Funnel Analysis

What is Funnel Analysis?

Funnel Analysis maps the user journey from first visit to purchase, showing how many users progress (or drop) at each step. It helps you identify where you’re leaking users and what to fix first.

Formula (core metrics):

• Step Conversion Rate = Count(step i+1) ÷ Count(step i)
• Drop-off = 1 − Step Conversion Rate
• Overall Conversion Rate (CR) = Purchases ÷ Sessions
• AOV = Revenue ÷ Purchases
• RPV (Revenue per Visitor) = AOV × CR

Typical ecommerce funnel: Sessions → Product View → Add to Cart → Begin Checkout → Purchase

 Why it matters?

• Focus on the highest leverage step: Fixing the biggest drop-off yields the fastest revenue lift.
• Clarity over noise: Step-by-step rates beat “overall CR” for diagnosis.
• Direct ROI path: Every +1 pp improvement in a constrained step compounds down the funnel.

KPIQ Perspective

• User view: “People see my products but don’t add to cart—where’s the friction?”
• Technical view: KPIQ maps the funnel (Sessions → Product View → Add to Cart → Checkout → Purchase), surfaces abnormal drop-offs by segment (device, channel, country/language, product/collection), and runs what-if simulations to quantify impact if a step improves (e.g., ATC +4 pp → +X orders, +€Y revenue). It then recommends targeted fixes tied to the weak step (e.g., sticky ATC, variant auto-select, shipping/returns clarity).

Actionable Insights

• ✅ Define the exact steps and keep the same denominator across steps (sessions vs. users) for clean math.
• ✅ Segment everything: mobile vs. desktop, new vs. returning, traffic source, country/language, product/collection.
• ✅ Prioritize by impact (size of drop × traffic volume × margin).
• ✅ For Product Page leaks (PV → ATC): make CTA visible (sticky), reduce variant friction, show shipping/returns early, and add credible social proof.
• ✅ For Checkout leaks: enable guest checkout, auto-complete, express wallets, and trim fields.
• ✅ Re-measure after each change; keep a simple “before/after” log.

Practical Example

Assume a Shopify store with the following last-30-days data:

Step 1 – Define Your Goal

Primary KPI: Increase Add to Cart Rate (PV → ATC) and downstream purchases.

Step 2 – Map the Funnel

• Sessions: 12,000
• Product Views: 7,200
• Add to Cart: 2,300
• Begin Checkout: 1,400
• Purchases: 700
• AOV: €42

Step 3 – Measure Each Step

• PV Rate = 7,200 ÷ 12,000 = 60.0%
• ATC Rate = 2,300 ÷ 7,200 = 31.9% (largest drop-off)
• Checkout Start = 1,400 ÷ 2,300 = 60.9%
• Purchase (from checkout) = 700 ÷ 1,400 = 50.0%
• Overall CR = 700 ÷ 12,000 = 5.8%
• RPV = €42 × 5.8% ≈ €2.45

Step 4 – Fix the Bottleneck (PV → ATC)

Implement: sticky “Add to Cart”, default variant pre-selected, shipping/returns badge above the fold, one 10–20s UGC clip.

Step 5 – Simulate Impact

If ATC improves from 31.9% → 36.0%:

• New ATC = 7,200 × 0.36 = 2,592
• Begin Checkout ≈ 2,592 × 60.9% = 1,580
• Purchases ≈ 1,580 × 50% = 790
• Order lift: +90 → +€3,780 revenue at AOV €42
• New RPV ≈ (790 × €42) ÷ 12,000 = €2.76 (≈ +13%)