Revenue Dashboards
Revenue dashboards are the daily and weekly views operators use to monitor store health — done well, they alert on anomalies; done badly, they're just decoration.
Revenue Dashboards
Charts and tables operators check daily or weekly to monitor revenue health and catch anomalies before they compound.
A revenue dashboard is the operating cockpit of an online store. It pulls together the handful of numbers that actually move the business — orders, average order value, conversion rate, refund rate, ad spend, contribution margin — and presents them in a view a non-analyst can read in under two minutes.
The best dashboards do two things at once. They give a stable baseline view of the week so you know what 'normal' looks like, and they flag deviations — a sudden 30% drop in mobile checkout completion, a spike in refunds on one SKU — early enough to act. Anything that doesn't do one of those two jobs is decoration.
Revenue dashboards sit inside the broader practice of revenue intelligence — the discipline of turning raw store, ad, and CRM data into decisions. The dashboard is the surface layer; the data model, alerting, and segmentation underneath are what make it trustworthy.
Most stores have too many dashboards, not too few. A useful setup is three views: a daily pulse (yesterday vs. trailing 7-day average), a weekly trading review (WoW and YoY on the core funnel), and a cohort view (LTV and repeat rate by acquisition month). If a chart doesn't belong to one of those jobs, it's noise.
Revenue Health Score = (Conversion Rate Δ% × 0.4) + (AOV Δ% × 0.3) + (Sessions Δ% × 0.3)
Conversion Rate Δ%
Conversion rate change
Percentage change in store conversion rate vs. the trailing 28-day average.
AOV Δ%
Average order value change
Percentage change in AOV vs. the trailing 28-day average.
Sessions Δ%
Sessions change
Percentage change in total sessions vs. the trailing 28-day average.
A Shopify apparel store reviews Monday's trading numbers. Conversion rate is down 5%, AOV is flat, sessions are up 10% on the trailing 28-day baseline.
Conversion Rate Δ%: -5%
AOV Δ%: 0%
Sessions Δ%: +10%
→ Revenue Health Score = (-5 × 0.4) + (0 × 0.3) + (10 × 0.3) = +1.0
Slightly positive overall, but the negative conversion delta is the signal worth investigating — traffic is masking a CRO problem.
A composite score like this isn't the source of truth — the underlying tiles are. Its job is to give a one-number pulse so anomalies bubble up without anyone having to scan twelve charts every morning.
Typical tiles on a working DTC revenue dashboard
| Tile | Cadence | Alert threshold | Why it's there |
|---|---|---|---|
| Net revenue (today vs. T-7 avg) | Daily | ±15% | Catches outages, broken checkout, ad pauses |
| Conversion rate by device | Daily | −10% mobile | Mobile regressions hide inside blended CR |
| Refund rate (rolling 7d) | Daily | >4% | Early warning on quality or sizing issues |
| Blended ROAS | Daily | <1.5 | Profitability check on paid spend |
| AOV by channel | Weekly | ±10% | Detects discount creep or promo cannibalisation |
| Repeat purchase rate (90d) | Weekly | <20% | Retention health, leading LTV indicator |
| Checkout completion rate | Weekly | <55% | Funnel leak signal before revenue moves |
Notice what's not on the list: vanity tiles like total page views, social followers, or 'sessions by browser'. Every tile should have an alert threshold and a named owner. If no one acts when it turns red, it shouldn't be on the dashboard.
Frequently asked questions
Seven to nine for the daily view, twelve to fifteen for the weekly trading review. Beyond that, readers stop scanning and start ignoring. If you need more, split into separate dashboards by job (acquisition, retention, merchandising).
Revenue intelligence is the full practice — data model, alerting, forecasting, and the decisions that come out of it. A revenue dashboard is the visible surface of that practice. You can have dashboards without intelligence, but they'll just be wallpaper.
GA4 is fine for traffic and conversion-path views but weak on revenue accuracy and cohorts. Shopify Analytics is accurate but shallow. A dedicated layer makes sense once you're blending ad spend, store data, and CRM — typically past €1M in annual revenue.
Both, for different jobs. Daily exists to catch breakage (a broken pixel, a paused campaign, a checkout bug). Weekly exists to spot trends (rising CAC, falling repeat rate). Confusing the two leads to over-reacting to noise on the daily view.
Start with ±2 standard deviations from the trailing 28-day average per metric, then tune. Tighter thresholds on revenue and checkout completion (these break fast); looser on retention metrics (these move slowly).
Three rules: every tile has an owner, every tile has an alert, and any tile that hasn't triggered a decision in 60 days gets removed. A dashboard that no one acts on is a maintenance tax with no payoff.
Yes — blended ROAS and contribution margin per order are more useful than gross revenue alone. A revenue chart that doesn't subtract spend hides the months where you grew unprofitably.
Group by acquisition source (paid social, paid search, organic, email, direct) and by new-vs-returning customer. Most decisions live at that intersection — paid social to new customers behaves nothing like email to returning ones.
Checkout completion rate broken out by device. Blended conversion rate hides mobile regressions for weeks, and mobile is where 60-70% of sessions live. If you only add one tile, add that one.
Quarterly review, not monthly. The tiles should be stable enough that the team builds intuition for 'normal' ranges. Constant restructuring resets that intuition and the dashboard stops being a baseline.
See Metricuno on your data
Bring your stack — Google Analytics, Stripe, a CRM, anything — and we'll walk through the metric tree that turns your funnel into one number.