Customer 360 - Entity resolution and centralized customer view & Record linkage unification of customer master
At customer360.dev, our mission is to help organizations centralize all customer data and make it easily accessible to business and data analysts. We believe that by providing a comprehensive view of customer interactions, organizations can make informed decisions that lead to better customer experiences and increased revenue. Our goal is to empower businesses with the tools and resources they need to create a customer-centric culture and drive growth through data-driven insights.
Customer360.dev is a website that focuses on centralizing all customer data in an organization and making it accessible to business and data analysts. This cheatsheet is designed to provide a comprehensive reference guide for anyone who is getting started with the concepts, topics, and categories covered on the website. The cheatsheet is divided into several sections, each of which covers a specific topic related to customer data management.
Section 1: Customer Data Management
Customer data management is the process of collecting, storing, and analyzing customer data to gain insights into customer behavior and preferences. The following are some of the key concepts related to customer data management:
1.1 Customer Data
Customer data refers to any information that is collected about a customer, including their name, address, email address, phone number, purchase history, and other demographic information. This data can be collected through various channels, including online forms, surveys, and social media.
1.2 Data Collection
Data collection is the process of gathering customer data from various sources, including online forms, surveys, and social media. It is important to ensure that the data collected is accurate, complete, and up-to-date.
1.3 Data Storage
Data storage refers to the process of storing customer data in a secure and organized manner. This can be done using a variety of tools, including databases, data warehouses, and cloud storage solutions.
1.4 Data Analysis
Data analysis is the process of analyzing customer data to gain insights into customer behavior and preferences. This can be done using various techniques, including data mining, machine learning, and statistical analysis.
Section 2: Customer Data Platforms
Customer data platforms (CDPs) are software solutions that are designed to centralize customer data from various sources and make it accessible to business and data analysts. The following are some of the key concepts related to customer data platforms:
2.1 Data Integration
Data integration is the process of integrating customer data from various sources, including CRM systems, marketing automation platforms, and social media. This can be done using various techniques, including API integrations and data connectors.
2.2 Data Cleansing
Data cleansing is the process of cleaning and standardizing customer data to ensure that it is accurate, complete, and up-to-date. This can be done using various tools, including data quality software and data validation rules.
2.3 Data Enrichment
Data enrichment is the process of adding additional data to customer records to provide more context and insights. This can be done using various techniques, including data appending and data enrichment services.
2.4 Data Segmentation
Data segmentation is the process of dividing customer data into smaller groups based on specific criteria, such as demographics, behavior, and preferences. This can be done using various techniques, including clustering and decision trees.
Section 3: Customer Analytics
Customer analytics is the process of analyzing customer data to gain insights into customer behavior and preferences. The following are some of the key concepts related to customer analytics:
3.1 Customer Segmentation
Customer segmentation is the process of dividing customers into smaller groups based on specific criteria, such as demographics, behavior, and preferences. This can be done using various techniques, including clustering and decision trees.
3.2 Customer Lifetime Value
Customer lifetime value (CLV) is the estimated value of a customer over their lifetime. This can be calculated using various metrics, including customer acquisition cost, retention rate, and average order value.
3.3 Customer Churn
Customer churn is the rate at which customers stop doing business with a company. This can be calculated using various metrics, including customer retention rate, customer satisfaction, and customer feedback.
3.4 Customer Acquisition
Customer acquisition is the process of acquiring new customers through various channels, including online advertising, social media, and email marketing. This can be done using various techniques, including lead generation and customer acquisition campaigns.
Section 4: Customer Data Privacy
Customer data privacy is the process of protecting customer data from unauthorized access, use, or disclosure. The following are some of the key concepts related to customer data privacy:
4.1 Data Protection
Data protection is the process of protecting customer data from unauthorized access, use, or disclosure. This can be done using various techniques, including encryption, access controls, and data masking.
4.2 Data Privacy Regulations
Data privacy regulations are laws and regulations that govern the collection, use, and disclosure of customer data. These regulations vary by country and region, and include laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
4.3 Data Breaches
Data breaches are incidents in which customer data is accessed, stolen, or disclosed without authorization. It is important to have a data breach response plan in place to minimize the impact of a breach.
4.4 Data Privacy Policies
Customer data management is a complex and ever-evolving field that requires a deep understanding of customer behavior, data analytics, and data privacy. This cheatsheet provides a comprehensive reference guide for anyone who is getting started with customer data management, customer data platforms, customer analytics, and customer data privacy. By following the best practices outlined in this cheatsheet, businesses can gain valuable insights into customer behavior and preferences, while also protecting customer data from unauthorized access, use, or disclosure.
Common Terms, Definitions and Jargon1. Customer Data: Information about customers that is collected and stored by an organization.
2. Customer360: A centralized database that contains all customer data in an organization.
3. CRM: Customer Relationship Management software that helps businesses manage customer interactions and data.
4. Data Analytics: The process of examining data to draw conclusions and insights.
5. Data Integration: The process of combining data from different sources into a single, unified view.
6. Data Warehouse: A large, centralized repository of data that is used for reporting and analysis.
7. ETL: Extract, Transform, Load - the process of moving data from one system to another.
8. Customer Segmentation: The process of dividing customers into groups based on shared characteristics.
9. Customer Lifetime Value: The predicted value of a customer over the course of their relationship with a business.
10. Customer Acquisition Cost: The cost of acquiring a new customer.
11. Churn Rate: The rate at which customers stop doing business with a company.
12. Net Promoter Score: A metric used to measure customer loyalty and satisfaction.
13. Customer Satisfaction Score: A metric used to measure customer satisfaction with a product or service.
14. Customer Journey: The path a customer takes from initial contact with a business to becoming a loyal customer.
15. Touchpoints: The various interactions a customer has with a business throughout their customer journey.
16. Customer Experience: The overall experience a customer has with a business.
17. Data Governance: The process of managing the availability, usability, integrity, and security of data.
18. Data Quality: The accuracy, completeness, and consistency of data.
19. Data Privacy: The protection of personal information from unauthorized access or use.
20. Data Security: The protection of data from unauthorized access, use, disclosure, disruption, modification, or destruction.
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