Weekly revenue breakdown

Q: How do you break down revenue by segment?

Define your segments once (SMB / mid-market / enterprise, or whatever fits your customer base), tag each account, and let your billing system enforce the tagging. Then a weekly automation can slice revenue consistently without manual recategorization.

Weekly automated revenue breakdown. AI splits new revenue by product, segment, geography, and channel and surfaces the biggest movers.

Use this template How automations work

Runs weekly Scheduled Delivered to Slack, Email

Weekly revenue breakdown

Active

Trigger

Runs weekly

Instructions

Generate a weekly revenue breakdown.

Delivery

Slack · Email

Example output

A realistic sample of what this automation delivers — your version draws on your data.

Weekly revenue breakdown Runs weekly · Sent to Slack, Email

Sample output

MTD revenue by segment $ thousands

Enterprise Mid-market SMB

Top line. $284K MTD (78% of plan, day 21/31). Net new MRR this week: $24,700 (+12%).

By segment

Enterprise: $148K (52%) — 3 new logos closed, 2 expansions.
Mid-market: $94K (33%) — strongest week since Q4.
SMB: $42K (15%) — down 8% MoM, mostly higher churn.

By plan

Growth: $164K (58%) ↑
Pro: $84K (30%)
Starter: $36K (12%) ↓

Watch. SMB churn is up 1.4 pts. Same cohort signal as the at-risk accounts report — early-stage usage drop after week 4. Onboarding fix is the lever.

The prompt

Copy, paste, customize.

Drop this into a Basedash automation. AI fills in the numbers from every source you've connected.

Instructions

Generate a weekly revenue breakdown.

Cover:

**Total.** Revenue this week, vs. last week and same week last year.

**By product / plan.** Share and absolute revenue. Movers ≥ 10%.

**By segment.** SMB / mid / enterprise.

**By geography.** Top three countries; ROW lumped.

**By acquisition channel.** Inbound / outbound / partner.

**By lifecycle.** New vs. existing customer revenue.

**Anomalies.** Highlight any category whose share moved > 5 percentage points week-over-week.

Numbers first; close with the most actionable observation.

Data sources