E-commerce events

A standard e-commerce taxonomy with revenue and AOV queries.

Standard e-commerce funnel:

viewed_product  →  added_to_cart  →  started_checkout  →  completed_checkout  →  refunded

Plus product browse signals (viewed_collection, searched) and post-purchase (reviewed_product).

Suggested taxonomy

Event	When	Required properties
viewed_product	product detail page renders	product_id, price_cents, currency, category
added_to_cart	"Add to cart" button	product_id, quantity, price_cents, currency
removed_from_cart	item removed from cart	product_id, quantity
started_checkout	checkout page load	cart_value_cents, currency, item_count
applied_discount	discount code applied	code, discount_cents
completed_checkout	order placed	order_id, amount_cents, currency, item_count, payment_method
refunded	refund issued	order_id, amount_cents, currency, reason

Money in cents as integers. currency always alongside any amount.

Instrument the storefront

import { track } from "@millimetric/track";

export function ProductPage({ product }: { product: Product }) {
  useEffect(() => {
    track("viewed_product", {
      product_id: product.id,
      price_cents: product.priceCents,
      currency: "usd",
      category: product.category
    });
  }, [product.id]);

  return (
    <button onClick={() => addToCart(product, 1)}>Add to cart</button>
  );
}

function addToCart(product: Product, qty: number) {
  cart.add(product, qty);
  track("added_to_cart", {
    product_id: product.id,
    quantity: qty,
    price_cents: product.priceCents,
    currency: "usd"
  });
}

Track the order from the server

// app/api/checkout/complete/route.ts
import { init, track, flush } from "@millimetric/track-node";

init({ key: process.env.AOA_SK!, host: process.env.AOA_HOST!, flushAt: 1 });

export async function POST(req: Request) {
  const order = await chargeAndPersist(await req.json());

  track({
    event: "completed_checkout",
    anonymous_id: order.anonymous_id,
    user_id: order.user_id,
    properties: {
      order_id: order.id,
      amount_cents: order.totalCents,
      currency: order.currency,
      item_count: order.items.length,
      payment_method: order.paymentMethod,
      is_first_purchase: order.isFirstPurchase
    }
  });

  await flush();
  return Response.json({ ok: true, order_id: order.id });
}

For Stripe webhooks, do the same in your webhook handler and pass order_id so you can dedupe on retry.

Refunds

track({
  event: "refunded",
  user_id: order.user_id,
  properties: {
    order_id: order.id,
    amount_cents: refund.amountCents,
    currency: order.currency,
    reason: refund.reason
  }
});

Querying

Revenue per day

SELECT
  toDate(timestamp) AS day,
  sum(JSONExtractInt(properties, 'amount_cents')) / 100.0 AS revenue_usd
FROM events
WHERE project_id = '...'
  AND event_name = 'completed_checkout'
  AND JSONExtractString(properties, 'currency') = 'usd'
  AND timestamp > now() - INTERVAL 30 DAY
GROUP BY day
ORDER BY day;

Average order value (AOV) by channel

SELECT
  argMin(source, timestamp)  AS source,
  argMin(medium, timestamp)  AS medium,
  count() AS orders,
  round(avg(JSONExtractInt(properties, 'amount_cents')) / 100.0, 2) AS aov_usd
FROM events
WHERE project_id = '...'
  AND event_name = 'completed_checkout'
  AND timestamp > now() - INTERVAL 30 DAY
GROUP BY anonymous_id
GROUP BY source, medium
ORDER BY orders DESC;

Funnel: view → cart → checkout → order

WITH steps AS (
  SELECT
    anonymous_id,
    minIf(timestamp, event_name = 'viewed_product')        AS s1,
    minIf(timestamp, event_name = 'added_to_cart')         AS s2,
    minIf(timestamp, event_name = 'started_checkout')      AS s3,
    minIf(timestamp, event_name = 'completed_checkout')    AS s4
  FROM events
  WHERE project_id = '...'
    AND timestamp BETWEEN '2026-05-01' AND '2026-05-17'
  GROUP BY anonymous_id
)
SELECT
  countIf(s1 IS NOT NULL) AS viewed,
  countIf(s2 IS NOT NULL AND s2 >= s1) AS added,
  countIf(s3 IS NOT NULL AND s3 >= s2) AS checked_out,
  countIf(s4 IS NOT NULL AND s4 >= s3) AS purchased
FROM steps;

Cohort retention (do they come back?)

For the cohort that purchased in week W0, what fraction purchased again in week W1, W2, …?

WITH cohorts AS (
  SELECT
    user_id,
    toMonday(min(timestamp)) AS cohort_week
  FROM events
  WHERE project_id = '...' AND event_name = 'completed_checkout'
  GROUP BY user_id
),
purchases AS (
  SELECT user_id, toMonday(timestamp) AS week
  FROM events
  WHERE project_id = '...' AND event_name = 'completed_checkout'
)
SELECT
  c.cohort_week,
  dateDiff('week', c.cohort_week, p.week) AS week_offset,
  uniq(c.user_id) AS users
FROM cohorts c
JOIN purchases p USING user_id
WHERE p.week >= c.cohort_week
GROUP BY c.cohort_week, week_offset
ORDER BY c.cohort_week, week_offset;

Common pitfalls

• amount instead of amount_cents. Floats are not your friend in revenue math. Always use integer cents and store currency explicitly.

• Tracking completed_checkout from the success page. Anyone refreshing it double-counts. Track from the server, on order creation, with event_id = order.id so you can dedupe in queries.

• Mixing currencies. Don't sum amount_cents across rows with different currency values. Convert in the query, or split by currency.

• No is_first_purchase flag. It's trivial to compute, easier in queries, and answers half the marketing-attribution questions cleanly.

See also

• Properties — full conventions.

• Marketing attribution dashboards — credit channels for revenue.

• Server-side events — why critical events should come from the server.