// Control which products appear first in collections using automated ranking rules, manual overrides, and performance-based sorting algorithms
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| name | merchandising-rules |
| description | Control which products appear first in collections using automated ranking rules, manual overrides, and performance-based sorting algorithms |
| category | business-operations |
| risk | safe |
| source | curated |
| date_added | 2026-03-12 |
| tags | ["merchandising","product-ranking","collections","sorting","curation","search-relevance","boosting"] |
| triggers | ["implement merchandising rules","build product ranking","automate collection curation","product sorting algorithm"] |
| tools | ["claude-code","cursor","gemini-cli","copilot","codex-cli","kiro","opencode"] |
| platforms | ["shopify","woocommerce","bigcommerce","custom"] |
| difficulty | intermediate |
Merchandising rules control which products appear first in your collections and search results. The goal is to surface products that are likely to convert — in-stock, popular, high-margin — while giving merchandisers manual control to pin hero products, hide out-of-stock items, and boost seasonal collections. Every major platform has some built-in sorting options; apps like Searchpie, Intelligems, or SearchPie add automated performance-based ranking.
| Platform | Recommended Tool | Why |
|---|---|---|
| Shopify | Shopify's built-in collection sorting + Kimonix or SearchPie | Shopify has built-in sort options; Kimonix and SearchPie add performance-based automated sorting with manual override capability |
| WooCommerce | WooCommerce's default sort + YITH WooCommerce Catalog Mode or WooCommerce Product Table | WooCommerce supports basic sorting; YITH and similar plugins add advanced catalog control |
| BigCommerce | Built-in Collection Sorting + SearchPie or Boost Commerce | BigCommerce has strong built-in category sorting; Boost Commerce adds advanced search merchandising |
| Custom / Headless | Algolia or Elasticsearch with a merchandising rules layer | Algolia has a built-in "Rules" and "Pinning" feature in its dashboard; Elasticsearch needs custom scoring rules |
Built-in sorting options (no app needed):
Smart (automated) collections:
Shopify Search & Discovery app (free):
Built-in sorting:
Category product display (manual control):
BigCommerce Search:
Performance-based ranking automatically promotes products that are selling well and pushes down slow-movers. This typically requires an app.
Using Shopify Search & Discovery (free) for simpler boosts:
Using YITH WooCommerce Ajax Product Filter + WooCommerce Visual Products Configurator:
total_sales meta field that increments with each saleShopify Search & Discovery — search boosts:
in_stock: true)clearance: truedesc(sales_30d) — sort by recent sales descendingdesc(conversion_rate) — secondary sort by conversion rate// Compute a merchandising score for a set of products
interface ProductMetrics {
productId: string;
unitsSold30d: number;
revenue30d: number;
conversionRatePct: number; // (add-to-carts / page views) × 100
grossMarginPct: number;
daysInStock: number; // 0 = out of stock
daysSinceAdded: number;
}
interface ScoreWeights {
salesVelocity: number; // e.g., 0.35
revenue: number; // e.g., 0.20
conversion: number; // e.g., 0.20
margin: number; // e.g., 0.15
recency: number; // e.g., 0.10 (newer products get a boost)
}
function scoreProducts(metrics: ProductMetrics[], weights: ScoreWeights): { productId: string; score: number }[] {
// Normalize each metric to 0–1 range across the set
const normalize = (values: number[]) => {
const min = Math.min(...values);
const max = Math.max(...values);
return values.map(v => max === min ? 0.5 : (v - min) / (max - min));
};
const salesScores = normalize(metrics.map(m => m.unitsSold30d));
const revenueScores = normalize(metrics.map(m => m.revenue30d));
const conversionScores = normalize(metrics.map(m => m.conversionRatePct));
const marginScores = normalize(metrics.map(m => m.grossMarginPct));
// Recency: newer = higher score (invert daysAdded)
const recencyScores = normalize(metrics.map(m => -m.daysSinceAdded));
return metrics.map((m, i) => {
const score =
salesScores[i] * weights.salesVelocity +
revenueScores[i] * weights.revenue +
conversionScores[i] * weights.conversion +
marginScores[i] * weights.margin +
recencyScores[i] * weights.recency;
// Out-of-stock products get pushed to the bottom
const adjustedScore = m.daysInStock === 0 ? score - 2 : score;
return { productId: m.productId, score: Math.round(adjustedScore * 1000) / 1000 };
}).sort((a, b) => b.score - a.score);
}
| Problem | Solution |
|---|---|
| Best-selling products dominate every collection indefinitely | Add a recency weight and a "new product boost" for items added in the last 14 days; this gives new products a chance to get exposure |
| Merchandising rule conflicts with another rule | In Shopify Search & Discovery and Kimonix, rules have priority order — define which rules override others and document the priority scheme |
| Smart collection adds products that shouldn't be there | Review your collection conditions carefully; using "any condition" instead of "all conditions" is the most common cause of unintended inclusions |
| Performance scores skewed by a single viral day | Use a rolling 30-day window for sales metrics, not all-time totals; cap extreme outliers at the 95th percentile |