Analysis overview and configuration
| Parameter | Value | _row |
|---|---|---|
| min_clicks | 5 | min_clicks |
| min_impressions | 50 | min_impressions |
| min_pageviews | 10 | min_pageviews |
| domain | mcpanalytics.ai | domain |
| content_type_regex | content_type_regex |
This cross-platform content performance analysis integrates Google Search Console (GSC) search visibility data with Google Analytics 4 (GA4) engagement metrics to classify 159 content pages into strategic quadrants. The analysis identifies which pages drive search traffic, which engage users, and where misalignments exist—enabling data-driven content optimization decisions.
Data preprocessing and column mapping
| Metric | Value |
|---|---|
| Initial Rows | 798 |
| Final Rows | 159 |
| Rows Removed | 639 |
| Retention Rate | 19.9% |
This section documents the data consolidation process that merged Google Search Console (GSC) and Google Analytics 4 (GA4) datasets through URL normalization. The 19.9% retention rate reflects the join operation's selectivity—only pages appearing in both platforms were retained for cross-platform performance analysis, which directly supports the stated objective of analyzing content performance by combining search visibility with engagement metrics.
The low retention rate is a direct consequence of the analysis design—the algorithm prioritizes data quality over volume by requiring URL matches across both GSC and GA4. This ensures that composite scores and quadrant assignments are based on complete information (search
| finding | value |
|---|---|
| Total pages analyzed | 159 |
| Match rate (GSC + GA4) | 25.1% |
| Star pages (high search + engagement) | 35 |
| SEO opportunities (high engagement, low search) | 45 |
| Content to optimize (high search, low engagement) | 45 |
| Best performing page | /articles/cox-proportional-hazards-practical-guide-for-data- |
| Top SEO opportunity | /whitepapers/whitepaper-pca |
| Biggest mismatch page | /articles/one-class-svm-practical-guide-for-data-driven-deci |
This analysis evaluates cross-platform content performance by matching Google Search Console (GSC) search visibility data with Google Analytics 4 (GA4) engagement metrics. The objective is to identify which content drives both search traffic and user engagement, and where strategic improvements can unlock value.
The portfolio is evenly split between three actionable segments. The 45 SEO opportunity pages represent the most attractive investment—they've already proven ability to engage users, requiring only improved search visibility
Quality matrix scatter plot classifying every matched page into quadrants based on search performance score vs engagement quality score
This section classifies your 159 matched pages into four performance quadrants based on search visibility (impressions, clicks, position) versus user engagement quality (bounce rate, session duration). Understanding this distribution reveals which content is working well, which needs optimization, and where untapped opportunities exist—directly supporting the goal of improving cross-platform content performance.
The distribution is nearly balanced across quadrants, indicating a diverse content portfolio with distinct optimization needs. The equal split between SEO Opportunities and Optimize Content (45 each) suggests your site has both untapped potential and underperforming high-traffic pages. The 22% Stars rate indicates room for improvement—most content isn't simultaneously strong in search and engagement, revealing systematic gaps in either discoverability or user experience.
Top performing pages ranked by composite search + engagement score — these pages excel at both attracting search traffic and engaging visitors
The Stars section identifies your highest-performing content—pages that simultaneously achieve strong search visibility and visitor engagement. These 35 pages represent proven content templates that successfully attract organic traffic while keeping users engaged, making them benchmarks for understanding what works across your content portfolio.
Star pages validate that your content strategy works when both SEO and UX elements align. The tight clustering of engagement scores (0.49–1.0, sd=0.13) suggests these pages share consistent quality attributes. However, the low average CT
High engagement but low search visibility pages ranked by engagement score — these are the highest-ROI targets for SEO investment
This section identifies 45 pages that demonstrate strong visitor engagement but rank poorly in search results (average position 10.7). These pages represent high-ROI SEO targets because they've already proven content-market fit through user behavior—improving their search visibility requires optimization rather than content creation.
These pages occupy a unique position in the content performance matrix: they convert engaged visitors but receive insufficient search exposure. The zero bounce rate and extended session durations confirm content quality and relevance. The gap between engagement strength (0.75 mean engagement score) and search visibility (position 10.7) indicates optimization opportunities rather than content failures. This cohort represents the highest-probability targets for SEO investment because ranking improvements directly amplify existing conversion potential.
This analysis assumes consistent GA4 tracking and GSC data from the same
High search visibility but low engagement pages with mismatch severity — these pages attract visitors but fail to engage them
| page_path | impressions | clicks | ctr | bounce_rate | avg_session_duration | mismatch_score |
|---|---|---|---|---|---|---|
| /articles/one-class-svm-practical-guide-for-data-driven-decisions | 1111 | 1 | 9.00e-04 | 1 | 0 | 0.8792 |
| /articles/bayesian-regularization-practical-guide-for-data-driven-decisions | 937 | 0 | 0 | 1 | 0 | 0.8555 |
| /articles/general-linear-models-glm-practical-guide-for-data-driven-decisions | 1451 | 4 | 0.0028 | 0.8889 | 1.5 | 0.8405 |
| /articles/price-elasticity-practical-guide-for-data-driven-decisions | 1923 | 2 | 0.001 | 0.8 | 1.4 | 0.8205 |
| /tutorials/how-to-use-mrr-analysis-in-stripe-step-by-step-tutorial | 697 | 2 | 0.0029 | 1 | 0 | 0.8144 |
| /articles/principal-component-analysis-pca-practical-guide-for-data-driven-decisions | 623 | 0 | 0 | 1 | 0 | 0.7988 |
| /articles/arima-practical-guide-for-data-driven-decisions | 1190 | 2 | 0.0017 | 0.8571 | 3.7 | 0.7894 |
| /tutorials/how-to-use-revenue-overview-in-stripe-step-by-step-tutorial | 371 | 1 | 0.0027 | 1 | 0 | 0.7269 |
| /articles/association-rules-apriori-practical-guide-for-data-driven-decisions | 979 | 2 | 0.002 | 0.75 | 7.9 | 0.6861 |
| /articles/intraclass-correlation-icc-practical-guide-for-data-driven-decisions | 1519 | 3 | 0.002 | 0.7143 | 46.1 | 0.6809 |
| /tutorials/how-to-use-discount-effectiveness-in-etsy-step-by-step-tutorial | 255 | 1 | 0.0039 | 1 | 0 | 0.6749 |
| /articles/customer-lifetime-value-ltv-practical-guide-for-data-driven-decisions | 219 | 0 | 0 | 1 | 0 | 0.6538 |
| /tutorials/how-to-use-listing-performance-comparison-in-etsy-step-by-step-tutorial | 197 | 0 | 0 | 1 | 0 | 0.6391 |
| /whitepapers/whitepaper-difference-in-differences | 191 | 0 | 0 | 1 | 0.2 | 0.6347 |
| /articles/xgboost-practical-guide-for-data-driven-decisions | 166 | 0 | 0 | 1 | 0 | 0.6155 |
| /whitepapers/whitepaper-factor-analysis | 166 | 0 | 0 | 1 | 0 | 0.6155 |
| /articles/propensity-score-matching-practical-guide-for-data-driven-decisions | 1018 | 3 | 0.0029 | 0.6667 | 26.9 | 0.6149 |
| /articles/holm-bonferroni-method-practical-guide-for-data-driven-decisions | 2327 | 3 | 0.0013 | 0.6364 | 114.2 | 0.6126 |
| /articles/logistic-regression-practical-guide-for-data-driven-decisions | 146 | 0 | 0 | 1 | 4.7 | 0.5925 |
| /whitepapers/whitepaper-fishers-exact | 563 | 3 | 0.0053 | 0.7143 | 4.4 | 0.5893 |
This section identifies 45 pages that achieve strong search visibility but fail to convert visitor interest into engagement. These pages attract traffic through search rankings but experience high bounce rates (mean 0.48, median 1.0), indicating a critical mismatch between search intent and page content. Understanding this gap is essential for the cross-platform analysis, as it reveals where SEO success masks content quality problems.
These pages represent a critical inefficiency in the content portfolio. While they successfully rank in search results, the extreme bounce rates and minimal session duration reveal that page content fails to satisfy visitor expectations set by search
Aggregate search and engagement metrics compared across content types (Articles, Tutorials, Homepage, etc.)
This section identifies which content formats drive the strongest cross-platform performance by comparing search visibility and user engagement metrics across six content types. Understanding format-level performance reveals whether certain content structures attract organic traffic but fail to engage users—or vice versa—informing both SEO and content quality strategies.
Articles attract significant organic search traffic but visitors don't engage deeply, suggesting content quality or relevance gaps. Conversely, Blog content achieves strong engagement despite lower search prominence, indicating format resonates with users once they arrive. The Homepage's exceptional performance across all metrics reflects its structural advantage in both discoverability and user interaction. This pattern directly supports the overall analysis
Diagnostic view of pages found in only one data source — GSC-only pages may have tracking gaps, GA4-only pages reveal non-search traffic
| page_path | impressions | clicks | ctr | position |
|---|---|---|---|---|
| /articles/spectral-clustering-practical-guide-for-data-driven-decisions | 2136 | 0 | 0 | 16.2 |
| /articles/price-elasticity-practical-guide-for-data-driven-decisions.html | 1350 | 0 | 0 | 10.5 |
| /articles/xgboost-practical-guide-for-data-driven-decisions.html | 1258 | 0 | 0 | 20.5 |
| /articles/session-based-recommendations-practical-guide-for-data-driven-decisions | 930 | 0 | 0 | 7 |
| /articles/holm-bonferroni-method-practical-guide-for-data-driven-decisions.html | 853 | 2 | 0.0023 | 8.8 |
| /blogs/content/stripe-card-brands-matter | 797 | 0 | 0 | 8.5 |
| /articles/cox-proportional-hazards-practical-guide-for-data-driven-decisions.html | 643 | 0 | 0 | 21.4 |
| /whitepapers/whitepaper-fishers-exact.html | 480 | 2 | 0.0042 | 13.1 |
| /articles/decision-trees-practical-guide-for-data-driven-decisions | 430 | 1 | 0.0023 | 18.1 |
| /whitepapers/whitepaper-icc.html | 412 | 1 | 0.0024 | 12.4 |
| /articles/intraclass-correlation-icc-practical-guide-for-data-driven-decisions.html | 411 | 1 | 0.0024 | 10.1 |
| /articles/var-vector-autoregression-practical-guide-for-data-driven-decisions.html | 411 | 1 | 0.0024 | 13.6 |
| /whitepapers/whitepaper-umap | 409 | 0 | 0 | 7.8 |
| /blogs/content/stripe-card-brands-matter.html | 338 | 0 | 0 | 8.9 |
| /whitepapers/whitepaper-chi-square | 323 | 0 | 0 | 9.6 |
| /articles/anova-practical-guide-for-data-driven-decisions | 315 | 0 | 0 | 14.5 |
| /whitepapers/whitepaper-kolmogorov-smirnov.html | 305 | 0 | 0 | 11.4 |
| /whitepapers/whitepaper-ancova.html | 291 | 0 | 0 | 13.6 |
| /tutorials/how-to-use-revenue-overview-in-stripe-step-by-step-tutorial.html | 281 | 0 | 0 | 9 |
| /whitepapers/whitepaper-tsne.html | 281 | 0 | 0 | 15.4 |
| page_path | screenPageViews | totalUsers | sessions | bounceRate |
|---|---|---|---|---|
| /login | 109 | 66 | 82 | 0.1585 |
| /signup | 60 | 50 | 51 | 0 |
| /tutorials/how-to-use-tax-and-fee-analysis-in-amazon-step-by-step-tutorial | 34 | 36 | 36 | 0.3889 |
| /machine-learning | 27 | 25 | 25 | 0.8 |
| /chat/f7e6be32-67bd-41c1-9954-d50dd09383ab | 17 | 1 | 6 | 0 |
| /catalog | 8 | 2 | 2 | 0 |
| /chat/cb62dedc-82c6-4df5-be70-57c045467b4b | 5 | 1 | 2 | 0 |
| /account-app | 4 | 1 | 1 | 0 |
| /chat/29f89cdf-58d2-43ad-8f71-3bc959990b42 | 4 | 1 | 1 | 0 |
| /chat/8d01257f-dc85-4cf0-9e39-8567859dfe38 | 4 | 1 | 3 | 0.3333 |
| /chat/9211bec2-4d03-41b4-bdeb-2ccd5bd10b81 | 4 | 1 | 2 | 0 |
| /chat/cae22414-bd52-421a-ac03-cb90dd445318 | 4 | 1 | 1 | 0 |
| /chat/db1bb4c3-3a2c-406f-bab3-dc4ba556d9f1 | 4 | 1 | 1 | 0 |
| /account | 3 | 3 | 3 | 0.6667 |
| /analysis/reports/commerce__amazon__orders__business_vs_individual | 3 | 3 | 3 | 0 |
| /authors | 3 | 3 | 3 | 0 |
| /blogs | 3 | 2 | 2 | 0 |
| /chat/13482160-e8d7-486e-9e0d-833d1aac19d9 | 3 | 1 | 1 | 0 |
| /chat/3ba85522-cffe-4dc9-bbfb-32806381a7f4 | 3 | 1 | 1 | 0 |
| /chat/4710ca8d-8182-4194-8dc1-d73553fec3be | 3 | 1 | 1 | 0 |
This section identifies pages that exist in only one data source, revealing potential tracking gaps and traffic source diversity. The 25.1% match rate indicates significant data fragmentation—292 pages appear in Google Search Console (GSC) but lack GA4 engagement data, while 183 pages show GA4 traffic without search visibility. Understanding these mismatches is critical for validating data completeness and identifying pages driven by non-search channels.
The low match rate reflects a portfolio with diverse traffic sources beyond organic search. GSC-only pages represent untapped conversion opportunities if GA4 tracking is properly installed. GA4-only pages (including login, signup