Testing Ecommerce Site Usability

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?

Marketers, are you still measuring email the old way? We get told email is dead, but everyone reading this has most likely read an email, logged in using it & made a purchase with it. So it's not dead, but how we judge its effectiveness hasn’t evolved fast. We’ve relied on open rates & click-through rates (CTR) — metrics that, frankly, are no longer fit for purpose. Why open rates are no longer reliable Open tracking depends on image loading, which Outlook often blocks, & Apple & Gmail preload by default. As a result, you might see machines open, not human ones. And proper visibility is vanishing with more “text-only” creatives or image-blocked environments. And CTR? It’s got its own problems Think about user intent. If a customer reads “50% off this weekend” in your subject line, they may just go straight to your site—no click needed. Even Gmail’s AI summarising content & extracting voucher codes means users engage without clicks. Email is quickly becoming a powerhouse for brand awareness, but it doesn't have the metrics to prove this. So, what should we look at? As the rest of adtech races toward incrementality, attention, and post-impression attribution, email needs to catch up. Here’s how: 1. Conversion Attribution (Beyond Last Click) Don't stop at click-based conversions. Track who received the email, & assign influence weightings to openers, clickers, & even non-clickers who later convert. This mirrors how display and social now assess "view-through" impact. 2. Frequency & Multi-Touch Engagement Did the recipient open on mobile in the morning, revisit via desktop, & convert on payday? That’s a multi-touch journey. Look at repeat site visits, device switching, & re-engagement post-send. 3. Pay Day or Trigger-Based Lift Create holdout groups and measure uplift around high-conversion moments (e.g., end-of-month). This mirrors the incrementality testing often used in paid social or programmatic, proving that email drives behaviour, not just volume. 4. Attention Metrics Use tools to estimate dwell time on emails or the time between opening& clicking. These are soft proxies for intent, similar to how platforms measure scroll depth, hover rate, and ad exposure time in other channels. 5. Site Quality Metrics Did email recipients spend longer on site, view more pages, or have higher AOVs? Your session quality tells you if email delivers high-intent traffic, something brands already monitor from Google Ads or affiliates. 6. Ask them! Simple, but powerful: survey your audience. What emails did they find valuable? Did it change their behaviour? Self-reported attribution, done well, can give you what click-tracking can’t. Email deserves more credit than. If adtech is shifting toward attention, incrementality, & deeper behaviour analysis, email should, too. Let's measure actual impact, not just opens & clicks. I bet you will discover that email isn't just for conversion but also a branding-building superpower.

A team celebrated a 31% lift in conversion rate after a big optimization push. I conducted an incrementality test to ensure accuracy. Real lift: 4%. The other 27 points were noise, seasonality, and people who would have converted anyway. We'd have hit our number with 80% of the budget. That's when I stopped trusting conversion rate as a primary metric. And I started looking at every dashboard in the building differently. Here are 5 reasons your CR is lying to you in 2026, and what to actually measure instead: 👉 Cookie deprecation killed half your tracking, but nobody updated the dashboard. Your "conversion rate" is now calculated against a known sample, not your full traffic. Fix: move to server-side tracking with consent-mode modeling, and start reporting a confidence range on every conversion metric, not a single number. 👉 AI assistants are now sending you traffic with no referrer. ChatGPT, Perplexity, Gemini, and Claude are increasingly the first touch in B2B buying journeys. The people who arrived because an AI recommended you are getting credited to "direct" or "organic." Fix: track branded search trends and direct traffic growth as leading indicators. If they're climbing, your AI-mediated demand is working, even if your dashboard can't see it. 👉 Most of your "conversions" would have happened anyway. Without incrementality testing, you're attributing conversions to campaigns that the customer was going to do regardless. The honest number is almost always 20 to 40% lower than your dashboard shows. Fix: run geo holdout tests at least quarterly. Pick 5 markets to turn off paid ads for 30 days and compare them. The gap is your real incremental lift. 👉 Last-click is still secretly running your reporting, even if you've moved on. The first touch (the creator post, the podcast, the AI mention) almost never gets the credit. Fix: pair your platform reports with marketing mix modeling (MMM) at least twice a year. MMM is the only attribution method that can see what your tracking can't. 👉 Your CR moves more from market timing than your work. When category demand spikes, your CR goes up. Fix: index your conversion rate against your category's search volume trend. If your CR rose 12% but category demand rose 18%, you didn't get better. You got carried. Here's the part that should sit uncomfortably with you: Most marketing teams in 2026 are reporting metrics that are 30-50% wrong, with confidence intervals that nobody publishes, on dashboards built before the AI search wave even started. What's the metric your team is still reporting that you privately know is broken? Follow #socialJJ to read more of my posts. 

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

Wait... Passing Core Web Vitals isn't fast enough??? For years I've helped brands "get to green", and passing Google's site speed target has become the default web performance goal for most websites. This week I was shocked to learn that at this speed, most brands are still leaving SIGNIFICANT money on the table. Site speed directly influences business outcomes. A faster site results in: - Lower bounce rates - Higher conversion rates - And therefore higher revenues, healthier business, happier customers. New real-world eCommerce performance data from across 700+ brands and 500M+ shopper sessions shows that continuing to optimize beyond Google's recommended targets, continues to boost conversion, and drop bounce rates. For LCP ("Looks fast") - Passing CWV (2.5s): average 1.49% conversion rate and 60.51% bounce rate - Conversion rates across all sessions, brands, device types, and platforms peak at 1.3s - Sessions at 1.3s average 2.21% conversion, and 44.64% bounce rate! Shaving 1.2 seconds off LCP, above and beyond Google's recommendation, shows a 26% lower bounce rate, and 48% higher conversion rate! The data also shows that optimizing LCP beyond 1.3s LCP shows diminishing returns, and becomes exceptionally expensive. And for INP ("Feels fast") - Conversion rate continues to improve all the way to 0ms INP.  - Driving INP to 0ms from Google's recommended 200ms results in 16.3% higher conversion rate - Bounce rate at 100ms INP is 10.3% lower than at Google's 200ms threshold. This is shocking to me, honestly. We have a lot of work to do! Explore for yourself at the link in the comments. #sitespeed #webperf #ecommerce #conversion #analytics #pagespeed #corewebvitals

We increased Champify's inbound demo requests 3x by moving from a form to a self-scheduling calendar flow. Here’s why this was bad (and why benchmarking on only MQLs is dangerous): In order for a demo request to be meaningful it has to convert into revenue. In month one our inbound –> stage 2 conversion rate dropped from 80% to 20%. Making access easier also increased the number of tire kickers: Here’s what we changed to bring our conversion rate back up: 1. Updated language and FAQs on the pricing page on the website to help visitors self-qualify 2. Built a workflow using Dock/Loom that would help hand-raisers on the edge of “good fits” learn more before taking a call with an AE 3. Provided more information on the website so people with lower intent did not have to book a demo ( 👀 looking forward to adding some interactive demos soon) Our conversion rate to qualified pipeline is back up above 60%.  TAKEAWAY: Marketing’s job isn’t MQLs. It’s impacting the full funnel. In 2024 the goal is efficient growth. If you’re only measuring top of funnel you’re never going to get there. It’s not just about  the volume of leads. It’s about the quality. Make sure you are watching your full-funnel conversion rates. And your top of funnel is turning into revenue.

🚨 𝗪𝗵𝗲𝗻 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗱𝗿𝗼𝗽𝘀, 𝗶𝘁’𝘀 𝗿𝗮𝗿𝗲𝗹𝘆 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗺𝗲𝗱𝗶𝗮 𝗯𝘂𝗱𝗴𝗲𝘁—𝗶𝘁’𝘀 𝗼𝗳𝘁𝗲𝗻 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝘁𝗵𝗮𝘁 𝘀𝗶𝗹𝗲𝗻𝘁𝗹𝘆 𝗱𝗿𝗮𝗶𝗻 𝘆𝗼𝘂𝗿 𝗥𝗢𝗜. We recently audited a Google Ads account that was spending heavily yet struggling with poor lead quality and conversion rates. Here’s what we found 👇 🔧 𝗞𝗲𝘆 𝗜𝘀𝘀𝘂𝗲𝘀: 1. Misaligned conversion goals: Incorrect optimizations were misguiding performance signals, costing 20-30% of the budget. 2. Faulty data tracking: Multiple touchpoints had broken tracking, skewing insights. 3. Poor campaign alignment: Deviations from Google’s best practices (e.g., incorrect creative dimensions and asset mismatches) led to ~40% of spends on improperly set-up campaigns. 4. Inefficient keyword grouping: A few high-volume, low-quality keywords were hoarding the budget, with only 3 keywords generating 80% of conversions. 💡 𝗢𝘂𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: 𝗥𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗼𝘃𝗲𝗿𝗵𝗮𝘂𝗹𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴, 𝘄𝗲 𝗯𝗲𝗴𝗮𝗻 𝗯𝘆 𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: - Realigned conversion goals to actual business outcomes - Rebuilt the tracking architecture from click to customer - Enforced Google’s campaign hygiene standards (creative specs, structure, etc.) - Restructured keyword groups based on intent signals 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗮𝗳𝘁𝗲𝗿 90 𝗱𝗮𝘆𝘀: ✅ Click-to-lead conversion rate: 0.7% → 1.6% (128% increase) ✅ Lead-to-customer rate: 30% → 40% (33% improvement) ✅ Overall acquisition efficiency: 2.7x better Sometimes the simplest tweaks can drive the biggest improvements. Let’s always go back to the basics when things aren’t working. #PerformanceMarketing #GoogleAds #DigitalMarketing #MarketingStrategy #GrowthHacking

Here's a handy little SEO script for detecting dynamic HTML content in the DOM, perfect for situations where elements should be present in the source code but only get added when a user hovers or clicks. An extreme example is the mobile burger menu. Google expects this content to be included in the rendered source code, but it may only appears in the DOM after the user interacts with the burger menu icon. To use this script: - Open the page you want to inspect. - Open Chrome Developer Tools - Paste the script (excluding the ``` markers) into console. - Interact with the page by clicking or hovering over elements. - Any new items added to the DOM during your interactions will be logged in the console. A simple yet powerful and potentially an important script. Perfect for SEOs when migrating to a JavaScript framework for the first time. ``` (function() {  // Set up MutationObserver to watch for DOM changes and log  const observer = new MutationObserver((mutations) => {   mutations.forEach((mutation) => {    if (mutation.type === 'childList') {     mutation.addedNodes.forEach((node) => {      if (node.nodeType === Node.ELEMENT_NODE) {       console.log('New HTML added to DOM:', {        tagName: node.tagName,        id: node.id,        class: node.className,        innerHTML: node.innerHTML,        timestamp: new Date().toISOString()       });       // Log the stack trace to help identify where the change came from       console.log('Stack trace:', new Error().stack);      }     });    }   });  });  // Start observing the entire document  observer.observe(document.body, {   childList: true,   subtree: true  });  console.log('DOM monitor initialized. Watching for new HTML elements...'); })(); ```

*Understanding ADF(Azure Data Factory) Concepts* 1)How do you optimize the performance of data movement in ADF ? **Optimizing Data Movement in Azure Data Factory:** 1. **Use Parallelism:** Maximize parallel copy activities and data partitioning for faster processing.  2. **Integration Runtime:** Choose Azure-IR or Self-hosted IR based on your data source and region.  3. **Efficient Staging:** Leverage staging storage like Azure Blob or ADLS for large-scale transfers.  4. **Compression & Formats:** Use compressed formats like Parquet/Avro for reduced data size.  5. **Monitor & Tune:** Continuously monitor pipelines and tweak settings for optimal throughput. #DataFactory #Azure #DataEngineering 2)how do you handle dynamic content and parameter in azure data factory pipelines? Handling Dynamic Content & Parameters in Azure Data Factory:    **Parameters: Define pipeline parameters to pass values at runtime for flexible configurations.    **Expressions: Use dynamic expressions with the @{} syntax for conditional logic and transformations.    **Variables: Store intermediate values using variables for reuse across activities.    **Mapping Data Flows: Leverage parameterized datasets for dynamic file paths or query logic.    **Debug & Test: Use ADF’s debug mode to validate dynamic content before deployment. #AzureDataFactory #DataEngineering #DynamicPipelines 3) if you have zip file how you are going to load the zip file in adls? **Loading ZIP Files into ADLS with Azure Data Factory:** 1. **Blob Storage Staging:** Upload the ZIP file to Azure Blob Storage as a staging area.  2. **Copy Activity:** Use ADF’s Copy Activity to move the ZIP file to Azure Data Lake Storage (ADLS).  3. **Unzip with Logic Apps or Azure Functions:** If extraction is required, integrate Logic Apps or Azure Functions to unzip and save contents to ADLS.  4. **Native Tools:** For large datasets, use tools like Azure Storage Explorer or AzCopy for efficient uploads. #AzureDataFactory #ADLS #DataEngineering #CloudStorage Deepak Goyal #Azure #AzureDataFactory 😇

Stop trusting your Google Ads conversion number. Audit it like your budget depends on it. Most teams check clicks, CPC, CPA, ROAS and still have no idea if Google is optimizing on clean data or broken signals. That’s why I built The Conversion Tracking Diagnostic. It’s not a generic tracking checklist. It’s the same diagnostic we run inside new Google Ads accounts to find the leaks that quietly wreck Smart Bidding, inflate CPA, double count conversions, and hide wasted spend. It starts with a 10 minute health check • compare Google Ads conversions against your CRM • spot double counting before Smart Bidding scales fake results • identify tracking leaks before Google misses real customers • audit Primary vs Secondary conversions so the right actions fuel bidding Then the deep diagnostic • check if auto-tagging is actually on • verify Conversion Linker fires sitewide • catch duplicate Google tags and duplicate conversion sources • make sure conversion tags fire once, on the right event, not every refresh • find broken redirects, stripped GCLIDs, and cross-domain tracking issues Then the modern 2026 tracking stack • Enhanced Conversions status and match rate • Consent Mode v2 for EU/EEA traffic • iOS 17+ link tracking protection risks • server-side tagging opportunities • GA4 and Google Ads linking issues Then the fix plan • build a leak inventory • rank issues by impact and effort • know what to fix first • follow a 90 day sequence to clean up your tracking stack Because broken tracking does not just make reports inaccurate. It teaches Google to optimize toward the wrong people. You pause winners. You scale losers. You trust CPA that is not real. And Smart Bidding burns budget chasing bad signals. This diagnostic helps you find every major Google Ads tracking leak in 60 to 90 minutes. Comment "TRACKING" and I’ll send you the copy. #GoogleAds #PPC #ConversionTracking #GoogleAdsTips #PaidSearch #PerformanceMarketing