Certification in Business Data Analytics (CBDA)

Customer retention rate is a measure of how many customers continue to use a product or service over a given period of time. It is an important indicator of customer loyalty, satisfaction, and value. Customer retention rate can help the bank evaluate how the website is being used by comparing the number of customers who visited the website before and after the launch of the new features. A high customer retention rate would suggest that the new features are attractive, useful, and engaging for the customers, while a low customer retention rate would indicate that the new features are not meeting the customers' needs or expectations. Customer retention rate can also help the bank identify the segments of customers who are more or less likely to use the website, and tailor their marketing and communication strategies accordingly.

Unsupervised learning is a category of data analysis techniques that does not require labeled data or predefined outcomes. Unsupervised learning aims to discover patterns, structures, or relationships in the data without any guidance or supervision. Clustering is a common example of unsupervised learning, where the data is grouped into clusters based on some similarity or distance measure. Clustering can help reveal customer segments, market trends, or product preferences, among other insights.

Prescriptive analytics is the type of analytics that provides recommendations or suggestions for optimal actions or decisions based on data analysis. Prescriptive analytics uses techniques such as optimization, simulation, and decision analysis to generate and evaluate various scenarios and outcomes. Prescriptive analytics can help financial institutions to use credit scoring to determine the best loan offers, interest rates, and repayment terms for their customers, as well as to manage risk and compliance. Prescriptive analytics is the most advanced and complex type of analytics, as it requires a high level of data quality, integration, and modeling, as well as human judgment and domain expertise.

A well-defined objective is one that is specific, measurable, achievable, relevant, and time-bound (SMART) 1. The goal of increasing sales of a mature product by reducing its price by 20% which would result in increased revenues of 2% over a 6-month period meets all these criteria, as it clearly states what the desired outcome is, how it will be measured, whether it is realistic and attainable, how it aligns with the organization's strategy, and when it will be achieved

According to the Guide to Business Data Analytics, data profiling is a technique that analyzes the structure, content, and quality of data sources. Data profiling can help identify data issues such as missing values, outliers, inconsistencies, duplicates, and errors. Data profiling can also provide information about the data types, formats, ranges, distributions, and relationships of data elements. Data profiling can help prepare data for data conversion, data cleansing, and data migration by providing a high level understanding of the current state of data and the potential challenges and risks involved in transforming and integrating data from different sources.

A line chart is the type of chart that the manager should use to effectively show the change in processing rate over time, because it is a technique that displays data as a series of points connected by straight lines.
A line chart can help the manager visualize the trends and patterns in the ticket volume and processing rate over the past year, and highlight the widening gap between them. A line chart can also show the seasonal variations and fluctuations in the data, and compare the performance of different categories or groups. Options A, B, and D are not suitable for showing the change in processing rate over time, because they are techniques that display data as proportions (A), comparisons (B), or accumulations (D) of different categories or groups at a single point in time or over a fixed period.

Database normalization is the process of organizing the data in a database to reduce redundancy and improve integrity, consistency, and performance 1. Database normalization rules are based on the concept of normal forms, which are levels of database design that meet certain criteria 2. One of the most common normal forms is the third normal form (3NF), which states that a table should not have any transitive dependencies, meaning that a non-key attribute should not depend on another non-key attribute 3. In the CustomerOrder entity, the set of attributes that will need to be normalized to avoid redundancies are ProductName and ProductQuantity, as they are non-key attributes that depend on another non-key attribute, Orderld. This means that the same product information may be repeated for different orders, which could lead to data inconsistency, duplication, or update anomalies. To normalize this set of attributes, a separate table should be created for the OrderDetails entity, which would have Orderld, ProductName, and ProductQuantity as its attributes, and Orderld and ProductName as its composite primary key.

The other sets of attributes do not need to be normalized to avoid redundancies, as they do not violate the 3NF. CustomerPhone and ProductName are non-key attributes that depend on the primary key, Customerld and Orderld respectively, which is allowed by the 3NF. Orderld and ProductName are part of the composite primary key of the OrderDetails entity, which is also allowed by the 3NF. Customerld and OrderDate are both primary keys of the Customer and Order entities respectively, which are also allowed by the 3NF.

Reliability is the degree to which a data collection method produces consistent results under the same conditions 1. In this case, the reliability of the study data might be compromised by the different interviewers (senior managers vs. external market research company), who might have different biases, expectations, or rapport with the employees. This could affect how the employees respond to the same set of questions, and thus introduce variability in the data. Validity, timeliness, and precision are not directly affected by the choice of interviewers, as they depend more on the quality, relevance, and accuracy of the questions and the data analysis.

Prescriptive analytics is the type of analytics that the retailer is using to maintain their pricing structure, because it is a technique that uses data and models to recommend the best course of action for a given situation. Prescriptive analytics can help the retailer optimize their prices based on the data collected from the competitors, the market conditions, and the customer preferences, and thus achieve their strategic goal of becoming the market leader.