Funnel Analysis 📉 Prompts

Purpose: Funnel Analysis helps product teams identify where users drop off in multi-step user journeys, enabling data-driven UX decisions to reduce friction and improve conversion.

Design Thinking Phase: Test

Time: 2–3 hours data pulling + 1–2 hours analysis

Difficulty: ⭐⭐

When to use:When users are abandoning onboarding or checkout flowsPre-launch testing of prototypes with defined journeysEvaluating the impact of UX experiments on completion rates

What it is

Funnel Analysis is a quantitative method that uses behavioural data to examine how users navigate step-by-step flows in digital products. By analysing completion and drop-off rates at each step, designers and product teams can pinpoint exactly where users are disengaging and why.

📺 Video by NNgroup. Embedded for educational reference.

Why it matters

Funnel Analysis provides concrete evidence of performance bottlenecks in your user experience. Rather than rely on anecdotal feedback or assumptions, you’re looking at real behavioural data to understand completion barriers. It’s particularly impactful when paired with qualitative methods like usability testing or user interviews. Together, they give depth (why?) and breadth (how many?) to the user story—essential for product decision-making and advocating for UX investments.

When to use

• Your product has a known drop-off at a key point (e.g., signup, checkout, onboarding)
• You’re comparing A/B variants to improve a flow
• You want a baseline experience metric before redesign

Benefits

• Rich Insights: Validates assumptions with hard data, helping prioritise UX work that matters.
• Flexibility: Applies across onboarding, ecommerce, account creation, support flows, and more.
• User Empathy: While quantitative, it surfaces key “drop moments” that spark further qualitative to understand emotion or intent.

How to use it

• Select a flow: Identify a clear, linear process — e.g., Checkout (Cart > Shipping > Payment > Confirmation).
• Map each step: Use tools like Mixpanel, Amplitude, or GA4 to define funnel stages (ensuring consistent event naming).
• Pull data: Look at a defined window (last 2 weeks, last sprint) with statistically relevant volume.
• Analyse drop-off: Where do users abandon most? Look for patterns across device, browser, or audience segments.
• Validate with heuristic review: Manually walk through friction points at each stage — low conversion often aligns with usability issues.
• Document findings: Include conversion rate per step, annotated screenshots, and hypotheses.
• Propose changes: Prioritise improvements by estimated lift vs effort (e.g., removing a field or redesigning a screen).

Example Output

Flow Analysed: Mobile checkout (4 steps)

Conversion rates:

• Step 1 (Cart View) → 100%
• Step 2 (Shipping) → 82%
• Step 3 (Payment Details) → 46%
• Step 4 (Review & Confirm) → 43%

Key Issues:

• Large drop at Payment step; forms are not mobile-optimised
• High percentage of exits from Android devices with low bandwidth

Recommendation: Test collapsible sections, autofill support, and simplify form validation

Common Pitfalls

• No event hygiene: Inconsistent tagging ruins accuracy. Align early with devs or analysts.
• Over-interpreting small data: Don’t act on funnels with < 100 users unless it’s early-stage testing.
• Too much focus on the “what” not “why”: Funnel data should guide further user research, not replace it.

10 Design-Ready AI Prompts for Funnel Analysis – UX/UI Edition

How These Prompts Work (C.S.I.R. Framework)

Each of the templates below follows the C.S.I.R. method — a proven structure for writing clear, effective prompts that get better results from ChatGPT, Claude, Copilot, or any other LLM.

C.S.I.R. stands for:

• Context: Who you are and the UX situation you're working in
• Specific Info: Key design inputs, tasks, or constraints the AI should consider
• Intent: What you want the AI to help you achieve
• Response Format: The structure or format you want the AI to return (e.g. checklist, table, journey map)

Level up your career with smarter AI prompts.Get templates used by UX leaders — no guesswork, just results.Design faster, research smarter, and ship with confidence.First one’s free. Unlock all 10 by becoming a member.

Prompt Template 1: “Audit a Drop-off Funnel for Mobile Checkout”

Audit a Drop-off Funnel for Mobile Checkout

Context: You are a UX Designer reviewing a mobile checkout experience for an ecommerce app.  
Specific Info: The flow has 4 steps and is showing a significant 50% drop at the payment stage.  
Intent: Uncover UX issues or design opportunities contributing to the drop-off.  
Response Format: Create a funnel breakdown (table format) with user actions, pain points, and 1 tactical fix per step.

If the payment method UX isn’t clear, ask follow-up diagnostic questions.  
Then, suggest one behavioural insight that could be tested with qualitative research.

Prompt Template 2: “Generate Hypotheses Based on Funnel Trends”

Generate Hypotheses Based on Funnel Trends

Context: You’re a Senior Product Designer analysing drop-offs across a 5-step onboarding flow.  
Specific Info: Step 3 (Profile Preferences) has the sharpest reduction in user progression.  
Intent: Ideate testable UX hypotheses that could explain the friction.  
Response Format: Bullet list of 5 hypotheses with a matching research validation method (quant or qual).

Ask if goal completions are clearly defined and distinguish between system errors or cognitive friction.

Prompt Template 3: “Summarise Segment-Level Funnel Analysis”

Summarise Segment-Level Funnel Analysis

Context: You're analysing funnel performance across device types.  
Specific Info: Desktop converts 62%, mobile drops to 41%, with unclear drop cause at Step 2.  
Intent: Create a summary highlighting behavioural differences and UI opportunities.  
Response Format: Comparative table or grouped bullet points, plus 2 follow-up prioritisation ideas.

Ask if mobile testing includes network/bandwidth constraints or limited interactions.

Prompt Template 4: “Draft Priority UX Recommendations from Funnel Data”

Draft Priority UX Recommendations from Funnel Data

Context: You're contributing to a QBR presentation with PMs and stakeholders.  
Specific Info: You’ve analysed an onboarding drop-off funnel with 3 key pain points mapped.  
Intent: Turn findings into executive-ready UX recommendations with clear business impact.  
Response Format: Slide-ready bullets with 1-line rationale (what/why/how to fix).

Request clarity on what KPIs matter most to the business (retention, activation, etc.).

Prompt Template 5: “Design a Multi-Variant Test Plan for Drop-off Point”

Design a Multi-Variant Test Plan for Drop-off Point

Context: You’re a UX strategist designing experiments to improve a product education flow.  
Specific Info: Drop-off at tutorial screen is 39%, suspected info overload.  
Intent: Recommend experimental design with variants and success metrics.  
Response Format: A/B test plan with control/variant, goal metric, sample size, and run time.

Ask what analytics tool is used and if users can skip the step.

Prompt Template 6: “Convert Funnel Data into UX Storyboards”

Convert Funnel Data into UX Storyboards

Context: You’re preparing for a design sprint and want to turn funnel issues into narratives.  
Specific Info: Two steps with high drop-off pre-survey and post-prototype.  
Intent: Translate behaviour into short user stories for design ideation.  
Response Format: 3 UX storyboard frames per failed step showing what’s happening and why.

Request demographic input if available for emotional context.

Prompt Template 7: “Review Funnel Definitions for Analytics Clean-up”

Review Funnel Definitions for Analytics Clean-up

Context: You’ve inherited an Amplitude setup and suspect event inconsistency.  
Specific Info: Users report duplicate data and misleading conversion at Step 1.  
Intent: Help audit funnel logic and propose more accurate event sequencing.  
Response Format: Step-by-step checklist with naming best practices and stakeholder questions.

Clarify if current events were authored by front-end or analytics team.

Prompt Template 8: “Summarise Funnel Impact for Non-designers”

Summarise Funnel Impact for Non-designers

Context: You’re briefing a CX or marketing team unfamiliar with UX funnels.  
Specific Info: Drop-off analysis shows a 25% abandonment tied to unclear email microcopy.  
Intent: Frame insights in business terms to support UX fixes.  
Response Format: Lay-language summary with before/after impact metrics if fixed.

Ask what channels these findings influence (email, ads, CRM).

Prompt Template 9: “Map Funnel Drop-offs Against Customer Emotions”

Map Funnel Drop-offs Against Customer Emotions

Context: You’re doing a hybrid analysis combining click data with verbatim feedback.  
Specific Info: Survey responses indicate frustration at Steps 2 and 4.  
Intent: Cross-map quantitative and qualitative data into a feelings-based journey map.  
Response Format: Annotated user journey chart combining data and emotion points.

Ask if survey verbatims include sentiment-tagged keywords already.

Prompt Template 10: “Simplify Complex Funnels into Microflow Insights”

Simplify Complex Funnels into Microflow Insights

Context: You’re optimising a product tour with 8+ steps and layered UI panels.  
Specific Info: Drop-off grows gradually with no obvious spike.  
Intent: Break down surface-level completions into micro-behaviours for design clues.  
Response Format: List micro-interactions per step, highlight 1-2 design tweaks.

Ask if eye-tracking or click heatmaps are available.

Recommended Tools

• Mixpanel – for funnel breakdowns and retention cohorts
• Amplitude – event-based funnel analysis with robust segmentation
• Hotjar / FullStory – for session replay paired with funnel data
• GA4 – for free funnel tracking in Google environments
• Figma & FigJam – rapid storyboarding of funnel journeys + drop-off mapping

Learn More

• NNGroup: UX Research & Design Articles
• Interaction Design Foundation – Topics
• Google Design Sprint Kit