finsilabs/product-analytics

Product Analytics

Overview

Product analytics reveals which products drive revenue, which are overstocked, and which product pages are losing shoppers before they add to cart. The core analyses — sell-through rate, dead stock identification, PDP conversion funnel, and category performance — give your buying and merchandising team the data they need to make confident reorder, markdown, and catalog decisions.

This skill guides you through running these analyses using your platform's built-in tools and dedicated apps, without building custom data pipelines.

When to Use This Skill

• When the buying team needs a weekly sell-through report to decide on reorders and markdowns
• When building a product performance dashboard for merchandisers
• When identifying dead stock that ties up capital
• When measuring which products have high views but low add-to-cart rates
• When ranking products for collection page sorting based on performance data
• When generating a catalog health report before a seasonal reset

Core Instructions

Step 1: Choose your product analytics tool by platform

| Platform | Tool | What It Provides | |----------|------|-----------------| | Shopify | Shopify Analytics (built-in) | Product-level revenue, units sold, sell-through (if cost entered); free | | Shopify | Inventory Planner (App Store) | Sell-through rates, days of supply, reorder recommendations, dead stock alerts | | Shopify | Google Analytics 4 (via Shopify's GA4 integration) | PDP views, add-to-cart rate, checkout funnel by product | | WooCommerce | WooCommerce Analytics (built-in) | Product revenue, units sold, orders by product; free | | WooCommerce | Metorik | Advanced product analytics including sell-through, cohort analysis by product, and dead stock reports | | BigCommerce | BigCommerce Analytics → Merchandising (built-in) | Product revenue, units sold, and conversion rate by product | | BigCommerce | Glew.io (App Marketplace) | Advanced sell-through, dead stock, and product lifecycle analytics | | All platforms | Google Analytics 4 + enhanced ecommerce | Views-to-cart-to-purchase funnel by product; requires GA4 setup with ecommerce tracking |

Step 2: Analyze sell-through rate

Sell-through rate measures how much of received inventory has been sold:

Sell-through % = Units Sold / (Units Sold + Units On Hand) × 100

A product at 80%+ sell-through is performing well. Below 30% after 60+ days suggests slow movement.

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Shopify

Using Shopify Analytics:

1. Go to Analytics → Reports → Inventory sold and remaining (this is the sell-through report)
2. Set the date range to the product's launch date or the beginning of the season
3. The report shows: Units received, Units sold, Units remaining, and % sold for each variant
4. Export to CSV for detailed analysis

Using Inventory Planner:

1. Install Inventory Planner from the Shopify App Store
2. Go to Inventory Planner → Reports → Sell-Through — shows sell-through rate by product and variant
3. Go to Inventory Planner → Reports → Days of Supply — shows how many days of stock remain at current sales velocity
4. Go to Inventory Planner → Replenishment — automatically recommends reorder quantities and timing

Key sell-through benchmarks by category:

• Fashion/seasonal items: Target 70%+ sell-through by end of season; anything below 40% at season end needs markdown
• Evergreen/perennial basics: 50–70% sell-through is normal (higher in-stock availability is intentional)
• Perishables/consumables: 85%+ (low days of supply is the goal)

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WooCommerce

Using WooCommerce Analytics:

1. Go to WooCommerce → Analytics → Products
2. Set date range to the period you want to analyze
3. View: Revenue, Quantity, Average price, Orders by product
4. Export to CSV; calculate sell-through manually by dividing quantity sold by (quantity sold + current stock)

Using Metorik:

1. Go to Metorik → Products — view all products with revenue, units sold, and refund data
2. Apply the Slow Moving filter to identify products with low recent sales relative to their stock levels
3. Create a Segment in Metorik for "products with 0 sales in the last 60 days" and monitor regularly

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BigCommerce

1. Go to Analytics → Merchandising → Products — shows revenue, units sold, and conversion rate per product
2. Go to Analytics → Merchandising → Inventory — shows current stock levels alongside recent sales velocity
3. Install Glew.io for sell-through rate calculations and dead stock alerts with automated weekly digest emails

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Step 3: Identify dead stock

Dead stock is inventory that has been on hand for a long time with minimal or no sales. It ties up working capital, occupies warehouse space, and often requires markdowns to liquidate.

Dead stock criteria (adjust by category):

• Fashion/seasonal: On hand 60+ days + sell-through < 20%
• Evergreen basics: On hand 120+ days + sell-through < 15%
• High-value items: On hand 90+ days + inventory value > $500

Finding dead stock by platform:

Shopify:

1. Go to Analytics → Reports → Inventory sold and remaining — filter for products with > 90 days since first available AND sell-through < 20%
2. Alternatively, install Inventory Planner → go to Reports → Excess Inventory for automated dead stock identification with capital-at-risk calculation

WooCommerce:

1. Go to WooCommerce → Analytics → Products — sort by "units sold ascending" to find products with minimal recent sales
2. Cross-reference with WooCommerce → Products → Inventory for current stock levels
3. Metorik makes this easier: go to Metorik → Products → Filter by: 0 sales in last 90 days AND stock > 0

Dead stock action guide:

| Days on Hand | Sell-Through | Recommended Action | |-------------|-------------|-------------------| | 60–90 days | < 20% | 10–15% markdown; add to promotional emails | | 91–120 days | < 15% | 20–25% markdown; feature in collections and homepage | | 120–180 days | < 10% | 30–40% markdown; run dedicated clearance campaign | | 180+ days | < 5% | 40–50% markdown or bundle with fast-movers; consider liquidation if markup still negative |

Step 4: Measure product page conversion (Views → ATC → Purchase)

A product with high traffic but low add-to-cart rate signals a page problem: pricing, description, images, or reviews.

Setting up product-level funnel tracking:

All platforms require Google Analytics 4 with Enhanced Ecommerce for PDP conversion tracking.

Shopify:

1. Go to Shopify → Online Store → Preferences → Google Analytics and add your GA4 Measurement ID
2. Or install Google & YouTube from the Shopify App Store (recommended — includes server-side events)
3. In GA4, go to Reports → Monetization → Ecommerce purchases → filter by item to see views, add-to-carts, and purchases per product
4. For a funnel view: go to GA4 → Explore → Funnel exploration and build a funnel: view_item → add_to_cart → begin_checkout → purchase; dimension by item_name

WooCommerce:

1. Install Google Analytics for WooCommerce by MonsterInsights or Site Kit by Google — both send WooCommerce product events to GA4 automatically
2. View product funnel the same way as Shopify in GA4

BigCommerce:

1. Go to BigCommerce → Analytics → Marketing → Connected Channels → Google Analytics and enable Enhanced Ecommerce
2. View product funnel in GA4

Key PDP conversion benchmarks:

• PDP view → Add to cart: 5–15% is typical; below 3% warrants investigation
• Add to cart → Purchase: 40–60% is typical

What low add-to-cart rate usually means:

• Price is too high relative to perceived value → A/B test price or add value (bundle, guarantee)
• Product images are poor quality or show the product unclearly → Improve photography
• Description does not address customer objections → Add FAQ section, size guide, or material details
• Reviews are low or absent → Activate review request automation

Step 5: Build a weekly catalog health report

Combine sell-through, dead stock, and conversion data into a weekly report for the buying team.

Report structure (can be a recurring Metorik digest, Inventory Planner export, or manual Shopify CSV export):

WEEKLY CATALOG HEALTH REPORT — Week of [Date]

HEADLINE METRICS
  Active SKUs: 284
  Dead stock count (>90 days, <15% ST): 23 SKUs ($41,200 at cost)
  Low stock / reorder needed (<14 days supply): 12 SKUs
  New arrivals launched this week: 8 SKUs

TOP PERFORMERS (Revenue, last 7 days)
  [Product A] — $12,400 — 78% sell-through — 14 days supply remaining
  [Product B] — $9,800 — 65% sell-through — 32 days supply remaining

PRODUCTS NEEDING ATTENTION
  Slow movers (on hand >90 days, <20% ST):
    [SKU X] — 180 days on hand — 8% ST — $4,200 inventory value — ACTION: 30% markdown
    [SKU Y] — 120 days on hand — 12% ST — $2,800 inventory value — ACTION: 20% markdown

  High views, low ATC (>200 views last 7 days, <3% ATC):
    [Product Z] — 340 views — 1.8% ATC — Review product description and pricing

Best Practices

• Report sell-through weekly, not monthly — a weekly cadence lets buyers intervene before products age into dead stock
• Always include inventory value (units × cost) in dead stock reports — a merchant cares more about $5,000 tied up in slow movers than 100 units of a $3 product
• Set different dead-stock thresholds by category — fashion items become dead stock faster (60 days) than perennial basics (180 days); configure category-specific thresholds in Inventory Planner or your reporting tool
• Use days of supply, not just inventory count — 500 units of a product selling 5/day (100 days of supply) is very different from 500 units selling 1/day (500 days); days of supply is the actionable metric
• Pair low-ATC-rate alerts with session recording — tools like Hotjar or Lucky Orange (Shopify App Store) let you watch real visitor sessions on high-traffic/low-converting product pages; often reveals issues invisible in metrics alone
• Include return rate in product health scoring — high-return products look good on revenue but erode margin; investigate and possibly discontinue before reordering

Common Pitfalls

| Problem | Solution | |---------|----------| | Dead stock report includes recently launched products | Exclude products launched in the last 30 days from dead stock analysis; they need time to ramp up before being flagged | | Sell-through over 100% | Inventory received was understated — check if inventory received captures all purchase orders including transfers and returns | | Days of supply calculation shows zero for products that are not selling | Handle zero-sales denominator as "effectively infinite stock" rather than division by zero; display as "No recent sales" in reports | | PDP conversion data does not match expectations | Verify GA4 Enhanced Ecommerce events are firing correctly on product pages; use GA4's DebugView to confirm view_item and add_to_cart events | | Product analytics slow on large catalogs | Materialize a weekly product performance summary table in your data warehouse or use Inventory Planner's pre-computed metrics instead of querying raw order data |

Related Skills

• @sales-reporting-dashboard
• @customer-analytics
• @ab-testing-ecommerce
• @profit-margin-analysis