Ecommerce Analyst20 prompts5 categoriesBeginner → Advanced19 prompts · 1 chains

Ecommerce Analyst AI Prompts

Ecommerce Analyst AI prompt library with 20 prompts in 5 categories. Copy templates for real workflows in analysis, modeling, and reporting. Browse 5 categories and copy prompts you can use as-is or adapt to your stack.

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Conversion Optimization

5 prompts
Conversion OptimizationIntermediatePrompt
01

Checkout Abandonment Recovery

Analyze checkout abandonment and design a recovery strategy. Checkout data: {{checkout_data}} CartAbandonment rate: {{abandonment_rate}} Average order value: {{aov}} 1. Abandonment by checkout step: Which steps lose the most customers? - Step 1 (Enter email/sign up): high drop here = account creation friction - Step 2 (Shipping details): drop here = unexpected shipping cost or complexity - Step 3 (Payment details): drop here = trust or payment method issue - Step 4 (Order review): drop here = final price concern or last-minute doubt Map drop-off rate at each step. 2. Abandonment reasons (from exit surveys or user research): - Unexpected shipping costs (primary reason cited in ~50% of cases) - Forced account creation - Complicated checkout process - Payment security concerns - Just browsing / not ready to buy - Found a better price elsewhere Which of these apply based on the data signals? 3. Immediate fix opportunities (no A/B test required): - Show shipping cost early: display on product page and cart, before checkout - Enable guest checkout: remove account requirement or make it optional after purchase - Add trust signals: SSL badge, money-back guarantee, security certifications near payment field - Reduce form fields: remove any non-required fields - Save progress: if user leaves, preserve their cart and form data 4. Cart abandonment email sequence: Email 1 (1 hour after abandonment): - Subject: 'You left something behind' - Content: items in cart, strong CTA, no discount yet - Goal: recapture the 30% who abandoned due to distractions Email 2 (24 hours): - Subject: 'Your cart is waiting - and here's 10% off' - Content: discount offer with urgency (expires in 48 hours) - Goal: recapture price-sensitive abandoners Email 3 (72 hours): - Subject: 'Last chance - your cart expires soon' - Content: urgency reminder, social proof on the products 5. Recovery revenue estimate: - Current recovered revenue from email sequence (if any) - Expected recovery rate with optimized sequence: industry benchmark 10-15% of abandoned carts - Annual revenue opportunity: abandonment volume x AOV x expected recovery rate Return: step-by-step abandonment analysis, root cause hypothesis, immediate fixes, email sequence design, and revenue recovery estimate.
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Conversion OptimizationBeginnerPrompt
02

E-commerce Conversion Funnel Audit

Audit the e-commerce conversion funnel from landing to purchase and identify the highest-priority optimization opportunities. Funnel data: {{funnel_data}} Platform: {{platform}} (Shopify, WooCommerce, Magento, custom) Traffic: {{monthly_sessions}} monthly sessions 1. Funnel stages and current conversion rates: - Session to Product View rate: sessions that viewed at least one product page - Product View to Add-to-Cart rate - Add-to-Cart to Checkout Initiation rate - Checkout Initiation to Purchase rate (checkout completion) - Overall session-to-purchase conversion rate Benchmark comparison by device: - Desktop: typical e-commerce CVR 2-4% - Mobile: typical e-commerce CVR 1-2% - How does this store compare? 2. Cart abandonment analysis: - Cart abandonment rate: (add-to-cart - purchases) / add-to-cart - Industry average: ~70%. If > 80%, significant optimization opportunity. - Checkout abandonment rate: where in the checkout flow do users leave? - Most common exit pages during checkout 3. Device and traffic source breakdown: - Conversion rate by device (desktop, mobile, tablet) - Conversion rate by traffic source (organic, paid, email, direct, social) - Which combination (source x device) has the highest and lowest CVR? - Mobile CVR vs desktop CVR gap: if > 50% lower on mobile, mobile UX is the priority 4. Product page conversion rate: - Add-to-cart rate by product category - Products with high traffic but low add-to-cart rate (< 5%): page or product issue? - Products with high add-to-cart but low purchase rate: cart abandonment issue for specific products 5. Checkout friction analysis: - How many steps in the checkout? (Target: 1-2 pages) - Is guest checkout available? (Forced account creation kills mobile conversions) - Payment options: does the store offer the top payment methods for its audience? - Form fields: any unnecessary fields that slow completion? 6. Priority recommendations: - Top 3 interventions by expected revenue impact - Expected lift in CVR for each intervention - Estimated annual revenue gain from each 0.1% CVR improvement at current traffic Return: funnel metrics table, abandonment analysis, device breakdown, product page analysis, checkout friction assessment, and prioritized recommendations.
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Conversion OptimizationAdvancedChain
03

Full E-commerce Analytics Chain

Step 1: Revenue attribution audit - review the attribution model in use. Identify if it is accurately crediting channels, including the impact of dark social and direct traffic. Recommend attribution improvements. Step 2: Conversion funnel audit - map the full conversion funnel from session to purchase. Identify the top 3 drop-off points. Segment the funnel by device, traffic source, and new vs returning. Step 3: Customer segmentation - build RFM segments for the full customer base. Size each segment, compute revenue contribution, and define the marketing action for each tier. Identify the At-Risk and Can't Lose Them segments as top priority. Step 4: Product analytics - apply the product performance matrix (traffic vs conversion). Identify the top 10 Hidden Gems to promote and the top 5 high-traffic, low-conversion pages to fix. Step 5: Pricing and promotion review - compute the revenue and margin impact of the last 3 promotions. Test whether any products are candidates for a price increase based on elasticity signals. Review discount addiction indicators. Step 6: Retention strategy - compute the second-purchase conversion rate and the average time between orders by category. Design a post-purchase email flow with specific timing and content for the top 3 product categories. Step 7: 90-day growth plan - synthesize findings into a prioritized growth plan: top 3 conversion improvements, top 3 retention actions, and top 2 acquisition efficiency improvements. For each: expected revenue impact, required investment, and measurement plan.
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Conversion OptimizationAdvancedPrompt
04

Personalization Opportunity Analysis

Identify and prioritize personalization opportunities to improve conversion and customer experience. Customer data: {{customer_data}} Behavioral data: {{behavioral_data}} Current personalization capabilities: {{current_capabilities}} 1. Personalization opportunity inventory: Where can personalization improve the experience? - Homepage hero: show category or product relevant to the visitor's history - Product recommendations: 'Recommended for you' based on browse/purchase history - Search results: personalized ranking based on category preferences - Email content: dynamically insert products based on browse history - Pricing/offer: segment-specific offers (first-time buyer discount vs loyalty reward) - Category landing: reorder products based on the visitor's likely preferences 2. Personalization value estimation: For each opportunity: - Baseline performance of the non-personalized version - Expected lift from personalization (cite studies or similar implementations) - Traffic volume affected - Estimated incremental revenue = Traffic x Baseline CVR x Expected Lift x AOV 3. Data requirements per opportunity: - What data is needed? (Purchase history, browse history, declared preferences, segment membership) - Is this data available and accessible in real-time? - Any privacy or consent requirements? (GDPR, CCPA) 4. Quick wins (no ML required): - New vs returning: show a 'welcome back' message and recently viewed products for returning users - Geo-based: show regionally relevant products or shipping estimates based on IP location - Cart-based: upsell products based on cart contents (highest affinity pair from market basket analysis) - Email click-based: if user clicked category X in last email, show category X products next time 5. Advanced personalization (requires ML or CDP): - Collaborative filtering: 'customers like you also bought' - Propensity scoring: predict likelihood to buy each product and rank recommendations accordingly - Churn prediction-based offers: detect at-risk customers and trigger a personalized retention offer 6. Measurement plan: For each personalization implementation: - Control group: show non-personalized version to 10-20% of users - Measure: CVR, AOV, and revenue per visitor - Minimum duration: 2 weeks, minimum conversions per variant: 100 Return: personalization opportunity map, value estimates, data requirements, quick win implementations, and measurement framework.
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Conversion OptimizationIntermediatePrompt
05

Product Page Optimization

Analyze product page performance and identify conversion optimization opportunities. Product page data: {{product_page_data}} (traffic, add-to-cart rate, purchase rate, heatmap data) Top products to analyze: {{product_list}} 1. Product page conversion hierarchy: - Views to Add-to-Cart rate by product (benchmark: 5-15% for most categories) - Add-to-Cart to Purchase rate (measures checkout conversion for this specific product) - Page exit rate before any engagement 2. Above-the-fold analysis: - Do the primary product images load quickly? (LCP < 2.5 seconds) - Is the price visible without scrolling? - Is the primary CTA (Add to Cart) visible without scrolling? - Is the product name and key value proposition immediately clear? - What does the heatmap show for click and scroll distribution? 3. Product image quality signals: - Number of images: research shows > 3 images increases conversion - Image types: do they include: hero shot, lifestyle image, detail/zoom, 360/video? - Mobile image display: do images load quickly and display correctly on mobile? 4. Product description analysis: - Is the primary benefit stated in the first sentence? - Are technical specifications separated from benefits? - Is social proof (review count, rating) prominently displayed near the CTA? - Is shipping information and return policy visible on the product page? 5. Social proof signals: - Review count and average rating visible near Add-to-Cart - User-generated content (UGC) or customer photos - 'X people are viewing this' or 'Only 3 left in stock' urgency signals - Are these signals accurate and trustworthy? (Fake scarcity damages trust) 6. Low-performing product deep dive: For each product with add-to-cart rate < 3%: - Is the price competitive vs comparable products? - Is the primary concern a product quality issue or a page presentation issue? - What do the top 10 reviews say? Any consistent complaints? 7. A/B test backlog for product pages: - 5 specific tests ranked by expected impact on add-to-cart rate Return: product page metrics table, above-the-fold assessment, image and content analysis, social proof audit, and A/B test backlog.
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Customer Analytics

5 prompts
Customer AnalyticsAdvancedPrompt
01

Customer Acquisition Cost Analysis

Analyze customer acquisition costs and LTV/CAC ratios across channels for this e-commerce business. Marketing spend data: {{spend_data}} (by channel, period) New customer data: {{new_customer_data}} (acquisition date, first order value, acquisition channel) LTV data: {{ltv_data}} 1. CAC calculation: CAC = Total Acquisition Spend / New Customers Acquired - Blended CAC: all channels combined - Channel CAC: paid search, paid social, email, influencer, affiliate, organic (fully-loaded) - Organic CAC: allocate content, SEO, and brand spend to organic-acquired customers - New customer only: exclude existing customer marketing spend from the CAC numerator 2. CAC payback period: Payback = CAC / (Monthly Gross Profit per New Customer) - Target for e-commerce: < 12 months - At current gross margin and purchase frequency: how many months to recover the acquisition cost? 3. LTV / CAC ratio by channel: - 12-month LTV and 24-month LTV per acquisition channel - LTV / CAC ratio per channel - Healthy benchmark: > 3x - Channels with LTV/CAC < 1: losing money on every customer acquired 4. Cohort-based payback curves: - For customers acquired in the last 6 cohorts: cumulative gross profit over time - At what month does each cohort recover its CAC? - Are newer cohorts recovering faster or slower? (Faster = improving efficiency) 5. Customer quality by channel: - Second purchase rate by acquisition channel (% making a second purchase within 90 days) - Average order frequency in year 1 by channel - Churn rate (no purchase in > 180 days) by channel - Channels bringing high-volume but low-quality customers: reconsider spending 6. Budget allocation implications: - Which channels should receive more budget based on LTV/CAC? - Which channels are over-funded relative to their LTV/CAC? - Maximum viable CAC per channel = Target LTV/CAC ratio x 12-month LTV Return: CAC by channel, payback analysis, LTV/CAC ratios, cohort payback curves, customer quality metrics, and budget allocation recommendations.
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Customer AnalyticsBeginnerPrompt
02

Customer Lifetime Value Analysis

Calculate and segment Customer Lifetime Value (LTV) for this e-commerce business. Order data: {{order_data}} (customer_id, order_date, order_value, product_category) Time period: {{period}} Business model: {{business_model}} (single purchase, subscription, repeat purchase) 1. Basic LTV metrics: - Average Order Value (AOV): total revenue / total orders - Purchase frequency: orders per customer per year - Customer lifespan: average months from first to last purchase (or 1 / annual churn rate) - Simple LTV = AOV x Purchase Frequency x Customer Lifespan - Gross profit LTV: multiply by gross margin % 2. Cohort-based LTV: - Group customers by acquisition month - For each cohort: cumulative revenue per customer through months 1, 3, 6, 12, 24 - LTV curve: how does cumulative revenue grow over time? - At what month does the cohort LTV begin to plateau? 3. LTV by acquisition channel: - Which channel brings customers with the highest 12-month LTV? - Which brings the most orders per customer? Which brings the highest AOV? - Compare to CAC by channel: LTV/CAC ratio per channel 4. LTV by first product category: - Do customers who first purchase from category A have higher LTV than category B? - Category with highest LTV first purchase: prioritize in acquisition marketing 5. LTV by customer segment: - One-time buyers (only 1 order): how many and what % of customers? What is their share of revenue? - Repeat buyers (2-4 orders): their LTV vs one-time buyers - Loyal customers (5+ orders): their LTV, AOV, and frequency vs average 6. Second purchase conversion: - What % of first-time buyers make a second purchase within 90 days? - Time to second purchase distribution - The second purchase is the most predictive event for long-term retention - What drives second purchase? (Category, time since first, email trigger) Return: LTV metrics table, cohort curves, channel LTV comparison, category LTV analysis, segment breakdown, and second-purchase insights.
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Customer AnalyticsIntermediatePrompt
03

Repeat Purchase and Retention Analysis

Analyze repeat purchase behavior and design a retention improvement strategy. Order data: {{order_data}} Customer base: {{customer_count}} total customers Business goal: {{goal}} (increase purchase frequency or reduce time between orders) 1. Repeat purchase metrics: - First-time buyer %: customers with only 1 order - Repeat buyer %: customers with 2+ orders - Loyal buyer %: customers with 5+ orders - % of revenue from repeat vs first-time buyers - If > 70% of revenue is from first-time buyers: the business depends on constant acquisition (expensive) 2. Time between purchases: - Median days between order 1 and order 2 - Median days between order 2 and order 3 - Does the inter-purchase interval increase or decrease with order number? - Purchase interval by product category (frequency-based categories vs occasion-based) 3. Second purchase conversion: - % of first-time buyers who make a second purchase within 30, 60, 90, 180 days - Second purchase conversion rate by acquisition channel - Second purchase conversion rate by first product category purchased - The single most important metric for retention: getting the first repeat purchase 4. Replenishment cycle analysis: - For consumable products: average repurchase interval per SKU - Products with predictable repurchase cycles (coffee, supplements, skincare) - These products are candidates for subscription programs 5. Churn definition and rate: - Define active customer: purchased in last {{active_window}} days - Churn: not active by this definition - Monthly churn rate: (customers at risk of churning who actually churn) / at-risk customers 6. Retention program recommendations: - Post-purchase email: trigger {{days}} days before expected next purchase based on category interval - Loyalty points: reward repeat purchases to incentivize second order - Subscription upsell: offer subscribe-and-save for products with predictable repurchase cycles - Win-back: at {{days}} days since last purchase, trigger win-back with incentive Return: repeat purchase metrics, time-to-repurchase analysis, second purchase conversion, churn rate, and retention program recommendations.
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Customer AnalyticsIntermediatePrompt
04

Returns and Refunds Analysis

Analyze product returns and refunds to identify root causes and financial impact. Returns data: {{returns_data}} (order_id, product_id, return_reason, return_date, refund_amount) Order data: {{order_data}} 1. Returns overview: - Return rate: returns / orders (by units and by order value) - Industry average by category: apparel 20-30%, electronics 5-10%, home goods 5-8% - How does this store compare? - Cost of returns: shipping + processing + inventory write-down as % of revenue 2. Return rate by product and category: - Products with highest return rates - Are certain categories structurally high-return? (Size/fit issues in apparel) - Products with increasing return rates: product quality issue or misleading listing? 3. Return reason analysis: - Classify return reasons: wrong size, defective/damaged, not as described, changed mind, arrived late - Volume and % for each reason - Controllable vs uncontrollable returns: - Controllable: not as described, wrong item sent, defective → operational fix - Uncontrollable: changed mind, wrong size ordered → policy and content optimization 4. Financial impact: - Gross return rate: returns / gross revenue - Net revenue = gross revenue - refunds - Contribution margin impact: for each return, the contribution margin of that order is lost, plus return cost - Annual cost of returns in dollars 5. Return prevention opportunities: - 'Not as described' returns → improve product descriptions, add more images, size guides - 'Wrong size' returns → add size recommendation feature or chat assistant - 'Defective' returns → supplier quality review - Products with > 25% return rate: review listing accuracy and product quality 6. Return policy analysis: - Does the current return policy drive returns? (e.g. free returns may incentivize 'bracket buying') - What would be the financial impact of moving from 30-day to 14-day return window? Return: returns overview, product/category return rates, reason analysis, financial impact, prevention opportunities, and policy analysis.
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Customer AnalyticsIntermediatePrompt
05

RFM Segmentation for E-commerce

Build an RFM (Recency, Frequency, Monetary) segmentation model for this e-commerce customer base. Order data: {{order_data}} (customer_id, order_date, order_value) Analysis date: {{analysis_date}} Total customers: {{customer_count}} 1. RFM metric computation: For each customer: - Recency (R): days since most recent purchase - Frequency (F): total number of orders - Monetary (M): total revenue generated 2. Quintile scoring: - Rank all customers on each dimension from 1 (worst) to 5 (best) - R score: 5 = purchased most recently, 1 = purchased least recently - F score: 5 = highest order frequency - M score: 5 = highest total spend - Combined RFM score: concatenate the three scores (e.g. '555', '411', '123') 3. Segment definition: - Champions (RFM 444-555): bought recently, buy often, high spend - Loyal Customers (R3-5, F3-5, M3-5): frequent but not cutting-edge recency - Potential Loyalists (R4-5, F1-2, M1-3): recent but low frequency - new customers showing promise - At-Risk (R1-2, F3-5, M3-5): used to be great customers but haven't bought recently - Can't Lose Them (R1, F4-5, M4-5): previously highest-value, now gone - Hibernating (R1-2, F1-2, M1-2): low on all dimensions - Lost (R1, F1, M1): low on all, no recent activity 4. Segment sizing and value: - Count and % of customers in each segment - Total revenue and % of revenue per segment - Average AOV and purchase frequency per segment 5. Marketing actions per segment: - Champions: VIP treatment, loyalty program invitation, referral ask - At-Risk: re-engagement campaign, exclusive offer, personal outreach for high-value - Can't Lose Them: win-back campaign with strong offer, survey why they left - Hibernating: last-chance reactivation email before sunset - Potential Loyalists: second-purchase nurture, loyalty points accelerator 6. Segment migration tracking: - Run the RFM model monthly - Track how many customers move between segments - Net migration to Champions: leading indicator of business health Return: RFM scoring methodology, segment definitions and sizes, revenue contribution table, marketing actions per segment, and migration tracking plan.
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Merchandising Analytics

4 prompts
Merchandising AnalyticsIntermediatePrompt
01

Cross-Sell and Upsell Analysis

Analyze cross-sell and upsell opportunities to increase average order value and customer LTV. Order data: {{order_data}} (order_id, customer_id, product_ids, quantities, values) Product catalog: {{catalog}} 1. Market basket analysis: Compute product affinity using association rules: - Support: % of orders containing both product A and product B - Confidence: given product A, what % of those orders also contain product B? - Lift: how much more likely is product B in the basket given product A (vs random chance)? Lift > 1: products are bought together more than by chance Find the top 20 product pairs by lift score (minimum support = 1% of orders, minimum confidence = 20%) 2. Category cross-sell patterns: - Which pairs of product categories are most frequently purchased together? - Which categories are under-crosssold? (Frequently ordered independently but rarely together) - Are there natural product bundles the data suggests? 3. Sequential purchase analysis: - After purchasing product A, what does the customer buy in their next order? - Time between purchase A and related purchase B: how long do we have to suggest the cross-sell? - This drives post-purchase email timing and content 4. Upsell opportunities: - Products where customers frequently upgrade to a higher-priced variant after initial purchase - Product categories where the second purchase is at a higher price point than the first - Which products serve as 'entry points' that lead to higher-value subsequent purchases? 5. Bundle creation recommendations: - Based on affinity analysis: top 5 product bundle opportunities - For each bundle: expected AOV lift, current % of customers who buy both individually - Bundle pricing strategy: slight discount vs individual sum (3-7% discount typical) 6. Implementation recommendations: - Product page 'Frequently Bought Together': use top affinity pairs - Cart page 'Add to your order': show the highest-confidence cross-sell for items in cart - Post-purchase email: trigger with the sequential purchase insight (timing and product) - Bundle listings: create bundles for top affinity pairs Return: top association rules table, category cross-sell matrix, sequential purchase analysis, upsell patterns, bundle recommendations, and implementation priorities.
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Merchandising AnalyticsAdvancedPrompt
02

Inventory Analytics

Analyze inventory performance and identify stock-out and overstock risks. Inventory data: {{inventory_data}} (SKU, units_on_hand, daily_sales_rate, lead_time_days, reorder_point) Sales data: {{sales_data}} 1. Inventory coverage analysis: - Days of Inventory Outstanding (DIO): units_on_hand / average_daily_sales_rate - Flag: DIO > 90 days = likely overstock - Flag: DIO < 14 days = reorder urgently (below safety stock for most lead times) - Distribution of DIO across the catalog 2. Stock-out risk assessment: - Products with DIO < lead_time_days: will stock out before reorder arrives - Products currently at zero inventory: lost sales occurring now - Lost revenue estimate from out-of-stocks: Out-of-stock loss = (days_out_of_stock x average_daily_revenue_when_in_stock) 3. Overstock identification: - Products with DIO > 90 days: cash and storage tied up unproductively - Total overstock value: units_excess x unit_cost (units_excess = inventory - 90 days supply) - Products with declining sales trend: overstock risk is growing over time 4. Reorder point calculation: Reorder Point = (Average Daily Sales x Lead Time) + Safety Stock Safety Stock = Z x Standard Deviation of Demand x sqrt(Lead Time) - Z = 1.65 for 95% service level, 2.05 for 98% - Compare current reorder points to calculated optimal reorder points 5. ABC classification: - A items: top 20% of products by revenue (80% of revenue) - high control - B items: next 30% of products (15% of revenue) - moderate control - C items: bottom 50% of products (5% of revenue) - minimal control - A items should never stock out; C items can have lower service levels 6. Inventory optimization actions: - Immediate: order products in stock-out risk category - Short term: markdown or bundle overstock items to release cash - Medium term: revise reorder points and safety stock levels for top-50 SKUs Return: DIO distribution, stock-out risk list with lost revenue, overstock list with excess value, optimized reorder points, ABC classification, and action priorities.
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Merchandising AnalyticsIntermediatePrompt
03

Product Catalog Performance Analysis

Analyze the performance of the product catalog to identify best sellers, underperformers, and merchandising opportunities. Product data: {{product_data}} (product_id, category, views, add-to-cart, orders, revenue, margin, inventory) Time period: {{period}} 1. Revenue and margin contribution: - Pareto analysis: what % of products generate 80% of revenue? (Typically 20%) - Top 20 products by revenue contribution and their % of total - Top 20 products by gross margin contribution - Products where high revenue rank and high margin rank diverge: investigate 2. Product performance matrix (2x2): X-axis: Conversion rate (add-to-cart to purchase) Y-axis: Traffic (product page views) - High traffic, high conversion (Stars): protect and feature prominently - High traffic, low conversion (Opportunities): fix the page or review the product offering - Low traffic, high conversion (Hidden Gems): increase visibility, promote more - Low traffic, low conversion (Dogs): review for discontinuation or significant improvement 3. Category-level analysis: - Revenue, orders, and AOV by category - Category conversion rate (add-to-cart from category page) - Category growth: which categories are growing YoY? Which are declining? - Cross-category purchase patterns: which categories are most frequently bought together? 4. Inventory vs demand alignment: - Out-of-stock rate per product: % of time in stock-out during the period - Stock-out revenue impact: estimated lost sales from stock-outs on high-demand products - Overstock: products with > 90 days of inventory coverage at current sell rate 5. New vs established product performance: - Products launched in the last 90 days: are they ramping or stalling? - Products > 180 days old with declining traffic: content refresh or markdown needed? 6. Pricing analysis: - Products with lowest conversion rate in their category: is price a factor? - Price elasticity signals: any products where a discount drove disproportionate volume increase? Return: Pareto analysis, product performance matrix, category analysis, inventory alignment, and pricing insights.
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Merchandising AnalyticsIntermediatePrompt
04

Search and Discovery Analysis

Analyze on-site search behavior and product discovery patterns to improve findability and merchandising. Search data: {{search_data}} (search_term, frequency, click-through, add-to-cart, purchase) Navigation data: {{navigation_data}} 1. Search usage metrics: - % of sessions using the search bar - Search users vs non-search users: conversion rate comparison - If search users convert at > 2x non-search users: search is a high-value entry point to optimize 2. Top search terms analysis: - Top 50 search terms by frequency - Click-through rate per search term: did the user find what they were looking for? - Add-to-cart rate per search term - Zero-results searches: searches that returned no results (high-priority fixes) 3. Zero-result and low-result searches: - What are customers searching for that the store does not carry? - Which zero-result searches represent a product gap opportunity? - Which low-result searches are caused by poor search configuration (typos, synonyms)? - Example: 'sneakers' returns 0 results but 'trainers' returns 200 (synonym issue) 4. Search-to-purchase funnel: - For the top 20 search terms: click rate, add-to-cart rate, and purchase rate - High search frequency but low purchase rate: search results not matching intent - Products receiving many searches but with low stock: restock priority 5. Category navigation analysis: - Which category pages have the highest bounce rate? - Click distribution on category pages: are users clicking the right products? - Category sort order: is the default sort (best sellers, new arrivals, featured) working? 6. Discovery optimization recommendations: - Add synonyms to search engine for top zero-result queries - Promote 'Hidden Gem' products (high conversion, low traffic) in search results and category pages - Create product collections or gift guides based on common search intent clusters - Improve autocomplete suggestions for top search terms Return: search usage metrics, top and zero-result analysis, search-to-purchase funnel, category navigation insights, and discovery optimization recommendations.
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Pricing Analytics

3 prompts
Pricing AnalyticsIntermediatePrompt
01

Discount and Promotion Analysis

Analyze the effectiveness of discounts and promotions and optimize the promotional strategy. Promotion data: {{promotion_data}} (promotion type, discount %, products, period, orders, revenue) Baseline data: {{baseline_data}} (same period last year or non-promotional baseline) 1. Promotion performance metrics: For each promotion: - Revenue during promotion vs baseline (lift) - Gross margin during promotion vs baseline (margin impact) - Orders count and AOV during vs baseline - New vs returning customer mix during promotion 2. Revenue vs margin trade-off: - Revenue lift (%) from promotion - Gross profit change (%) from promotion (may be negative even if revenue is up) - Break-even volume: how much incremental volume is needed to offset the margin give-away? Break-even lift = Discount % / (Gross Margin % - Discount %) Example: 20% discount on a 40% margin product requires 100% volume increase to maintain gross profit 3. Promotion types comparison: - % off: drives broadest participation, margins most exposed - BOGO (Buy One Get One): volume driver, effective for high-margin products - Free shipping threshold: AOV driver (set threshold at 30% above current AOV) - Gift with purchase: drives specific product trial, protects revenue - Flash sale (time-limited): urgency driver, but trains customers to wait for discounts 4. Customer behavior post-promotion: - Are customers acquired during promotions at lower LTV than non-promotion customers? - Do promotional buyers return at similar or lower rates than full-price buyers? - Are existing customers simply shifting their planned purchases to discount periods? 5. Discount addiction signals: - Is there a trend toward increasing discount frequency to maintain revenue? - What % of revenue is generated at a discount? - Are customers waiting for promotions before purchasing? (Signal: conversion rate declines in weeks before promotions) 6. Optimized promotional calendar: - Based on analysis: which promotion types drive the best net margin impact? - Recommended promotional cadence: how often and at what depth? - Products that should never be promoted (inelastic, premium positioning) Return: promotion performance table, break-even analysis, type comparison, post-promotion behavior analysis, discount addiction signals, and promotional calendar recommendation.
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Pricing AnalyticsAdvancedPrompt
02

Dynamic Pricing Strategy

Design a data-driven dynamic pricing framework for this e-commerce business. Product catalog: {{catalog}} Demand data: {{demand_data}} Competitor pricing feed: {{competitor_prices}} (if available) Objective: {{objective}} (maximize revenue, maximize margin, protect market share) 1. Dynamic pricing applicability: Not all products are suitable for dynamic pricing. Score each category: - High suitability: commodity products, seasonal products, high price sensitivity, active competitor pricing changes - Low suitability: branded products, luxury, items where price stability builds trust 2. Demand-based pricing rules: Adjust price based on current demand signals: - High demand (sell-through rate > target): small price increase to protect margin - Low demand (sell-through rate < target): small price decrease to drive velocity - Near out-of-stock: price increase to ration remaining inventory - Overstock: price decrease to accelerate sell-through 3. Competitive pricing rules (if competitor data available): - Price matching rule: stay within 5% of the lowest verified competitor price for A-category products - Price leadership rule: for unique or differentiated products, ignore competitor pricing - Floor price: never go below the minimum margin threshold (cost + minimum margin %) 4. Time-based pricing: - Day-of-week elasticity: are there purchase patterns that suggest different price sensitivity by day? - Seasonal pricing: pre-season vs peak season vs clearance pricing for each category - End-of-season markdown schedule: structured markdown cadence as season ends 5. Guardrails and controls: - Maximum price change per day: ± 10% to avoid customer perception issues - Price floor per SKU: ensures margin is never destroyed - Minimum price stability: some products (premium, gift) should not fluctuate frequently - Human review threshold: any suggested price change > 20% requires human approval 6. Testing and measurement: - A/B test dynamic pricing vs static: randomize products into test and control - Measure: revenue per visit, gross margin %, sell-through rate - Be aware: customers on the same product may see different prices (manage perception risk) Return: product category suitability matrix, demand-based and competitive pricing rules, time-based strategy, guardrails, and testing framework.
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Pricing AnalyticsIntermediatePrompt
03

Price Elasticity and Optimization

Analyze price sensitivity and identify optimal pricing for key products. Sales and pricing data: {{pricing_data}} (product, price, units_sold, date) Promotion history: {{promotion_data}} Competitor pricing: {{competitor_prices}} (if available) 1. Price elasticity estimation: Price Elasticity of Demand (PED) = % change in quantity / % change in price - PED < -1: elastic demand (price decrease drives proportionally more volume) - PED > -1: inelastic demand (price changes have limited volume effect) - PED = -1: unit elastic Estimation approach: - Collect price change events (price increases, promotions, sales) for each product - Compare volume before and after each price change - Control for seasonality by using same period YoY or a holdout comparison 2. Revenue-maximizing price: At the estimated elasticity, the revenue-maximizing price is: Optimal Price = Marginal Cost / (1 + 1/PED) - Calculate for products with reliable elasticity estimates - Compare optimal price to current price: are products over or under-priced? 3. Margin-impact analysis: - If we raise price by 5%: at what elasticity does revenue decrease? - If revenue decreases but volume decreases proportionally less: gross profit may still increase - Calculate: (Price lift) x (Volume loss) = Net margin impact at different elasticity assumptions 4. Promotional discount analysis: - Which products historically show the highest volume lift from promotions? - What discount depth is needed to move volume without destroying margin? - Products where discount lift is < 20% volume increase: promotions destroy margin without compensating volume 5. Competitive pricing analysis: - Price index: our price / lowest competitor price per product - Products where we are more than 15% above the lowest competitor price and showing low conversion - Products where we are below average market price: potential for modest price increase 6. Pricing recommendations: - Products to test a price increase on (inelastic, below optimal price) - Products to review pricing strategy (elastic, above optimal price) - Promotional calendar recommendations based on elasticity Return: elasticity estimates per product, revenue-maximizing price calculations, margin impact analysis, discount effectiveness, and pricing recommendations.
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Traffic and Acquisition Analytics

3 prompts
Traffic and Acquisition AnalyticsIntermediatePrompt
01

Affiliate and Influencer Analytics

Analyze affiliate and influencer channel performance for this e-commerce business. Affiliate data: {{affiliate_data}} (affiliate_id, clicks, orders, revenue, commission) Influencer data: {{influencer_data}} (creator_id, platform, post_date, promo_code, orders, revenue) Commission rates: {{commission_rates}} 1. Affiliate performance metrics: For each affiliate: - Clicks, orders, conversion rate, revenue - Commission paid and commission rate - Net revenue = Revenue - Commission - ROAS (net): Net Revenue / Commission - Average order value from this affiliate's referrals - New vs returning customers referred 2. Affiliate quality analysis: - Affiliates with high click volume but low conversion: traffic quality issue (incentivized or misaligned audience) - Affiliates with high CVR and high AOV: premium partners, worth higher commission rates - Coupon code affiliates vs content affiliates: which generate higher new customer rates? - Coupon affiliates often capture customers who would have bought anyway (low incrementality) 3. Influencer performance: For each influencer post: - Impressions, reach, clicks (if tracked), orders attributed (promo code) - Revenue, EMV (Earned Media Value) - Cost per acquisition from influencer: fee / orders - Compare CPA to paid social CPA as a benchmark 4. Influencer content effectiveness: - Which content formats drove the most orders? (Reel vs story vs feed post vs YouTube) - Which platforms performed best for this product category? - Timing: how quickly do influencer-driven orders arrive? (Typically 48-72 hours peak, then decay) 5. Commission structure optimization: - Are commission rates optimized per affiliate tier? - Which affiliates would respond to a higher commission and generate enough incremental revenue? - Are any affiliates receiving high commissions without driving incremental customers? 6. Recommendations: - Top 5 affiliates to invest more in (performance-based tiering) - Affiliates to pause or restructure (low quality, possible fraud signals) - Influencer categories to scale based on performance analysis Return: affiliate performance table, quality analysis, influencer performance metrics, commission optimization, and investment recommendations.
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Traffic and Acquisition AnalyticsIntermediatePrompt
02

E-commerce Traffic Source Analysis

Analyze traffic sources and their contribution to e-commerce revenue. Analytics data: {{analytics_data}} (sessions, source/medium, channel, orders, revenue) Time period: {{period}} Marketing spend by channel: {{spend_data}} 1. Revenue by channel: - Revenue attributed by channel: organic search, paid search, direct, email, paid social, organic social, referral, affiliate - Each channel: sessions, orders, conversion rate, AOV, revenue - Revenue per session by channel (best efficiency metric for cross-channel comparison) 2. Channel conversion rate comparison: - Email typically converts at 2-5%: highest-converting channel - Organic search: 1-3% - Paid search (branded): 3-8% - Paid search (non-branded): 1-2% - Social (paid): 0.5-1.5% - Direct: 2-4% - How does each channel compare to these benchmarks? 3. Paid channel ROI: - For each paid channel: spend, attributed revenue, ROAS - True ROI (gross profit basis): (Revenue x Gross Margin - Spend) / Spend - Which paid channels have ROAS above the threshold to justify continued investment? 4. Organic vs paid balance: - Organic revenue as % of total: is the business too dependent on paid traffic? - Paid traffic shut-off risk: if all paid channels stopped tomorrow, what would revenue be? - Cost of revenue: what % of revenue is consumed by marketing spend? 5. New customer vs returning customer by channel: - Which channels drive new customers vs returning customers? - Channels bringing primarily returning customers: potential attribution issue (assisting role) or wasted prospecting spend 6. Traffic quality signals: - Bounce rate by channel - Pages per session by channel - Session duration by channel - Low-quality channels (high bounce, low engagement): review targeting and landing page alignment Return: revenue by channel table, conversion rate benchmark comparison, paid channel ROI, organic vs paid balance, new vs returning mix, and traffic quality signals.
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Traffic and Acquisition AnalyticsAdvancedPrompt
03

Paid Search Performance Analysis

Analyze Google Shopping and paid search performance for this e-commerce store. Google Ads data: {{ads_data}} (campaign, ad group, keyword, impressions, clicks, conversions, revenue, spend) Time period: {{period}} 1. Account-level performance: - Total spend, revenue, ROAS, and CPA - ROAS target: {{target_roas}} (typically 4-8x for e-commerce) - Is the account meeting the ROAS target? Overall and by campaign type? 2. Campaign type breakdown: - Branded search: keywords containing the brand name - Non-branded search: generic product and category keywords - Shopping campaigns: Google Shopping product listing ads - Performance Max: automated campaign type - ROAS and CPA per campaign type - Branded terms typically have very high ROAS (5-15x) but limited incrementality 3. Product/keyword performance: - Top 20 keywords/products by revenue - Bottom 20 keywords/products by ROAS (candidates for bid reduction or pause) - ROAS distribution: what % of spend is above / below the ROAS target? 4. Shopping campaign analysis: - Product feed health: any products disapproved or not showing? - Impression share: what % of available impressions are we capturing? - Lost impression share: due to budget vs due to rank - Top products by Shopping revenue and their Shopping CPA 5. Auction insights: - Who are the top auction competitors? - Impression share comparison vs competitors - Are we winning or losing on top-of-page rate? 6. Optimization opportunities: - Budget allocation: which campaigns are limited by budget but have high ROAS? Increase budget. - Bid adjustment: keywords with ROAS > 2x target: consider raising bids to capture more volume - Negative keywords: terms generating clicks but no conversions: add as negatives - Product feed improvements: products with high impressions but low CTR need title/image optimization Return: account performance summary, campaign type breakdown, product/keyword analysis, Shopping health check, auction insights, and optimization recommendations.
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