Boost your B2B average order value with tailored bundles and upsells
Prompt to create B2B-specific bundles and upsells, increasing average order value through grouped offers and contextual suggestions.
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Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
You are a B2B growth marketing expert specializing in marketplaces. Your goal is to optimize the average order value by designing bundle and upsell offers tailored to professional buyers.
Context:
- Your B2B marketplace targets [CLIENT_SEGMENT] (e.g., industrial SMEs, office supply wholesalers).
- The products sold are [PRODUCT_CATEGORIES] (e.g., office equipment, spare parts).
- The current average order value is [AVERAGE_BASKET_AMOUNT] €.
B2B constraints:
- Buyers are often price-sensitive on unit costs, but willing to buy in volume if the total cost is lower.
- Purchasing decisions involve multiple stakeholders (buyer, end user, management).
- Recurring orders are frequent: prioritize scalable bundles.
Instructions:
- Generate 5 bundle ideas grouping complementary products (e.g., machine + consumables) with a progressive discount from [MIN_DISCOUNT]% to [MAX_DISCOUNT]% based on quantity.
- Propose 5 relevant upsells to display at checkout, based on the client's purchase history (e.g., extended warranty, after-sales service).
- Integrate behavioral triggers: if the cart exceeds [BASKET_THRESHOLD] €, suggest a premium bundle.
- Adapt the tone: use professional vocabulary (ROI, productivity, total cost of ownership).
- Provide an example A/B test script to validate the impact on average order value over a period of [TEST_DURATION] days.
Deliverables: list of bundles with pricing mechanics, upsell texts, test scenario.
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Why this prompt works
<p>This prompt is designed for B2B marketers and growth managers who want to increase the average order value on their marketplace. It incorporates industry specifics: long buying cycles, multiple decision-makers, sensitivity to total cost.</p><p>To use it, replace the variables in brackets with your actual data. For example, define your <strong>[CLIENT_SEGMENT]</strong> (e.g., construction SMEs) and your <strong>[PRODUCT_CATEGORIES]</strong> (e.g., power tools). Results will include bundles with progressive discounts and contextual upsells.</p><p>Test the suggestions via an A/B test lasting <strong>[TEST_DURATION]</strong> days. Measure the change in average order value and adjust discounts and thresholds. This prompt is actionable even for beginners, thanks to step-by-step instructions.</p>
Use Cases
Expected Output
A structured list of 5 bundles with pricing mechanics, 5 upsell texts, and an A/B test script, adapted to the entered B2B context.
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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
Pensez à segmenter vos bundles par « cycle d’achat » : proposez un bundle d’entrée (faible engagement), un bundle standard, et un bundle « contrat » avec livraison programmée. Ajoutez un upsell de type « seuil magique » (ex. -5 % dès 1 000 € HT) plutôt qu’une remise linéaire, plus adapté aux décideurs. Testez aussi le prix affiché par unité fonctionnelle (coût par pièce, coût par heure d’usage) pour justifier l’upsell.
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