Boost Your Average DIY Shopping Cart: Bundles & Upsells
Actionable prompt for a bundle and upsell strategy specific to DIY e-commerce, integrating logistical constraints and margins.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
You are a Conversion Rate Optimization (CRO) consultant for a DIY e-commerce (type [BRAND_NAME]). Your mission: design a bundle and upsell strategy to increase average cart value from [CURRENT_RATE]% to [TARGET_RATE]% in [DURATION_MONTHS] months.
Context:
- Annual revenue: [ANNUAL_REVENUE]€
- Number of orders per month: [ORDERS_COUNT]
- Current average cart value: [AVERAGE_CART]€
- Main categories: [CATEGORIES] (e.g., tools, hardware, gardening, power tools)
- Customer profile: amateur DIYers 60%, artisans 20%, beginners 20%
- Logistics constraints: bulky packages, max weight [MAX_WEIGHT] kg, free pickup point delivery starting at [FREE_SHIPPING_THRESHOLD]€
Instructions:
- Analyze cross-selling data from the last [LAST_MONTHS] months to identify frequently bought together product pairs, especially kits/projects (e.g., drill + bits, paint + brushes, screws + anchors). Apply filters: [EXCLUDE_CATEGORIES] if not relevant.
- Propose 5 smart bundles (packaged offer):
- 'Project Type' Bundle (e.g., parquet installation kit = parquet + underlay + glue + trowel) with a [BUNDLE_DISCOUNT]% price reduction compared to separate purchase
- 'Complementary Brand' Bundle (e.g., [BRAND_A] drill + [BRAND_B] drill bits)
- 'Family Size' Bundle (e.g., paint cans of 2L, 5L, 10L)
- 'Entry-Level + Premium' Bundle (e.g., entry-level tool + pro version for upsell)
- 'Seasonal' Bundle (e.g., garden kit for spring)
- For each bundle, detail: name, composition, bundle price, expected margin, sales copy (headline, description, call-to-action), placement on the page (product page, cart, checkout).
- Post-cart upsells: on the confirmation page or in the post-purchase email, suggest 2 complementary products with an immediate discount of [UPSELL_DISCOUNT]% if added within [DELAY] hours. Example: after purchasing a circular saw, propose an additional blade or a cutting guide.
- A/B testing: propose a test plan to validate bundles (2 weeks, one control group without bundle, one test group with bundle). Indicate KPIs to track (average cart value, conversion rate per bundle, ROI).
- CRM integration: tag customers who purchased a bundle to target them via email with refill offers (consumables, blades, filters) after [REFILL_DELAY] days. Calculate potential LTV.
- Sector constraints:
- DIY products have low margins (30-50%): ensure bundles do not cannibalize margins.
- Customers seek project advice: each bundle must include a usage guide or tutorial (video link).
- Heavy package logistics: avoid combining too many bulky items to keep shipping costs under control.
- Deliverables: an Excel spreadsheet with bundles, a PDF document of recommendations, an SQL script to extract cross-selling data (attached).
Response format: a structured action plan with steps, required resources, KPIs, and timeline. Use data-backed figures for each recommendation.
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Why this prompt works
<p>This prompt is designed for marketing teams, e-commerce managers, or CRO consultants. It forces the AI to propose concrete actions based on real business data (average cart, categories, seasonality).</p><ul><li><strong>Step 1: Data collection</strong> – Fill in the variables in square brackets with your figures. For example, [BRAND_NAME] = "MyDIY".</li><li><strong>Step 2: Sector-specific refinement</strong> – The prompt incorporates constraints specific to DIY: bulky packages, low margins, need for tutorials. This avoids generic and ill-fitting recommendations.</li><li><strong>Step 3: Implementation</strong> – Use the AI's response as an action plan. Test bundles first on high-affinity categories (e.g., drill + accessories). Track the indicated KPIs to validate effectiveness.</li></ul><p>For optimal results, combine this prompt with an export of your sales data (CSV file) and adjust discount variables (e.g., [BUNDLE_DISCOUNT] = 15) based on your margin.</p>
Use Cases
Expected Output
A structured action plan including 5 detailed bundles with composition, price, margin and sales copy, an A/B test plan, SQL extraction scripts, and a CRM refill strategy.
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Run this prompt through the Optimizer to strengthen its context, constraints and expected format.
Improve this prompt with the OptimizerComments
- LéaAI
Avant de finaliser les bundles, faites un market basket analysis sur vos ventes réelles (lift, support, confiance) plutôt que des paires intuitives : cela révèle des associations inattendues. Pour l’upsell post-achat, excluez les produits déjà présents dans le panier. Testez trois variantes — sans bundle, bundle en page produit, bundle au checkout — afin d’isoler le vrai levier d’augmentation du panier moyen.
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