By ANil [source]
This dataset provides an in-depth look at the profitability of e-commerce sales. It contains data on a variety of sales channels, including Shiprocket and INCREFF, as well as financial information on related expenses and profits. The columns contain data such as SKU codes, design numbers, stock levels, product categories, sizes and colors. In addition to this we have included the MRPs across multiple stores like Ajio MRP , Amazon MRP , Amazon FBA MRP , Flipkart MRP , Limeroad MRP Myntra MRP and PaytmMRP along with other key parameters like amount paid by customer for the purchase , rate per piece for every individual transaction Also we have added transactional parameters like Date of sale months category fulfilledby B2b Status Qty Currency Gross amt . This is a must-have dataset for anyone trying to uncover the profitability of e-commerce sales in today's marketplace
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- 🚨 Your notebook can be here! 🚨!
This dataset provides a comprehensive overview of e-commerce sales data from different channels covering a variety of products. Using this dataset, retailers and digital marketers can measure the performance of their campaigns more accurately and efficiently.
The following steps help users make the most out of this dataset: - Analyze the general sales trends by examining info such as month, category, currency, stock level, and customer for each sale. This will give you an idea about how your e-commerce business is performing in each channel.
- Review the Shiprocket and INCREF data to compare and analyze profitability via different fulfilment methods. This comparison would enable you to make better decisions towards maximizing profit while minimizing costs associated with each method’s referral fees and fulfillment rates.
- Compare prices between various channels such as Amazon FBA MRP, Myntra MRP, Ajio MRP etc using the corresponding columns for each store (Amazon MRP etc). You can judge which stores are offering more profitable margins without compromising on quality by analyzing these pricing points in combination with other information related to product sales (TP1/TP2 - cost per piece).
- Look at customer specific data such as TP 1/TP 2 combination wise Gross Amount or Rate info in terms price per piece or total gross amount generated by any SKU dispersed over multiple customers with relevant dates associated to track individual item performance relative to others within its category over time periods shortlisted/filtered appropriately.. Have an eye on items commonly utilized against offers or promotional discounts offered hence crafting strategies towards inventory optimization leading up-selling operations.?
- Finally Use Overall ‘Stock’ details along all the P & L Data including Yearly Expenses_IIGF information record for takeaways which might be aimed towards essential cost cutting measures like switching amongst delivery options carefully chosen out of Shiprocket & INCREFF leadings away from manual inspections catering savings under support personnel outsourcing structures.?By employing a comprehensive understanding on how our internal subsidiaries perform globally unless attached respective audits may provide us remarkably lower operational costs servicing confidence; costing far lesser than being incurred taking into account entire pallet shipments tracking sheets representing current level supply chains efficiencies achieved internally., then one may finally scale profits exponentially increases cut down unseen losses followed up introducing newer marketing campaigns necessarily tailored according playing around multiple goods based spectrums due powerful backing suitable transportation boundaries set carefully
- Analysing the difference in profitability between sales made through Shiprocket and INCREFF. This data can be used to see where the biggest profit margins lie, and strategize accordingly.
- Examining the Complete Cost structure of a product with all its components and their contribution towards revenue or profitability, i.e., TP 1 & 2, MRP Old & Final MRP Old together with Platform based MRP - Amazon, Myntra and Paytm etc., Currency based Profit Margin etc.
- Building a predictive model using Machine Learning by leveraging historical data to predict future sales volume and profits for e-commerce products across multiple categories/devices/platforms such as Amazon, Flipkart, Myntra etc as well providing m...
http://www.gnu.org/licenses/fdl-1.3.htmlhttp://www.gnu.org/licenses/fdl-1.3.html
This dataset contains transaction data from a fictitious SaaS company selling sales and marketing software to other companies (B2B). In the dataset, each row represents a single transaction/order (9,994 transactions), and the columns include:
Here is the Original Dataset: https://ee-assets-prod-us-east-1.s3.amazonaws.com/modules/337d5d05acc64a6fa37bcba6b921071c/v1/SaaS-Sales.csv
| # | Name of the attribute | Description | | -- | --------------------- | -------------------------------------------------------- | | 1 | Row ID | A unique identifier for each transaction. | | 2 | Order ID | A unique identifier for each order. | | 3 | Order Date | The date when the order was placed. | | 4 | Date Key | A numerical representation of the order date (YYYYMMDD). | | 5 | Contact Name | The name of the person who placed the order. | | 6 | Country | The country where the order was placed. | | 7 | City | The city where the order was placed. | | 8 | Region | The region where the order was placed. | | 9 | Subregion | The subregion where the order was placed. | | 10 | Customer | The name of the company that placed the order. | | 11 | Customer ID | A unique identifier for each customer. | | 13 | Industry | The industry the customer belongs to. | | 14 | Segment | The customer segment (SMB, Strategic, Enterprise, etc.). | | 15 | Product | The product was ordered. | | 16 | License | The license key for the product. | | 17 | Sales | The total sales amount for the transaction. | | 18 | Quantity | The total number of items in the transaction. | | 19 | Discount | The discount applied to the transaction. | | 20 | Profit | The profit from the transaction. |
This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Sales transactions from an SME (small and medium enterprise) in Chemical Products industry. Data holds sales date, customer, product, price, quantity, city and sales person information. Data Set can be useful for performance tracking and monitoring, customer segmentation, financial forecasting, anomaly detection etc. Columns and details: DATE: Date of sales in DD/MM/YYYY hh:mm format SKU: Stock Code of the product CUSTOMER: Customer Code CITY: City ID PRICE: Sales price of the product QUANTITY: Number of items in the transaction SALESPERSON : Responsible sales person
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
2. Citation
Please cite the following papers when using this dataset:
3. 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 |
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Research Domain:
The dataset is part of a project focused on retail sales forecasting. Specifically, it is designed to predict daily sales for Rossmann, a chain of over 3,000 drug stores operating across seven European countries. The project falls under the broader domain of time series analysis and machine learning applications for business optimization. The goal is to apply machine learning techniques to forecast future sales based on historical data, which includes factors like promotions, competition, holidays, and seasonal trends.
Purpose:
The primary purpose of this dataset is to help Rossmann store managers predict daily sales for up to six weeks in advance. By making accurate sales predictions, Rossmann can improve inventory management, staffing decisions, and promotional strategies. This dataset serves as a training set for machine learning models aimed at reducing forecasting errors and supporting decision-making processes across the company’s large network of stores.
How the Dataset Was Created:
The dataset was compiled from several sources, including historical sales data from Rossmann stores, promotional calendars, holiday schedules, and external factors such as competition. The data is split into multiple features, such as the store's location, promotion details, whether the store was open or closed, and weather information. The dataset is publicly available on platforms like Kaggle and was initially created for the Kaggle Rossmann Store Sales competition. The data is made accessible via an API for further analysis and modeling, and it is structured to help machine learning models predict future sales based on various input variables.
Dataset Structure:
The dataset consists of three main files, each with its specific role:
Train:
This file contains the historical sales data, which is used to train machine learning models. It includes daily sales information for each store, as well as various features that could influence the sales (e.g., promotions, holidays, store type, etc.).
https://handle.test.datacite.org/10.82556/yb6j-jw41
PID: b1c59499-9c6e-42c2-af8f-840181e809db
Test2:
The test dataset mirrors the structure of train.csv
but does not include the actual sales values (i.e., the target variable). This file is used for making predictions using the trained machine learning models. It is used to evaluate the accuracy of predictions when the true sales data is unknown.
https://handle.test.datacite.org/10.82556/jerg-4b84
PID: 7cbb845c-21dd-4b60-b990-afa8754a0dd9
Store:
This file provides metadata about each store, including information such as the store’s location, type, and assortment level. This data is essential for understanding the context in which the sales data is gathered.
https://handle.test.datacite.org/10.82556/nqeg-gy34
PID: 9627ec46-4ee6-4969-b14a-bda555fe34db
Id: A unique identifier for each (Store, Date) combination within the test set.
Store: A unique identifier for each store.
Sales: The daily turnover (target variable) for each store on a specific day (this is what you are predicting).
Customers: The number of customers visiting the store on a given day.
Open: An indicator of whether the store was open (1 = open, 0 = closed).
StateHoliday: Indicates if the day is a state holiday, with values like:
'a' = public holiday,
'b' = Easter holiday,
'c' = Christmas,
'0' = no holiday.
SchoolHoliday: Indicates whether the store is affected by school closures (1 = yes, 0 = no).
StoreType: Differentiates between four types of stores: 'a', 'b', 'c', 'd'.
Assortment: Describes the level of product assortment in the store:
'a' = basic,
'b' = extra,
'c' = extended.
CompetitionDistance: Distance (in meters) to the nearest competitor store.
CompetitionOpenSince[Month/Year]: The month and year when the nearest competitor store opened.
Promo: Indicates whether the store is running a promotion on a particular day (1 = yes, 0 = no).
Promo2: Indicates whether the store is participating in Promo2, a continuing promotion for some stores (1 = participating, 0 = not participating).
Promo2Since[Year/Week]: The year and calendar week when the store started participating in Promo2.
PromoInterval: Describes the months when Promo2 is active, e.g., "Feb,May,Aug,Nov" means the promotion starts in February, May, August, and November.
To work with this dataset, you will need to have specific software installed, including:
DBRepo Authorization: This is required to access the datasets via the DBRepo API. You may need to authenticate with an API key or login credentials to retrieve the datasets.
Python Libraries: Key libraries for working with the dataset include:
pandas
for data manipulation,
numpy
for numerical operations,
matplotlib
and seaborn
for data visualization,
scikit-learn
for machine learning algorithms.
Several additional resources are available for working with the dataset:
Presentation:
A presentation summarizing the exploratory data analysis (EDA), feature engineering process, and key insights from the analysis is provided. This presentation also includes visualizations that help in understanding the dataset’s trends and relationships.
Jupyter Notebook:
A Jupyter notebook, titled Retail_Sales_Prediction_Capstone_Project.ipynb
, is provided, which details the entire machine learning pipeline, from data loading and cleaning to model training and evaluation.
Model Evaluation Results:
The project includes a detailed evaluation of various machine learning models, including their performance metrics like training and testing scores, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). This allows for a comparison of model effectiveness in forecasting sales.
Trained Models (.pkl files):
The models trained during the project are saved as .pkl
files. These files contain the trained machine learning models (e.g., Random Forest, Linear Regression, etc.) that can be loaded and used to make predictions without retraining the models from scratch.
sample_submission.csv:
This file is a sample submission file that demonstrates the format of predictions expected when using the trained model. The sample_submission.csv
contains predictions made on the test dataset using the trained Random Forest model. It provides an example of how the output should be structured for submission.
These resources provide a comprehensive guide to implementing and analyzing the sales forecasting model, helping you understand the data, methods, and results in greater detail.
This dataset is generated for the purpose of analyzing furniture sales data using multiple regression techniques. It contains 2,500 rows with 15 columns, including 7 numerical columns and 7 categorical columns, along with a target variable (revenue) which represents the total revenue generated from furniture sales. The dataset captures various aspects of furniture sales, such as pricing, cost, sales volume, discount percentage, inventory levels, delivery time, and different categorical attributes like furniture type, material, color, and store location.
Guys please upload your notebook of this dataset so that others can also learn from your work
This dataset was created by Ahmed ABO GHALY
At CompanyData.com (BoldData), we provide verified company data sourced directly from official trade registers. Our global IT company dataset gives you access to 6 million IT businesses worldwide, including software firms, tech consultancies, system integrators, SaaS providers, and other IT service companies. Every record is sourced from authoritative local registries, ensuring unmatched accuracy, coverage, and compliance.
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https://brightdata.com/licensehttps://brightdata.com/license
Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features
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Get Started Today Request a sample or customize your dataset to fit your unique...
This dataset was created by Amey Mane
taken from this Kaggle competition:
Dataset Description
In this competition, you will predict sales for the thousands of product families sold at Favorita stores located in Ecuador. The training data includes dates, store and product information, whether that item was being promoted, as well as the sales numbers. Additional files include supplementary information that may be useful in building your models.
File Descriptions and Data Field Information
train.csv… See the full description on the dataset page: https://huggingface.co/datasets/t4tiana/store-sales-time-series-forecasting.
50 Million Rows MSSQL Backup File with Clustered Columnstore Index.
This dataset contains -27K categorized Turkish supermarket items. -81 stores (Every city of Turkey has a store) -100K real Turkish names customer, address -10M rows sales data generated randomly. -All data has a near real price with influation factor by the time.
All the data generated randomly. So the usernames have been generated with real Turkish names and surnames but they are not real people.
The sale data generated randomly. But it has some rules.
For example, every order can contains 1-9 kind of item.
Every orderline amount can be 1-9 pieces.
The randomise function works according to population of the city.
So the number of orders for Istanbul (the biggest city of Turkey) is about 20% of all data
and another city for example orders for the Gaziantep (the population is 2.5% of Turkey population) is about 2.5% off all data.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1611072%2F9442f2a1dbae7f05ead4fde9e1033ac6%2Finbox_1611072_135236e39b79d6fae8830dec3fca4961_1.png?generation=1693509562300174&alt=media" alt="">
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1611072%2F1c39195270db87250e59d9f2917ccea1%2Finbox_1611072_b73d9ca432dae956564cfa5bfe42268c_3.png?generation=1693509575061587&alt=media" alt="">
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1611072%2Fa908389f33ae5c983e383d17f0d9a763%2Finbox_1611072_c5d349aa1f33c0fc4fc74b79b7167d3a_F3za81TXkAA1Il4.png?generation=1693509586158658&alt=media" alt="">
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.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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.
Success.ai’s Technographic Data for the North American IT Industry provides unparalleled visibility into the technology stacks, operational frameworks, and key decision-makers powering 30 million-plus businesses across the region’s tech landscape. From established software giants to emerging SaaS startups, this dataset offers verified contacts, firmographic details, and in-depth insights into each company’s technology adoption, infrastructure choices, and vendor partnerships.
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Ethical and Compliant
Data Highlights:
Key Features of the Dataset:
Technographic Decision-Maker Profiles
Advanced Filters for Precision Targeting
AI-Driven Enrichment
Strategic Use Cases:
Sales and Account-Based Marketing
Product Development and Roadmap Planning
Competitive Analysis and Market Entry
Partnership and Ecosystem Building
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Leverage high-quality B2B data with 468 enriched attributes, covering firmographics, financial stability, and industry classifications. Our AI-optimized dataset ensures accuracy through advanced deduplication and continuous updates. With 30+ years of expertise and 1,100+ trusted sources, we provide fully compliant, structured business data to power lead generation, risk assessment, CRM enrichment, market research, and more.
Key use cases of B2B Data have helped our customers in several areas :
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By ANil [source]
This dataset provides an in-depth look at the profitability of e-commerce sales. It contains data on a variety of sales channels, including Shiprocket and INCREFF, as well as financial information on related expenses and profits. The columns contain data such as SKU codes, design numbers, stock levels, product categories, sizes and colors. In addition to this we have included the MRPs across multiple stores like Ajio MRP , Amazon MRP , Amazon FBA MRP , Flipkart MRP , Limeroad MRP Myntra MRP and PaytmMRP along with other key parameters like amount paid by customer for the purchase , rate per piece for every individual transaction Also we have added transactional parameters like Date of sale months category fulfilledby B2b Status Qty Currency Gross amt . This is a must-have dataset for anyone trying to uncover the profitability of e-commerce sales in today's marketplace
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This dataset provides a comprehensive overview of e-commerce sales data from different channels covering a variety of products. Using this dataset, retailers and digital marketers can measure the performance of their campaigns more accurately and efficiently.
The following steps help users make the most out of this dataset: - Analyze the general sales trends by examining info such as month, category, currency, stock level, and customer for each sale. This will give you an idea about how your e-commerce business is performing in each channel.
- Review the Shiprocket and INCREF data to compare and analyze profitability via different fulfilment methods. This comparison would enable you to make better decisions towards maximizing profit while minimizing costs associated with each method’s referral fees and fulfillment rates.
- Compare prices between various channels such as Amazon FBA MRP, Myntra MRP, Ajio MRP etc using the corresponding columns for each store (Amazon MRP etc). You can judge which stores are offering more profitable margins without compromising on quality by analyzing these pricing points in combination with other information related to product sales (TP1/TP2 - cost per piece).
- Look at customer specific data such as TP 1/TP 2 combination wise Gross Amount or Rate info in terms price per piece or total gross amount generated by any SKU dispersed over multiple customers with relevant dates associated to track individual item performance relative to others within its category over time periods shortlisted/filtered appropriately.. Have an eye on items commonly utilized against offers or promotional discounts offered hence crafting strategies towards inventory optimization leading up-selling operations.?
- Finally Use Overall ‘Stock’ details along all the P & L Data including Yearly Expenses_IIGF information record for takeaways which might be aimed towards essential cost cutting measures like switching amongst delivery options carefully chosen out of Shiprocket & INCREFF leadings away from manual inspections catering savings under support personnel outsourcing structures.?By employing a comprehensive understanding on how our internal subsidiaries perform globally unless attached respective audits may provide us remarkably lower operational costs servicing confidence; costing far lesser than being incurred taking into account entire pallet shipments tracking sheets representing current level supply chains efficiencies achieved internally., then one may finally scale profits exponentially increases cut down unseen losses followed up introducing newer marketing campaigns necessarily tailored according playing around multiple goods based spectrums due powerful backing suitable transportation boundaries set carefully
- Analysing the difference in profitability between sales made through Shiprocket and INCREFF. This data can be used to see where the biggest profit margins lie, and strategize accordingly.
- Examining the Complete Cost structure of a product with all its components and their contribution towards revenue or profitability, i.e., TP 1 & 2, MRP Old & Final MRP Old together with Platform based MRP - Amazon, Myntra and Paytm etc., Currency based Profit Margin etc.
- Building a predictive model using Machine Learning by leveraging historical data to predict future sales volume and profits for e-commerce products across multiple categories/devices/platforms such as Amazon, Flipkart, Myntra etc as well providing m...