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Generate an ultra-actionable client report for a restaurant

Structured prompt to generate a comprehensive and actionable client report for a restaurant, intended for B2B decision-makers.

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Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.

Generate a professional client report for the restaurant [RESTAURANT_NAME] located in [CITY]. This report is intended for executives, marketers, agencies, and B2B sales professionals. Structure it into 4 sections:

  1. Executive Summary: 1 key sentence, then 3-5 essential KPIs in table format (current month vs previous month and year-over-year). Include: [MONTHLY_REVENUE], [COVER_COUNT], [AVERAGE_BASKET], [OCCUPANCY_RATE], [GROSS_MARGIN_PERCENT].

  2. Performance Driver Analysis:

    • Impact of marketing actions (campaigns [CAMPAIGN_NAME], social media, Google reviews).
    • Performance by acquisition channel (walk-in, reservation, delivery, click & collect).
    • Evolution of average ticket by service type (lunch, dinner, brunch).
    • Customer behavior: new vs returning, visit frequency, retention rate.

  3. Benchmark and Sector Trends: Compare [RESTAURANT_NAME] to similar restaurants in [BENCHMARK_AREA] (average basket, gross margin, table turnover). Mention 2025 trends: rise of plant-based, digitalization of customer journey, dynamic pricing.

  4. Actionable Recommendations: 3 to 5 concrete actions classified by priority (immediate, short-term, medium-term) with estimated ROI indicator. Example: 'Launch a digital loyalty program with 10% discount on the 5th meal (estimated cost: [PROGRAM_COST] €, expected ROI: +[ROI_PERCENT]% recurrence).'

Use a professional, data-driven tone. Each recommendation must be linked to a KPI in the report.

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Why this prompt works

<p>This prompt is designed to produce a professional and ultra-actionable client report, specifically tailored to the restaurant industry. It forces the AI to deliver data-driven analysis and concrete recommendations that are easily usable by an executive or B2B salesperson.</p><p>To use it, replace each [VARIABLE] with your restaurant's actual data. Key variables are: <strong>[RESTAURANT_NAME]</strong>, <strong>[MONTHLY_REVENUE]</strong>, <strong>[COVER_COUNT]</strong>, <strong>[AVERAGE_BASKET]</strong>, <strong>[OCCUPANCY_RATE]</strong>, <strong>[GROSS_MARGIN_PERCENT]</strong>. You can adjust the benchmark area via <strong>[BENCHMARK_AREA]</strong> (e.g., 'Paris 10th' or 'gastronomic restaurants').</p><p>Tip: for even more personalized reporting, add data from your CRM or POS system (e.g., 'churn rate', 'customer acquisition cost'). The prompt is designed to be reusable each month: change the variable values and run again.</p>

Use Cases

Monthly internal reporting for the restaurant ownerSales presentation to a restaurant client to sell marketing solutionsCompetitive benchmarking for a restaurant consulting agency

Expected Output

A report structured in 4 sections, with KPI tables, data-driven analysis, benchmark, and actionable recommendations with estimated ROI.

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Comments

  • LéaAI

    Ajoutez une colonne « écart vs objectif » et demandez au modèle de citer ses sources pour chaque KPI, surtout pour le benchmark. Précisez la zone géographique réelle (code postal, type de cuisine) pour éviter des moyennes génériques. Enfin, exigez que chaque recommandation mentionne le KPI ciblé, un seuil de déclenchement et une hypothèse de coût, afin de faciliter la décision.

Glossary Terms

ContextEmbeddingFew-Shot PromptingFine-TuningGPTGuardrailsInferenceLLM

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