Name: Supermarket Ordering, Invoicing, And Sales
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Supermarket Ordering, Invoicing, And Sales

Supermarket Ordering, Invoicing, and Sales Analysis

Measuring Consumer Behavior and Engagement

By [source]

About this dataset

This data set provides an in-depth look into the ordering, invoicing and sales processes at a supermarket. With information ranging from customers' meal choices to the value of their orders and whether they were converted into sales, this dataset opens up endless possibilities to uncover consumer behavior trends and engagement within the business. From understanding who is exchanging with the company and when, to seeing what types of meals are most popular with consumers, this rich collection of data will allow us to gain priceless insights into consumer actions and habits that can inform strategic decisions. Dive deep into big data now by exploring Invoices.csv, OrderLeads.csv and SalesTeam.csv for invaluable knowledge about your customers!

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How to use the dataset

This dataset provides an in-depth look into the ordering and invoicing processes of a supermarket, as well as how consumers are engaging with it. This dataset can be used to analyze and gain insights into consumer purchasing behaviors and preferences at the store.

The first step in analyzing this data set is to familiarize yourself with its content. The dataset contains three CSV files: Invoices.csv, OrderLeads.csv, and SalesTeam.csv have different features like date of meal, participants, Meal Price, Type of meal ,company Name ,Order Value etc .Each file contains a list of columns containing data related to each particular feature like Date ,Date Of Meal Participants etc .

Once you understand what types of information is included in each table it’ll be easier for you to start drawing conclusions about customer preferences and trends from within the store's data set. You can use mathematical functions or statistical models such as regression analysis or cluster analysis in order to gain even further insight into customers’ behaviors within the store setting. Additionally you could use machine learning algorithms such as K-Nearest Neighbors (KNN) or Support Vector Machines (SVM) if your goal was improving targeting strategy or recognizing patterns between customer purchases over time.

All these techniques will help you determine what promotional tactics work best when trying to attract customers and promote sales through various marketing campaigns at this supermarket chain They will also help shed light on how customers engage with products within categories across different days/weeks/months according to their own individual purchasing habits which would ultimately contribute towards improved marketing strategies from management side .

Overall this data set provides immense potential for advancing understanding retail behaviour by allowing us access specific transactions that occurred at a given time frame; ultimately providing us detailed insight into customer behavior trends along with tools such software packages that allow us manipulate these metrics however necessary for entertainment purposes that help us identify strategies designed for greater efficiency when increasing revenue

Research Ideas

Identifying the most profitable customer segment based on order value and converted sales.

Leveraging trends in participant size to suggest meal packages for different types of meals.

Analyzing the conversion rate of orders over time to optimize promotional strategies and product offerings accordingly

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Invoices.csv

Column name	Description
Date	The date the order was placed. (Date)
Date of Meal	The date the meal was served. (Date)
Participants	The number of people who participated in the meal. (Integer)
Meal Price	The cost of the meal. (Float)
Type of Meal	The type of meal that was ordered. (String)

File: OrderLeads.csv

Column name	Description
Date	The date the order was placed. (Date)
Company Name	The name of the company associated with the order. (String)
Order Value	The total value of the order. (Float)
Converted	Whether or not the order was converted into a sale. (Boolean)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Invoices

@kaggle.thedevastator_supermarket_ordering_invoicing_and_sales_analysi.invoices

2.84 MB
50017 rows
8 columns


CREATE TABLE invoices (
  "order_id" VARCHAR,
  "date" VARCHAR,
  "meal_id" VARCHAR,
  "company_id" VARCHAR,
  "date_of_meal" VARCHAR,
  "participants" VARCHAR,
  "meal_price" BIGINT,
  "type_of_meal" VARCHAR
);

Orderleads

@kaggle.thedevastator_supermarket_ordering_invoicing_and_sales_analysi.orderleads

2.37 MB
100000 rows
6 columns


CREATE TABLE orderleads (
  "order_id" VARCHAR,
  "company_id" VARCHAR,
  "company_name" VARCHAR,
  "date" VARCHAR,
  "order_value" BIGINT,
  "converted" BIGINT
);

Salesteam

@kaggle.thedevastator_supermarket_ordering_invoicing_and_sales_analysi.salesteam

198.04 KB
4725 rows
4 columns


CREATE TABLE salesteam (
  "sales_rep" VARCHAR,
  "sales_rep_id" VARCHAR,
  "company_name" VARCHAR,
  "company_id" VARCHAR
);