Customers of a business leave due to dissatisfaction of services, attracted by competitors and so on. Customers stop buying the products or services for a few months and never come back. Data scientists or Data Analysts analyze the reason behind customer churn, predict probable churn in the future and give solutions.
The customer churn dataset that can be downloaded from this site consists of 500 rows and each row consists of information about customer details, subscription type, support access, transaction frequency and last purchase date based on which the customer whether he is retained or churned is inferred. It is a time-series dataset. The columns in the customer churn dataset consists of ' CustomerID ', 'Age', 'Gender', 'Annual Income', 'Subscription', 'Customer Support Access', 'Last Purchase Date', 'Transaction Frequency', 'Customer Churn'. Two datasets of customer churn can be downloaded among which one as train and the other as test dataset and a predictive model using 'Logistic Regression' or 'Random Forest Classification' or 'XGBoost Classifier' can be developed and evaluated.
Employees of a company are the assets of that company. A company invests on its employees by training them, giving them experience which the industry needs. It is a company ' s utmost need to retain its employees to the extend as possible so that the cost of filling the position and other expenses for a new recruit can be reduced. Thus, a company always keep in check of employee retention rate to control its profit and loss.
The employee retention dataset that can be downloaded from this site consists of employee details, and with the information such as salary, benefits, recognition, whether unhappy towards management, absence rate whether the employee is retained or not can be found out. The columns in the employee retention data include of ' EmployeeID ', 'Age', 'Gender', 'Salary', 'Benefits', 'Recognition', 'Unhappy towards management', 'Absence Rate', Employee Retention Rate'. Two different employee retention datasets can be downloaded of which one as train and other as test. A predictive model can be built to predict employee retention rate.
'ABC logistics' has to keep track of the order details to improve its logistics operations. It records the details of order and delivery. The data that is recorded help to find the key performance indicators of the business. The Key Performance Indicators (KPIs) are a measure of business performance and helps to identify whether the business objectives are met.
. The Order Logistics Management dataset that can be downloaded from this site consists of columns: 'Order ID', ' OrderDate ', 'Delivery Date', 'order_perfect', 'Weight', 'back_order', ' BackOrderID ', 'unplanned_shipment', and 'delivery_status'. Dashboards showing the statistics of delivery and logistics, the KPI's can be created using the dataset. The delivery status can be predicted with any classification model.
The finance statements show financial position of a company. There are mainly three financial statements which are Income statements, Cash Flow statements and Balance Sheet. The income statement shows the revenues and expenses of a company and its net profit. The cash flow statement shows cash inflow and outflow. The balance sheet shows the total assets and liabilities of a company.
Finance dataset of a fictitious company that is presented in this website consists of columns: 'Month', 'Year', 'Revenue(Budget)', 'Revenue(Actual)', 'Cost of Goods Sold(Budget)', 'Cost of Goods Sold(Actual)', 'Distribution Expenses(Budget)', 'Distribution Expenses(actual)', 'Marketing and Administration(Budget)', 'Marketing and Administration(Actual)', 'Research and Development(Budget)', 'Research and Development(actual)', 'Depreciation(Budget)', 'Depreciation(Actual)', 'Interest(Budget)', 'Interest(Actual)', 'Total Taxes(Budget)', 'Total Taxes(Actual)', 'Cash Balance(Budget)', 'Cash Balance(Actual)', 'Total Assets(Budget)', 'Total Assets(Actual)', Liabilities_and_Equity(Budget)', 'Liabilities_and_Equity(Actual)', 'Financial Position'.
Finance dashboard created from the finance dataset summarizes the income, cash flow and balance sheet statements and depicts the present financial position of the company. The finance dataset can also be used to predict the future financial position of the company.