Boost Your Average Sports Basket with Targeted Bundles & Upsells
Prompt to generate a strategy for optimizing average basket through bundles and upsells, specific to the sports sector with technical and seasonal constraints.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
You are a B2B e-commerce expert specializing in the sports sector. Analyze the following data to propose a strategy for optimizing the average basket through bundles and upsells, tailored to [TYPE_OF_SPORT] and [SEASON].
Context:
- Product categories: [CATEGORY_LIST] (e.g., running shoes, road bikes, dumbbells, etc.)
- Current average basket: [CURRENT_AVERAGE_BASKET] €
- Objective: increase of [PERCENTAGE_OBJECTIVE]% in 3 months
- Customer behavior: [CLIENT_BEHAVIOR] (e.g., 60% buy a single item, 30% two items, 10% three or more)
- Seasonality: [SEASON] (e.g., pre-summer, back-to-school sports season, winter)
Specific constraints for the sports sector:
- Bundles must respect technical compatibilities (e.g., ski bindings-boots, bicycle frame sizes)
- Upsells must take season into account (e.g., offer a breathable jersey with running shorts in summer)
- Cross-sell suggestions must be based on recurring purchases (e.g., buyers of treadmills -> interested in cardio accessories)
Rules:
- Propose 3 distinct bundles: one for beginners, one for intermediate, one for expert, with bundle price (discount 10-20% depending on [PRODUCT_MARGIN]).
- For each bundle, define 2 relevant upsells (one higher-end product and one accessory) with a hook text.
- Justify each choice with industry data (retention rate, seasonality, buying behavior).
- Include tracking KPIs: bundle adoption rate, average basket increase, ROAS of upsell campaigns.
Provide a structured answer in a table: Bundle name, Products, Discount, Upsell1, Upsell2, Justification, KPIs.
Variables: [TYPE_OF_SPORT], [SEASON], [CATEGORY_LIST], [CURRENT_AVERAGE_BASKET], [PERCENTAGE_OBJECTIVE], [CLIENT_BEHAVIOR], [PRODUCT_MARGIN]
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Why this prompt works
<p>This prompt allows B2B e-commerce sports marketers to create bundles and upsells tailored to their catalog and season. It integrates sector specifics: equipment compatibility, seasonality of sports practices, and purchasing behavior by skill level.</p><p>To use it, replace the variables in square brackets with your real data (e.g., TYPE_DE_SPORT = 'running', SAISON = 'spring'). The prompt generates an actionable table with bundles, upsells, and KPIs. Adapt discounts according to your margin.</p><p>Tips: test first on a single category (e.g., running), then deploy. Use the provided KPIs to measure impact and iterate. This prompt is compatible with all generative AI models.</p>
Use Cases
Expected Output
A structured table with 3 bundles (beginner, intermediate, expert), for each bundle: name, product list, discount, 2 upsells with hook text, data-driven justification, and tracking KPIs.
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