Is checkout too slow?

Why this matters

Checkout speed is one of the few performance metrics that ties directly to revenue. The industry rule of thumb: every additional second of checkout time costs you ~3% conversion. A 4-second checkout vs an 8-second checkout is the difference between profit and a marketing budget that feels mysteriously inefficient.

The aggregate “average checkout time” hides the truth. The metric that matters is the mobile p99 — what your slowest 1% of mobile users experience. That’s where the conversion bleed happens, because mobile p99 is typically 4-8x worse than desktop p50, and it’s where impulse-buyers live.

GA4 won’t show you this without custom dimensions. Shopify Analytics gives you “store performance” which is a server-side metric, not a per-shopper timing. Agentry computes it from your existing event timestamps in one HogQL query.

What you get

• Percentile distribution (p50, p90, p99) of order_completed_ts - checkout_started_ts, in seconds
• Split by device (mobile / desktop / tablet)
• A publishable dashboard so you can watch it after every checkout optimization

Walk through it

agentry_analytics_query {
  project_id: "default",
  hogql: "WITH starts AS (SELECT distinct_id, properties.$device_type AS device, timestamp AS start_ts FROM events WHERE event = 'checkout_started' AND timestamp >= now() - INTERVAL 30 DAY), purchases AS (SELECT distinct_id, timestamp AS purchase_ts FROM events WHERE event = 'order_completed' AND timestamp >= now() - INTERVAL 30 DAY), durations AS (SELECT s.device AS device, dateDiff('second', s.start_ts, p.purchase_ts) AS duration_s FROM starts s INNER JOIN purchases p ON s.distinct_id = p.distinct_id AND p.purchase_ts > s.start_ts AND p.purchase_ts < s.start_ts + INTERVAL 1 HOUR) SELECT device, count() AS n, round(quantile(0.5)(duration_s)) AS p50_s, round(quantile(0.9)(duration_s)) AS p90_s, round(quantile(0.99)(duration_s)) AS p99_s FROM durations GROUP BY device ORDER BY n DESC"
}

{
  "rows": [
    { "device": "Desktop", "n": 2840, "p50_s": 38,  "p90_s": 96,  "p99_s": 280 },
    { "device": "Mobile",  "n": 1240, "p50_s": 72,  "p90_s": 320, "p99_s": 1840 },
    { "device": "Tablet",  "n": 120,  "p50_s": 58,  "p90_s": 198, "p99_s": 720 }
  ]
}

agentry_publish_query {
  recipe_id: "checkout_duration_percentiles",
  description: "Checkout Duration p50/p90/p99 by Device (30d)",
  params: { window_days: 30 }
}

{ "publication_id": "pub_chk_1", "public_url": "https://agentry.sh/q/pub_chk_1?key=agp_…" }

The output

Checkout Duration (30d)

DEVICE     N         p50      p90       p99
Desktop  2,840    38s ▎    96s ▎▎    280s ▎▎▎▎
Mobile   1,240    72s ▎▎  320s ▎▎▎▎  1,840s ▎▎▎▎▎▎▎▎▎▎▎▎▎▎▎▎▎▎▎ ★ FIX
Tablet     120    58s ▎▎  198s ▎▎▎    720s ▎▎▎▎▎▎▎▎▎▎▎

KEY NUMBERS:
  Desktop p50 (good baseline):       38s
  Mobile  p50:                       72s  (1.9x desktop)
  Mobile  p90:                       320s (3.3x desktop)
  Mobile  p99:                     1,840s (6.6x desktop)  ← optimization target

REVENUE IMPACT (rough, using 3%-per-second heuristic):
  Reducing mobile p90 from 320s → 200s would lift mobile conversion by ~12%
  At your current 1,240 mobile completions / 30d, that's +150 orders / month
  At avg order $69 (your current AOV), ~$10k/month uplift

Public dashboard:
  https://agentry.sh/q/pub_chk_1?key=agp_xxx

SUGGESTED INVESTIGATIONS:
  - Replace Stripe Elements with Payment Request / Apple Pay on mobile
  - Preconnect to api.stripe.com on checkout page load
  - Lazy-load the confirmation page JS bundle on /checkout

Setting it up

You need two events with timestamps and a device-type tag:

// Detect device once at page load
const deviceType = /Mobi|Android/i.test(navigator.userAgent) ? "Mobile"
                 : /Tablet|iPad/i.test(navigator.userAgent) ? "Tablet"
                 : "Desktop";

async function track(event: string, properties: Record<string, unknown> = {}) {
  await fetch(`https://api.agentry.sh/v1/analytics/${process.env.AGENTRY_PROJECT_ID}/`, {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${process.env.AGENTRY_DSN}`,
      "Content-Type": "application/json",
      "User-Agent": "myshop/1.0",  // REQUIRED — Cloudflare 403s default UAs
    },
    body: JSON.stringify({
      event,
      distinct_id: visitorId,
      properties: { ...properties, $device_type: deviceType },
    }),
  });
}

// At checkout page mount:
track("checkout_started", { cart_value: cart.total });

// At order confirmation page:
track("order_completed", { order_id: order.id, total: order.total });

If you use the PostHog-shaped alias /v1/track/, $device_type is parsed from the User-Agent automatically — no manual sniff needed.

For sub-second precision, the recipe also works if you send a single checkout_completed_in_s property on order_completed (computed client-side as Date.now() - checkoutStartedAt). HogQL is happier with explicit durations than with timestamp diffs at scale.

Variations

• “Same query but split by BROWSER as well — old Safari might dominate the slow tail.”
• “Split by checkout payment method — is Stripe Link faster than card form?”
• “Compute the same percentiles for product_page_load_ms and add_to_cart_response_ms. Where else is the friction?”
• “Watch this metric: alert me if mobile p90 increases by >20% week-over-week.” (uses agentry_create_alert)