User Behaviour Analytics

Your Complete Guide to Understanding Visitor Behaviour

What You'll Learn

This guide shows you how to use SERP360's Page Analysis dashboard to understand exactly how visitors interact with your webpages. You'll discover where people click, how long they spend reading, and what makes them convert or leave.

What you can do:

• Track visitor scrolling patterns across your pages
• See which content sections get the most engagement
• Compare converted customers with visitors who drop off
• Filter by traffic source, device type, and date ranges
• Generate AI-powered insights with actionable recommendations

Compare Converted vs Drop-off Users

What Are User Cohorts?

Cohorts are groups of visitors who share similar characteristics or behaviours. Your dashboard tracks several types of cohorts:

Conversion-based cohorts:

• Converted Users: Visitors who completed your conversion goal (purchase, signup, download, etc.)
• Drop-off Users: Visitors who started your conversion process but didn't complete it
• All Users: Combined data from all visitors regardless of conversion status

Traffic source cohorts:

• Google referrers: Visitors arriving from search results
• Social media cohorts: Users from Facebook, Twitter, LinkedIn, etc.
• Direct traffic: Visitors who typed your URL directly
• Email campaigns: Users clicking from email marketing
• Paid advertising: Traffic from Google Ads, Facebook Ads, etc.

Device cohorts:

• Mobile users: Smartphone visitors
• Desktop users: Computer-based visitors
• Tablet users: iPad and tablet visitors

Each cohort reveals different behaviour patterns and optimization opportunities.

How Cohort Views Work

Single Cohort Analysis: Select specific cohorts to focus on particular user segments:

• Conversion cohorts: See how converted vs drop-off users behave differently
• Traffic source cohorts: Understand how Google users differ from social media visitors
• Device cohorts: Compare mobile vs desktop interaction patterns
• Combined filtering: Analyse converted mobile users from Google, for example

Comparison View: Select "Comparison" to see converted and drop-off users side by side:

• Spot critical differences in scroll patterns between user types
• Identify content that converts vs content that loses visitors
• Find zones where behaviour diverges between successful and unsuccessful journeys

Reading Cohort Data

What to look for in converted user data:

• Sections where they spend more time than average
• Content they scroll back to review
• Click patterns that lead to conversion
• Device-specific successful behaviours

What to look for in drop-off user data:

• Points where they exit the page
• Sections they skip or scroll through quickly
• Areas with high confusion (frequent revisits)
• Barriers that prevent conversion completion

What comparison view reveals:

• Content that works differently for each group
• Conversion tipping points
• Device-specific conversion barriers
• Traffic source behaviour differences

Practical Cohort Applications

Reduce drop-off rates:

1. Analyse where drop-off users typically exit
2. Review content and design at those points
3. Simplify complex sections or add clarifying information
4. Test different approaches with similar user segments

Increase conversion rates:

1. Identify what converted users focus on
2. Make those elements more prominent
3. Replicate successful patterns in other sections
4. Guide drop-off users toward conversion-focused content

Optimise for different audiences:

1. Compare behaviour across traffic source cohorts using filters
2. Create targeted experiences for high-converting referrer cohorts
3. Address specific concerns of different user segments
4. Develop device-specific strategies based on cohort performance
5. Test messaging that resonates with specific traffic source cohorts

Set Up Your Filters

Accessing Your Dashboard

Navigate to the Page Analysis section in your main menu. You'll see a comprehensive dashboard with several filter controls at the top and multiple charts below.

Understanding the Filter Controls

Before diving into your data, set up your filters to focus on what matters most:

Domain Select: Choose which website you want to analyse.

Page Select: Pick a specific webpage or select "All Pages" for overview data.

Referrer Filter: See data from specific traffic sources:

• All Referrers: Everyone who visits your page
• Google.com: Visitors from Google search
• Facebook.com: Social media traffic
• Direct: People who typed your URL directly

User Type Filter: Compare different visitor behaviours:

• All: Everyone who visits
• Converted: Visitors who completed your goal (purchase, signup, etc.)
• Drop-off: Visitors who started but didn't complete your conversion process
• Comparison: Side-by-side view of converted vs drop-off visitors

Date Range: Choose your timeframe (7, 14, 30, or 90 days).

Device Type: Filter by desktop, mobile, tablet, or all devices.

Read Your Performance Charts

1. Scroll Depth vs Time Spent

What it shows: How long visitors spend reading at each section of your page compared to how deep they scroll.

How to read it: This line chart plots time spent (vertical axis) against scroll depth (horizontal axis). Look for peaks where visitors linger and valleys where they rush through content.

Key insights:

• High peaks = engaging content that holds attention
• Flat lines = visitors skimming without reading
• Sharp increases = content that makes people slow down and read carefully
• Device differences = mobile users often spend less time but may scroll further

2. Clicks vs Scroll Depth

What it shows: Where visitors click at different parts of your page.

How to read it: Each bar represents clicks in 5% scroll zones (0-5%, 5-10%, etc.). Higher bars mean more clicking activity in that section.

Key insights:

• High clicks near the top = strong call-to-action placement
• No clicks in conversion areas = buttons might be hard to find
• Unexpected click spikes = visitors trying to interact with non-clickable elements
• Comparison mode = see how converted vs drop-off users click differently

3. Average Scroll Velocity

What it shows: How quickly visitors scroll through different sections, compared to average reading speed (200 words per minute baseline).

How to read it: The chart shows scroll speed as percentage deviation from normal reading pace. Positive numbers mean faster scrolling, negative numbers mean slower.

Key insights:

• Slower than baseline (-20% to -50%) = careful reading or engaging content
• Faster than baseline (+30% to +100%) = skimming or searching behaviour
• Extreme speeds (+200%) = visitors jumping past content entirely
• Device patterns = mobile users typically scroll faster than desktop

4. Zone Revisit Frequency

What it shows: How often visitors scroll back to re-read specific sections of your page.

How to read it: Higher percentages mean visitors frequently return to that section, which can indicate either valuable content or confusing information.

Key insights:

• High revisit rates (>30%) = either very valuable content or confusing sections
• Low revisit rates (<10%) = content that's clear on first read
• Pattern spikes = key decision points or important information
• Comparison differences = converted users may revisit pricing, while drop-offs revisit unclear sections

5. Referrer Pages vs Sessions

What it shows: Which websites send you the most traffic and how many pages those visitors typically view.

How to read it:

• Column chart (single page selected): Shows session volume from each referrer
• Scatter plot (all pages view): X-axis shows pages referred, Y-axis shows total sessions

Key insights:

• High sessions, low pages = visitors arriving but not exploring further
• High sessions, high pages = quality traffic that engages deeply
• Referrer quality = compare conversion rates across different traffic sources
• Traffic source behaviour = social media vs search vs direct traffic patterns

Analyse Different User Behaviours

Converted vs Drop-off Analysis

When you select "Comparison" mode, you'll see data for both converted visitors and those who dropped off. This comparison reveals crucial insights:

What to look for:

• Where converted users spend more time = content that drives conversions
• Where drop-off users exit = friction points that need attention
• Different scroll patterns = content that resonates differently with each group

Common patterns:

• Converted users often scroll further and spend more time on key sections
• Drop-off users may skip important information or get stuck on confusing content
• Device differences can reveal mobile vs desktop conversion barriers

Run a Full AI Analysis Report

Understanding Comprehensive Analysis

The Comprehensive Analysis helps you turn visitor behaviour data into specific improvements for your website. This AI system examines how people interact with your pages and provides detailed recommendations.

How It Works

Step 1: Select your page, filters, and date range Step 2: Click "Comprehensive Page Analysis" Step 3: Wait 30-60 seconds while SERP360's AI processes your data Step 4: Receive a detailed report with zone-by-zone insights

What makes it special:

• Context-aware analysis - Considers your specific filters (converted users, mobile traffic, etc.)
• Screenshot integration - Analyses your actual page layout alongside behaviour data
• Actionable recommendations - Provides exact copy suggestions and UX improvements
• Priority scoring - Tells you what to fix first for maximum impact

Understanding Your SERP360 AI Report

Executive Summary

Three high-impact opportunities with specific metrics. These aren't generic suggestions – they're based on your actual visitor data.

Example insight: "67% of mobile visitors drop off at 45% scroll depth where your pricing section begins, while desktop users (23% drop-off) continue reading. Consider mobile-specific pricing presentation."

Zone-by-Zone Analysis

For each 5% section of your page, you'll see:

What We Saw:

• Engagement metrics - Exact click and visitor numbers
• Reading behaviour - Scroll velocity and time spent data
• Content elements - What's actually visible in that zone

What It Means:

• Behavioural interpretation - Why visitors act this way in this section
• User intent analysis - What visitors are trying to accomplish
• Problem identification - Specific friction points or missed opportunities

Detailed Recommendations

Each recommendation includes:

Priority Classification:

• Immediate - Critical issues affecting >50% of visitors or causing 0% engagement
• Near-term - Important improvements with 20-50% visitor impact
• Can-wait - Optimisations with <20% impact or already performing well

Complete Implementation Guide:

• Zone identification - Exact scroll depth location (e.g., "15-25%")
• Current state - What exists now based on your screenshot
• Recommended change - Detailed implementation steps
• Copy suggestions - Exact headlines, button text, or content improvements in quotes
• Justification - Specific metrics explaining why this will help

Context-Aware Intelligence

The AI adapts its analysis based on your selected filters:

Analysing Converted Users:

• Focuses on successful patterns that drive conversions
• Identifies high-converting content and layout elements
• Recommends replicating successful patterns in low-performing zones

Analysing Drop-off Users:

• Identifies friction points and conversion barriers
• Focuses on reducing cognitive load and improving clarity
• Recommends simplification and clearer guidance

Comparison Mode:

• Compares behaviour patterns between converted and drop-off visitors
• Identifies differentiating factors between users who convert and those who abandon
• Recommends changes to reduce drop-off rates and increase conversions

Traffic Source Analysis:

• Google traffic - Focuses on search intent matching and information hierarchy
• Social media traffic - Emphasises visual engagement and social proof elements
• Direct traffic - Leverages brand familiarity for deeper engagement

Device-Specific Insights:

• Mobile recommendations - Touch-friendly design, simplified navigation, faster loading
• Desktop insights - Detailed content presentation, complex interaction patterns
• Cross-device patterns - Identifies where mobile and desktop experiences should differ

Advanced AI Features

Screenshot Analysis Integration

The SERP360 AI examines your actual webpage screenshot to:

• Identify specific UI elements causing issues
• Recommend exact placement improvements
• Suggest visual hierarchy changes
• Provide pixel-perfect design recommendations

Pattern Recognition

The system identifies:

• Content formats that drive conversions for your specific audience
• Layout patterns that correlate with higher conversion rates
• Navigation issues that cause visitor drop-off
• Conversion funnel optimisations based on user flow data

Competitive Intelligence

When possible, the AI considers:

• Industry-standard engagement patterns
• Content type best practices (blog posts vs product pages vs landing pages)
• Device-specific user expectations
• Traffic source behaviour norms

Getting Maximum Value from AI Analysis

Before Running Analysis

Ensure sufficient data:

• Minimum 100 visitors in selected timeframe
• At least 2 weeks of tracking data
• Multiple device types represented

Choose meaningful filters:

• Focus on specific user segments or traffic sources
• Compare time periods around major changes
• Analyse high-traffic, low-conversion pages first

Implementing Recommendations

Start with "Immediate" priority items:

1. Address 0% engagement zones first
2. Fix major drop-off points (>50% visitor loss)
3. Implement quick copy improvements

Plan "Near-term" improvements:

1. Schedule design changes requiring development
2. Coordinate content updates across teams
3. Test one recommendation at a time

Consider "Can-wait" optimisations:

1. Add to future roadmap planning
2. Bundle with other design updates
3. Use for A/B testing inspiration

Measuring Success

Track these metrics post-implementation:

• Scroll depth improvements in target zones
• Time spent increases in optimised sections
• Click-through rate improvements on updated CTAs
• Overall conversion rate changes

Re-run analysis:

• Wait 2-3 weeks after changes
• Compare new page analysis report with original
• Identify successful patterns for other pages

Adding Recommendations to Your Workflow

Project Management Integration

Click the "+" button on any recommendation to automatically create project tasks with:

• Clear titles and descriptions
• Implementation requirements
• Priority levels and deadlines
• Background data and success metrics

Team Collaboration

For designers:

• Specific UI/UX improvement requirements with pixel-perfect guidance
• Visual hierarchy recommendations based on engagement data
• Mobile vs desktop design considerations and responsive patterns
• Colour, typography, and layout optimizations for conversion zones

For copywriters:

• Exact headline and content suggestions with character limits
• Tone and messaging guidance based on user behaviour patterns
• User-intent-based copy direction for different scroll zones
• Content structure recommendations (bullets vs paragraphs, length guidelines)
• Call-to-action copy optimization with A/B testing suggestions

For developers:

• Technical implementation requirements for UX improvements
• Performance improvement suggestions based on drop-off patterns
• Cross-device compatibility notes and responsive design requirements
• Loading optimization for high-engagement content sections

Troubleshooting Page Analysis

"No Data Available" Errors

Common causes:

• Insufficient visitor volume in timeframe
• Selected filters too restrictive
• Page tracking not properly configured

Solutions:

• Extend date range to capture more visitors
• Broaden device or referrer filters
• Verify tracking implementation

Unexpected Recommendations

Why this happens:

• Analysis identifies patterns humans might miss
• User behaviour differs from designer assumptions
• Mobile vs desktop experiences vary significantly

How to validate:

• Cross-reference with raw chart data
• Test recommendations on small user segments
• Compare with industry benchmarks

Generic-Sounding Suggestions

This might indicate:

• Insufficient data for personalised insights
• Very common user behaviour patterns
• Need for longer data collection period

Improvement strategies:

• Collect data for additional weeks
• Focus analysis on specific user segments
• Combine multiple page analyses for broader insights

Understand Traffic Source Differences

Referrer-Specific Analysis

Different traffic sources behave differently. Use the referrer filter to understand:

Google Search Traffic:

• Often looking for specific information
• May scroll quickly to find relevant sections
• Usually has clear intent

Social Media Traffic:

• More likely to skim content initially
• May need stronger visual hooks
• Often mobile-heavy

Direct Traffic:

• Familiar with your brand
• May have specific goals in mind
• Often converts at higher rates

Device-Specific Patterns

Mobile Visitors:

• Scroll faster but may read less thoroughly
• Need clear, concise content sections
• Require larger, thumb-friendly buttons

Desktop Visitors:

• More likely to read detailed content
• Can handle more complex layouts
• Often spend longer on decision-making

Implement Your Improvements

Quick Wins

Immediate Content Improvements

Immediate UX Improvements

Advanced Optimisations

Longer-term Content Improvements

Content restructuring based on user flow:

• Reorganise page sections based on visitor scroll patterns
• Create content funnels that guide users through logical progression
• Develop mobile-specific content strategies for different engagement patterns
• Build content libraries based on high-performing section templates

Personalised content strategies:

• Create different content versions for various traffic sources
• Develop device-specific content presentations
• Build user journey-specific landing pages
• Implement dynamic content based on referrer data

Content performance optimization:

• A/B test headlines in zones with low engagement
• Experiment with content length in high drop-off areas
• Test visual content placement based on scroll velocity data
• Optimize content reading level for faster comprehension

Longer-term UX Improvements

Conversion funnel optimization:

• Design progressive disclosure patterns based on scroll depth data
• Create device-specific user interface adaptations
• Implement smart form placement based on engagement zones
• Build responsive design patterns that adapt to user behaviour

Performance and loading optimization:

• Optimize page loading for sections with high immediate drop-off
• Implement lazy loading for content below high-engagement zones
• Reduce cognitive load in areas where users revisit frequently
• Create seamless transitions between content sections

Advanced interaction design:

• Implement scroll-triggered animations to guide attention
• Create sticky elements for high-conversion content
• Design contextual help for areas with high confusion (revisit patterns)
• Build interactive elements that respond to user engagement data

Adding Insights to Your Project Management

Click the "+" button on any AI recommendation to add it directly to your project management system. This creates actionable tasks with:

• Clear implementation steps
• Priority levels
• Background data and justification

Fix Common Data Issues

Build Your Review Process

Regular Review Schedule

Data-Driven Decision Making

Before making changes:

1. Gather at least 2 weeks of data
2. Look for consistent patterns across devices
3. Consider seasonal or campaign influences
4. Test one change at a time to measure impact

After implementing changes:

1. Monitor for 1-2 weeks minimum
2. Compare before and after data
3. Document what worked (and what didn't)
4. Apply successful patterns to other pages

Getting the Most Value

Focus on:

• Pages with high traffic but low conversion rates
• Sections where converted and drop-off users behave differently
• Mobile vs desktop performance gaps
• Seasonal pattern changes

Remember:

• Small improvements can have big impacts
• Consistent monitoring beats sporadic deep dives
• User behaviour insights are goldmines for content strategy

Quick Reference

Key Metrics to Watch

Filter Combinations That Reveal Insights

• Mobile + Converted users: See what works on mobile
• Google traffic + Drop-off: Find search intent mismatches
• Desktop + Comparison: Identify desktop-specific conversion barriers
• Social referrers + All users: Understand social media visitor behaviour

Need Help?

For technical support or questions about implementing recommendations, contact our SERP360 support team