Alun Lucas’ Post

Cracking the Code: Understanding Google Ads Algorithm for Keyword Grouping Step 2: Advanced Grouping with search terms data Although google ads platform has a lot of in-built report capabilities, it has its limitations so for this step we will be exporting the data out of google ads into a sheet & or directly connect google ads data source into your choice of visualization tool. Both Google Looker studio & Power BI will be efficient for the task, for this particular analysis I am using Power BI. Throughout this step, our sole aim is to unravel the art of grouping. For now, impressions remain our only ad metric of interest. First visualization gives you an idea on what the top trending terms are & how they are distributed. One thing you can look for here is whether or not the spread looks even. We will relate that back to why that might be in “search term trigger analysis across existing keywords” Next chart is to analyze the search term variety for each keyword. Here you will be observing if some keywords are triggering more terms than others & if anything specific stands out about the nature of that keyword Chart 3 evaluates impression volume per term relative to its corresponding keyword. While a tabular form is viable, I'm partial to its depiction as a stacked chart. This format simplifies pattern identification through color blocks. Additionally, the individual representation of each column—100% of a stacked column—de-emphasizes impression counts and fosters a focus on term distribution within keywords Chart 4: For this one, we are distributing it into two steps to ensure we can focus on one thing at a time. This step can be seen as a vice-versa of the second visualization. So if you compare these two side by side & apply some cross filtering for each value, you will uncover some patterns which may differ from one brand to another. One thing that you will find very common in most data sets at this stage is that there is usually a lot of variety in search terms as compared to your triggering keyword, so the values for this chart are going to be much more even throughout as compared to our second visualization #5 This might seem like a lot of data so let me help you with how to read & what to observe & get out of this one. Firstly, start with the rows of totals, you can sort this from high to low or low to high. A general observation is that the broad match has a much higher search term spread & from the search terms side, the long tail tends to be on the lower end of your totals column. Later you will be deciding on your preferred match types & keyword elimination based on your observations in this step. If you have different ad groups, you can also analyze cross-category search term triggers if there are any & why they might be happening, we will be getting deeper on the action plan in the next steps for now, stay focused on grouping & understanding the algorithm #KeywordAnalysis #PowerBI #SearchTermAnalysis #DataAnalytics

SEO is easy, ✅ Initial Search Console Analysis ✅ Search Console Segmented Query Analysis ✅ Search Console Device-Based Non Brand Query/Impression Analysis ✅ Keyword/Query Trajectory Analysis ✅ Competitor SERP Performance Over Time ✅ Website Index Analysis ✅ Supplemental / Omitted Result Analysis ✅ Page Level Query Performance Analysis ✅ Cannibalisation Audit ✅ Low-Quality Content Audit ✅ Initial Site Content Audit ✅ Duplicate & Cannibalising Content Evaluation ✅ Query to Landing Page Relevancy Alignment ✅ Site URL Architecture Analysis ✅ URL Consistency Evaluation ✅ Full Tag Evaluation (Canonicals, HREFLANG etc) ✅ Search Console Coverage Evaluation ✅ Landing Page Construction (Template Evaluation) ✅ Title Performance (CTR) / Title Rewrites ✅ Meta Description Evaluation ✅ Site Crawl & Reported Header Codes ✅ Re-Direction & Re-Direct Chains ✅ Internal Linking Audit ✅ Use of Headings & Content Relation Evaluation ✅ Page Accessibility Element Review ✅ Site Navigation & Footer Evaluation ✅ Domain Trust Factors Review ✅ Sub Domain Evaluation ✅ Link Audit (Full Referring Domain Review) ✅ Link Performance Over Time & Distribution ✅ Anchor Text Audit ✅ Competitor Link Performance Evaluation ✅ Link Spam Review ✅ Page Experience / Core Web Vitals Review ✅ Google Tag Checks ✅ Google Analytics Initial Audit ✅ Overall SEO Performance Summary ✅ Aggregated Query / Click to Query Vol Analysis ✅ Primary & Secondary Impression Audit ✅ Competitor Site Content Evaluation ✅ Query Intent to Landing Page Evaluation ✅ Topical Clustering Analysis ✅ Search Console Link Evaluation (Internal) ✅ Search Console Link Evaluation (External) ✅ Above the Fold vs Centre Fold vs Low Fold Content Evaluation ✅ Other Known Brand Based Domain Evaluation ✅ Site Behaviour Flow Analysis ✅ Segmented Site Behaviour Flow Analysis ✅ Guided Recommendations ✅ SEO Priority Resolution List ✅ Aggregated Query / Click to Query Vol Analysis ✅ Primary & Secondary Impression Audit ✅ Competitor Site Content Evaluation ✅ Query Intent to Landing Page Evaluation ✅ Topical Clustering Analysis ✅ Search Console Link Evaluation (Internal) ✅ Search Console Link Evaluation (External) ✅ Above the Fold vs Centre Fold vs Low Fold Content Evaluation ✅ Other Known Brand Based Domain Evaluation ✅ Site Behaviour Flow Analysis ✅ Segmented Site Behaviour Flow Analysis ✅ Guided Recommendations ✅ SEO Priority Resolution List ✅ SERP Analysis (SERP Changes Over Time) ✅ Wayback Analysis & Site Review ✅ Competitor Wayback Analysis & Site Review ✅ NLP Topical Analysis ✅ Search Console Reported Link Audit (vs Third Party) ✅ Link Equity Distribution Evaluation (Internal vs. External) ✅ Tiered Link Audit ✅ SERP Aggregated Link Power Audit ✅ Historic Penalty Checks / Reviews ✅ Google Update & Query Coverage Review ✅ Landing Page Engagement Factors (LPEF) ✅ Content Strategy / Support Guidance ✅ Link Building & Digital PR #seo #seoiseasy #seo2024 #seoconsultant

Understanding web analytics is crucial for online success. The right tools can turn data into actionable insights. Here are my favorite tools for web analytics that help me make informed decisions. Google Analytics ↦ Google Analytics is a must-have. It's free, powerful, and comprehensive. It provides detailed insights into your website traffic. Adobe Analytics ↦ Adobe Analytics offers advanced features. It's perfect for large enterprises needing in-depth analysis. Hotjar | by Contentsquare ↦ Hotjar is fantastic for understanding user behavior. Heatmaps and session recordings reveal how visitors interact with your site. Crazy Egg ↦ Crazy Egg is great for visual analytics. It shows where users click and how far they scroll. Easy to set up and insightful. Mixpanel ↦ Mixpanel focuses on user engagement. It tracks actions and provides insights into user behavior and retention. Matomo (formerly Piwik) ↦ Matomo is an open-source alternative. It offers full control over your data and powerful analytics features. Kissmetrics ↦ Kissmetrics is excellent for tracking individual user behavior. It helps you understand the customer journey and improve conversions. Clicky ↦ Clicky offers real-time analytics. It provides instant insights into your website traffic and user behavior. Chartbeat ↦ Chartbeat is designed for content creators. It offers real-time analytics to help you optimize content performance. Heap | by Contentsquare ↦ Heap automatically captures all user interactions. It saves time and ensures you don’t miss any data. Statcounter ↦ Statcounter is simple and effective. It offers essential analytics features without the complexity. Segment ↦ Segment simplifies data collection. It integrates with multiple tools, providing a unified view of your analytics. Amplitude ↦ Amplitude focuses on product analytics. It helps you understand user engagement and product usage. These tools have transformed my approach to web analytics. They provide the insights needed to optimize and grow. PS: What are your favorite tools for web analytics? #WebAnalytics #DigitalMarketing #AnalyticsTools #GoogleAnalytics #AdobeAnalytics #Hotjar #CrazyEgg #Mixpanel #Matomo #Kissmetrics #Clicky #Chartbeat #Heap #Statcounter #Segment #Amplitude #GrowthHacks #DataDrivenMarketing #MarketingSuccess

Want to understand how people are using and accessing your site? Well, this article may help! We've put together a list of 21 web analytics tools recommended by experts. For each entry, we include: 👉 An expert opinion 👉 Top features 👉 Alternatives 👉 Pricing So you have all the information you need in one place. These aren't in order of which we think is best, so please don't get offended! But we do like Google Analytics the best 😎 Special thanks to all the software and contributors! 1. Google Analytics 2. Open Web Analytics - (Luma Abu-ghazaleh, Uberall) 3. Optmyzr - (Navah Hopkins) 4. Fathom Analytics - (Danielle Hu, The Wanderlover) 5. Clicky - (Kseniia Mykolaienko, Parentaler) 6. Matomo 7. Hotjar 8. GoatCounter - (Jonathan Merry, Monefize) 9. Microsoft Clarity - (Steph Andrusjak) 10. Ahrefs - (Shane McEvoy( 11. Mixpanel - (Luciano Colos, PitchGrade) 12. Qlik 13. Cloudflare Analytics - (Burak Özdemir) 14. Kissmetrics (Max, Schwartzapfel Lawyers P.C.) 15. TinyAnalytics (Saikat Ghosh, Technource) 16. Woopra (Carly Syms Hill, virtualholidayparty.com) 17. HubSpot 18. Semrush 19. Adobe Analytics 20. Google Search Console (MEIPING MAK, Weave Asia) 21. Similarweb And thanks to Featured for helping to bring together these amazing experts. Check out the full article below! ⬇️ https://lnkd.in/gCDTP4ik

Google Analytics Removes 4 Models, Adds Custom Metrics Google Analytics drops four attribution models and adds custom metric combinations. Google Analytics is removing four attribution models in October 2023. A new "calculated metrics" feature will allow custom combinations of metrics. Users should audit reports and test new models now to prepare. #GoogleAnalytics #seo

Upgrade your Analytics 360 properties to Google Analytics 4 to preserve measurement and campaign performance. Good to Share #google #digitalmarketing #analytics360 #adwords #googleads

Google Analytics Removes 4 Models, Adds Custom Metrics Highlights: 1. Google Analytics is removing four attribution models in October 2023. 2. A new "calculated metrics" feature will allow custom combinations of metrics. 3. Users should audit reports and test new models now to prepare. Google has announced updates coming soon to Google Analytics 4 (GA4) properties. As of mid-October 2023, specific attribution models will be removed from the platform. At the same time, Google is introducing a new “calculated metrics” feature that allows you to create custom metrics tailored to your business needs. According to a statement from Google, the First Click, Linear, Time decay, and Position-based attribution models will be phased out for all GA4 properties next month. The default attribution will switch to paid and organic data-driven models for any accounts currently using those models. Other models like Last Click will remain available. Removing the rule-based attribution models marks a shift toward automated, AI-driven attribution in Google Analytics. Implications Of Removing Rule-Based Attribution Removing the four attribution models could impact marketers who have optimized campaigns and strategies around those models. This could push Google Analytics users toward Google’s AI-powered attribution models. This reliance on Google’s proprietary systems could make it harder to understand how attribution works. To offset this change, Google Analytics is launching calculated metrics – a way for users to combine standard or custom metrics using mathematical formulas. As an example, Google said a calculated “Item margin” metric could subtract “Item COGS” from “Item price.” “You can adjust any metric to fit your business needs or logic,” explains Google’s announcement. Calculated metrics allow weighting, discounting, and combining other metrics. Utilizing Calculated Metrics According to Google, calculated metrics enable direct decision-making by letting users incorporate fundamental business logic into the metrics. Users with admin access can build up to five calculated metrics per standard property or 50 for Analytics 360 properties. The new feature will be available across reports, explorations, and the Analytics API. While calculated metrics provide more customization, there are potential downsides. Allowing users to create complex calculated metrics could lead to confusion or inconsistencies across teams and reports. Proper governance will ensure that calculated metrics are well-documented and structured logically. Users will likely need training on how to build metrics that provide actual value rather than overly complex formulas. Preparing For The Changes To avoid these changes, Google recommends auditing your existing attribution setups. Any reports or strategies relying on the four removed models must be transitioned to alternate attribution methods. #seo #seonews #seotrends #googleseo #trending

Here's a list of 21 web analytics tools that I've collated by asking experts who use them. Special thanks to all the software and contributors! 1. Google Analytics 2. Open Web Analytics - (Luma Abu-ghazaleh, Uberall) 3. Optmyzr - (Navah Hopkins) 4. Fathom Analytics - (Danielle Hu, The Wanderlover) 5. Clicky - (Kseniia Mykolaienko, Parentaler) 6. Matomo 7. Hotjar 8. GoatCounter - (Jonathan Merry, Monefize) 9. Microsoft Clarity - (Steph Andrusjak) 10. Ahrefs - (Shane McEvoy) 11. Mixpanel - (Luciano Colos, PitchGrade) 12. Qlik 13. Cloudflare Analytics - (Burak Özdemir) 14. Kissmetrics (Max, Schwartzapfel Lawyers P.C.) 15. TinyAnalytics (Saikat Ghosh, Technource) 16. Woopra (Carly Syms Hill, virtualholidayparty.com) 17. HubSpot 18. Semrush 19. Adobe Analytics 20. Google Search Console (MEIPING MAK, Weave Asia) 21. Similarweb For each piece of software, I've included: 👉 An expert opinion 👉 Top features 👉 Alternatives 👉 Pricing So you have all the information you need in one place. Thanks Featured for helping to bring together these amazing experts. Check it out ⬇️ https://lnkd.in/gyBi--ct

As I'm still on the job hunt, let me take you on a deeper dive into a project in my portfolio - using #Tableau to create a workbook of multiple KPI Dashboards to assess User Trends, Retention, and Demographic data from publicly available Last.fm datasets (2005 - 2009). The Dashboard contains 5 different slides, each aimed at a different aspect of the company's goals and KPI tracking. This dashboard is both an example of my Tableau skills, and an understanding of identifying and tracking KPIs within a company over time. Ranging from looking at which Months during what Year(s) had the highest DAU, MAU, and DAU/MAU Ratio, to tracking those Users logins across a year to see when the service was most popular (and providing a Top N list of the busiest days) - the Dashboard tackles Active User KPIs in the first 2 slides. Hovering a column on the charts even showcases Day over Day/Month over Month changes, and showcases Total Plays, Active Users, and which Artists had the most Plays on those days (and how many Users tuned in). Year over year Churn of Users is tracked alongside changes in Total Users, User Session Length and demographics such as Male, Female, and Non-Identifying users. It also tracks total Churn for each individual year, showcasing what years had "soft Churn" where Users with the same ID came back later in the year against how many total Churned Users there were while providing definitions and analysis in case this was presented without a verbal component. The last two slides take a look at User metrics broken down by Country. Slide 4 has each country's total unique Users, how much of the Userbase that number makes up, and breaks down Gender, Age, and listening metrics such as Plays, Plays per User, and Artists listened to. The final slide is an interactive Map that takes those User metrics and creates a "Top N" artist plot, showcasing where in the World those artists may be popular and breaking down the most popular Songs and Artists with just a hover over the Country. It also showcases the biggest "Superfan" of each artist by displaying the User ID with the most Plays for that artist when hovered over. All of the Data was transformed using Tableau - I did not edit the data set using any other methods (no Excel, no SQL, etc). Take a look at the visualization and play around with it for yourself on my Tableau Public Profile: https://lnkd.in/gNBS6sjr #Dashboards #dashboarddesign #dataanalysis #dataanalytics #data #datavisualization #datavisualizationtools #lookingforwork #lookingforajob

Google Analytics is a web analytics service provided by Google that allows website owners and marketers to track and analyze user interactions with their websites. It provides valuable insights into how visitors engage with a website, what content they view, where they come from, and much more. Here are some key features and aspects of Google Analytics: 1.Traffic Analysis: Google Analytics helps you understand how users find your website. It tracks data related to the source of traffic, whether it's from search engines, social media, direct visits, or referrals from other websites. 2.User Behavior: It provides information on user behavior, such as the pages they visit, how long they stay on a page, and the actions they take (like clicking on links or making purchases). 3.Audience Insights: Google Analytics offers demographic and geographic data about your website visitors, allowing you to understand your audience better. 4.Conversion Tracking: You can set up conversion goals in Google Analytics to track specific actions you want users to take on your site, such as filling out a contact form, making a purchase, or signing up for a newsletter. 5.E-commerce Tracking: For e-commerce websites, Google Analytics can track product sales, revenue, and other transaction-related data. 6.Real-Time Reporting: It provides real-time data on the number of users currently on your website, the pages they are viewing, and their geographic location. 7.Custom Reporting: Google Analytics allows you to create custom reports and dashboards tailored to your specific needs. 8.Event Tracking: You can track user interactions that don't lead to a new page load, such as video views or file downloads. 9.Mobile App Analytics: In addition to web tracking, Google Analytics can be used to track user interactions within mobile applications. 10.Data Integration: It can be integrated with other Google products, such as Google Ads and Google Search Console, to provide a comprehensive view of your online presence. 11.Tag Manager Integration: Google Tag Manager can be used to manage and deploy tracking tags for Google Analytics without modifying your website's code. Google Analytics offers both free and premium versions (Google Analytics 360) with more advanced features for enterprise-level users. To use Google Analytics, you need to set up an account, add a tracking code to your website, and configure your account to collect and display the data you're interested in. This data is then presented in an easy-to-understand format within the Google Analytics dashboard, allowing you to make data-driven decisions to improve your website's performance and user experience.