Master B2B Real Estate Sales Objections
Structured prompt to generate responses to real estate sales objections, with key variables and precise output format.
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You are a negotiation and closing expert in B2B real estate. You help a real estate agency's sales team brilliantly respond to objections from their clients (landlords, developers, investors). For each objection below, generate a structured response in 3 parts: 1/ REFRAMING: rephrase the objection to show you understand the client's point of view. 2/ ARGUMENTS: 2-3 solid arguments, with figures if possible, using local market data. 3/ FOLLOW-UP QUESTION: an open-ended question that engages the client and makes them think about your agency's added value. Use a professional but warm tone, and adapt the language level to [CLIENT_TYPE] (e.g., landlord, developer, institutional investor). The objection is: "[OBJECTION]". Additional context: the property is located in [CITY] and the local market is experiencing [MARKET_TREND] (e.g., rising prices, declining rental demand). Ensure each response highlights the agency's expertise in [SPECIALTY] (e.g., property management, luxury sales, commercial real estate). End each response with a suitable call-to-action, such as a meeting proposal or a visit offer.
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Why this prompt works
<p>This prompt is designed for sales and marketing managers of real estate agencies. It helps prepare personalized responses to common objections from B2B clients (owners, developers, investors). By integrating variables like client type, objection, city, market trend, and agency specialty, you get contextualized and persuasive responses.</p><ul><li><strong>Reframing</strong>: Show you understand the client's concerns before responding.</li><li><strong>Argument with data</strong>: Use local data to make your arguments credible.</li><li><strong>Follow-up question</strong>: Engage the client in a conversation rather than just answering.</li></ul><p>Usage example: copy the prompt into your AI interface, replace the variables with real information, and get a ready-to-use response. You can also create a library of responses for the most common objections.</p>
Use Cases
Expected Output
A structured text with three parts (reframing, argument, follow-up question) and a final call-to-action. Each part is clearly separated and adapted to the specified client type.
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- LéaAI
Pour crédibiliser l’argumentaire, remplacez les variables par des données locales réelles (prix au m², taux de vacance, délais de vente) issues de sources publiques. Adaptez aussi la question de relance au profil : un investisseur attend un ROI concret, un promoteur un calendrier. Testez chaque réponse sur des objections récentes de votre portefeuille.
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