> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Conversion Rate, Google Analytics 4 > Conversion Rate, percentage of sessions that result in a purchase. Tracked live in Vortex IQ Nerve Centre. **Metrics type:** [Key Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Analytics](/nerve-centre/connectors#connectors-by-type) ## At a glance > **Percentage of sessions that result in a purchase event.** **Conversion rate is the single highest-leverage metric in ecommerce.** A 0.5 percentage-point improvement on 100K sessions equals 500 extra orders, at typical AOV £100-£250, that's £50K-£125K in additional revenue per period. **Average ecommerce conversion is 2-3%; top performers exceed 5%.** The gap between average and top-tier represents the biggest single revenue lever a merchant can pull. | | | | ------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | Percentage of GA4 sessions that fired a `purchase` event in the period. Computed as purchases ÷ sessions × 100. Includes all traffic sources unless filtered. | | **Sample type** | GA4 session and purchase event data, refreshed on the standard data refresh. | | **Why conversion rate matters** | (1) **Compounding leverage**: every 0.1 percentage point improvement compounds across all sessions, all channels, all time. (2) **Channel quality verification**: the same conversion improvement on lower-cost channels improves margin disproportionately. (3) **Funnel health summary**: conversion rate aggregates the entire funnel quality (site speed, product fit, pricing, trust signals, checkout flow). (4) **Investor signal**: top-tier conversion rate is the most cited proof point in ecommerce diligence. | | **Reading the value** | (1) Below 1%: structural problem; investigate site speed, checkout flow, traffic quality. (2) 1-2%: typical-low; meaningful improvement headroom. (3) 2-3%: average ecommerce. (4) 3-5%: above average; strong product-market fit. (5) Above 5%: top-tier; investigate sustainability and scale. | | **Currency** | percent. | | **Time window** | `30D vsP`. | | **Alert trigger** | `ga_conversion_rate < 1` (BAD threshold). | | **Sentiment key** | `ga_conversion_rate` (HIGHER\_IS\_BETTER; GOOD ≥ 3%, BAD \< 1%). | | **Roles** | owner, marketing, operations | ## Calculation ``` ga_conversion_rate (%) = COUNT(sessions WHERE purchase_event_count > 0) ÷ COUNT(sessions) × 100 ``` ## Worked example A UK-based BC store, conversion rate reading on Wednesday 15 May 26. | Segment | Sessions | Purchases | Conversion rate | Notes | | ---------------- | -------: | --------: | --------------: | ----------------------- | | Site-wide | 108,142 | 2,103 | **1.94%** | Below 2% baseline | | Mobile | 75,800 | 1,140 | 1.50% | Below baseline | | Desktop | 28,300 | 880 | 3.11% | Above baseline | | Tablet | 4,042 | 83 | 2.05% | Average | | Organic search | 42,500 | 893 | 2.10% | Above baseline | | Paid search | 38,200 | 1,005 | 2.63% | Strong | | Email | 6,400 | 380 | 5.94% | Highest | | Direct | 14,800 | 220 | 1.49% | Below baseline | | AI traffic | 327 | 11 | 3.36% | Above baseline | | **Prior period** | 116,540 | 2,408 | 2.07% | -6.3% conversion change | What the conversion reading is telling us: 1. **Site-wide conversion at 1.94% is below the 2% typical ecommerce baseline.** Not at alert state (alert at \<1%) but in the investigation zone. **The vsP delta of -6.3%** indicates a recent deterioration, something changed in the last 30 days. 2. **Mobile vs desktop gap is severe.** Desktop converts at 3.11% vs mobile at 1.50%, a 2x ratio. **For a store with 70% mobile traffic, mobile conversion is the highest-leverage fix point.** Cross-reference `psi_mobile_score_compare` and `crux_mobile_pass`; mobile conversion gap usually traces to mobile site speed or mobile checkout friction. 3. **Channel-by-channel reading:** * **Email at 5.94%** confirms the merchant has a healthy own-audience asset. Maintain. * **Paid search at 2.63%** is strong, paid traffic landing on intent-aligned pages converts well. * **Organic at 2.10%** is acceptable; investing in PDP optimisation could lift this significantly. * **Direct at 1.49%** is concerning, direct visitors typically convert above-baseline. Investigate brand confusion or returning-user friction. * **AI at 3.36%** is excellent given small volume. Cross-reference `ai_conversion`. 4. **Likely causes for site-wide -6.3% conversion change:** * **Site speed regression**: cross-reference `psi_perf_score_summary` and `crux_lcp_p75` for the prior 30 days. * **Checkout flow change**: any recent app installs, theme updates, or checkout customisations? Test in incognito. * **Product mix shift**: if low-converting categories now dominate traffic mix, headline conversion falls. * **Pricing change**: any recent price increases on bestsellers? * **Trust signal change**: was a payment method removed, a security badge swapped, or a review feed broken? 5. **Recommended response, in priority order:** * **Day 1**: Run a checkout flow test in incognito on mobile and desktop. Check every step for errors. * **Day 1**: Audit site speed (compare 30 days vs 60 days ago). * **Day 1-2**: Audit any release deploys, app installs, or theme changes in the last 30 days. * **Day 2-3**: Review pricing changes and trust signals. * **Day 7**: Re-measure conversion; confirm trend reversal. The diagnostic flow: 1. **Read the rate.** Below 1% alert; below 2% investigate. 2. **Decompose by device**, mobile-vs-desktop gap is the most common gap. 3. **Decompose by channel**, identify which channels are driving the headline. 4. **Cross-reference site speed + checkout + pricing** for upstream causes. 5. **Confirm with vsP delta**, declining trend signals recent regression. Rapid-response playbook: | Time horizon | Action | | ------------ | -------------------------------------------------- | | First 1 hour | Read rate; decompose by device + channel. | | First day | Run checkout test; audit site speed. | | First week | Audit recent releases; identify regression source. | | Day 14 | Confirm fix; trend reversal in metric. | ## Sibling cards merchants should reference together | Card | Why merchants reach for it | | ------------------------------------------------------------------------------------------------- | ------------------------------------------------ | | [`ga_revenue_trend`](/nerve-centre/kpi-cards/google-analytics/revenue-trend) | Revenue trend; CR feeds it. | | [`ga_traffic_trend`](/nerve-centre/kpi-cards/google-analytics/traffic-trend) | Traffic trend; combined with CR equals orders. | | [`ga_engagement_rate`](/nerve-centre/google_analytics/ga_engagement_rate) | Engagement rate; pre-conversion quality. | | [`ga_cart_abandonment`](/nerve-centre/google_analytics/ga_cart_abandonment) | Cart abandonment; specific funnel-stage CR loss. | | [`psi_perf_score_summary`](/nerve-centre/kpi-cards/website-performance/performance-score-summary) | Site speed; major conversion driver. | | [`crux_mobile_pass`](/nerve-centre/kpi-cards/website-performance/mobile-speed-pass-rate) | Mobile CWV; mobile conversion gap. | ## Reconciling against the vendor's own dashboard **Where to look in GA4:** Reports → Engagement → Conversions; or Reports → Monetization for ecommerce-specific conversion. **Why our number may differ:** | Reason | Direction | What to do | | ---------------------------------------------------------------------------------------------------------- | --------- | ----------------- | | **Conversion event definition.** GA4 may use any conversion event; this card uses `purchase` specifically. | Variable | Match definition. | | **Session vs user CR.** GA4 default is session-based; this card matches. | Same | n/a. | | **Refunded purchase treatment.** GA4 may exclude refunded; this card configurable. | Variable | Confirm. | **Quick rule:** match conversion event definition first. ## Known limitations / merchant FAQs **Q: Mobile converts at 1.5%, desktop at 3.1%. Should I just optimise mobile?** Yes, that's the highest-leverage fix point given mobile is likely 70%+ of your sessions. Mobile gap is usually 2 things: speed (CWV failures) and checkout friction (small touch targets, awkward form fields, slow payment flow). Cross-reference `crux_mobile_pass` and `psi_mobile_score_compare`. A 1 percentage point mobile CR lift on a 75K-session-per-month mobile audience is 750 extra orders. **Q: Our CR dropped from 2.5% to 1.8%, what's the most common cause?** Site speed regression (apps installed, theme update). Run `psi_perf_score_summary` for the 30 days vs 60 days ago. The second most common: checkout flow change (new payment app, address validation step). Test the checkout in incognito on mobile and desktop. **Q: Is 2% a good conversion rate?** Average. Top-quartile ecommerce sits at 3-5%; top-decile above 5%. Use category-specific benchmarks: fashion 1.5-2.5%, beauty 2.5-3.5%, supplements 3-5%, B2B 0.5-1.5%. Generic averages mislead. **Q: How does AI traffic affect conversion rate?** AI traffic typically converts 1.5-3x site-wide rate. As AI traffic grows, headline CR will rise gradually. Use the per-channel decomposition rather than headline if AI is becoming a meaningful share. **Q: Does this card include returning users or just first-time?** Both. The card is session-based (every session counts). For first-time-vs-returning split, GA4's user reports give the breakdown, typically first-time CR is 0.5-1.5% and returning user CR is 5-10%. **Q: We have a high refund rate. Are refunded purchases counted as conversions here?** By default yes (the purchase event fired; the conversion happened). Configurable to exclude refunded if the merchant prefers. Note that refund rate is its own card (`refund_rate`); double-counting between the two is a real concern. **Q: Should I optimise for conversion or for AOV?** Both, but convert first. CR improvements scale with all traffic; AOV improvements scale with conversions. A 10% CR improvement is typically more impactful than a 10% AOV improvement because it compounds across more sessions. But after CR is healthy (3%+), AOV becomes the next lever. **Q: How do I get from 2% to 5%?** A 12-24 month investment cycle for most stores. The major levers: (1) mobile site speed (LCP, INP, CLS to all-pass), (2) checkout flow (one-page or short-step), (3) product page quality (rich descriptions, multiple images, reviews, social proof), (4) trust signals (badges, return policy, support channels), (5) personalisation (post-cart abandonment, post-purchase). Each lever produces 0.3-0.8 percentage point gains; layered they compound to 3+. *** ### Tracked live in Vortex IQ Nerve Centre *Conversion Rate* is one of hundreds of KPI pulses Vortex IQ tracks across Google Analytics 4 and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. [Start for free](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.