Funnel Analysis 📉 Prompts

SUMMARY

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.