> ## 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.

# Products, returns & discounts

> Triggers in Ocular for return rates, discount depth, product performance trends, and AOV — to diagnose catalog, promotion, and product-mix health.

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

## 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?

***

## 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** {/* TODO: relink to /use-your-data/sales-performance once that page is published. */}

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?

***

## 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** {/* TODO: relink to /use-your-data/sales-performance once that page is published. */}

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?

***

## How is a specific product or SKU trending over time?

**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** {/* TODO: relink to /use-your-data/sales-performance once that page is published. */}

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?

## Next

<CardGroup cols={2}>
  <Card title="Customers & retention" icon="users" href="/overview/start-with-the-question/customers-and-retention">
    Cohorts, CAC payback, LTV, repeat purchase.
  </Card>

  <Card title="Back to overview" icon="circle-question" href="/overview/start-with-the-question">
    All categories and the quick reference table.
  </Card>
</CardGroup>