Content teams move fast — publishing articles, testing emails, launching landing pages. But tracking what’s actually working? That’s where things get messy. 

Most platforms collect plenty of data (clicks, scrolls, sign-ups), but making sense of it is another story.

This is where SQL(Structured Query Language) comes in. As a simple language, it’s designed to help marketers work with data stored in content databases. It enables tracking and boosting content performance, simplifies content data analysis, and drives conversions.

In this quick guide, you’ll find ready-to-run SQL queries that turn scattered click logs into clear, conversion-driving insights.

The importance of monitoring content performance

Once your content piece goes live, you have two options: publish and walk away or publish and watch. The first costs nothing now but sacrifices growth opportunities. 

The second turns feedback into an advantage, transforming each click into clear guidance and actionable results. 

Every scroll, click, and sign-up tells a story. The teams that know how to track and learn from that data are the ones that keep improving. If you don’t do that, your competitors will, and then it’s pretty much a game over for you.

What to focus on when monitoring content performance

Keep your eyes on the seven essential markers that prove readers stick, share, and eventually buy:

1. Conversion rate: assessing if content drives business goals
2. Organic traffic growth: more visits signal rising interest in your pages
3. Traffic sources mix: know which channels deliver readers, not just numbers
4. Backlinks earned: external votes that nudge Google and expand reach
5. Average time on site: longer stays hint that the content matches visitor intent
6. Click-through rate: compelling calls persuade readers to take the next step
7. SERP ranking lift: higher positions capture attention before clicks even happen

Track these seven, and you’ll spot quick wins — like updating headlines that stall or doubling down on topics fans devour.

Finally, remember that data alone won’t rewrite copy; people will. Review numbers as a team, debate the “why,” and let each new post borrow the lessons from the previous one.

Why SQL still works for content performance analysis

SQL plugs straight into your data so you get answers fast, without waiting on devs.

Structured Query Language — better known as SQL, was first sketched at IBM in the early 1970s and was published formally in 1974. Half a century later, its syntax remains almost unchanged, which is exactly why it endures.

SQL is declarative: you declare what you want, not how to get it. This declarative style makes SQL marketing analytics both powerful and approachable for busy teams. It keeps queries readable, e.g., “show me page views this week, grouped by URL”, and shareable among teammates who have never written a line of code elsewhere.

Here’s what makes SQL the best friend for marketers for slicing through content metrics:

1. Quickly combines data from different marketing tables
2. Enables data filtering using commands that feel close to natural language
3. Effortlessly calculates summaries (e.g., totals, averages, conversions, or counts) for key performance indicators (KPIs)
4. Compatible with all major data storage systems used in marketing and analytics
5. Free to learn, no license required

The format grows with you, too. When your content appears on a broader set of blogs, or you ramp up your guest posting strategy, you can pull each domain’s data into one SQL query for side-by-side evaluation and comparison in like-for-like.

Crucially, the language doesn’t lock you into a single vendor. Whether your data lives in BigQuery, Snowflake, or a scrappy MySQL box, the same SQL command (called a SELECT statement) used to pull specific data from a database travels with you, along with insights on views, conversions, and leads.

So, while many shiny modern tools promise one-click magic, SQL still wins on clarity, portability, and time-tested effectiveness. Master a handful of common SQL structures (called patterns) now, and every future analytics tool or data platform will feel familiar.

Basic SQL queries for non-technical teams

Not everyone wants to live in a command-line window, but a few short SQL snippets (this term in SQL means small, ready-to-paste blocks of full SQL queries) will save you hours of spreadsheet work. Copy, paste, and watch the numbers appear.

1. Page-views leaderboard

To instantly spot your top traffic drivers, paste this page-views leaderboard snippet:

SELECT page_url,
       COUNT(*) AS views
FROM   page_views
GROUP  BY page_url
ORDER  BY views DESC;

This snippet returns one row for each unique page in your site, with a views column that counts exactly how many times that page was viewed. By sorting those rows with ORDER BY views DESC, you instantly see your highest-traffic pages at the top of the list, making it easy to spot your biggest content winners without any duplicate URLs in the results.

2. Unique visitors per article

To see how many individual readers each article attracts, run the unique visitors per article query:

SELECT page_url,
       COUNT(DISTINCT user_id) AS uniques
FROM   page_views
GROUP  BY page_url
ORDER  BY uniques DESC;

This metric and relevant snippet filter out repeated visits and show you the real size of your audience for each piece. 

3. Average time on page

Run this bigger SQL query to find out exactly how long, on average, users stick around each page:

WITH hits AS (
  SELECT user_id,
         page_url,
         MIN(event_time) AS first_hit,
         MAX(event_time) AS last_hit
  FROM   page_events
  GROUP  BY user_id, page_url
)
SELECT page_url,
       ROUND(AVG(EXTRACT(EPOCH FROM last_hit - first_hit))/60, 1) AS avg_minutes
FROM   hits
GROUP  BY page_url
ORDER  BY avg_minutes DESC;

Even a rough time-on-page metric tells you which pieces hold attention.

Run these three snippets each week, drop the results into a simple chart, and you’ll know exactly where to double down, where to cut, or update your content.

How to segment and personalize insights with SQL

SQL's possibilities for analyzing content performance stretch far beyond basic engagement metrics. This chapter will explain how to segment performance data by industry, location, device, or traffic source using basic filters.

1. Segmenting by traffic source

Knowing the source of the incoming traffic that your content generates is the first step to understanding why visitors land on your pages at all. 

Organic, paid, and referral channels each signal different user intent — searchers arrive with questions, ad-clickers arrive with expectations, and referral readers arrive with borrowed trust. Splitting them apart lets you tailor messages instead of lumping every session into a single bucket.

One quick way to see the spread is to run:

SELECT traffic_source,
       COUNT(*) AS visits
FROM   page_views
WHERE  traffic_source IN ('organic','paid','referral')
GROUP  BY traffic_source
ORDER  BY visits DESC;

That single line gives you a tidy count for each channel, so you can spot imbalances at a glance. Add AND page_url = '/your-landing-page' if you want to focus on one piece of content.

Next, go deeper with conversion data:

SELECT traffic_source,
       COUNT(*) FILTER (WHERE converted = TRUE) * 100.0 / COUNT(*) AS conversion_rate
FROM   sessions
GROUP  BY traffic_source;

Now you’ll see whether your ad budget really outperforms organic, or if referrals secretly win on loyalty. Armed with that intel, you can boost spend on channels that convert, tighten copy where engagement lags, or chase guest posts on sites already sending high-quality traffic. 

Small SQL tweaks, big clarity. That’s the essence of SQL marketing analytics — With the right queries, you can spot what’s really working across your channels.

2. Segmenting by user type

Statistics without context are just tales, as numbers alone don’t tell the full story, and can even mislead you if you don’t understand what’s behind them. 

Context arrives when you tag sessions by the industry they belong to, the map where they come from, and the screen in their hand.

Industry velocity first:

SELECT industry,
       COUNT(*) FILTER (WHERE event = 'demo_request') AS demos
FROM   users
JOIN   events USING (user_id)
GROUP  BY industry;

A quick tip: industries requesting demos deserve dedicated case studies.

Now, add a regional reach:

SELECT region,
       COUNT(*) AS sessions
FROM   sessions
GROUP  BY region;

Plot this on an interactive map and you might discover untapped pockets of interest. 

If you make geographic segmentation of your audience a habit, your outreach will be less about guessing and more like shooting a sniper rifle with a scope.

Device shift:

SELECT device_type,
       SUM(conversions) * 1.0 / COUNT(*) AS conv_rate
FROM   sessions
GROUP  BY device_type;

If you spot lower mobile conversion, consider re-evaluating checkout flow on small screens or optimizing SEO for mobile devices in general.

Segmenting by user type isn’t extra polish; it’s the steering wheel for your roadmap.

How segmentation helps inform personalization or retargeting

Personalization and retargeting thrive on context. Segmentation hands you that context in tidy rows and columns.

Instead of treating every bounce as a lost sale, you can see if the user bounced after reading FAQs or after hitting the pricing wall.

Suppose you see that manufacturing executives from Germany often browse your ROI calculators at 8 a.m. their time. Armed with that intel, you can schedule LinkedIn ads promoting a German-language calculator white paper right before breakfast. 

Meanwhile, U.S. freelancers who binge on your “getting started” content on mobile might receive a simplified onboarding email instead of a dense PDF guide.

Here’s how segmented data powers personal touches:

• Schedule ads when users browse your pages.
• Tailor CTAs to device context (e.g., shorter and sharper CTAs on mobile).
• Show currency based on the user’s current location.
• Highlight features by industry pain (e.g., in healthcare, users look for safety testing and adherence to regulations).
• Adjust the tone for new visitors, who need to be engaged first.

These tweaks feel small, yet they add up to highly personalized user experiences. That’s the difference between an ignored impression and a remembered brand, and when a matching retargeting ad reinforces those tailored touches, it draws hesitant prospects back to complete the journey.

Analyzing content conversion performance

Clicks and page views are nice, but conversions pay the bills. In this chapter, we’ll show how a few targeted SQL queries pinpoint which articles drive sign-ups, and which merely rack up impressions.

Using SQL to identify high-converting content pages

A page that converts at 3% is worth more than one that converts at 0.3%, even if the latter brings twice the traffic. SQL helps quantify that truth.

Combine views and events:

SELECT pv.page_url,
       COUNT(*) FILTER (WHERE ev.event_type = 'signup') AS signups,
       COUNT(*)                                        AS views,
       ROUND(
         COUNT(*) FILTER (WHERE ev.event_type = 'signup') * 100.0 / COUNT(*),
         1
       ) AS signup_rate
FROM   page_views pv
LEFT   JOIN events ev ON pv.session_id = ev.session_id
GROUP  BY pv.page_url
ORDER  BY signup_rate DESC;

Pages with stellar signup rates become templates for future content. That’s the kind of insight you only get from SQL in marketing, where data illuminates creative wins.

You should analyze their structure, placement of CTAs, and reading level, and do the same for your competitors’ pages. As a matter of fact, interactive website lead generation tools — like pricing estimators — often double form submissions.

For laggards, try testing new button colors, shorter forms, or stronger social proof. Repeat monthly and watch the lagging pages climb.

Using SQL to optimize lead-generating CTAs

Most marketers judge a CTA by gut feeling — red button, blue button, who knows? 

SQL replaces hunches with proof. By pairing click events with the pages that host them, you can see which words, colors, or positions actually persuade.

Start with a simple count:

SELECT cta_id,
       COUNT(*) AS clicks
FROM   cta_clicks
GROUP  BY cta_id
ORDER  BY clicks DESC;

Good, but if you only look at the total number of clicks on each CTA, the busiest pages will naturally rack up more clicks simply because more people saw them, not necessarily because their CTAs are more effective.

What you can do next is normalize by view, i.e., adjust raw click totals based on how many times the CTA was seen, so you get a true effectiveness rate rather than just a popularity count:

WITH views AS (
  SELECT page_url, COUNT(*) AS pageviews
  FROM   page_views
  GROUP  BY page_url
)
SELECT cc.cta_id,
       pv.page_url,
       COUNT(*) AS clicks,
       v.pageviews,
       ROUND(COUNT(*)*100.0 / v.pageviews, 2) AS click_rate
FROM   cta_clicks cc
JOIN   page_views pv ON cc.session_id = pv.session_id
JOIN   views      v ON pv.page_url   = v.page_url
GROUP  BY cc.cta_id, pv.page_url, v.pageviews
ORDER  BY click_rate DESC;

CTAs with high click rates, but modest impressions, are low-hanging fruit — place them higher on the page or reuse them elsewhere. CTAs with traffic but no love need copy tweaks or, perhaps, color and shape/size changes.

Using SQL to find underperforming content

Big traffic is flattering, but it can hide ineffective or low-quality content. Your goal is to pinpoint pages that attract visitors yet fail to keep them reading, clicking, or converting. 

SQL offers a quick, repeatable way to surface those problems so you can fix them before Google demotes them and readers bounce for good.

Start by pairing page views with average time on page:

WITH views AS (
  SELECT page_url,
         COUNT(*) AS total_views
  FROM   page_views
  GROUP  BY page_url
),
dwell AS (
  SELECT page_url,
         ROUND(AVG(time_on_page_seconds),1) AS avg_seconds
  FROM   session_stats
  GROUP  BY page_url
)
SELECT v.page_url,
       v.total_views,
       d.avg_seconds
FROM   views v
JOIN   dwell d USING (page_url)
WHERE  v.total_views > 500
  AND  d.avg_seconds < 30
ORDER  BY v.total_views DESC;

Pages with >500 views and <30 seconds average dwell time top the priority fix list. Maybe the introduction makes big claims, but the rest feels disappointing, or large images push your key text too far down.

 Check formatting, tighten the lead, or add a clearer CTA.

Next, layer in conversions to spot high-traffic, low-yield pages:

SELECT p.page_url,
       v.total_views,
       c.leads,
       ROUND(c.leads*100.0 / v.total_views,2) AS conv_rate
FROM   views v
JOIN   conversions c USING (page_url)
JOIN   popular_pages p USING (page_url)
WHERE  conv_rate < 0.5;      -- adjust threshold

If a page converts under 0.5%, try shorter forms, clearer headlines, or extra social proof nearby. Running these queries regularly turns guesswork into a clear improvement list, saving time and protecting your ROI.

How to turn SQL insights into client-ready content dashboards with secure access

If your content performance data lives in SQL databases like MySQL, PostgreSQL, or SQL Server, adding Softr to your workflow makes that data much easier to use and show clients. This is especially true for non-technical teams needing fast answers instead of raw tables.

Softr connects directly to your SQL data and gives you a clean, visual interface to segment and share insights without writing additional code or relying on developers.

Why it works for measuring content performance:

• Fast and easy to set up: Pre-built templates and a drag-and-drop builder let you launch in minutes — ideal for small teams that don’t want a complex setup process.
• Real-time deep sync: Connect your data directly to Softr with real-time, two-way data sync — no CSV exports or manual updates. Changes made in your Softr app update your SQL database and vice versa.
• Custom views for every stakeholder: Build tailored dashboards for marketing, sales, or clients, each with different filters, layouts, and permissions. Everyone sees what matters to them in real time, without duplicate work.
• Secure data handling: Control access with custom user groups and granular permissions, ensuring sensitive data is only visible to the right users.
• Segment SQL data for end users: Use dropdown filters and visual toggles to show performance by source, device, industry, or campaign.
  
  Supported SQL databases include:
  
  • PostgreSQL
  • SQL Server
  • MariaDB
  • MySQL 

See how Make built a Softr app on top of MySQL data.

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Why SQL is your simplest next step

The main takeaway? Data can feel heavy. SQL keeps it simple — helping you analyze what’s working and stay focused on what matters.

From spotting top-performing pages to diagnosing leaks in conversion flow, a handful of clear queries delivers answers faster than any one-click dashboard. 

SQL allows you to keep things simple. Just by running your scripts weekly and letting the numbers steer, you can achieve many things:

• Track the core engagement metrics.
• Rank pages by conversion rate before traffic.
• Normalize CTA clicks by views.
• Slice results by channel, device, and region.
• Promote high-rate pages and revive low dwell pieces.
• Turn every insight into one next action.

SQL’s portability means you can lift the core queries given in this article into any future warehouse without rewriting a line. Easy, right? 

Want to turn those SQL insights into something your whole team (or client) can actually use? Softr lets you build custom content dashboards on top of your SQL data — no code, no delays.

Connect your database, add filters, and share live insights in minutes.

👉 Try Softr for free and start building content dashboards your whole team can use — no code required.