# Cross Sell Opportunity Engine > Identifies and prioritizes cross-sell opportunities by analyzing product affinity, purchase sequences, basket composition, and customer segment behavior for CPG and retail e-commerce. Use when a user needs to increase basket size, improve category penetration, design product bundles, or build recommendation strategies. Triggers on requests for cross-sell analysis, product affinity, bundle optimization, recommendation engine design, or category expansion. - Skill: `writer/cross-sell-opportunity-engine` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds add writer/cross-sell-opportunity-engine` - Raw SKILL.md: https://api.skillmd.com/api/skills/writer/cross-sell-opportunity-engine/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: writer (https://skillmd.com/u/writer) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/writer/cross-sell-opportunity-engine --- # Cross-Sell Opportunity Engine ## Overview This skill discovers high-probability cross-sell opportunities by mining transactional data for product co-purchase patterns, sequential purchase paths, and segment-level category gaps. It produces ranked product pair recommendations, bundle designs, and channel-specific activation strategies to grow AOV and customer category penetration in CPG and retail e-commerce. ## When to Use - Identifying which products to recommend alongside or after a purchase - Designing product bundles or kits with data-driven composition - Building personalized recommendation strategies for email, site, or ads - Analyzing category penetration gaps across customer segments - Planning cross-category marketing campaigns - Optimizing product page "Frequently Bought Together" sections - Increasing AOV through strategic product pairing ## Required Inputs | Input | Required | Description | |---|---|---| | Transaction Data | Yes | Order-level data: customer ID, order ID, products purchased, categories, revenue | | Product Catalog | Yes | Product names, categories, subcategories, price points, margin | | Time Period | Yes | Analysis window (minimum 6 months; 12+ months preferred) | | Customer Segments | Recommended | Pre-defined segments for segment-specific analysis | | Product Margin Data | Recommended | Gross margin per product/category for profitability-weighted recommendations | | Browse/Search Data | No | Product page views, searches, add-to-cart without purchase | | Return Data | No | Product return rates (to avoid recommending high-return combinations) | ## Methodology ### Step 1 — Market Basket Analysis (Association Rules) Apply association rule mining to discover product co-purchase patterns: **Key Metrics**: ``` Support(A→B) = Transactions containing both A and B ÷ Total transactions Minimum threshold: 1% (for large catalogs) to 5% (for small catalogs) Confidence(A→B) = Transactions containing both A and B ÷ Transactions containing A Minimum threshold: 20% Interpretation: "Of customers who bought A, XX% also bought B" Lift(A→B) = Confidence(A→B) ÷ Support(B) Minimum threshold: 1.5 Interpretation: >1 means A and B are bought together more than random chance Lift of 2.0 means customers are 2× more likely to buy B when they buy A ``` **Output: Product Affinity Matrix**: ``` Product A | Product B | Support | Confidence | Lift | Avg Joint AOV Facial Cleanser | Moisturizer | 12% | 45% | 3.2 | $58 Protein Powder | Shaker Bottle | 8% | 62% | 5.1 | $72 Coffee Beans | Coffee Filters | 15% | 38% | 2.8 | $42 Baby Formula | Baby Wipes | 11% | 55% | 3.5 | $65 ``` ### Step 2 — Sequential Purchase Path Analysis Identify products commonly purchased in sequence (not same basket): ``` Sequence Analysis: For each customer, order products by purchase date Identify frequent transitions: Product A (order N) → Product B (order N+1) Sequence Probability = P(B follows A) = Count(A→B sequences) ÷ Count(A purchases) Time-to-Next: Median days between purchase of A and subsequent purchase of B ``` **Output: Purchase Sequence Map**: ``` Step 1 Product | Step 2 Product | Seq. Probability | Median Gap | Revenue Opportunity Starter Kit | Full-Size Product | 42% | 18 days | $35/customer Shampoo | Conditioner | 38% | 25 days | $18/customer Face Serum | Eye Cream | 29% | 35 days | $42/customer Dog Food | Dog Treats | 51% | 12 days | $15/customer ``` Use sequence timing to determine optimal cross-sell trigger timing in lifecycle flows. ### Step 3 — Category Penetration Gap Analysis Analyze what percentage of customers purchase across multiple categories: ``` Category Penetration Rate = Customers who purchased in Category X ÷ Total customers Cross-Category Rate = Customers purchasing in 2+ categories ÷ Total customers ``` **Category Penetration Matrix**: ``` | Bought Skincare | Bought Haircare | Bought Body | Bought Supplements Skincare Buyers | 100% | 28% | 35% | 12% Haircare Buyers | 32% | 100% | 22% | 8% Body Care Buyers | 40% | 19% | 100% | 15% Supplement Buyers | 18% | 9% | 21% | 100% ``` **Gap Opportunity Sizing**: ``` Opportunity = (Target Penetration - Current Penetration) × Segment Size × Category AOV Example: Skincare buyers into Haircare: (40% target - 28% current) × 10,000 × $32 = $38,400 ``` ### Step 4 — Profitability-Weighted Scoring Not all cross-sells are equally valuable. Score by incremental profit: ``` Cross-Sell Score = Lift × Confidence × Margin Contribution × (1 - Return Rate) Where: Lift: From association rules (>1.5) Confidence: Probability of co-purchase (0–1) Margin Contribution: Gross margin $ of the cross-sell product Return Rate: Historical return rate of the product pair ``` Rank all product pairs by Cross-Sell Score to prioritize high-value, high-probability, low-risk recommendations. ### Step 5 — Segment-Specific Cross-Sell Mapping Different segments have different cross-sell propensities: ``` Segment | Top Cross-Sell | Confidence | Best Channel | Offer Needed? New (1st order) | Complementary accessory | 35% | Post-purchase email | Yes (10% off) Developing (2-3) | Adjacent category entry | 28% | Personalized email | Bundle discount Established (4+) | Premium/upgrade product | 22% | On-site reco | No (relationship) VIP | New product / exclusive | 40% | Early access email | No (access) Price-Sensitive | Value bundle | 45% | Promotion email | Yes (bundle %) ``` ### Step 6 — Bundle Design & Optimization Create data-driven bundles from cross-sell analysis: **Bundle Types**: | Type | Logic | Example | Discount Strategy | |---|---|---|---| | **Complementary** | High-lift product pairs | Cleanser + Moisturizer | 10%–15% vs. separate | | **Starter Kit** | Entry products for new customers | 3 best-sellers mini sizes | 20%–25% value | | **Replenishment** | Regular-use items at volume | 3-pack protein bars | 10% + free shipping | | **Discovery** | Low-penetration categories | "Try our haircare" sample set | 30%+ value (trial size) | | **Gift Set** | Seasonal/gifting occasions | Holiday collection | Premium packaging, 10% | | **Subscribe & Save** | Recurring need items | Monthly essentials box | 15% ongoing discount | **Bundle Pricing Framework**: ``` Bundle Price = Σ(Individual Prices) × (1 - Bundle Discount %) Target Bundle Margin = Individual Product Margin - (Bundle Discount × Revenue) Ensure bundle margin remains >40% of individual margin Cannibalization Check: Will the bundle reduce full-price individual sales? - If >30% of bundle buyers would have bought items separately → flag risk - Mitigate: limit bundle availability, make it new-customer exclusive ``` ### Step 7 — Activation Strategy Deploy cross-sell recommendations across channels: **On-Site**: - Product page: "Frequently Bought Together" (top 3 by lift) - Cart page: "Complete Your Routine" (complementary items not in cart) - Post-purchase page: "Customers Also Bought" (sequential purchase products) - Search results: Boost cross-sell products when primary product is queried **Email**: - Post-purchase (Day 2): "Pair it with..." recommendation (top affinity product) - Replenishment (pre-cycle): "Add to your reorder" with cross-sell suggestion - Monthly digest: Personalized "recommended for you" based on purchase history - Category introduction: "Discover our [new category]" for low-penetration segments **Paid Media**: - Dynamic product ads: Cross-sell catalog to past purchasers of complement products - Retargeting: Show cross-sell products to recent single-category buyers - Lookalike audiences built from multi-category buyers (highest LTV proxy) **In-App / SMS**: - Push notification: "Your [product] pairs perfectly with [cross-sell]" - SMS: Flash cross-sell offers for time-sensitive bundles ## Output Specification 1. **Product Affinity Matrix**: Top 20+ product pairs ranked by cross-sell score 2. **Sequential Purchase Map**: Top purchase paths with timing and probability 3. **Category Penetration Analysis**: Cross-category rates with gap opportunities sized in revenue 4. **Segment Cross-Sell Recommendations**: Per-segment top cross-sell products and channels 5. **Bundle Recommendations**: 3–5 bundle designs with pricing, margin analysis, and use case 6. **Activation Playbook**: Channel-specific deployment plan for top cross-sell opportunities 7. **Revenue Opportunity Summary**: Total addressable cross-sell revenue by priority tier ## Examples **Input**: "Analyze cross-sell opportunities for our natural skincare brand. 5 product categories (cleanser, serum, moisturizer, SPF, eye cream), 20,000 customers, 12 months of data." **Output**: Affinity matrix reveals cleanser→moisturizer (lift 3.8, confidence 52%) and serum→eye cream (lift 2.9, confidence 31%) as top pairs. Category penetration shows only 18% of cleanser buyers have tried SPF — $95K revenue gap. Recommended "Complete Your Routine" bundle (cleanser + serum + moisturizer) at 15% off, projected 12% attachment rate. Sequential analysis: SPF is purchased on average 45 days after first skincare purchase — trigger email at day 30 post-first-order. **Input**: "We sell pet food and accessories. How do we increase basket size? Current AOV is $38, goal is $48." **Output**: Top cross-sell: dog food → treats (lift 4.2), cat food → litter (lift 3.1), any food → toy/accessory (lift 1.8). Bundle design: "Monthly Essentials" (food + treats + dental chew) at 12% discount, projected AOV $52. Activation: add "Complete Your Pet's Order" module to cart page, post-purchase email with treat recommendation at day 5. Estimated AOV lift: $38 → $47 through on-site cross-sell (70% of impact) and email (30%). ## Guidelines - Minimum support threshold of 1% — product pairs below this lack statistical reliability - Always check lift, not just confidence — high confidence on a popular product may have low lift - Exclude product pairs with high combined return rates (>15%) from recommendations - Account for cannibalization: bundles shouldn't just discount what customers would buy anyway - Segment-specific recommendations outperform one-size-fits-all by 2–3× in conversion rate - For CPG consumables, sequence timing is critical — recommend before the customer needs to reorder - Test bundle pricing sensitivity: some customers prefer "save $X" framing over "X% off" - On-site recommendations should be limited to 3–4 products to avoid decision paralysis - Track cross-sell attribution carefully: distinguish incremental purchases from planned multi-item orders - Refresh affinity analysis quarterly as product catalog and customer behavior evolve ## Validation Checklist - [ ] Association rules meet minimum support (>1%), confidence (>20%), and lift (>1.5) thresholds - [ ] Cross-sell score incorporates both probability and profitability - [ ] Sequential purchase timing is analyzed with median gap days - [ ] Category penetration gaps are sized in revenue opportunity - [ ] Bundle designs include margin analysis and cannibalization assessment - [ ] Recommendations are segment-specific, not generic - [ ] Activation strategy covers on-site, email, and paid channels - [ ] High-return product combinations are excluded or flagged - [ ] Revenue opportunity is quantified with clear assumptions - [ ] Refresh cadence for affinity analysis is established