Revenue review

Snowflake orders joined with BigQuery CRM accounts. Refreshed daily.

ordersCell 2 · SQL

Snowflake orders

SELECT order_id, account_id, order_month, amount_usd
FROM analytics.marts.fct_orders
WHERE order_month >= '2026-01-01'

FilterAll columns 48,213 rows

order_idUtf8	account_idUtf8	order_monthDate	amount_usdDecimal(12, 2)
ord_90412	acc_1182	2026-09-01	18,400.00
ord_90413	acc_0457	2026-09-01	2,950.00
ord_90414	acc_2210	2026-09-01	41,200.00

accountsCell 7 · SQL

BigQuery accounts

SELECT account_id, segment, region
FROM crm.accounts

FilterAll columns 3,402 rows

account_idUtf8	segmentUtf8	regionUtf8
acc_1182	Enterprise	North America
acc_0457	SMB	EMEA
acc_2210	Enterprise	APAC

revenueCell 8 · SQL

Notebook DuckDB revenue

SELECT o.order_month AS month, a.segment, a.region,
       SUM(o.amount_usd) AS revenue
FROM orders o JOIN accounts a USING (account_id)
GROUP BY ALL ORDER BY month

FilterAll columns 81 rows

monthDate	segmentUtf8	regionUtf8	revenueDecimal(14, 2)
2026-09-01	SMB	North America	145,672
2026-09-01	SMB	EMEA	85,127
2026-09-01	SMB	APAC	47,859

periodCell 9 · Python

period = alkera.ui.range_slider(
    months(revenue), value=("Jan", "Sep"), label="Months",
)

MonthsJan – Sep 2026

kpisCell 10 · Python

in_period = revenue.filter(period.contains("month"))
alkera.hstack([
    alkera.callout(f"**{usd(in_period)}** revenue", kind="success"),
    alkera.callout(f"**{share(in_period, 'Enterprise'):.0%}** Enterprise"),
    alkera.callout(f"**{usd(monthly(in_period))}** per month"),
])

$15.8MRevenue9 months

58%Enterprise shareJan – Sep 2026

$1.8MAverage per monthAll segments

trendCell 11 · Python

alkera.chart(revenue.filter(period.contains("month"))).line(
    x="yearmonth(month)", y="sum(revenue)", color="segment",
).title("Monthly revenue by segment").tooltip()

by_regionCell 12 · Python

alkera.chart(revenue.filter(is_q3)).bar(
    y="region", x="sum(revenue)", color="segment",
).title("Q3 revenue by region").tooltip()

mixCell 13 · Python

alkera.chart(revenue.filter(period.contains("month"))).pie(
    theta="sum(revenue)", color="segment", inner_radius=54,
).title("Revenue mix").tooltip()

Takeaways

Enterprise grew fastest, about 5% a month, and drives most of the growth.

North America leads; EMEA is gaining on Mid-market deals.

SMB is flat. Pricing changes ship in Q4.