Six layers turn your commerce data into decisions. Most tools stop at layer one.
Each layer adds intelligence. By the time data reaches your team, it's not a number — it's an instruction.
Shopify orders and customers. NetSuite inventory and B2B revenue. GA4 traffic. Google Ads and Meta spend. Klaviyo email performance. All synced every 15 minutes. Every data point carries a freshness timestamp.
The engine scans three domains continuously. Revenue anomalies: day-over-day drops or spikes detected using 30-day rolling percentile bands. Customer churn: purchase intervals exceeding 1.5x their normal pattern. Inventory risk: critical stock levels with meaningful revenue at risk.
Every signal gets a trust score from 0% to 100%. Factors: source reliability (metric engine 90%, customer data 85%, inventory 80%), data completeness, and time-of-day. At 6 AM, when only 20% of revenue data has arrived, signals are automatically suppressed — not hidden, but explicitly marked as unreliable.
Related signals merge into insights. A revenue drop + customer churn = one unified insight, not two separate alerts. Each insight is scored on three dimensions: revenue impact (0-40 points), urgency (0-35 points), and confidence (0-25 points). The highest-scoring insight surfaces first.
Insights connect into a narrative. “Revenue spiked 169% but 182 customers are churning. The spike may mask a retention problem.” Seven business patterns recognized: unstable growth, supply crisis, demand erosion, retention issue, inventory alert, revenue anomaly, and full crisis. Every number comes from real data.
Each insight produces concrete tasks assigned to the right team. “Call this customer — $3,866 LTV, 9 days inactive.” “Reorder this SKU — $1,298/day at risk.” Tasks have targets, instructions, deadlines, and suggested owners. Sales reps see tasks, not dashboards.
This is where most analytics tools fail — and where Platox is fundamentally different.
Platox compares your performance against category context — broad for directional signals, deep for specific patterns.
When a signal is weak or directional, Platox looks at the broader category. Is the entire segment declining, or just your brand?
When a signal is specific, Platox zooms into subcategory data. A problem in “outdoor lighting” is different from a general “home & garden” slowdown.
Market context is a decision layer, not a separate dashboard. It helps the engine explain why.
This is a real output from the Decision Engine.
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