The DTC Finance Dashboard a CFO Can Actually Use

Finance dashboards most DTC brands run are marketing dashboards with a finance label

Pick a random DTC brand under $30M revenue. Ask to see the finance dashboard. You will likely see gross revenue, returning customer rate, and a paid media ROAS number. Every one of those metrics is useful for marketing. None of them answer the questions a CFO needs to answer.

TL;DR ▸ Most DTC finance dashboards show vanity metrics, not decision-grade metrics ▸ A real finance dashboard answers cash, margin, cohort profitability, and forecast questions ▸ The 18 metrics below cover what an operator or CFO actually needs to see daily ▸ Build the pipeline once, the dashboard is the easy part

This post is the metric list we install when CFOs bring us in. Each metric has a definition, a source, and a decision it enables. Our analytics and reporting service builds these dashboards from the data layer up.

What a finance dashboard actually answers

Before listing metrics, the CFO questions a dashboard must answer:

1. Are we profitable this month and this quarter
2. Are new customers profitable after all costs
3. How much cash do we have and how much will we have in 90 days
4. Which acquisition channels are earning their spend
5. What is our working capital cycle doing to free cash flow
6. What is our inventory situation, stockout risk and overstock risk
7. Which cohorts are driving or dragging LTV

Every metric on the dashboard should map to one of these questions. If it does not, it is a marketing metric or a vanity metric, not a finance metric.

The 18 metrics

Cash and liquidity

1. Cash on hand — the total across all bank accounts, updated daily. The single most important number.
2. Cash runway in months — cash on hand divided by average monthly burn, recalculated weekly.
3. Inventory dollars on hand — cost basis of inventory, shows working capital locked up.
4. Accounts payable and receivable balance — the net of what you owe vs what you are owed.

Margin and profitability

5. Gross margin percent — revenue minus COGS divided by revenue, by week and by month.
6. Contribution margin percent — gross margin minus fulfillment and payment processing, divided by revenue.
7. Net contribution margin after ads — contribution margin minus paid media spend, divided by revenue. This is the profitability reality check.
8. Average fulfillment cost per order — pick, pack, ship, and packaging cost per order, monitored for drift.

Acquisition economics

9. Blended customer acquisition cost — total marketing spend divided by new customers acquired, including organic acquisition.
10. Paid customer acquisition cost — paid marketing spend divided by paid-attributed new customers, a harder number.
11. New customer contribution margin on first order — first order contribution margin minus customer acquisition cost. Is this negative, flat, or positive.
12. Payback period in days — the days to recover CAC through contribution margin on repeat orders. Shorter is better.

Cohort and retention

13. 90 day cohort LTV — contribution margin per customer in the first 90 days, by acquisition month.
14. Repeat customer rate — percent of customers with 2+ orders, by acquisition cohort.
15. Subscription month-two survival — if applicable, the percent of subscribers still active at the second shipment.

Inventory and operations

16. Days of inventory on hand — inventory in units divided by daily sales rate, by SKU.
17. Stockout rate — percent of SKUs out of stock at any time, rolling 7 day.
18. Return rate — percent of units returned, by SKU and by channel.

These 18 together describe the business. Drop any of them and a question goes unanswered.

What each metric tells you

A brief note on decision use for each:

The data pipeline

The dashboard is the display layer. The real work is the pipeline underneath.

Source systems:

• Shopify for orders, customers, and inventory
• Recharge, Skio, or Stay AI for subscription data
• Klaviyo for email engagement
• Meta Ads, Google Ads, TikTok Ads for media spend
• Shipping platform for fulfillment cost
• Bank and accounting platform for cash and AR/AP
• 3PL or WMS for inventory movements

Our LTV modeling service stitches these sources into a cohort view. The general architecture:

1. Source systems export daily to a warehouse via Fivetran, Hightouch, or custom ETL
2. dbt models transform raw data into fact and dimension tables
3. Cohort and unit economics calculations run as scheduled dbt transformations
4. BI tool reads from the transformed layer
5. Dashboard refreshes daily on a schedule

Building this takes 6 to 12 weeks for a mid-size brand. The payoff is that every future metric is one SQL query away, not a weeks-long integration project.

The forecast layer

A static dashboard is a scorecard. A forecast layer turns it into a planning tool.

Forecasts that matter: ▸ Revenue forecast 30, 60, 90 days out with confidence intervals ▸ Cash position forecast driven by AR, AP, and expected receipts ▸ Inventory position forecast based on sell-through rates ▸ CAC and payback forecast based on current cohort performance

Forecasts should be rebuilt weekly and compared to prior forecast to calibrate. A forecast that is always off in the same direction tells you the model has a bias. Fix the model.

Read DTC cash flow forecasting for the cash forecast methodology.

The weekly review ritual

The dashboard is not useful if no one looks at it. Build a weekly review ritual.

The structure we recommend:

1. 30 minute weekly finance review, same day and time every week
2. Attendees: CEO, CFO, head of operations, head of marketing
3. Open the dashboard live, do not pre-screenshot
4. Go metric by metric, note any that moved more than 10 percent
5. Assign an owner and action for any red-flag metric
6. Document decisions in a shared log

The ritual matters. A dashboard that is looked at on Fridays at 10am gets built and maintained. A dashboard that people remember to check gets stale.

Common anti-patterns

Bad dashboards share patterns:

Too many metrics. 40+ metrics on one screen means nothing is highlighted. The 18 above are the core. Anything beyond should be in a secondary tab.

Metrics without targets. A number without context is ambiguous. Every metric should have a target, a yellow threshold, and a red threshold. Color-code the dashboard to surface issues.

Mixed time windows. Some metrics show trailing 7 days, others trailing 30, others month to date. This creates apples-to-oranges comparisons. Standardize on two windows, typically trailing 28 and month to date.

Revenue without margin. A dashboard that shows gross revenue without contribution margin encourages growth that loses money. Always pair revenue with margin.

Vanity metrics. Session count, pageviews, email opens. These are useful for diagnostic work, not for finance. Keep them off the finance dashboard.

The operator view vs the board view

The dashboard described above is the operator view, updated daily, used for tactical decisions. The board view is different. It aggregates to monthly, shows 24 month trends, and focuses on strategic metrics like LTV to CAC ratio, gross margin trend, and market share estimates.

Both views read from the same data pipeline. The operator view is built for frequency. The board view is built for narrative.

The ecommerce customer lifetime value post covers LTV calculation in detail. The break-even ROAS guide covers the paid acquisition math.

Subscription-specific additions

For subscription brands, add four metrics:

▸ MRR or ARR, the recurring revenue base ▸ Net revenue retention by cohort ▸ Involuntary churn rate, card failures not recovered ▸ Expected second charge rate for the current cohort

The subscription dunning recovery flow covers the involuntary churn lever. The first month churn fix covers the second charge problem.

Getting started

If you do not have this dashboard today, the starter path:

1. Week 1: audit what data you can pull from Shopify, ads platforms, and accounting
2. Week 2: build the simplest version in Looker Studio or Google Sheets
3. Week 3: establish the weekly review ritual with placeholder metrics
4. Week 4 to 12: build the warehouse and dbt layer with a data engineer
5. Week 12 onward: migrate the dashboard to the warehouse-backed version

Do not wait for the perfect pipeline to start the ritual. The ritual is the point. The pipeline improves data quality over time.

Our analytics and reporting service covers this as a full-service build. The customer experience service covers the operational feedback loop that turns the metrics into actions.

What to do this week

▸ List the 18 metrics and check which you can report on today ▸ Identify the highest priority gaps, usually cohort LTV and true CAC ▸ Stand up the weekly finance review with a placeholder dashboard ▸ Assign each metric an owner who is accountable for its accuracy ▸ Define target, yellow, and red thresholds for each metric ▸ Start building the warehouse pipeline in parallel, do not block the ritual

A CFO-ready dashboard is not fancy software. It is a curated metric list, a reliable pipeline, and a ritual that forces the team to look at the numbers weekly. The software is the last thing you build.