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Documentation Index

Fetch the complete documentation index at: https://docs.ocular.dev/llms.txt

Use this file to discover all available pages before exploring further.

For each trigger below: what happened, where to go, how to read it, the decision you’re making.

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Which products have the highest return rates?

Trigger: Returns are eating margin (you saw it in the P&L’s Refunds step). You need to trace the problem to specific products. Or customer complaints are spiking for particular SKUs and you want to quantify the financial impact. Chart Builder: Sales · Measures: percentage_orders_returned, Net Orders, total_returned_units · Dimension: product_name · Table. Sort by percentage_orders_returned descending. Filter to ≥ 50 Net Orders to exclude low-volume noise. Refund turnaround: Add Average Days to Refund Return. Products with both high return rates and long refund times pressure both margin and cash flow. Connection to P&L: High-return products feed the Refunds & Cancellations step. If that step is large, this chart tells you which products are responsible. Decision: Which products need investigation (sizing? quality? misleading PDP?), and which should come off promotion or be discontinued?

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Are my discounts driving new orders or subsidising existing customers?

Trigger: Running promotions but unsure whether they’re acquiring new customers or training loyal buyers to wait for discounts. Usually surfaces in promo planning or when repeat-customer AOV drops. Go to: Sales Performance → Discount Affinity by Customer Type Compares discount usage between new and repeat customers, broken down by product category. If repeat customers account for a larger share of promo orders than new customers, you’re conditioning loyalty to discount. Chart Builder (deeper): Sales · Measures: Gross Orders (With Promotion), Gross Orders (No Promotion), Average Discount Rate · Dimension: Customer Visit Type · Breakdown: product_category. Seasonal context: Add occasion_name as filter/breakdown to compare sale events vs. standard periods. Holidays often mask repeat-buyer discount dependency. Decision: Tighten discount eligibility (new-customer-only, first-order-only), restructure the discount, or pull always-on promo codes?

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How deep are we discounting, and is it getting worse?

Trigger: ASPs are declining, or the P&L’s Discounts step has grown QoQ. Seasonal blip or structural pricing problem? Go to: Sales Performance → Discount Depth Across Product Categories Heatmap of discount depth across categories over time. Darker cells = heavier discounting. Set a 12-month range — categories with consistently dark cells across the year are structurally discount-dependent (pricing/product issue, not promo). P&L connection: The Discounts step aggregates this. If Discounts is a big step, use the heatmap to find which categories drive it and whether it’s trending worse. Decision: Is this a structural pricing problem (adjust base prices, remove always-on discounts) or a seasonal pattern I can manage tactically?
Trigger: Reviewing a specific product — new launch, declining hero SKU, or discontinuation candidate. You need revenue + volume trend, and to separate organic demand from discount-driven sales. Go to: Sales Performance → Product Performance Over Time Line chart with built-in period-over-period comparison. Products in YoY decline with stable promo activity → likely product-market-fit issue. Chart Builder (deeper): Sales · Measures: Net Revenue, Net Units Sold · Dimension: Full Date (Monthly) · Filter: product_sku = [your SKU] · Line. Add Gross Orders (With Promotion) as a second series to overlay promo activity — separates organic growth from discount-driven. Decision: Growing on its merits or propped up by promotions — invest more, hold steady, or phase out?

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Customers & retention

Cohorts, CAC payback, LTV, repeat purchase.

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