Financial transactions are not typically considered a source of customer data, as they are more related to business operations rather than customer insights.

Data analytics for customer insights aims to achieve all of these objectives by analyzing customer data to gain insights into their behavior, preferences, and needs.

Clustering is a data analysis technique that groups similar data points together, helping to identify patterns and trends in customer data.

Data visualization plays a crucial role in customer insights by making data more accessible, identifying outliers, and communicating insights effectively to stakeholders.

Abundance of customer data is not typically considered a challenge in data analytics, as it provides more opportunities for insights and analysis.

Data interpretation involves translating raw data into meaningful information and insights that can be used for decision-making.

Customer churn prediction, product recommendation, and customer lifetime value analysis are all common types of predictive analytics used in customer insights.

Customer segmentation aims to achieve all of these goals by dividing customers into groups based on shared characteristics, enabling targeted marketing, improved customer service, and overall enhanced customer experiences.

Customer lifetime value (CLTV) is a metric used to measure the total value of a customer over their lifetime, rather than their satisfaction level.

Data warehousing involves collecting, storing, and managing customer data in a central repository for easy access and analysis.

Prescriptive analytics is not typically considered a common type of data analytics used in customer insights, as it focuses on providing specific recommendations for actions, which is beyond the scope of customer insights.

Data-driven decision-making involves using data to inform and support decisions, rather than relying solely on intuition or experience.

Website analytics are not typically considered a type of customer feedback, as they provide data on website usage and behavior rather than direct feedback from customers.

Customer discovery involves identifying and understanding customer needs and wants through various research methods.

Decision trees are not typically considered a common type of data analysis used in customer insights, as they are more commonly used in machine learning and predictive modeling.