This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

This is a case study for the company to improve sales

Business Goal
Date: 2023/09/15
Dataset: Sales quantity of a certain brand from January to December 2022 and from January to September 2023.

Please describe what you observe (no specific presentation format required). Among your observations, identify at least three valuable insights and explain why you consider them valuable.
If more resources were available to you (including time, information, etc.), what would you need, and what more could you achieve?

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Sample question & answer 1. Product insights: identify the product sales analysis, such as BCG matrix 2. Store insights: identify the sales performance of the sales 3. Supply chain insights: identify the demand 4. Time series forecasting: identify tread, seasonality

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

This dataset contains sales data, including order dates, order IDs, item details, costs, and revenues, primarily featuring USB novelty items and mugs.

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

1. Citation

Please cite the following papers when using this dataset:

I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

1. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File

Period

Number of Samples (days)

product 1 2020.xlsx

01/01/2020–31/12/2020

363

product 1 2021.xlsx

01/01/2021–31/12/2021

364

product 1 2022.xlsx

01/01/2022–31/12/2022

365

product 2 2020.xlsx

01/01/2020–31/12/2020

363

product 2 2021.xlsx

01/01/2021–31/12/2021

364

product 2 2022.xlsx

01/01/2022–31/12/2022

365

product 3 2020.xlsx

01/01/2020–31/12/2020

363

product 3 2021.xlsx

01/01/2021–31/12/2021

364

product 3 2022.xlsx

01/01/2022–31/12/2022

365

product 4 2020.xlsx

01/01/2020–31/12/2020

363

product 4 2021.xlsx

01/01/2021–31/12/2021

364

product 4 2022.xlsx

01/01/2022–31/12/2022

364

product 5 2020.xlsx

01/01/2020–31/12/2020

363

product 5 2021.xlsx

01/01/2021–31/12/2021

364

product 5 2022.xlsx

01/01/2022–31/12/2022

365

product 6 2020.xlsx

01/01/2020–31/12/2020

362

product 6 2021.xlsx

01/01/2021–31/12/2021

364

product 6 2022.xlsx

01/01/2022–31/12/2022

365

product 7 2020.xlsx

01/01/2020–31/12/2020

362

product 7 2021.xlsx

01/01/2021–31/12/2021

364

product 7 2022.xlsx

01/01/2022–31/12/2022

365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature

Description

Unit

Day

day of the month

-

Month

Month

-

Year

Year

-

daily_unit_sales

Daily sales - the amount of products, measured in units, that during that specific day were sold

units

previous_year_daily_unit_sales

Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year

units

percentage_difference_daily_unit_sales

The percentage difference between the two above values

%

daily_unit_sales_kg

The amount of products, measured in kilograms, that during that specific day were sold

kg

previous_year_daily_unit_sales_kg

Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year

kg

percentage_difference_daily_unit_sales_kg

The percentage difference between the two above values

kg

daily_unit_returns_kg

The percentage of the products that were shipped to selling points and were returned

%

previous_year_daily_unit_returns_kg

The percentage of the products that were shipped to selling points and were returned the previous year

%

points_of_distribution

The amount of sales representatives through which the product was sold to the market for this year

previous_year_points_of_distribution

The amount of sales representatives through which the product was sold to the market for the same day for the previous year

Table 1 – Dataset Feature Description

1. Structure and Format

4.1 Dataset Structure

The provided dataset has the following structure:

Where:

Name

Type

Property

Readme.docx

Report

A File that contains the documentation of the Dataset.

product X

Folder

A folder containing the data of a product X.

product X YYYY.xlsx

Data file

An excel file containing the sales data of product X for year YYYY.

Table 2 - Dataset File Description

1. Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957406 (TERMINET).

References

[1] MEVGAL is a Greek dairy production company

Analysis of ‘Sample Sales Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kyanyoga/sample-sales-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Sample Sales Data, Order Info, Sales, Customer, Shipping, etc., Used for Segmentation, Customer Analytics, Clustering and More. Inspired for retail analytics. This was originally used for Pentaho DI Kettle, But I found the set could be useful for Sales Simulation training.

Originally Written by María Carina Roldán, Pentaho Community Member, BI consultant (Assert Solutions), Argentina. This work is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License. Modified by Gus Segura June 2014.

--- Original source retains full ownership of the source dataset ---

This dataset is based on the Sample Leads Dataset and is intended to allow some simple filtering by lead source. I had modified this dataset to support an upcoming Towards Data Science article walking through the process. Link to be shared once published.

Standard error reference tables for the Retail Sales Index in Great Britain.

Nothing ever becomes real till it is experienced.

-John Keats

While we don't know the context in which John Keats mentioned this, we are sure about its implication in data science. While you would have enjoyed and gained exposure to real world problems in this challenge, here is another opportunity to get your hand dirty with this practice problem.

Problem Statement :

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Data :

We have 14204 samples in data set.

Variable Description

Item Identifier: A code provided for the item of sale

Item Weight: Weight of item

Item Fat Content: A categorical column of how much fat is present in the item: ‘Low Fat’, ‘Regular’, ‘low fat’, ‘LF’, ‘reg’

Item Visibility: Numeric value for how visible the item is

Item Type: What category does the item belong to: ‘Dairy’, ‘Soft Drinks’, ‘Meat’, ‘Fruits and Vegetables’, ‘Household’, ‘Baking Goods’, ‘Snack Foods’, ‘Frozen Foods’, ‘Breakfast’, ’Health and Hygiene’, ‘Hard Drinks’, ‘Canned’, ‘Breads’, ‘Starchy Foods’, ‘Others’, ‘Seafood’.

Item MRP: The MRP price of item

Outlet Identifier: Which outlet was the item sold. This will be categorical column

Outlet Establishment Year: Which year was the outlet established

Outlet Size: A categorical column to explain size of outlet: ‘Medium’, ‘High’, ‘Small’.

Outlet Location Type: A categorical column to describe the location of the outlet: ‘Tier 1’, ‘Tier 2’, ‘Tier 3’

Outlet Type: Categorical column for type of outlet: ‘Supermarket Type1’, ‘Supermarket Type2’, ‘Supermarket Type3’, ‘Grocery Store’

Item Outlet Sales: The number of sales for an item.

Evaluation Metric:

We will use the Root Mean Square Error value to judge your response

Dataset

This dataset was created by selvam mts

Released under MIT

Contents

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

Note:- Only publicly available data can be worked upon

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Dataset

This dataset was created by Waqas Ali Naqvi

Contents

It contains the following files:

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Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

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The given dataset appears to be a sales dataset containing information about different orders. Here is a description of the data:

1. Row ID: An identifier for each row in the dataset.
2. Order ID: Unique identifier for each order.
3. Order Date: The date when the order was placed.
4. Ship Date: The date when the order was shipped.
5. Ship Mode: The mode of shipping chosen for the order.
6. Customer ID: Unique identifier for each customer.
7. Customer Name: Name of the customer who placed the order.
8. Segment: The segment to which the customer belongs (e.g., consumer, corporate).
9. Country: The country where the order was placed (in this case, United States).
10. City: The city where the order was placed.
11. State: The state where the order was placed.
12. Postal Code: The postal code associated with the order's location.
13. Region: The region of the country where the order was placed.
14. Product ID: Unique identifier for each product.
15. Category: The category to which the product belongs (e.g., furniture, office supplies).
16. Sub-Category: The sub-category to which the product belongs (e.g., bookcases, chairs).
17. Product Name: The name of the product.
18. Cost: The cost of the product.
19. Price: The price at which the product was sold.
20. Profit: The profit made from the sale of the product.
21. Quantity: The quantity of the product ordered.
22. Sales: The total sales generated from the order (quantity multiplied by price).

The dataset provides detailed information about each order, including customer details, product details, sales information, and shipping information. It can be used to analyze various aspects of the sales data, such as profitability, customer segments, product categories, and regional sales performance.

This dataset was generated for use with Nile's Sales Assistant example: https://github.com/niledatabase/niledatabase/tree/main/examples/ai/sales_insight It includes:

Simulated sales conversations for 5 different fictional companies. Chunked and embedded version of these conversations (embeddings use OpenAI's text-embedding-3-small model).

The chunks and embeddings can be directly loaded to a vector databases and searched using vector similarity methods. The example's ./ingest directory… See the full description on the dataset page: https://huggingface.co/datasets/gwenshap/sales-transcripts.

📊 Supplement Sales Data (2020–2025) Overview This dataset contains weekly sales data for a variety of health and wellness supplements from January 2020 to April 2025. The data includes products in categories like Protein, Vitamins, Omega, and Amino Acids, among others, and covers multiple e-commerce platforms such as Amazon, Walmart, and iHerb. The dataset also tracks sales in several locations including the USA, UK, and Canada.

Dataset Details Time Range: January 2020 to April 2025

Frequency: Weekly (Every Monday)

Number of Rows: 4,384

Columns:

Date: The week of the sale.

Product Name: The name of the supplement (e.g., Whey Protein, Vitamin C, etc.).

Category: The category of the supplement (e.g., Protein, Vitamin, Omega).

Units Sold: The number of units sold in that week.

Price: The selling price of the product.

Revenue: The total revenue generated (Units Sold * Price).

Discount: The discount applied on the product (as a percentage of original price).

Units Returned: The number of units returned in that week.

Location: The location of the sale (USA, UK, or Canada).

Platform: The e-commerce platform (Amazon, Walmart, iHerb).

Use Cases This dataset is ideal for:

Time-series forecasting and sales trend analysis 📈

Price vs. demand analysis and revenue prediction 📊

Sentiment analysis and impact of promotions (Discounts) on sales 🛍️

Product performance tracking across different platforms and locations 🛒

Business optimization in the health and wellness e-commerce sector 💼

Potential Applications Build predictive models to forecast future sales 📅

Analyze the effectiveness of discounts and promotions 💸

Create recommendation systems for supplement products 🧠

Perform exploratory data analysis (EDA) and uncover trends 🔍

Model return rates and their effect on overall revenue 📉

Why This Dataset? This dataset provides an excellent starting point for those interested in building business intelligence tools, e-commerce forecasting models, or exploring health & wellness trends. It also serves as a perfect dataset for data science learners looking to apply regression, time-series analysis, and predictive modeling techniques.

Sales data for all Islanders Board Of Industry & Service (IBIS) stores.

Dataset Card for "sales-conversations"

This dataset was created for the purpose of training a sales agent chatbot that can convince people. The initial idea came from: textbooks is all you need https://arxiv.org/abs/2306.11644 gpt-3.5-turbo was used for the generation

  Structure

The conversations have a customer and a salesman which appear always in changing order. customer, salesman, customer, salesman, etc. The customer always starts the conversation Who ends the… See the full description on the dataset page: https://huggingface.co/datasets/goendalf666/sales-conversations.

Turnover and volume of sales in wholesale and retail trade - monthly data