Analytics & Performance Tracking
I Thought My Website Was Doing Great… Until I Looked at These 3 Google Analytics Reports
My blog’s visitor numbers were climbing steadily; I felt successful! But then I dove deeper into Google Analytics. The Behavior > Site Content > Exit Pages report showed users fleeing from key service pages. The Acquisition > All Traffic > Source/Medium report revealed my hyped social media efforts drove traffic with near 100% bounce rates. And the Conversions > Goals > Overview report showed abysmal completion rates for my contact form. Vanity traffic metrics masked the reality: my site wasn’t engaging users or achieving goals. Data revealed the crucial flaws.
The Only 5 Website Metrics I Track (And Why They’re Gold)
Feeling overwhelmed by the dozens of metrics in Google Analytics, I decided to focus ruthlessly on just five that directly impacted my business blog’s growth: 1. Organic Traffic: Shows SEO effectiveness. 2. Conversion Rate (Goal Completions): Measures if traffic turns into leads/subscribers. 3. Bounce Rate by Landing Page: Identifies weak content. 4. Average Session Duration: Gauges engagement. 5. Top Referring Sites: Shows where valuable partnerships/traffic originates. Tracking these core metrics keeps me focused on actions that drive meaningful results, not just chasing pageviews.
“My Bounce Rate is 80%!” – How I Diagnosed and Fixed It
Seeing an 80% bounce rate on my new landing page felt like a punch to the gut. Visitors were arriving and leaving immediately. My diagnosis involved Google Analytics (checking bounce rate by source and device) and Hotjar (heatmaps/recordings). GA showed the bounce rate was worst on mobile. Hotjar revealed users scrolled briefly then left. The culprit? A slow-loading hero image and a headline that didn’t match my ad copy. Optimizing the image and aligning the headline drastically reduced the bounce rate to under 40%.
Setting Up Google Analytics 4 (GA4): My Painless Guide for Beginners
I procrastinated setting up GA4, fearing a complex migration from Universal Analytics. When I finally tackled it for a new project website, it was surprisingly straightforward for a basic setup. I created a new GA4 property, followed the Setup Assistant prompts, chose “Data Streams” > “Web,” and copied the Measurement ID. Using a simple WordPress plugin (like GA Google Analytics), I pasted the ID, and tracking started within minutes. While advanced features require learning, the initial setup process itself was quite painless. Don’t delay!
How I Use Heatmaps to See EXACTLY What Users Do On My Website
Analytics told me which pages users visited, but not what they did there. My product page wasn’t converting well. I installed Microsoft Clarity (a free heatmap tool). The click maps showed users repeatedly clicking on an image expecting it to enlarge (it didn’t). Scroll maps revealed most users never reached the testimonials section lower down. Based on this visual evidence of actual user interaction, I made the image clickable and moved testimonials higher, leading to improved engagement and conversions. Heatmaps reveal the hidden story.
A/B Testing My Call-to-Action: The Small Change That Increased Clicks by 27%
My landing page CTA button read “Download Now.” It felt standard. Curious, I decided to A/B test it against a more benefit-oriented version: “Get Your Free Checklist.” Using Google Optimize (free A/B testing tool), I split traffic between the two versions for two weeks. The results were clear: the “Get Your Free Checklist” button had a 27% higher click-through rate. This simple wording change, emphasizing the value received, significantly impacted user action and demonstrated the power of testing even small elements.
Understanding User Flow in Google Analytics (And Optimizing the Journey)
I wanted to understand how visitors navigated my site after landing on the homepage. The Behavior > Behavior Flow report in Google Analytics visualized the common paths. I noticed a significant portion of homepage visitors went to my ‘About’ page, but then dropped off instead of proceeding to ‘Services’. Realizing the ‘About’ page lacked a clear next step, I added a prominent call-to-action button linking directly to the Services page, successfully optimizing that user journey and reducing drop-offs.
I Discovered My Most Popular Content Was NOT What I Thought (Thanks, Analytics!)
I poured hours into writing comprehensive “ultimate guides” for my blog, assuming they were my star performers. I checked the Behavior > Site Content > Landing Pages report in Google Analytics, filtering for organic traffic. To my surprise, my #1 landing page wasn’t an ultimate guide, but a short, specific “how-to” post I wrote years ago! This data completely shifted my content strategy, showing me the power of targeting specific, problem-solving queries over just broad topics. Analytics revealed true audience preference.
Setting Up Conversion Tracking: How I Know Which Marketing Efforts Pay Off
I was spending time on SEO, social media, and email marketing for my service website but had no idea what actually generated leads. I finally set up conversion tracking in Google Analytics. I defined Goals for key actions: submitting the contact form and clicking the phone number on mobile. Now, the Conversions > Goals reports clearly show which traffic sources (Organic Search, specific social campaigns via UTMs, email) are driving actual leads, allowing me to focus my efforts and budget on the channels that deliver real business value.
The Free Alternative to Hotjar I Use for Session Recordings
I loved the insights from Hotjar’s session recordings (watching replays of user visits) but couldn’t justify the cost for my small blog. I discovered Microsoft Clarity. It’s a completely free analytics tool offering heatmaps, scroll maps, and crucially, detailed session recordings. I could watch how users navigated, where they hesitated, and where they encountered frustration (“rage clicks”), gaining invaluable qualitative insights into user experience and identifying usability issues – all without paying a cent. It’s an amazing free resource.
“Dark Traffic” in Analytics: Where Are These Visitors Really Coming From?
My Google Analytics showed a large chunk of traffic categorized as “(direct) / (none)”. I initially thought these were all people typing my URL directly. I learned much of this is actually “dark traffic” – its true source obscured. This includes clicks from email clients, messaging apps, links shared in documents, traffic from secure (HTTPS) to non-secure (HTTP) sites, and sometimes misconfigured tracking. Understanding dark traffic means acknowledging GA source data isn’t always perfect and emphasizes the importance of using UTM parameters for trackable campaigns.
How I Use UTM Parameters to Track Campaign Performance Like a Pro
I promoted my new ebook via email, a Facebook ad, and a link in my Twitter bio. All pointed to the same landing page. Without tracking, I wouldn’t know which channel worked best. I started using UTM parameters – short codes added to URLs (?utm_source=facebook&utm_medium=cpc&utm_campaign=ebook_launch). Now, in Google Analytics’ Campaigns report, I can see exactly how many visits, leads, and even sales originated from each specific link, proving the Facebook ad drove the most conversions. UTMs provide crucial campaign clarity.
My Custom Google Analytics Dashboard That Shows Me Everything at a Glance
Logging into Google Analytics felt overwhelming; I wasted time navigating different reports to find key info. I created a Custom Dashboard. Using widgets, I pulled just the essential data onto one screen: overall traffic trend, conversion rate, top 5 landing pages, traffic sources by goal completion, and bounce rate. Now, my daily check takes 60 seconds, gives me an immediate overview of site health and performance against goals, and helps me quickly spot any anomalies without getting lost in endless data tables.
The Difference Between Users, Sessions, and Pageviews (Finally Explained!)
These core Google Analytics metrics used to confuse me. Here’s the simple analogy I use: Imagine a single person (User) visits your website twice today. Each visit is a Session. During their first session, they look at 3 different pages (Pageviews). During their second session, they look at 2 pages. So, for today, you’d have 1 User, 2 Sessions, and 5 Pageviews. Understanding this basic distinction is fundamental to interpreting website traffic data correctly and avoiding miscommunications about performance.
I Found Out Why People Were Leaving My Checkout Page (Analytics Detective Work)
My e-commerce store saw lots of “Add to Carts,” but few completed purchases. Using the Goal Funnel Visualization report in Google Analytics for my checkout process, I saw a massive drop-off specifically between the cart page and the step where users entered shipping information. Hypothesis: shipping costs were the issue. I A/B tested displaying estimated shipping costs directly on the cart page. Conversions improved significantly! Analytics pinpointed the exact friction point in the funnel.
Using Google Search Console Data to Improve My Website Content
Google Search Console (GSC) is an SEO goldmine often overlooked for content insights. I regularly check the Performance report > Queries tab. It shows search terms people used where my site appeared in results. I look for queries with high impressions but low click-through rates (CTR). This tells me Google thinks my page is relevant, but my title/description isn’t compelling enough. Rewriting those snippets based on the queries people actually use significantly boosts CTR and traffic.
How I Measure the ROI of My Blog Content Using Analytics
Blogging took significant time, but was it paying off? To measure ROI, I first set up Goal tracking in Google Analytics for lead form submissions (assigning an estimated value per lead, say fifty dollars). Then, I analyzed which blog posts drove visitors who eventually completed that goal (using landing page reports or multi-channel funnels). By comparing the estimated value generated from blog-referred leads against the time/cost invested in creating that content, I could demonstrate a positive return on investment for my blogging efforts.
The “Site Search” Report: Uncovering What Your Visitors Are Desperate to Find
I enabled Site Search tracking in Google Analytics (under Admin > View Settings). The Behavior > Site Search > Search Terms report became incredibly valuable. It showed me exactly what keywords visitors were typing into my website’s own search bar. I discovered people were frequently searching for topics I hadn’t covered adequately or for features hidden deep in my navigation. This direct user feedback guided my content creation strategy and highlighted areas where site navigation needed improvement.
My Monthly Analytics Review Process: Turning Data into Action
Data without action is useless. My monthly Google Analytics review process ensures insights lead to improvements: 1. Review KPIs: Check key metrics (traffic, conversions, bounce rate) against last month and last year. Note trends. 2. Analyze Channels: Which sources drove the most/least valuable traffic? 3. Review Content: Identify top and worst performing pages/posts. 4. Identify Issues: Look for concerning trends (e.g., mobile bounce rate increasing). 5. Formulate Actions: Based on findings, define 1-3 specific, actionable tasks for the next month (e.g., “Update underperforming blog post X,” “Test new CTA on page Y”).
Avoiding “Vanity Metrics”: Focusing on Data That Drives Real Growth
When I first started using analytics, I obsessed over total Pageviews and social media Likes. These felt good but didn’t impact my business goals. I learned to ignore these “vanity metrics.” Instead, I focused on metrics directly tied to growth: Conversion Rate (leads/sales), Cost Per Acquisition (CPA), Customer Lifetime Value (CLV), and metrics indicating engagement quality like Average Session Duration or Goal Completion Rate. Focusing on actionable data that reflects actual business health led to smarter decisions and sustainable growth.
How I Segment My Audience in Google Analytics for Deeper Insights
Looking at overall website data often hides important nuances. I started using Segments in Google Analytics. For example, I created segments for “Mobile Traffic,” “Organic Traffic,” “Visitors from USA,” and “New vs. Returning Visitors.” Comparing metrics across these segments revealed crucial insights: my mobile conversion rate was terrible, organic visitors engaged longer than social referrals, and returning visitors were far more likely to convert. This allowed me to tailor strategies and optimizations for specific audience groups effectively.
The Impact of Ad Blockers on Your Website Analytics (And How to Account for It)
I noticed a discrepancy between my server logs (showing total hits) and Google Analytics pageviews. A likely culprit? Ad blockers. Many ad blockers also block common analytics tracking scripts like Google Analytics. This means my GA data was likely underreporting my actual traffic, potentially by 10-30% or more depending on my audience. While precise measurement is difficult, it’s important to be aware that client-side analytics data may not capture 100% of visitors due to blockers and privacy tools.
I Compared My Analytics Before and After a Redesign – The Surprising Shifts
After launching a major website redesign focused on improving mobile usability, I eagerly compared Google Analytics data for the month before and after launch. Key shifts: Mobile bounce rate decreased by 20%. Average session duration on mobile increased significantly. Crucially, the mobile conversion rate for my primary goal (form submission) improved by nearly 50%. While desktop metrics remained stable, the data clearly validated that the mobile-focused redesign successfully improved user experience and goal completion for that critical segment.
Using Analytics to Identify Your Website’s Weakest Pages (And How to Fix Them)
My website had hundreds of pages; some performed poorly, dragging down overall metrics. I used the Behavior > Site Content > All Pages report in Google Analytics, sorting by Bounce Rate (high to low) and % Exit (high to low). This quickly identified my “weakest” pages – the ones users abandoned most often. I then analyzed these specific pages qualitatively: Was the content thin? Was the CTA unclear? Did it load slowly? Fixing the issues on these specific underperformers improved overall site engagement.
My “Aha!” Moment with Website Analytics That Changed Everything
I struggled for months trying various marketing tactics with mediocre results. My “Aha!” moment came while digging into the Acquisition > All Traffic > Referrals report in Google Analytics. I noticed a tiny amount of traffic coming from a very specific, niche online forum. While the volume was low, the Average Session Duration and Goal Conversion Rate for that referral source were exceptionally high. I realized focusing my engagement efforts on that specific community was far more valuable than broad, untargeted marketing. Data revealed my true audience.
I Discovered “Ghost Spam” Was Skewing My Analytics – Here’s How I Filtered It
My Google Analytics reports showed traffic spikes from weird referral sources like “seo-scammer-website.com” with 100% bounce rate and 0 seconds session duration. This wasn’t real traffic; it was “ghost spam” – bots hitting the GA tracking code directly without visiting my site. It inflated my traffic numbers inaccurately. I fixed it by creating Filters in GA Admin: one to include traffic only from my valid hostname, and others to exclude known spam referrers. Cleaning this spam provided much more accurate data.
How I Use “Event Tracking” in GA4 to Understand Micro-Conversions
Beyond just pageviews, I wanted to understand how users interacted within pages. Using GA4’s event tracking (set up via Google Tag Manager), I started tracking “micro-conversions”: clicks on outbound links, PDF downloads, video plays (start, progress, complete), and scroll depth percentage. Monitoring these events provided valuable insights into content engagement and user interest levels, revealing which elements were most effective even if users didn’t complete a macro goal like a form submission on that visit.
The “User Explorer” Report: Following Individual User Journeys on My Site
To understand complex user behavior, especially for debugging conversion funnels, I used the Explore > User explorer report in GA4. This allowed me to drill down into anonymized data for individual users, seeing the exact sequence of pages they visited, events they triggered, and time spent across multiple sessions. While privacy-protected, observing these specific user journeys helped identify confusing navigation loops, points of friction, and common paths taken (or avoided) leading up to conversion or abandonment.
I Set Up “Intelligence Alerts” in Analytics to Catch Problems Before They Grow
A sudden drop in traffic or conversions could go unnoticed for days if I didn’t manually check analytics constantly. I leverage GA4’s Insights & recommendations feature, which automatically detects anomalies (like unusual traffic spikes/drops or conversion rate changes) and surfaces them on the dashboard. I also configured Custom Insights to email me immediately if specific critical metrics (e.g., daily revenue drops by > 40%, 404 errors surge) crossed defined thresholds. These alerts provide proactive warnings about potential issues.
The Difference Between “Client-Side” and “Server-Side” Analytics Tracking (And Why It Matters)
Standard Google Analytics uses client-side tracking: a JavaScript snippet runs in the user’s browser. This is easy to implement but can be blocked by ad blockers or browser privacy settings. Server-side tracking sends data directly from my website’s server to the analytics platform. It’s more accurate (bypassing blockers) and gives more control over data sent, but is significantly more complex and costly to set up (often requiring Google Tag Manager’s server container). Understanding this helps choose the right tradeoff between ease-of-implementation and data accuracy/control.
How I Use Google Data Studio to Create Beautiful Custom Analytics Reports
Presenting raw Google Analytics data to clients or my team was often confusing. I started using Google Data Studio (now Looker Studio) – a free tool. I connected my GA4 data source and easily created custom, branded reports with interactive charts, graphs, and scorecards highlighting only the Key Performance Indicators (KPIs) that mattered most. It transformed dense data into clear, visual stories, making insights much easier to understand, share, and act upon compared to navigating standard GA reports.
Tracking “Scroll Depth”: Are People Actually Reading My Long Articles?
I invested heavily in long-form blog content but worried users weren’t reading past the first few paragraphs. I implemented scroll depth tracking using Google Tag Manager, sending events to GA4 when users scrolled past 25%, 50%, 75%, and 90% of a page. Analyzing these events showed that on my key articles, a high percentage of users were scrolling down significantly (often >75%), validating that the content was engaging and worth the investment in length. It provided concrete data on reader engagement.
The “Exit Pages” Report: Why Are People Leaving FROM THESE Pages?
The Engagement > Pages and screens report in GA4, when analyzed for exits, revealed which pages were the final ones users viewed before leaving my site. I noticed my pricing page had an unusually high exit rate. Investigating the page, I realized the pricing tiers were confusing, and there wasn’t a clear next step or comparison. Simplifying the pricing layout and adding a clear “Contact Sales” CTA significantly reduced exits from that critical page, keeping more potential customers engaged.
How I Track My Website’s Performance Against My Competitors (Ethically)
While I can’t see competitors’ private analytics, I use ethical methods for benchmarking. Tools like SimilarWeb provide estimated traffic volume, traffic sources, and audience demographics for competitor domains (use estimations cautiously). I monitor their public activities: content publishing frequency, social media engagement levels, keyword rankings (using SEO tools like Semrush), and advertising efforts (using ad libraries). This external view provides valuable context for my own site’s performance and helps identify potential strategic opportunities or threats in the market.
Understanding “Assisted Conversions” in Google Analytics (It’s Not Just Last Click!)
My GA reports showed most sales attributed to “Direct” traffic, making other channels seem ineffective. I explored the Advertising > Attribution > Model comparison report in GA4. Comparing the default “Last click” model to others like “First click” or “Data-driven” revealed that channels like social media or initial organic searches often played a crucial role earlier in the customer journey, “assisting” conversions even if they weren’t the final touchpoint. This highlighted the importance of nurturing channels throughout the funnel.
I Used “Form Analytics” to Discover Why My Contact Form Had a Low Completion Rate
My contact form page had views, but few actual submissions came through. Standard analytics couldn’t tell me why. I integrated Hotjar Forms (or similar tools like Microsoft Clarity forms). The analysis showed most users started filling out the form but abandoned it specifically on the “Budget” field, which was required. Many also hesitated on the phone number field. Making the budget field optional and clarifying why the phone number was needed dramatically increased the form’s completion rate.
The Best Free Alternatives to Google Analytics (And Why You Might Consider Them)
While Google Analytics is powerful, privacy concerns and GA4’s complexity led me to explore free alternatives. Matomo (formerly Piwik) offers a self-hosted option giving full data ownership. Open Web Analytics (OWA) is another open-source, self-hosted choice. For simpler needs focused purely on privacy-first stats, platforms like Umami or Plausible Analytics (while often paid, have very limited free tiers or open-source versions) offer minimalist, cookieless tracking. These alternatives prioritize data control and user privacy over GA’s extensive feature set.
How I Track “Offline Conversions” Back to My Website Traffic
My local service business gets leads both online (forms) and offline (phone calls). To measure the website’s full impact, I needed to track calls. I used dynamic call tracking software (like CallRail). It displays unique trackable phone numbers on my website based on the visitor’s source (e.g., Google Ad click vs. organic search). When someone calls that number, the system attributes the call back to the original online source. Integrating this data allowed me to see the true ROI of website traffic generating valuable offline phone conversions.
The “Real-Time” Analytics Report: Uses and Misuses
Launching a new ad campaign, I found myself glued to the GA4 Real-Time report, watching visitor dots appear. While exciting, it’s mostly a vanity check. Practical uses: 1. Verifying tracking code is working immediately after installation. 2. Checking if campaign UTM parameters are registering correctly as traffic arrives. 3. Monitoring immediate visitor surges during a live event or promotion. Misuses: Making significant strategic decisions based on small, fluctuating real-time numbers. It’s a snapshot, not deep analysis.
I Set Up Custom “Dimensions and Metrics” in GA4 – Unlocking Deeper Insights
Standard GA4 reports didn’t capture everything important for my membership site, like the member’s subscription level or login status. I learned to set up Custom Dimensions (for attributes like “Subscription Plan”) and Custom Metrics (for counts like “Articles Read”) via Google Tag Manager sending data to GA4. Now, I can segment reports by subscription plan to see how different member tiers engage, providing far more granular, business-specific insights than default reporting allowed.
How I Track the Performance of My “Lead Magnets” Using Analytics
I offered three different PDF guides as lead magnets on my site, but didn’t know which was most effective at attracting quality subscribers. For each lead magnet, I created a unique Thank You page shown after signup. I then set up GA4 conversions based on visits to each specific Thank You page URL. Now, I can easily see in my conversion reports exactly how many downloads each lead magnet gets and, crucially, which traffic sources are driving downloads for each specific offer.
The “Attribution Modeling” Mess: Which Model Should You Actually Use?
Attribution modeling (assigning credit for conversions across multiple touchpoints) feels confusing in GA4. Last-click is default but incomplete. First-click oversimplifies. Linear ignores timing. Data-driven (GA4’s preferred) is complex but aims to be most accurate. My approach: Understand that no model is perfect. Use the Model Comparison Tool to see how different models value your channels. Focus on trends and relative contributions rather than absolute precision. Often, using the default Data-Driven model and comparing it occasionally to Last Click provides sufficient directional insight.
I Used Analytics to Personalize My Website Content – And It Worked!
My website served two distinct audience segments with different needs. Analytics showed clear differences in content consumption based on referral source. Using Google Optimize integrated with Analytics, I set up personalization rules. Visitors arriving from Source A (interested in Topic X) saw a homepage banner promoting content about Topic X. Visitors from Source B (interested in Topic Y) saw a banner promoting Topic Y. This simple analytics-driven personalization increased click-through rates on the homepage banners significantly.
How to Spot “Data Sampling” in Google Analytics (And What to Do About It)
When running complex reports with long date ranges in Universal Analytics (less common in standard GA4 reports), I sometimes saw a yellow shield icon indicating the data was sampled (based on a subset, not all sessions). This can lead to inaccuracies. Solutions: In UA, shorten the date range, simplify the report (fewer dimensions), or increase sampling precision (if available). In GA4, standard reports are generally unsampled, but complex explorations might be. Being aware helps interpret data cautiously. For absolute precision on huge datasets, BigQuery export or GA360 might be needed.
My “KPI Dashboard” for My Website: Tracking What Truly Matters for Growth
Lost in endless GA reports, I created a focused Key Performance Indicator (KPI) dashboard using Looker Studio. It tracks only the vital signs for my business website: 1. Total Leads/Sales (Goal Completions). 2. Conversion Rate. 3. Average Lead/Sale Value. 4. Organic Traffic Sessions. 5. Cost Per Acquisition (if running ads). 6. Email Subscribers Acquired. Reviewing this single dashboard weekly keeps me laser-focused on the metrics that truly indicate progress towards my core business objectives, cutting through the noise.
The “Content Grouping” Feature in Analytics: Organizing Your Data for Clarity
My blog had hundreds of posts across various categories (e.g., ‘Beginner Tips’, ‘Advanced Strategies’, ‘Case Studies’). Analyzing performance by individual post was overwhelming. I set up Content Grouping in GA. I defined rules based on URL structure or page titles to group posts into logical categories. Now, the Behavior > Site Content > Content Grouping report lets me easily compare the overall traffic, engagement, and conversion performance of entire content categories, revealing broader strategic insights about which topics resonate most.
How I Use Analytics to Improve My Website’s Internal Linking Strategy
Internal linking boosts SEO and user engagement, but I wasn’t sure where to add links effectively. My process using GA: Identify my top landing pages (high traffic, good engagement) using the Landing Pages report. Identify important target pages I want to rank better but have less traffic. Then, strategically add relevant, contextual internal links from the high-traffic pages to the important target pages. Analytics helps prioritize which pages have “link equity” to share and which pages would benefit most from receiving it.
The “Mobile vs. Desktop” Performance Gap I Found in My Analytics (And How I Fixed It)
My overall website conversion rate looked decent. However, segmenting Google Analytics data by Device Category revealed a huge gap: Desktop conversion rate was 4%, while Mobile was only 1.5%! Investigating further (using mobile speed tests and usability checks), I found the mobile checkout process was slow and difficult to use. Optimizing images specifically for mobile, simplifying the mobile form layout, and ensuring large tap targets significantly improved the mobile conversion rate, closing that performance gap.
I Used Funnel Visualization to Optimize My Sales Process – Big Wins!
My online course had a 4-step signup funnel: Landing Page > Info Page > Checkout Page > Thank You Page. I set this up as a Goal Funnel Visualization in Universal Analytics (or Exploration Funnel in GA4). The visualization clearly showed a massive 60% drop-off between the Info Page and the Checkout Page. Analyzing the Info Page, I realized the “Enroll Now” button was below the fold. Moving the button higher significantly reduced the drop-off and increased overall course enrollment by over 20%.
My “Pre-Holiday” Analytics Check: Ensuring My Site is Ready for Traffic Surges
Running an e-commerce store, holiday traffic spikes are crucial but risky. My pre-holiday analytics checklist: Review Past Performance: Analyze last year’s holiday traffic sources, peak times, and top-selling products in GA. Check Capacity: Ensure server resources can handle projected traffic increases (discuss with host). Test Speed: Run load tests simulating high traffic. Verify Tracking: Double-check conversion tracking, coupon codes, and promotion UTMs are working perfectly. Optimize Key Pages: Ensure top landing/product pages are fast and mobile-friendly. Using past data prepares for future success.