What SKU-level metrics reveal about customer intent

Why aggregate metrics hide purchase behavior signals

Store-wide metrics show 3.2% conversion rate, $86 average order value, 8,400 monthly sessions generating $23,100 revenue. These aggregate numbers suggest uniform customer behavior and consistent performance across catalog. But SKU-level analysis reveals dramatically different patterns hidden within averages.

Product A: 4.8% conversion rate, $124 AOV, 12% of traffic, 18% of revenue. Product B: 2.1% conversion, $68 AOV, 22% of traffic, 8% of revenue. Product C: 6.2% conversion, $210 AOV, 8% of traffic, 21% of revenue. Each product attracts different customer intent, converts at different rates, generates different transaction values. Aggregate metrics conceal these behavioral differences.

Understanding SKU-level performance reveals what customers actually want versus what you think they want based on traffic distribution. High-traffic low-conversion products indicate awareness without purchase intent. Low-traffic high-conversion products show strong intent among aware customers. Traffic-to-revenue mismatch signals merchandising optimization opportunities.

Product performance patterns expose customer decision-making processes, price sensitivity variations, feature importance differences, and purchase confidence levels. These insights guide inventory investment, marketing focus, pricing strategy, and catalog development more effectively than aggregate metrics suggesting uniform customer behavior.

Peasy’s top 5 products view shows which items drive revenue. Combine with traffic and order data to calculate product-specific conversion rates revealing intent patterns across catalog. SKU-level analysis transforms generic "improve conversion" goals into specific "increase Product B conversion by reducing friction" or "drive more traffic to high-converting Product C" actions.

High-traffic low-conversion products

Products attracting significant traffic but converting poorly indicate awareness without purchase readiness, research-phase customers, or interest without intent. These patterns suggest different optimization approaches than low-traffic products.

Awareness-stage traffic: Product receives traffic from informational searches, review queries, comparison keywords. Visitors researching category, learning about options, gathering information. Not yet purchase-ready despite high engagement. Traffic volume reflects topic interest rather than transaction intent.

Example: "best running shoes for beginners" drives traffic to running shoe category page. Visitors explore options, read descriptions, compare features. Conversion rate 1.8% because most visitors still researching, not ready to buy. Traffic volume high from broad search interest. Revenue contribution low from early-stage intent.

Optimization approach: Provide educational content helping visitors progress toward purchase readiness. Comparison guides, feature explanations, buying guides, FAQ sections. Capture email addresses for nurturing rather than expecting immediate conversion. Build trust and expertise positioning you as preferred vendor when purchase decision timing arrives.

Price resistance signals: Product attracts traffic indicating interest but converts poorly suggesting price objection. Visitors want product but perceive value-price mismatch. They compare alternatives, seek discounts, abandon without purchasing. Traffic validates demand; conversion reveals pricing problem.

Product priced $180 in category where competitors offer similar items $120-140. Traffic share 18% (high interest) but conversion 2.3% (low intent). Visitors discover product through search or browsing, evaluate favorably based on features, then balk at price premium. Price sensitivity prevents conversion despite product-market fit on features.

Optimization approach: Justify premium through value communication (quality, durability, warranty, brand trust), introduce lower-priced alternative capturing price-sensitive segment, or adjust pricing matching customer willingness-to-pay revealed through conversion data. High traffic low conversion on premium products often indicates pricing above market acceptance rather than product inadequacy.

Feature mismatch or confusion: Product attracts traffic from keyword match or category association but actual product doesn’t match visitor expectations. Traffic driven by SEO success or category prominence, not genuine product-visitor fit. Mismatch becomes obvious on product page, visitor leaves without converting.

Search for "laptop backpack" lands on technical hiking backpack with laptop sleeve. Product technically matches query (has laptop sleeve) but doesn’t match searcher intent (professional laptop bag, not hiking gear). Traffic arrives, mismatch apparent immediately, conversion fails. High traffic from keyword ranking, low conversion from intent mismatch.

Optimization approach: Improve targeting matching traffic to appropriate products, adjust product descriptions clarifying actual use case and features, or develop products genuinely matching high-traffic intent if market opportunity exists. Sometimes high-traffic low-conversion indicates catalog gap rather than optimization problem.

Low-traffic high-conversion products

Products with limited traffic but strong conversion rates indicate high purchase intent among aware visitors, specialized demand from targeted segments, or underpromoted products with strong product-market fit. These patterns suggest scaling opportunities through increased visibility.

Targeted niche demand: Product serves specific customer segment with clear need and strong intent. Awareness limited (low traffic) but fit excellent among aware visitors (high conversion). Traffic consists of qualified prospects rather than casual browsers, driving superior conversion despite modest volume.

Example: Specialized running shoe for overpronation correction. Only runners with specific gait issues search for product. Traffic volume 3% of total catalog traffic (small segment). Conversion rate 7.2% (double category average) because visitors searching have specific need and limited alternatives. Revenue contribution 8% despite low traffic from high AOV and conversion.

Optimization approach: Increase visibility to target segment through content marketing addressing specific problem, paid advertising targeting precise audience, and improved search optimization for niche keywords. Scaling requires finding more qualified traffic without diluting into broader low-intent audience reducing conversion. Quality growth over quantity growth.

Underpromoted winners: Product performs well among visitors who find it but receives insufficient promotional support or visibility. Merchandising, navigation placement, search ranking, or marketing focus directs traffic elsewhere despite this product’s strong conversion. Hidden gem in catalog awaiting discovery.

Product buried in category page position 12, receives only 2% of category traffic. Among visitors who scroll to find it, conversion rate 6.8% shows strong appeal. Revenue contribution 4% despite low visibility. Moving to prominent position or featuring in recommendations could dramatically increase revenue from proven conversion efficiency.

Optimization approach: Increase visibility through homepage featuring, category page promotion, email marketing, paid advertising, cross-sell recommendations from other products. Strong conversion validates product-market fit, justifying investment in awareness-building. Test promotional investment measuring incremental revenue versus cost.

Repeat purchase drivers: Product shows low traffic but high conversion because traffic consists heavily of returning customers making repeat purchases rather than new customers discovering. Consumables, replacement parts, or subscription items demonstrate this pattern. First purchase happened previously through other product, repeat purchases targeted and intentional.

Water filter replacement cartridge: low search volume and discovery traffic (most people buy filter system first, cartridges later). Among visitors finding cartridge page, conversion 9.4% because they own system and need replacement. Traffic modest but highly qualified consisting of existing system owners.

Optimization approach: Improve discovery among system owners through post-purchase email, reorder reminders, subscription offerings, and cross-sell from system product pages. Focus on customer retention and lifetime value rather than new customer acquisition. Build automatic replenishment reducing customer effort and increasing recurring revenue.

Premium products and purchase confidence

High-price products often show lower traffic (fewer searchers at premium price points) but strong conversion among qualified visitors willing and able to pay. Price itself filters traffic toward qualified prospects improving conversion efficiency despite reducing volume.

$850 premium product receives 4% of category traffic but converts at 5.2% versus category average 3.4%. Traffic self-selects for prospects with budget and quality prioritization. Visitors clicking premium product already mentally prepared for higher price point, reducing price shock and objection. Conversion reflects qualified audience rather than broad traffic.

This pattern validates premium positioning and pricing strategy. Low traffic acceptable when conversion and AOV produce strong revenue contribution. Attempting to increase traffic through discounting or mass marketing dilutes audience quality reducing conversion efficiency. Premium products thrive with targeted visibility to qualified buyers rather than broad awareness.

Traffic-revenue mismatch analysis

Comparing traffic share to revenue share reveals optimization priorities. Products with higher revenue share than traffic share demonstrate efficiency deserving increased visibility. Products with higher traffic share than revenue share indicate awareness without monetization requiring conversion optimization or traffic reallocation.

High-efficiency products: Product receives 8% of traffic but generates 15% of revenue. Strong conversion and/or high AOV create outsized revenue contribution relative to visibility. These products demonstrate proven market demand and effective positioning. Scaling traffic to these products likely produces proportional revenue growth.

Calculate efficiency ratio: revenue share divided by traffic share. 15% revenue / 8% traffic = 1.88 efficiency ratio. Products with efficiency ratios above 1.3 show strong performance justifying increased marketing investment, improved merchandising placement, and catalog expansion in similar categories.

Low-efficiency products: Product receives 22% of traffic but generates 8% of revenue. Poor conversion and/or low AOV create revenue underperformance relative to visibility. Traffic acquisition working (high volume) but monetization failing (low revenue). Optimization priority should emphasize conversion improvement rather than traffic growth.

Efficiency ratio: 8% revenue / 22% traffic = 0.36 efficiency. Products with ratios below 0.7 indicate problems: pricing too low, conversion barriers present, wrong audience attracted, or product-market fit questionable. Investigate cause before investing in more traffic amplifying inefficient patterns.

Merchandising optimization: Rebalance catalog visibility matching revenue contribution rather than traffic distribution. Feature high-efficiency products prominently, reducing visibility of low-efficiency products until conversion improves. Homepage, email, and paid advertising should emphasize proven revenue drivers rather than traffic attractors.

Current homepage features Product A (22% traffic, 8% revenue, 0.36 efficiency). Replace with Product C (8% traffic, 15% revenue, 1.88 efficiency). Traffic shift from A to C likely improves total revenue through better conversion and AOV on equivalent session volume. Test visibility reallocation measuring revenue impact.

Category-level demand patterns

Product performance patterns within categories reveal customer preference hierarchies, price tier demand distribution, and feature importance. Understanding category dynamics guides product development, inventory allocation, and assortment strategy.

Price tier distribution: Analyze revenue contribution across price tiers: budget (under $50), mid-range ($50-100), premium ($100-200), luxury (over $200). Category showing 60% revenue in mid-range tier indicates mainstream market. Category with 45% luxury tier suggests affluent customer base or quality-focused positioning.

Traffic distribution versus revenue distribution across tiers reveals price sensitivity. Budget products receiving 40% traffic but 15% revenue indicate browsing without purchasing from price-focused shoppers. Premium products receiving 15% traffic but 40% revenue show purchase concentration among quality-oriented buyers. Distribution guides inventory investment and catalog focus.

Feature variation performance: Compare similar products with different features. Color variants, size options, material choices, feature sets. Performance differences reveal customer preferences often hidden in aggregate data.

Black model: 45% of variant traffic, 52% of variant revenue. Blue model: 30% traffic, 28% revenue. Red model: 25% traffic, 20% revenue. Black clearly preferred, deserves inventory priority. Red underperforms, consider discontinuing or clearing inventory. Data reveals preference more reliably than opinions or assumptions.

Seasonal demand shifts: SKU-level tracking shows seasonal product demand changes more precisely than category aggregates. Winter products building demand October-November, peaking December-January, declining February-March. Tracking individual SKU timing rather than category timing enables optimized inventory purchasing and promotional timing.

Product A peaks November, product B peaks January despite both being "winter" category. A captures early holiday buying. B captures post-holiday purchasing with gift cards and resolved intent. Different promotional strategies and inventory timing appropriate for each SKU despite category similarity.

New product performance indicators

New product launch performance reveals market reception, positioning effectiveness, and scaling potential. Early SKU-level metrics predict whether products warrant continued investment or require repositioning.

Strong launch signals: Conversion rate matching or exceeding category average within first month indicates good product-market fit. Above-average AOV suggests customers perceive value justifying price. Steady or growing traffic (without paid advertising increases) shows organic discovery and word-of-mouth working. Review velocity (reviews per week) healthy compared to sales volume indicates customer satisfaction and engagement.

New product month 1: 3.8% conversion (category average 3.4%), $96 AOV (category average $86), 180 units sold, 12 reviews (6.7% review rate). Positive indicators suggesting successful launch warranting continued support and inventory investment. Product achieving average or better performance immediately suggests strong foundation for growth.

Weak launch signals: Conversion rate significantly below category average (under 2.5% when category runs 3.4%) indicates positioning, pricing, or product issues. Below-average AOV suggests discount dependency or bundling required for sales. Declining traffic despite promotional support shows weak word-of-mouth and retention. Low review rates indicate insufficient satisfaction for customer advocacy.

New product month 1: 1.9% conversion, $68 AOV, 85 units sold, 2 reviews (2.4% review rate). Warning signs requiring investigation before scaling. Product underperforming suggests fundamental problems better addressed through repositioning or discontinuation rather than marketing investment amplifying weak foundation.

Iteration requirements: Mixed signals — strong conversion but low AOV, or high traffic but weak conversion — suggest specific problems addressable through iteration. Strong conversion low AOV indicates price too low, capture value through price increase or upsells. High traffic weak conversion suggests messaging mismatch, improve product page or adjust targeting.

Inventory and assortment decisions

SKU-level performance should drive inventory investment allocation, catalog expansion direction, and discontinuation decisions. Revenue contribution, conversion efficiency, and growth trajectory determine which products deserve continued support.

Inventory investment prioritization: Allocate inventory budget proportionally to revenue contribution adjusted for efficiency. Product generating 18% of revenue with 1.5 efficiency ratio deserves more inventory investment than product generating 18% of revenue with 0.8 efficiency. Performance predicts future demand more reliably than gut instinct.

Calculate days of inventory and target stock levels based on sales velocity and efficiency. High-efficiency fast-moving products require deeper inventory preventing stockouts during demand spikes. Low-efficiency slow-moving products need minimal inventory avoiding capital lockup in underperforming SKUs.

Catalog expansion guidance: Successful products indicate demand for similar items. Develop variants, expanded size ranges, color options, or complementary products. High-converting premium product suggests market for even more premium option. Strong budget product indicates opportunity for adjacent budget category.

Product C (outdoor camping lantern) shows 6.2% conversion and 21% revenue share. Develop related products: backup batteries, carrying cases, camping lights for different uses. Proven customer demand for category justifies assortment expansion capturing more wallet share from engaged customer base.

Discontinuation candidates: Products with sustained low conversion (under 2%), minimal revenue contribution (under 3%), declining trajectory, or low efficiency (under 0.6) become discontinuation candidates. Catalog clutter from underperforming products distracts from strong performers and complicates inventory management.

Apply 80/20 principle: 20% of SKUs typically generate 80% of revenue. Remaining 80% of SKUs producing 20% of revenue collectively may include individual products worth discontinuing. Ruthless editing focuses resources on winners rather than supporting mediocre performers.

Using Peasy for SKU-level insights

Peasy’s top 5 products view shows highest-revenue SKUs daily. Track ranking changes revealing shifting performance. Products moving up rankings show growth momentum. Products falling show declining performance requiring investigation.

Combine Peasy product data with traffic and order counts calculating conversion rates. Product generating $4,200 monthly revenue at $84 AOV sold 50 units. From 1,200 product page sessions yields 4.2% conversion rate. Compare across products identifying high-converters and low-converters.

Monitor revenue concentration: top 5 products percentage of total revenue. Increasing concentration (top 5 growing from 42% to 58% of revenue) suggests portfolio narrowing and vulnerability to single-product problems. Decreasing concentration indicates healthy diversification spreading risk across broader catalog.

Track product position changes week-over-week. Product falling from #2 to #4 in top 5 lost revenue share to other products. Investigate cause: competitive pressure, seasonality, quality issues, or intentional cannibalization from new launches. Position changes signal performance shifts worth understanding.

Set monitoring thresholds for investigation. Product falling 20%+ in revenue week-over-week triggers analysis. New product failing to reach top 10 within first month triggers review. Consistent bottom performer remaining in catalog 6+ months without improvement triggers discontinuation consideration.

FAQ

How many products should be in my top 5 consistently?

Healthy catalogs show 60-70% consistency month-over-month in top 5 products (3-4 same products), with 1-2 positions rotating among next tier. High consistency (all 5 identical monthly) suggests dangerous concentration. Low consistency (different top 5 each month) indicates lack of clear winners or volatile performance. Some stability with some rotation demonstrates both bestseller strength and catalog depth.

Should I discontinue products with low traffic and low conversion?

Generally yes, unless serving strategic purpose (completes product line, satisfies niche need, retains specific customer segment). Products with under 2% conversion and under 3% revenue share draining resources through inventory investment, merchandising attention, and opportunity cost. Exception: new products needing time to establish or seasonal products during off-season.

What conversion rate difference justifies different marketing strategies?

Products converting 40%+ different than category average warrant specialized approaches. Product at 5.4% when category averages 3.2% (+69% better) deserves increased visibility and traffic investment. Product at 1.9% when category averages 3.2% (-41% worse) needs conversion optimization before traffic growth. Smaller differences (within ±20%) acceptable as normal variation.

How quickly should new products reach top product positions?

Strong new products reach top 10 within 1-2 months depending on catalog size and promotional support. Failure to crack top 20 within 3 months suggests weak market reception unless intentionally positioned as niche offering. Product launch success measured by revenue contribution growth trajectory rather than absolute position timing.

Should I promote low-traffic high-conversion products or high-traffic low-conversion products?

Promote low-traffic high-conversion products first. Proven conversion validates product-market fit, justifying awareness investment. High-traffic low-conversion products need conversion fixes before traffic increases worth pursuing. Throwing traffic at unconverting products wastes budget. Find converting products, then scale traffic to them.

What does increasing top 5 concentration indicate?

Growing top 5 revenue share (from 42% to 58%+) signals concerning concentration increasing business vulnerability to bestseller problems. Causes include: rest of catalog underperformance, successful bestseller growth outpacing other products, or catalog expansion failure. Increasing concentration requires diversification focus developing additional strong performers reducing single-product dependency.