Dataset

This dataset was created by Mark Medhat

Released under Apache 2.0

Contents

The growth of supermarkets in most populated cities are increasing and market competitions are also high. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset.

The dataset contains sales details of different stores of a supermarket chain that has multiple stores in different parts of the US. With columns such as: - Ship Mode - Segment - Country - City - State - Postal code - Region - Category - Sub-category - Sales - Quantity - Discount - Profit

Column Descriptors - Data Features

• Date – The date of purchase.
• Customer Type – Whether the customer is a member or a visitor.
• Gender – The gender of the customer.
• Product Category – The category of the purchased product (e.g., Snacks, Vegetables, Fruits, etc.).
• Unit Price – The price per unit of the product.
• Quantity – The number of units purchased.
• Total Sales – The total sales amount for the transaction (Unit Price × Quantity).

Dataset

This dataset was created by Ryann

Contents

Dataset

This dataset was created by Kirollos Ashraf

Released under Apache 2.0

Contents

Dataset

This dataset was created by Kalaivani Mani

Contents

Tabulating and Visualizing Supermarket Data

In this portfolio, I present an analysis of supermarket data, focusing on total sales, product categories, highest-spending customers, states with the highest and lowest sales, top-selling regions, and the most profitable city. This analysis provides valuable insights into supermarket performance and customer behavior.

Total Sales:

This chart illustrates the total sales over a specific time period. It serves as a key indicator of the supermarket's financial performance, showing revenue trends.

Product Categories:

A pie chart displays the distribution of sales across various product categories. It helps identify which product categories are the most popular and which may require additional marketing efforts.

Highest-Spending Customer:

The bar chart reveals the highest-spending customer, allowing the supermarket to recognize and reward loyal customers, while also gaining insights into their preferences.

States with the Highest Sales:

A map or bar chart showcases the states with the highest sales. This data can inform inventory management and marketing strategies.

Top-Selling Regions:

A bar chart displays the regions that generate the most sales, enabling the supermarket to concentrate resources where they are most effective.

Most Profitable City:

The pie chart reveals the city with the highest sales, providing insights into localized market dynamics.

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Power BI:

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Dataset

This dataset was created by Bijay Bera

Contents

Dataset

This dataset was created by raja28perumal

Released under CC0: Public Domain

Contents

Coles is a leading supermarket, retail, and customer service brand in Australia, boasting over 800 outlets nationwide and holding a 27% Australian market share.

ColesStoreData 1. Coles_StoreID - is a unique ID that uniquely identifies each store. 2. Store_Location - provides information on the store’s location in different states. 3. Customer_Count - is the average customer count accounted for in the store. 4. Staff_Count - is the number of employees who work at the stores. 5. Store_Area - is the store’s size, expressed in square meters.

ColesSalesData 1. Coles_StoreIDNo - is the unique ID that identifies each store. 2. Expec_Revenue - is the Expected Revenue in $M a store is supposed to generate. 3. Gross_Sale - is the Gross Sale in $M a store has generated. 4. Sales_Cost - is the cost incurred in $M after the Sale has occurred. 5. Targeted_Quarter - indicates the quarter within a fiscal year to which the sales data corresponds. 6. Coles_Forecast - provides details about whether the store's sales align with the expected revenue, specifically whether the net sales (computed as Gross_Sale - Sales_Cost) meet the expected goal. The forecasted values are determined based on predefined conditions. If the net sales equal or exceed the Expected Revenue, they are categorized as "On Target." If they fall short of the Expected Revenue, they are categorized as "Below Target".

This synthetic dataset can be used for a variety of purposes, including market analysis, sales forecasting, and training machine learning models. It provides a representative sample of data that can be analyzed and used to make informed business decisions without exposing real customer information.

Grocery Sales Prediction

This dataset provides a rich resource for researchers and practitioners interested in retail sales prediction and analysis. It contains information about various grocery products, the outlets where they are sold, and their historical sales data.

Product Characteristics:

Item_Identifier: Unique identifier for each product. Item_Weight: Weight of the product item. Item_Fat_Content: Categorical variable indicating the fat content of the product (e.g., low fat, regular). Item_Visibility: Numerical attribute reflecting the visibility of the product in the store (likely a promotional measure). Item_Type: Category of the product (e.g., Snacks, Beverages, Bakery). Item_MRP: Maximum Retail Price of the product. Outlet Information:

Outlet_Identifier: Unique identifier for each outlet (store). Outlet_Establishment_Year: Year the outlet was established. Outlet_Size: Categorical variable indicating the size of the outlet (e.g., Small, Medium, Large). (Note: This data may have missing values) Outlet_Location_Type: Categorical variable indicating the type of location the outlet is in (e.g., Tier 1 City, Tier 2 City, Upstate). Outlet_Type: Categorical variable indicating the type of outlet (e.g., Supermarket, Grocery Store, Convenience Store). Sales Data:

Item_Outlet_Sales: The historical sales data for each product-outlet combination. Profit: The profit margin earned on each product sold. Potential Uses

This dataset can be used for various retail sales analysis and prediction tasks, including:

Demand forecasting: Build models to predict future sales of individual products or product categories at specific outlets. Promotion optimization: Analyze the effectiveness of different promotional strategies (reflected by Item_Visibility) on sales. Assortment planning: Optimize product selection and placement within stores based on sales history and outlet characteristics. Outlet performance analysis: Compare the performance of different outlets based on sales figures and profit margins. Customer segmentation: Identify customer segments with distinct purchasing behavior based on product types and outlet locations. By analyzing these rich data points, retailers can gain valuable insights to improve their sales strategies, optimize inventory management, and maximize profits.

Dataset

This dataset was created by Ziad abdelfattah Djoual

Released under MIT

Contents

The dashboard is designed to provide an intuitive and visually appealing interface for users to explore key performance indicators (KPIs) and sales data. It includes interactive visualizations and filtering capabilities to allow for detailed analysis at various levels of granularity.

This component utilizes historical sales data and time series analysis techniques to generate sales forecasts for the next 15 days. These forecasts enable proactive decision-making and resource allocation within the supermarket.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

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...

Introduction

The collected data sets come from the multi-branch store computer system. The data shows: stocking, sales, sales statistics, characteristics of products sold from January 2018 - December 2018.

About the store:

Store was open in 2009 and is located in Poland. The shop area is 120m2. We offer general food-and basic chemistry, hygienic articles. We have fresh bread from 4 different bakers,sweets, local vegetables, dairy, basic meat(ham,sausages), newspaper, home chemistry etc. Interior is basic.

Location: Shop is located in city that population is around 28 000 people. Shop is placed in mid of house estate( block of flats), near is sports field. The store is open every day: Monday-Saturday from 06:00 to 22:00, Sunday from 10:00 to 20:00. The store has 4 employees. Work in the store takes place on 3 shifts. First: 06:00- 12:00, second: 10:00-16: 00/18:00 and third: 16: 00-22: 00.

Competition

1. The nearest competition: There is another grocery store nearby (30 m). The second store is smaller - also a delicatessen, but half smaller. They offer similar products for daily use- bread, dairy, some meat and general foods. I'm not sure about alcohol and how wide their offer is. However in our store the offer is richer(bread is delivered from 4 different bakers). To know exactly what are the differences I need check details.

2. Grocery stores in the town:

3. 1 hypermarket

4. 8 supermarkets

5. 25 groceries stores

6. Shopping trends in Poland Connected to our location: People tend to do general food shopping in supermarkets. If they need daily fresh things, something is missing or they need some special product (not valid at supermarket) they do shopping at groceries like ours. Still in Poland people prefer to go to shop in the neighborhood to do: quicker shopping/talk to people/or just throw out rubbish and do shop at once. To do bigger shopping they go by car to supermarket e.g. after work or on weekend.

Online shopping: E-commerce are 1% of the sales of the FMCG goods market in Poland. It is starting to be popular in bigger cities like Warsaw, Krakow etc. Not popular in our city.

Health trend: -Three-quarters of Polish consumers agree with the statement that "you are what you eat". Therefore, we pay more attention to what we eat and do not save on food products

Convenience trend: According to the expert, the habits of buyers will not change so quickly, and the fact is that Poles like to shop flat - Polish shoppers visit 4 shops a month on average. Also the vast majority of them tend to make smaller purchases, which confirms the most popular shopping mission - replenishing stocks. However, the shopping experience is pleasant in the third place among buyers' motivation and selection of the store. 8 out of 10 buyers prefer to shop in a well-organized store with a nice atmosphere. This is one of the reasons for the development of the convenience channel. He also responds very well to other needs of Polish consumers, because Poles definitely have less and less time, so shopping must be fast and convenient. In this situation, the price is not the most important - 30% of Polish buyers declare that anything that saves their time is worth the higher price.

Costumer

Our costumer is located in the neighborhood leave in house estate (block of flats). During events of the sport field our opening hours are adjusted to get more costumers from event. Moreover, during trade free Sundays we have costumers from City. Some of the costumer work abroad and come to our shop when they are at home and have special order- e.g. cigarettes packages.

Demographic of city and wellness of inhabitants

Average age of people is 40 years old. Gender split is equal between men and women. Majority of population are marriages 60% and city has positive natural increase. Unemployment rate is low and similar to country rate- around 7%. Average monthly gross salary is around 3800 PLN gross .This is between minimum and average salary in Poland. (Minimum wage in Poland is :2250 PLN gross and average wage is : 4272 PLN gross.) Occupation split of people is : 40 % industry and construction, 30% agricultural sector, 11%service sector and other. Companies in the city are micro and small ( only few big companies). City is not touristic. In general situation in city is good-budget revenues are growing year to year. Additionally, polish government gives social funds for every second children starts from 2017 and now in 2019 it is going to be extended to every children, without limits. This should boost economy.

In general- Costumer in the city has good shopping condition.

The main problems faced by the owners are:

• • Overhaul of the owners - the store employs 4 employees, but the owners' great involvement in the current operation of the store means that they are unable to assess the situation and take actions to...