Analytics & reporting
The 6 capabilities
The spine — reads every signal and surfaces what matters this week.
Shop-wide health in one view; 4 per-area dashboards for drill-down.
Infrastructure for experiments across copy, layout, journey, and offer.
Review collection and conversion-rate audit of your storefront.
Cross-channel revenue + ROAS view; ties to attribution.
Monday-morning email; engine audit catches misconfigured rules and journeys.
What this typically unlocks
| Outcome | Result |
|---|---|
| Hours/week on dashboard reading | −6h |
| "What should we do this week?" answered in seconds | vs. hours of cross-tool analysis |
| Decisions made on data (not anecdote) | +200% |
| Data-grounded board updates | prepared in 15 min vs. 4 hours |
| Time to detect a problem | 2–4h vs. 1–3 weeks |
How they compose
DIE is the spine. Every other surface (Command Centre, dashboards, digest, alerts) reads from DIE's pre-digested signals — so "this week's biggest opportunity" stays consistent across surfaces.
Real merchant scenarios
Scenario A — Founder gets weekly decision in 5 minutes
Solo founder, $500K/year. Pre-platform: ~4 hours every Monday reading Shopify + Klaviyo + Meta dashboards.
Post-platform: Weekly digest Monday 7am with top 3 wins + top 3 opportunities + recommended actions. 5 min to read; 20 min to action. 3.5 hours saved every week.
Scenario B — Mid-market replaces 3 BI tools
$10M brand using Looker + custom dashboards + manual weekly report.
| Item | Before | After |
|---|---|---|
| Looker | $2K/month | $0 |
| Custom dashboards maintenance | ~$1K/month | $0 |
| Manual reporting analyst | ~16 hrs/month | 4 hrs |
Annual savings: ~$36K + 144 analyst hours.
Scenario C — Agency gives clients clean dashboards
Agency manages 12 brands. Pre: built each report manually in Google Sheets, ~3 hrs/client/week. Post: weekly digest auto-generated per client, agency-branded. Spends 30 min/client reviewing.
Per-client time saved: 2.5 hrs × 12 = 30 hrs/week back.
See also
- Decision Intelligence — the engine
- Command Centre + dashboards — view layer
- Sales engine — most data sources
- Orchestration — copilot is conversational layer
Why this exists — the long version
Most stores have more analytics than they can act on. Shopify shows revenue. Klaviyo shows email metrics. Meta shows ROAS. Google Analytics shows traffic. Each dashboard answers its own narrow question; none answers the questions an operator actually has: "what should I do this week?", "where is the business slipping?", "what's working that I should do more of?"
Analytics & reporting on this platform is built for decisions, not data. The Decision Intelligence Engine (DIE) reads every signal — sales, engagement, attribution, predictive LTV, anomaly state — and surfaces the 3–5 things that matter most this week. Command Centre rolls up shop-wide health into one view. Per-area dashboards drill into specifics. The weekly digest emails you what to act on every Monday morning.
The shift this enables: from reading dashboards to making decisions. The team's analytical bandwidth goes 5–10× further because the platform pre-digests; humans focus on judgement.