metric-language for: “How much money does each visitor generate?” → ROI calculations are now much easier: if your RPV is $2.50 and your cost per visitor is $1.80, congrats! You know you’re profitable. Next time you review your store's performance, start with one question: how much revenue did each visitor generate? Everything else is just context. P.S. - Want access to an interactive calculator that lets you stay on top of RPV + surface opportunities to improve it? Shoot me a message, and I’ll add you to the early release list for it (completely free).

What CR doesn’t tell you But 7 components do You fixed the Conversion Rate, but nothing changed. Because CR is just the tip of the iceberg. It doesn’t explain the customers' journey. And definitely not the drop-offs. With Nick Valiotti, PhD we mapped 7 elements of conversion that reveal where your funnel actually leaks. That's what's under the water: 1/ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁 𝗥𝗮𝘁𝗲 = Product page views / Sessions Shows if users are landing on high-interest products or generic pages. 2/ 𝗖𝗮𝗿𝘁-𝘁𝗼-𝗩𝗶𝗲𝘄 𝗥𝗮𝘁𝗲 = Add to carts / Product views Reveals product appeal + pricing clarity. 3/ 𝗖𝗮𝗿𝘁 𝗢𝗽𝗲𝗻 → 𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝗦𝘁𝗮𝗿𝘁 = Checkout starts / Carts opened Do people commit after opening the cart? 4/ 𝗦𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗠𝗲𝘁𝗵𝗼𝗱 → 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 = Purchases / Shipping method selected Highlights issues with delivery cost, speed, or trust. 5/ 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗠𝗲𝘁𝗵𝗼𝗱 → 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 = Purchases / Payment method selected Do people quit after choosing how to pay? 6/ 𝗣𝗿𝗼𝗺𝗼 𝗖𝗼𝗱𝗲 → 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 = Purchases / Promo code applied Reveals whether discounts drive actual commitment. 7/ 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲-𝘁𝗼-𝗩𝗶𝗲𝘄 𝗥𝗮𝘁𝗲 = Purchases / Product views The real conversion beyond CR. These metrics tell you why CR changed. Not just that it did. 🤓 Save this if you want to audit your funnel like a pro

Your Google Ads Metrics Are Lying to You! 🚨 Most lead gen advertisers in India focus on CPL (Cost Per Lead), CPC, and CTR—thinking lower costs mean better results. But in 2025, a ₹500 lead is useless if it doesn’t convert into revenue. If you’re not tracking the right metrics, you might be: ❌ Generating ₹50 leads that never turn into customers ❌ Wasting budget on low-intent clicks ❌ Scaling campaigns based on vanity metrics Let’s fix that. The 5 Google Ads Metrics That You Can Consider for Lead Gen in 2025 1. Cost Per Qualified Lead (CPQL) > Cost Per Lead (CPL) Not all leads are equal. A high-intent lead is worth more than a random form fill. ✅ CPQL = Ad Spend / Sales-Qualified Leads (SQLs) 📊 If a ₹500 lead has a 40% close rate, it’s better than a ₹100 lead with a 5% close rate. 2. Lead-to-Customer Conversion Rate > Total Conversions 100 leads mean nothing if only 5 convert into paying customers. ✅ Formula: (Customers / Leads) * 100 📊 A high conversion rate means your campaigns are attracting the right audience. 3. Cost Per Revenue-Generating Lead > Cost Per Click (CPC) Clicks don’t pay the bills—customers do. ✅ If you spend ₹50,000 on ads and generate 50 SQLs, but only 5 turn into paying customers, what’s the real cost per acquisition? 📊 A ₹1,000 CPL is fine if it generates ₹50,000 in revenue per customer. 4. Pipeline Value > ROAS A campaign that delivers high-value deals is better than one with a high ROAS but small-ticket sales. ✅ Pipeline Value = Sum of potential revenue from all SQLs. 📊 Example: If Google Ads generates ₹5,00,000 in pipeline revenue from 100 SQLs, your ad spend should be based on revenue impact, not just CPL. 5. Revenue Per Lead (RPL) > Cost Per Lead (CPL) Instead of just tracking lead cost, track how much revenue each lead generates. ✅ Formula: Total Revenue from Ads / Number of Leads. 📊 If 10 leads at ₹500 each bring in ₹1,00,000 in sales, RPL = ₹10,000 per lead. That’s the real performance metric. 🚀 In 2025, successful lead gen advertisers in India are not just measuring lead volume, They’re tracking lead quality, conversion rates, and revenue impact. If you’re still optimizing for low CPL and dependent on sales to do most of the heavy lifting, It’s time to rethink your strategy. #digitalmarketing #leadgen #marketing

I can predict which ad accounts will scale by just looking at 4 metrics. 𝗔𝗳𝘁𝗲𝗿 𝟮𝟱𝟬+ 𝗮𝘂𝗱𝗶𝘁𝘀 𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝟱 𝘆𝗲𝗮𝗿𝘀, 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗳𝗼𝘂𝗻𝗱: The ones that scale past $300k/month all have these 4 metrics dialed in. These 4 building blocks are what actually make up your ROAS. Instead of flying blind, these to see consistent, predictable results: 𝟭. 𝗖𝗹𝗶𝗰𝗸-𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗥𝗮𝘁𝗲 (𝗖𝗧𝗥) Your creative either stops the scroll or it doesn't. Double your CTR from 1% to 2%? You've doubled your traffic without spending an extra dollar. → Industry benchmark: 1.5-2.5% 𝙃𝙤𝙬 𝙩𝙤 𝙤𝙥𝙩𝙞𝙢𝙞𝙯𝙚 𝘾𝙏𝙍: • Better hooks • stronger visuals • clearer messaging 𝟮. 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 (𝗖𝗥) The percentage of clicks that actually buy. Most accounts break here. 𝙃𝙤𝙬 𝙩𝙤 𝙤𝙥𝙩𝙞𝙢𝙞𝙯𝙚 𝘾𝙍: • Match your ad to your landing page • Remove checkout friction, show pricing in ads • Avoid clickbait ads that destroy CR 𝟯. 𝗔𝘃𝗲𝗿𝗮𝗴𝗲 𝗢𝗿𝗱𝗲𝗿 𝗩𝗮𝗹𝘂𝗲 (𝗔𝗢𝗩) Most brands ignore it completely. When it can be a complete game changer. 𝙃𝙤𝙬 𝙩𝙤 𝙤𝙥𝙩𝙞𝙢𝙞𝙯𝙚 𝘼𝙊𝙑: • Create product bundles • Have Tiered pricing • Implement free shipping thresholds 𝟰. 𝗖𝗼𝘀𝘁 𝗣𝗲𝗿 𝗠𝗶𝗹𝗹𝗲 (𝗖𝗣𝗠) Most brands think they can't control CPM. They're wrong. 𝙃𝙤𝙬 𝙩𝙤 𝙙𝙚𝙘𝙧𝙚𝙖𝙨𝙚 𝘾𝙋𝙈: • Make ads that look native to the platform • Drive organic engagement • Use trending formats These 4 metrics make up the 𝗥𝗢𝗔𝗦 𝗠𝗮𝘁𝗿𝗶𝘅. Brands that optimize all 4 scale profitably and consistently. We have a custom ROAS Matrix calculator we’ve used to help audit 250+ accounts. It shows you exactly which metric is killing your performance. And today I'm giving it away for free. To get access, all you need to do is: 1. Connect with me Ciaran Finn 2. Comment ‘ROAS’ below And I’ll send the ROAS calculator straight over to you. Feel free to repost if you find it valuable, to help someone in your network discover how they can improve their ROAS without anymore headaches.

𝗜𝗺𝗮𝗴𝗶𝗻𝗲 𝘁𝗵𝗶𝘀: You’re the head of marketing, and your CEO asks, “𝗪𝗵𝗮𝘁’𝘀 𝗼𝘂𝗿 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗿𝗮𝘁𝗲?” Pause. Breathe. And never give a single, blended number. Here’s why: Blended conversion rates lump together traffic sources with different goals and behaviors. It’s the fastest way to mislead your CEO—and derail your strategy. Instead, here’s how you should answer: “Great question! We have multiple traffic sources, each serving different purposes. Which one would you like to dive into?” 𝗪𝗵𝗲𝗻 𝘁𝗵𝗲𝘆 𝗶𝗻𝗲𝘃𝗶𝘁𝗮𝗯𝗹𝘆 𝗮𝘀𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝘀𝗼𝘂𝗿𝗰𝗲𝘀, 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀: 1. Demand Capture → Paid Search / Affiliates → Pricing Page / Demo Request 2. Education / Exploratory → Main Website Pages → Blog & Resources Each source has unique intent—and requires a tailored measurement approach. 𝗙𝗼𝗿 𝗗𝗲𝗺𝗮𝗻𝗱 𝗖𝗮𝗽𝘁𝘂𝗿𝗲, 𝗯𝘂𝘆𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗻𝘁 𝗶𝘀 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿. 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗿𝗮𝘁𝗲𝘀 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗮𝗿𝗼𝘂𝗻𝗱 𝟱%. 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗼 𝘁𝗿𝗮𝗰𝗸: → Landing Page Conversion Rates → Conversions to Opportunity → Opportunity to Revenue 𝗙𝗼𝗿 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 / 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗼𝗿𝘆, 𝗶𝗻𝘁𝗲𝗻𝘁 𝗶𝘀 𝗹𝗼𝘄𝗲𝗿. 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗿𝗮𝘁𝗲𝘀 𝗮𝗿𝗲 𝘁𝘆𝗽𝗶𝗰𝗮𝗹𝗹𝘆 𝘂𝗻𝗱𝗲𝗿 𝟭%. 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗶𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗱 𝗯𝘆 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 𝗹𝗶𝗸𝗲: → Assisted Conversions → Chat Engagements → Avg. Session Duration → New Visitors vs. Returning Visitors → Keyword Rankings → Brand vs. Non-Brand Clicks The key takeaway? Blended metrics hide insights that drive action. Specificity isn’t just better—it’s essential. Friends don’t let friends give blended conversion rates to CEOs. Let’s keep marketing data meaningful. 🚀 Have you faced this situation before? How did you handle it?

Your e-commerce site-wide conversion rate is hiding the truth from you. It's the metric everyone cites in the Monday morning meeting. "We're at 2.8%." Great. What does that actually tell you? Nothing. It's the average temperature of the Atlantic Ocean. Useless for deciding if you need a wetsuit in Miami. The metric that actually matters is your View to Buy Rate. Product level. Page by page. Unique Purchases ÷ Unique Product Views (For the same product). That's the report that will change how you spend your time, which is even more important than money. Right now, you probably have a product getting 10,000 views a month and 50 purchases. Your marketing team is doing their job. They're sending traffic. The ads are working. The page isn't. Something on that PDP is killing the sale. Maybe the photos look like they were taken on a flip phone. Maybe the ad is making a claim the product doesn't back up. Maybe the product description reads like it was copy-pasted from a factory spec sheet. But nobody's looking at it that way. Because everyone's staring at the site-wide number and arguing about whether to increase ad spend. Every dollar you spend driving traffic to a page with a bad View to Buy rate is a dollar you're setting on fire. You're paying for a crowd to walk into the store, get to the shelf, and walk right back out. What's your favorite product level performance report?

Did You Know Conversion Rate’s Hidden Power on Amazon? 🛒💡 Many sellers focus only on reducing their ACoS (Advertising Cost of Sale) and fail. Here’s why boosting your conversion rate can be the ultimate game-changer for your Amazon profitability. 1. Small Change, Big Impact: Shifting your conversion rate from 1% to 2% isn’t a small tweak—it’s a 100% increase in sales from the same traffic! That’s double the revenue without increasing your ad spend. This is why reviewing your conversion rate should be a top priority for managing ACoS. 2. Understanding Conversion Rate on Amazon: Conversion rate on Amazon is seen as your unit session percentage: total units sold divided by the number of sessions (visitors). Unlike traditional e-commerce, where conversion rate specialists are common, Amazon sellers often overlook this crucial metric. 3. Why Sellers Overlook It: Many new or small brands focus on lowering bids to manage ACoS, believing the issue is only with their advertising. In reality, a lower conversion rate could be the root problem. Competitors with higher conversion rates can outbid you and remain profitable. 4. Factors Influencing Conversion Rate: - Product Listings: Quality images, detailed infographics, and consistent branding in your A+ content are essential. - Reviews and Ratings: Competing against sellers with thousands of positive reviews requires focusing on gathering your own. - Pricing: Test different price points; a slight change can significantly impact conversion rate and profitability. 5. Steps to Boost Conversion Rate: - Optimize Your Listing: Use high-quality visuals, consistent branding, and persuasive bullet points. - Run A/B Tests: Use Amazon’s Manage Your Experiments tool to test main images, titles, etc. Incremental gains, like moving from 15% to 15.5%, add up over time. - Analyze Competitors: Regularly compare your listing to your top 10 competitors. Identify their strengths and integrate similar strategies into your own. 6. Simple Yet Effective Strategy—Price Testing: Price testing is one of the most straightforward ways to affect conversion rate. Adjust prices up or down and monitor how it influences conversion rates. Even a slight increase in price could lead to more revenue without sacrificing conversion rate. Takeaway: A higher conversion rate means better ACoS and overall profitability. Keep testing, adapting, and optimizing to stay ahead. Found this post insightful? Never miss out on any of my posts, sign up to my newsletter right now - Link in Bio. #AmazonPPC #AmazonAdvertising #EcommerceTips #AmazonSellers #PPCstrategy #DigitalMarketing #AmazonSEO #AmazonGrowth #EcommerceMarketing #AdOptimization #Amazon

After generating $1 BILLION+ dollars for clients, we can tell you Conversion Rate Optimization isn't about guessing. So want to know what ACTUALLY moves the needle in Conversion Rate Optimization? - It's not random A/B tests. - It's not changing button colors. - It's not "gut feelings." Here's the process we recommend at SiteTuners: 1️⃣ Start with your analytics. Look for the crucial signals: - Where are users dropping off? - Where's the engagement lacking? - How much time are people spending on site? - Which pages are they leaving from? 2️⃣ Add heat mapping. This is where it gets interesting. You need: - Video recordings of real user sessions - Accumulated heat maps showing visitor behavior - Clear data on what's actually happening on your site 3️⃣ Create informed hypotheses. Before testing, calculate: - Expected uplift from the change - Required effort to implement - Potential ROI of the test Here's what most people miss... Testing has real costs: 1. Heat mapping tools 2. Testing software 3. Development time 4. Traffic split for testing So this is important to know… Not every test is worth running. Just because you have an idea doesn't mean it deserves your resources. Let the data guide your decisions: - Use analytics for statistical proof - Watch heat maps for behavioral insights - Calculate the math before testing Stop guessing with your conversion rates. Start letting real data drive your optimization.