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Food Delivery Customer Profile

Food Delivery Customer Profile


Prepared By :

[Your Name]

Company Name:

[Your Company Name]

Date Prepared :

[Date]

I. Demographic Information

  • Age: 28-35

  • Gender: Male

  • Location: New York City, New York

  • Household Size: 2 (Single or with partner)

  • Income Level: $50,000 - $75,000 annually

II. Psychographic Details

  • Food Preferences: Italian cuisine, sushi, and burgers

  • Dietary Restrictions: None

  • Ordering Frequency: 3-4 times per week

  • Preferred Delivery Times: Evenings (6 PM - 9 PM)

  • Preferred Payment Methods: Credit Card

III. Ordering Behavior

  • Average Order Value: $30 - $40

  • Most Ordered Items: Margherita pizza, California rolls, classic cheeseburger

  • Preferred Restaurants or Cuisine Types: Mama Mia's Pizzeria, Tokyo Sushi House, The Burger Joint

  • Special Occasion Ordering Patterns: Orders sushi for date nights on Fridays

IV. App or Website Usage Patterns

  • Frequency of App/Website Visits: Daily

  • Time Spent Browsing Menu Options: 10-15 minutes per visit

  • Preferred Device: Mobile (iOS)

V. Feedback and Reviews

  • Rating Given for Previous Orders: 4-5 stars

  • Comments or Suggestions Provided: "Great service, loved the pizza!"

  • Frequency of Providing Feedback: Occasionally

VI. Loyalty and Engagement

  • Participation in Loyalty Programs: Enrolled in Mama Mia's Pizzeria rewards program

  • Frequency of Engaging with Marketing Campaigns: Opens promotional emails regularly

  • Referral History: Referred 2 friends in the past year

VII. Customer Service Interactions

  • Frequency of Contacting Customer Support: Rarely

  • Nature of Inquiries or Complaints: The order was missing an item once, quickly resolved

  • Resolution Time: The issue was resolved within 30 minutes

VIII. Geographic Insights

  • Delivery Location Frequency: Mostly delivers to Midtown Manhattan

  • Areas with High Concentration of Orders: Financial District and Chelsea

  • Seasonal Variations in Ordering Patterns: More frequent orders during winter months

IX. Trends and Predictive Analytics

  • Seasonal Trends: Orders more sushi during the summer months

  • Predictive Analysis for Future Ordering Behavior: Likely to try new Italian restaurants in the upcoming months

  • Cross-Selling or Upselling Opportunities: Promote combo deals including pizza, sushi, and burgers

X. Recommendations for Personalization

  • Tailored Promotions or Discounts: Offer discounts on sushi combos on Fridays

  • Menu Recommendations Based on Past Orders: Highlight new burger options on the menu

  • Targeted Communication Strategies: Send personalized emails with recommendations based on past orders

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