SEO: rewrite meta descriptions, add FAQ schema, add CTA box to all articles

- Rewrite meta descriptions on 4 high-impression articles (churn, compliance, data quality, ecommerce)
- Fix data-quality-validation-pipelines title & description to capture zero-click statistical validation queries
- Add FAQPage schema to churn prediction and data quality articles
- Add service CTA box to article-footer.php (appears on all blog articles)
- Add responsive CSS for CTA box in main.css

blog/articles/predictive-analytics-customer-churn.php

@@ -4,7 +4,7 @@ header('Strict-Transport-Security: max-age=31536000; includeSubDomains');
 
 // Article-specific SEO variables
 $article_title = "Predictive Analytics for Customer Churn: Reduce Churn by 35% (2026 Guide)";
-$article_description = "Cut B2B SaaS churn by 35% with predictive models. Feature engineering, UK benchmarks & free checklist. Start reducing churn today.";
+$article_description = "See how UK B2B SaaS companies reduce churn by up to 35% using predictive analytics. Practical guide with feature engineering steps, UK benchmarks, free checklist & real model examples.";
 $article_keywords = "customer churn prediction, predictive analytics, machine learning, customer retention, churn model, data science";
 $article_author = "UK Data Services Analytics Team";
 $canonical_url = "https://ukdataservices.co.uk/blog/articles/predictive-analytics-customer-churn.php";
@@ -1785,5 +1785,46 @@ $read_time = 14;
     });
     </script>
 <script src="../../assets/js/cro-enhancements.js"></script>
+
+<script type="application/ld+json">
+{
+  "@context": "https://schema.org",
+  "@type": "FAQPage",
+  "mainEntity": [
+    {
+      "@type": "Question",
+      "name": "What is predictive analytics for customer churn?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "Predictive analytics for customer churn uses machine learning models to identify customers who are likely to cancel or stop using your service, allowing you to intervene proactively. Models are trained on historical behaviour data including usage patterns, support tickets, billing history and engagement metrics."
+      }
+    },
+    {
+      "@type": "Question",
+      "name": "How much can predictive analytics reduce churn?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "UK B2B SaaS companies using predictive churn models typically see churn reductions of 25-35%. Results depend on model accuracy, the quality of intervention strategies, and how early at-risk customers are identified. The 35% figure assumes a well-tuned model with a 90-day prediction horizon and an active customer success programme."
+      }
+    },
+    {
+      "@type": "Question",
+      "name": "What data do you need to predict customer churn?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "The most predictive features include: product usage frequency and depth, support ticket volume and sentiment, login frequency, feature adoption breadth, billing history and payment failures, NPS scores, and contract renewal dates. You need at least 6-12 months of historical data with known churn outcomes to train a reliable model."
+      }
+    },
+    {
+      "@type": "Question",
+      "name": "Is predictive churn modelling suitable for small UK SaaS companies?",
+      "acceptedAnswer": {
+        "@type": "Answer",
+        "text": "Yes, though you need a minimum dataset of roughly 500-1000 historical churn events to train a statistically reliable model. Smaller companies can start with simpler logistic regression models and progress to gradient boosting as data accumulates. Even basic early-warning scoring outperforms reactive customer success approaches."
+      }
+    }
+  ]
+}
+</script>
 </body>
 </html>