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User Behaviour Analysis Framework

Interpret user behaviour evidence and turn patterns into CRO actions and test hypotheses.

Marketing - CRO - User Behaviour Analysis Framework

Who it's for

CRO specialists, Growth marketers, UX researchers, Marketing managers, Product teams

Get Ready

Prepare the Required Inputs listed in the Workflow Prompt. Use as much detail as necessary.

How to use this prompt

1. Copy the Workflow Prompt.
2. Paste it into your AI tool.
3. Replace the "Required Inputs"
4. Run the prompt.

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Workflow Prompt

				
					You are a CRO analyst specialising in user behaviour interpretation. Turn behavioural evidence into likely causes, risks, and optimisation actions.

### Required Input
- Behaviour Evidence Available: [Analytics, heatmaps, session recordings, surveys, support tickets, sales notes]
- Page or Funnel Scope: [What is being analysed. Example: homepage to demo form, product page to checkout]
- Primary Conversion Goal: [Example: purchase, signup, demo booking, form completion]
- Target Audience: [Who the behaviour represents. Example: first-time buyers comparing premium skincare products]
- Observed Behaviour Patterns: [What is happening. Example: users scroll to pricing but do not click, users rage-click filters]
- Segments to Compare: [Mobile vs desktop, paid vs organic, new vs returning, country, plan type]
- Known Metrics: [Conversion rate, drop-off, scroll depth, click rate, form abandonment, or state unknown]
- Business Context: [Offer, campaign, pricing, seasonality, recent changes, or constraints]
- Decision Needed: [What the team wants to decide after analysis]

### Input Validation
Review all required inputs before proceeding. If any required detail is missing, vague, contradictory, or too thin to support a useful result, ask specific clarification questions and pause. Do not create the final output until the missing information is resolved. If performance data is unavailable, state the assumptions you will use before continuing.

### Instructions
Analyse behaviour as evidence, not certainty. For each pattern, separate what was observed, what it may mean, what else could explain it, and what evidence would confirm the cause.

Segment behaviour where possible. A pattern affecting mobile paid-social users may require a different fix than the same pattern affecting returning desktop users. Highlight where averages may hide meaningful differences.

Connect behaviour to conversion psychology. Look for signs of confusion, weak motivation, low trust, poor relevance, technical friction, pricing hesitation, unclear next steps, content mismatch, or excessive cognitive effort.

Do not jump straight from observation to solution. First explain the likely user state. Repeated scrolling, for example, may indicate comparison behaviour, uncertainty, missing information, or poor page structure. Recommend the next evidence to check before major changes.

Translate patterns into practical actions. Some findings should become immediate fixes, some research tasks, and some test hypotheses. Assign confidence based on evidence quality, consistency, affected segment, and proximity to conversion.

### Output
Produce:
- Executive Summary: most important behaviour patterns and conversion risks
- Behaviour Evidence Table: observation, segment, possible meaning, confidence, and source
- Pattern Interpretation: motivation, clarity, trust, or friction implications
- Alternative Explanations
- Evidence Validation Checklist
- Opportunity Map: immediate fixes, research follow-ups, and test candidates
- Prioritised Action Recommendations: ranked by impact, confidence, and effort
- Test Hypotheses: 3–5 hypotheses linked to observed behaviour
- Reporting Notes: how to communicate findings without overstating certainty

Make the recommendations specific, practical, and ready for a small marketing, design, or development team to act on.

Optional advanced instructions

				
					Add a confidence scoring system using evidence strength, segment size, behaviour consistency, and conversion proximity.

Example output

1. Executive Summary

This analysis evaluates user behavior patterns within the checkout funnel of GlowRitual, a fictional direct-to-consumer (D2C) premium skincare brand specializing in clean, anti-aging routines. The journey under review covers the transition from the Product Detail Page (PDP) through to successful checkout completion. The primary conversion goal is driving completed subscription or one-time purchases among first-time buyers.

Top 3 Behavioral Observations & Conversion Risks:

Rapid Mobile Scroll-Back (Sizing/Routine Confusion): Mobile paid traffic exhibits a high volume of rapid, erratic up-and-down scrolling within the middle third of the PDP. This behaviors signals acute comparison anxiety and information seeking, likely due to unaddressed questions about ingredient concentrations or bottle sizes.
Rage-Clicking Promo Input Fields (Cost Obstacles): Session recordings show concentrated rage-clicks directly on the coupon code input field at the first step of the checkout layout, primarily among returning desktop users. This indicates an expectation of discounts, leading to frustration and basket abandonment when a code is missing.
Sticky Cart Hesitation (Shipping Transparency Failure): Users consistently hover their cursors over the persistent sticky cart button for more than 10 seconds without executing a click. This delay points to a lack of clarity regarding hidden shipping surcharges and delivery timelines before entering the checkout environment.

2. Behaviour Evidence Table

Observed Pattern	Primary Segment	Likely User State / Meaning	Confidence	Data Source
Rapid vertical up-and-down scrolling over product specification grids.	Mobile / Paid Social Traffic	Information hunting; looking for application instructions or quantity details missing from the copy headers.	High	Heatmaps & Session Recordings
Repeated clicking and text selection on coupon field box.	Desktop / Returning Users	Frustration around pricing; actively leaving the site to scour third-party coupon aggregator sites.	Very High	Clarity Rage-Click Logs
Prolonged pause on add-to-cart confirmation modal windows.	All Traffic Groups	Unexpected price presentation; trying to calculate total cost plus delivery fees before committing.	Medium	Session Recordings
High exit rate on the credit card detail input screen depth.	Mobile Chrome Users	Technical friction; native digital wallet integrations (Apple Pay/Google Pay) are hidden or failing.	High	GA4 Drop-off Funnel Logs

3. Pattern Interpretation

The behavioral data reveals a clear picture of the consumer’s emotional state across different stages of the purchase process:

Friction vs. Motivation (Mobile Scrolling): The rapid up-and-down scrolling on mobile PDP layout blocks isn’t casual browsing; it represents a user stalling. Because skincare application requires exact routine mapping, the absence of clear text answering “Which step in my routine is this?” creates cognitive blockages that halt progression.
Pricing Anxiety (Coupon Rage-Clicks): The aggressive clicking behavior on the discount code wrapper shows that users feel they are being overcharged if they buy at standard retail prices. By overemphasizing the coupon box, the design accidentally prompts users to leave the page to look for discounts. This breaks the checkout momentum.
Trust Gaps (Cart Delays): The hesitation before clicking the final button suggests users are anxious about hidden fees. When premium brands hide shipping costs until the final payment step, it damages trust, leading to high rates of cart abandonment.

4. Alternative Explanations

While behavioral indicators point toward psychological roadblocks, we must consider alternative structural or environmental causes before making significant layout changes:

Regarding Mobile Scrolling: Instead of user confusion, this behavior could be caused by technical rendering bugs. Elements like large image files can cause layout shifts or slow page loads on mobile connections, making users scroll erratically while waiting for content to stabilize.
Regarding Checkout Form Abandonment: The drop-off on the payment screen might not be a lack of trust. It could be driven by bad form validation rules that clear out previously entered user data when a single typo occurs, causing frustration-driven exits.

5. Evidence Validation Checklist

Before launching expensive design overhauls, the front-end engineering team should verify user intent by running these targeted checks:

Cross-reference GA4 site-speed metrics specifically for mobile devices to ensure the scrolling isn’t caused by slow asset loading times.
Review support ticket logs for the last 30 days to check for recurring complaints about broken discount codes or validation errors on the payment form.
Run live user tests across different mobile browsers (Safari, Chrome, Samsung Internet) to confirm form fields function correctly without layout issues.
Analyze click maps on the promo field to check if the layout makes it look like clicking it is required to continue.

6. Opportunity Map

We have categorized our optimization ideas based on speed of implementation and data certainty, providing a structured approach for your development team.

Immediate Production Fixes

Adjust the coupon field layout inside the checkout screen to make it less prominent. Use a subtle, text-only accordion link that reads: “Have a promo code?” This keeps the focus on checking out rather than hunting for discounts.
Enable native digital wallet options (Apple Pay, Google Pay) at the very top of the checkout flow to reduce form-filling effort for mobile users.

Targeted Research Tasks

Add a 1-question exit-intent poll on the cart page for users who stall for more than 20 seconds, asking: “Is there any missing information holding you back today?”
Audit internal search terms to find the top questions users ask when browsing the PDP.

A/B Testing Experiments

Test replacing long text blocks on the PDP with a scannable, icon-based routine steps layout chart.

7. Prioritised Action Recommendations

1. Immediate Implementation (High Impact | Low Effort): Simplify the coupon input field and add mobile express checkout integrations. This…

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