Food Mart Customer Cost Forecast Dataset

Columns

• food_category: The category of food items, such as Vegetables or Snack Foods.
• food_department: The department within the store where the food type belongs, for instance, Produce or Snack Foods.
• food_family: The broader classification of food, such as Food or Non-Consumable.
• store_sales(in millions): The total sales of the store, expressed in million US dollars (mean: 6.54, standard deviation: 3.46, range: 0.51 to 22.9).
• store_cost(in millions): The operational cost or expense of the store, in million US dollars (mean: 2.62, standard deviation: 1.45, range: 0.16 to 9.73).
• unit_sales(in millions): The quantity of units sold in stores, in millions (mean: 3.09, standard deviation: 0.83, range: 1 to 6).
• promotion_name: The name of the specific media promotion, such as "Weekend Markdown" or "Two Day Sale".
• sales_country: The country where the sale took place, primarily USA or Mexico.
• marital_status: The marital status of customers (S for single, M for married).
• gender: The gender of customers (F for female, M for male).
• total_children: The total number of children in a customer's household (mean: 2.53, standard deviation: 1.49, range: 0 to 5).
• education: The educational attainment level of the customer, including "Partial High School" and "High School Degree".
• member_card: Indicates the type of member card held by the customer, such as Bronze or Normal.
• occupation: The occupation of the customer, e.g., Professional or Skilled Manual.
• houseowner: A boolean flag indicating whether the customer is a homeowner (true/false).
• avg_cars_at home(approx): The approximate average number of cars per customer's home (mean: 2.2, standard deviation: 1.11, range: 0 to 4).
• avg. yearly_income: The range of the customer's yearly income, based on provided details (e.g., "$30K - $50K", "$10K - $30K").
• num_children_at_home: The number of children at home as per customer details (mean: 0.83, standard deviation: 1.3, range: 0 to 5).
• brand_name: The brand name of the product, such as Hermanos or Ebony.
• SRP: The Suggested Retail Price or Manufacturer's Recommended Price of the item (mean: 2.12, standard deviation: 0.93, range: 0.5 to 3.98).
• gross_weight: The gross weight of the item (mean: 13.8, standard deviation: 4.62, range: 6 to 21.9).
• net_weight: The net weight of the item (mean: 11.8, standard deviation: 4.68, range: 3.05 to 20.8).
• recyclable_package: A binary indicator if the food item's package is recyclable.
• low_fat: A binary indicator if the food item is low fat.
• units_per_case: The number of units available per case on store shelves (mean: 18.9, standard deviation: 10.3, range: 1 to 36).
• store_type: The type of store available, e.g., Supermarket or Deluxe Supermarket.
• store_city: The city where the store is located, such as Tacoma or Salem.
• store_state: The state where the store is present, e.g., WA or OR.
• store_sqft: The total area of the store in square feet (mean: 28,000, standard deviation: 5,700, range: 20,300 to 39,700).
• grocery_sqft: The grocery area available in square feet (mean: 19,100, standard deviation: 3,990, range: 13,300 to 30,400).
• frozen_sqft: The frozen food area available in square feet (mean: 5,310, standard deviation: 1,580, range: 2,450 to 9,180).
• meat_sqft: The meat area available in square feet (mean: 3,540, standard deviation: 1,050, range: 1,640 to 6,120).
• coffee_bar: A binary indicator if a coffee bar is available in the store.
• video_store: A binary indicator if a video store or gaming store is available.
• salad_bar: A binary indicator if a salad bar is available in the store.
• prepared_food: A binary indicator if prepared food is available in the store.
• florist: A binary indicator if flower shelves are available in the store.
• media_type: The media source used for the campaign, such as "Daily Paper, Radio" or "Product Attachment".
• cost: The cost incurred on acquiring a customer, in US dollars (mean: 99.3, standard deviation: 30, range: 50.8 to 150).

Distribution

Usage

• Developing regression models to predict the cost of acquiring new customers.
• Analysing the impact of different media types and promotions on acquisition costs.
• Identifying key customer demographics and store features that influence campaign effectiveness and cost efficiency.
• Strategic planning for marketing budgets and resource allocation within the retail sector.

Coverage

License

Who Can Use It

• Data scientists and machine learning engineers building predictive models for marketing costs.
• Marketing managers and analysts seeking to understand and optimise customer acquisition strategies.
• Business intelligence professionals looking for insights into retail operations and promotional effectiveness.
• Academics and researchers studying consumer behaviour, retail economics, or media effectiveness.

Dataset Name Suggestions

• Food Mart Media Campaign Cost Prediction
• US Retail Customer Acquisition Cost Data
• Convenience Store Marketing Spend Analysis
• Food Mart Customer Cost Forecast Dataset

Attributes