Customer Analytics Context It is highly important to attract new customers and at the same time avoid contract terminations, also known as churns, in order to drive their revenue generating attempts in the telecommunications industry. There are various reasons that causes the customers to terminate their contracts. One good example is the better price offers and others includes: more interesting packages, bad service experiences or change of customers' personal situations. analytics provides valuable capabilities to predict customer churn and also define the underlying reasons that drive it. The churn metric is mostly shown as the percentage of customers that cancel a product or service within a given period (mostly months). Company XYZ, a telecommunications company, was successful at bringing in new customers with its multiple rate structures and promises of higher savings than the competition. But while Company XYZ had no trouble earning new business, it wasn't as successful at keeping it. The company was experiencing high levels of churn or attrition that threatened to diminish the value of its customer acquisition efforts. Data In this case, data was collected from a company's data warehouse. Each row represents a customer, each column contains customer's attributes described on the column Metadata. The raw data contains 7043 rows (customers) and 21 columns (features). The data set includes information about: Customers who left within the last month - the column is called Churn Services that each customer has signed up for - phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information how long they've been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers - gender, age range, and if they have partners and dependents Requirements 1. One of the most fundamental skill that a Business Analytics professional analyst should possess and being the first step of data analysis, is to be able to identify and clean some of the data quality issues using MS excel. Hence, the first requirement is to parse and identify all possible data quality issues in the given dataset. a. Identify the exact type of data quality issue b. List down all the column/ field names with issues found. c. Remove the observation/s (row/s) from the dataset, 2. An analyst should be able to present the data visually to make it more comprehensible and visually impactful to the decision-makes in the telco company. Using the Data visualization techniques, you have learned in this module, create and apply all the necessary visualization using excel charts and present them into a MS PowerPoint deck. a. Use some of the descriptive statistics if deemed fit to the problem, b. Use the most relevant charts/ graphs based on the problem, using excel, c. Aesthetics of the Data Visualization should be taken into consideration. d. Data Storytelling techniques should be taken into consideration, 3. Lastly, to make the visualization much more sensible and impactful to the decision-makers, you should include findings and insights from the data. What did you observe? What and where it could possibly go wrong? What can you suggest to the decision-makers to possibly curb the main problem? a. Identify trends and patterns from the data. b. Insights, findings, and/or recommendation can be done in bullets or can be in a compact narrative