User Story 1:
Title: Precondition – Collecting Customer Data
As a marketing analyst, I want to collect comprehensive customer data, including demographics, purchase history, and online behavior, in order to calculate the Customer Lifetime Value (CLV).
Precondition:
– The marketing team has access to various data sources such as CRM systems, transactional databases, and web analytics tools.
– The data sources are integrated and provide a unified view of customer information.
– Data privacy and security measures are in place to ensure compliance with regulations.
Post condition:
– Customer data is collected and stored in a centralized database.
– The data is cleansed and standardized to ensure accuracy and consistency.
– The data is continuously updated and maintained to reflect the latest customer information.
Potential business benefit:
– Accurate and comprehensive customer data enables the calculation of CLV, which helps in making informed marketing decisions.
– Understanding CLV allows the marketing team to allocate resources effectively and prioritize high-value customers.
Processes impacted:
– Data collection and integration processes are impacted as they need to be designed to capture and consolidate customer data from various sources.
– Data cleansing and standardization processes are impacted to ensure the accuracy and consistency of the collected data.
– Data maintenance processes are impacted to keep the customer data up to date.
User Story description:
As a marketing analyst, I want to collect comprehensive customer data from various sources, including demographics, purchase history, and online behavior. This data will be used to calculate the Customer Lifetime Value (CLV). By having access to accurate and up-to-date customer information, we can make informed marketing decisions and allocate resources effectively. The data collection process should be designed to capture and consolidate customer data from different sources, ensuring accuracy and consistency. The collected data should be stored in a centralized database and continuously updated to reflect the latest customer information.
Key Roles Involved:
– Marketing Analyst
– Data Engineer
– Data Scientist
Data Objects description:
– Customer Data: Includes demographics, purchase history, and online behavior information.
– CRM Systems: Customer Relationship Management systems that store customer information.
– Transactional Databases: Databases that store transactional data such as purchase history.
– Web Analytics Tools: Tools that track and analyze online user behavior.
Key metrics involved:
– Customer Lifetime Value (CLV): A metric that represents the predicted revenue a customer will generate over their entire relationship with the company.
– Customer Segmentation: The process of dividing customers into distinct groups based on their characteristics and behaviors.
– Customer Acquisition Cost (CAC): The cost associated with acquiring a new customer.
– Return on Investment (ROI): The measure of profitability resulting from marketing efforts.