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This comprehensive glossary demystifies the key terms and trends in the customer data platform (CDP) scene in 2024. It aims to be a valuable resource for marketers, data analysts, and business leaders alike to surround themselves with the most talked about language in the field.
A
A/B Testing: A marketing practice to compare two versions of a message or asset to determine which variant performs best.
AdTech: Any technology that is aimed at supporting advertising activities; especially systems that work with digital media.
AI-Driven Personalization: The use of artificial intelligence to tailor customer interactions and experiences based on data analysis and prediction.
Analytics CDP: A type of customer data platform with the primary features of assembling, sharing and analyzing unified customer profiles, as well as predictive analytics.
Anonymization: The process of removing or encrypting personally identifiable information from customer data to ensure privacy while retaining the data's utility for analysis. :
API Integration: The process of connecting different software systems so they can share data and functionalities through Application Programming Interfaces.
Artificial Neural Network: A machine learning model that analyzes vast amounts of customer data to identify patterns and predict behaviors, enhancing personalization and decision-making.
B
Batch Processing: The execution of data processing tasks on a large volume of customer data at once, typically scheduled at regular intervals, to update, analyze, or transform data efficiently.
Behavioral Data: Information about the actions customers take, such as clicks, purchases, and interactions with a website or app.
Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
C
Channel Preference: The tendency of a customer to favor certain communication or interaction channels over others, which is tracked and utilized to personalize and optimize marketing efforts.
Churn Prediction: The use of data analytics to predict which customers are likely to stop using a service or product.
Collaborative Filtering: A recommendation method that predicts a customer's preferences by analyzing patterns and behaviors of similar customers within the platform's dataset.
Cookie: A small data file stored on a user's device that tracks and stores user behavior and preferences to improve personalized marketing and customer experience
Composable CDP: A flexible customer data platform that allows businesses to integrate and customize various data and marketing components to meet their specific needs and workflows.
Contact Fatigue: The diminishing responsiveness and engagement of customers due to excessive or overly frequent communications and interactions from a brand
Contextual Advertising: The practice of delivering targeted ads to customers based on the content they are currently engaging with, leveraging customer data to ensure relevance and effectiveness.
Control Group: A segment in a test that continues to receive unchanged marketing messages, allowing comparison with a segment receiving a new campaign, channel outreach, or other changes.
Cross-Channel Messaging: A marketing tactic that aims to deliver marketing messages to a customer using multiple channels (email, in-app, social media, etc.) to increase reach and engagement across a variety of communication touch-points. Learn more about the topic here.
Customer Acquisition: The process of identifying, targeting, and converting potential customers into actual customers through data-driven marketing strategies and personalized engagement.
Customer Acquisition Cost: The total expense incurred by a company to acquire a new customer, calculated by integrating data on marketing and sales expenditures and divided by the number of new customers gained.
Customer Centricity: A business approach that prioritizes creating a positive customer experience and value at every stage of the customer journey, leveraging comprehensive and unified customer data to personalize interactions and meet customer needs effectively.
Customer Data Platform: Software system that centralizes and unifies customer data from various sources to create a comprehensive, single customer view for more personalized and effective marketing.
360° Customer View: A comprehensive view of a customer that includes all interactions and data points from various channels and touchpoints.
Customer Journey: The entire process that an individual goes through from the moment they become aware of a brand to the conversation into a purchase, and a long-term customer.
Customer Journey Analysis: The process of tracking and analyzing the various touchpoints and interactions a customer has with a brand to understand their behavior, preferences, and decision-making processes.
Customer Journey Mapping: The process of creating a visual representation of the customer’s experience with a company across all touchpoints and channels.
Customer Journey Orchestration: The process of coordinating and optimizing customer interactions across various touchpoints and channels to deliver a seamless, personalized experience throughout the entire customer lifecycle. Learn more about the topic here.
Customer Segmentation: The practice of dividing a customer base into groups of individuals that share similar characteristics, such as age, gender, interests, and buying habits. Learn more about the topic here.
Customer Retention: The process of keeping existing customers engaged and satisfied to encourage repeat business and long-term loyalty to a brand or product.
D
Data Activation: The process of utilizing collected customer data to create personalized marketing campaigns, optimize customer experiences, and drive business actions across various channels. Learn more about the topic here.
Data Cleansing: The process of detecting and correcting or removing inaccuracies, inconsistencies, and errors in customer data to ensure high-quality and reliable information for analysis and decision-making.
Data Clean Room: A secure environment where multiple parties can aggregate and analyze their data collaboratively without revealing individual-level data to each other, ensuring privacy and compliance.
Data Enrichment: The process of enhancing existing customer data by integrating additional information from external sources to provide a more comprehensive and detailed view of customers.
Data Governance: The management of the availability, usability, integrity, and security of the data employed in an organization.
Data Integration: The process of combining data from different sources to provide a unified view.
Data Management Platform (DMP): A technology that collects, organizes, and analyzes large sets of audience data from various sources to create detailed customer profiles and enable targeted advertising and marketing efforts.
Data Privacy: The handling and protection of personal information and data in compliance with legal and regulatory requirements.
Data Pseudonymization: The process of transforming personal data into pseudonyms, or fake identifiers, to protect individual privacy while still allowing data to be analyzed and utilized for marketing and analytics purposes.
Data Standardization: The process of converting different data formats into a consistent, uniform format to ensure compatibility, accuracy, and ease of analysis across the platform.
Data Warehouse: A centralized repository that stores large volumes of structured customer data from various sources, designed for query and analysis to support business intelligence activities.
E
Event Data: Data generated from user actions or events, such as logins, page views, clicks, and purchases.
ETL (Extract, Transform, Load): A process in data warehousing responsible for pulling data out of the source systems and placing it into a data warehouse.
Experimentation: The systematic testing of different marketing strategies, messages, and customer experiences using controlled experiments to determine their effectiveness and optimize future campaigns based on data-driven insights.
F
First-Party Data: Information collected directly by a company from its customers and owned by that company.
First-Party Cookie: Data stored on a user's device by the website they are currently visiting, used to enhance user experience and retain information like login status and preferences. Learn more about the topic here.
First Touch Attribution: An attribution model that assigns 100% of the credit for a customer's conversion to the first interaction or touchpoint they had with the brand.
Fractional Attribution: An attribution model that assigns partial credit to multiple marketing touchpoints based on their relative contribution to a customer's conversion journey.
G
GDPR (General Data Protection Regulation): A regulation in EU law on data protection and privacy in the European Union and the European Economic Area.
Golden Record: A single, comprehensive, and accurate view of a customer created by merging and reconciling data from multiple sources to ensure consistency and reliability in customer insights and interactions.
H
Hashing: The process of converting customer data into a fixed-length string of characters, which serves as a unique identifier, to enhance data security and privacy while still allowing for data matching and analysis.
Historical Processing: The analysis and utilization of past customer data to identify trends, patterns, and insights that can inform future marketing strategies and customer interactions
Hyper-Personalization: The use of real-time data and AI to deliver more 1:1 personalization, relevant content, product, and service information to each user.
I
Identity Management: The process of accurately identifying, matching, and merging data from various sources to create a unified and consistent profile for each customer. Learn more about the topic here.
Identity Resolution: The process of matching different identifiers across devices and touchpoints to a single individual.
Identity Stitching: The process of combining data from multiple sources and touchpoints to create a unified and comprehensive profile for each individual customer.
J
Journey Orchestration: The management and coordination of customer interactions across various channels and touchpoints. Understand how it works here.
K
Known Individual: An individual connected to at least one personal identifier that can be linked to a specific person in the real world.
L
Last Touch Attribution: An attribution model that assigns 100% of the credit for a customer's conversion to the final interaction or touchpoint they had with the brand before converting.
Life Stage: The means to define the current relationship of a customer to a business, described as a sequence of states (prospect/lead, new customer, existing customer, at-risk customer, churned customer).
Lifetime Value: A metric that estimates the total revenue a business can expect from a customer throughout their entire relationship with the company.
Look-Alike Modeling: A method that identifies and targets potential new customers by finding individuals who exhibit similar characteristics and behaviors to an existing group of high-value customers.
M
MarTech: (Marketing Technologies): The software and tools that assist in achieving marketing goals and objectives through the use of technology.
Marketing Attribution: The process of identifying and assigning credit to the various marketing channels and touchpoints that contribute to a customer's conversion or desired action.
Marketing Automation: The use of software to automate and streamline marketing tasks and workflows, such as email campaigns, social media posts, and targeted advertisements, based on customer data and behavior to improve efficiency and personalization.
Marketing Cloud: A suite of integrated digital marketing tools and services that leverage customer data to manage, personalize, and optimize marketing campaigns across various channels, such as email, social media, and mobile apps.
Machine Learning: A type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed.
Master Data Management (MRM): The comprehensive method of managing and governing an organization's critical data to ensure a single, accurate, and consistent source of truth across the enterprise, enhancing data quality and integrity for better decision-making and customer insights.
Media Mix Modeling: The analytical technique used to evaluate the impact of different marketing channels and strategies on sales and conversions, by analyzing historical data to optimize future media spend and marketing efforts.
Metadata: The data that provides information about other data, such as details about data sources, structure, and attributes, which helps in organizing, managing, and making sense of the customer data stored within the platform.
Mobile Moment: A mobile moment is an occasion in which a user takes out their mobile device to get what they want, right when they want it, and it’s up to mobile marketers to find ways to build experiences into these moments.
Multi-Channel Marketing: Marketing strategy that uses multiple channels to reach customers, such as email, social media, and websites.
Multi-Step Campaign: A marketing strategy that involves a series of coordinated, sequential interactions and touchpoints with customers, designed to guide them through a predefined journey towards a specific goal or conversion.
Multi-Touch Attribution: The method of evaluating the contribution of each customer interaction across various touchpoints in the customer journey, to determine their impact on conversion and optimize marketing efforts accordingly.
Multivariate Testing: A marketing technique to simultaneously compare two or more versions of a message to understand which variant performs better than the other.
N
Next Best Action: A strategy that uses customer data and predictive analytics to determine and recommend the most effective, personalized action or interaction to take with a customer at any given moment. Learn more about the topic here.
Normalization: The process of organizing data to reduce redundancy and improve data integrity.
NoSQL: A type of database that allows for the storage and retrieval of unstructured and semi-structured data, enabling flexible, scalable, and efficient handling of diverse customer data types beyond traditional relational databases.
O
Omnichannel Marketing: An approach to sales and marketing that provides customers with a seamless experience across various channels, whether online or offline.
Opt-In Prompt: A message that requests permission from users to activate channels like push or SMS.
Out of the Box Data Model: A pre-built, standardized data schema provided by the platform that can be readily used to organize and analyze customer data without the need for extensive customization.
P
Personalization: Tailoring content, offers, and experiences to individual users based on their data and preferences.
Personal Identifier: Any piece of information that can uniquely identify an individual customer, such as a name, email address, phone number, or customer ID.
Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Learn more about the topic here.
Privacy Native: Systems and technologies designed with built-in features that prioritize and ensure the protection and confidentiality of customer data throughout all processes and interactions.
Push Notification: The automated messages sent directly to a customer's mobile device or web browser, based on customer data and behavior, to engage and inform them about updates, offers, or other relevant information.
Pseudonymization: The process of replacing private identifiers in customer data with fictitious names or codes to protect privacy while still allowing data to be linked and analyzed.
Q
Quality Assurance (QA): Ensuring the quality and reliability of data through systematic processes and procedures.
R
Real-Time Data: Information that is delivered immediately after collection without delay.
Real Time Interaction: The capability to engage with customers immediately and dynamically based on their current actions and context, using up-to-date data to deliver personalized and relevant responses or offers.
RestAPI: A set of web service protocols that allow different software systems to communicate and interact with the platform, enabling the integration, retrieval, and manipulation of customer data using standard HTTP methods.
Retention Marketing: Strategies aimed at keeping existing customers engaged and reducing churn.
Reverse ETL: The process of extracting data from a data warehouse, transforming it as needed, and loading it into operational systems, such as CRM or marketing tools, to enable real-time use and actionability. Understand the difference between CDPs and Reverse ETL here.
Retargeting: A marketing message with an aim to remind the user about the brand or product when the user does not engage or take a specific action.
Retargeting Campaign: A marketing program aimed at convincing a customer to purchase a product they had apparently considered buying but did not purchase
RFM: Recency, Frequency, and Monetary value, a method used to analyze and segment customers based on how recently they made a purchase, how often they make purchases, and how much they spend, to optimize marketing strategies and customer engagement.
Rich Push: A push notification that is able to feature an image, GIF or video as well as text only.
S
Second-Party Data: Data that an organization collects from another company directly.
Segmentation: A marketing tactic of breaking users into groups or categories to provide each with more personalized content. Learn more about the topic here.
Send Time Optimisation: The strategy of analyzing customer data to determine the most effective time to send marketing messages, ensuring higher engagement and response rates.
SFTP: Secure File Transfer Protocol, a method used to securely transfer files containing customer data between systems over a network, ensuring data privacy and integrity.
Single Customer View (SCV): A unified representation of all data known about a customer, accessible and actionable in real-time.
Single Sign On: An authentication process that allows users to access multiple applications and systems with one set of login credentials, streamlining user experience and improving security management.
SQL: Structured Query Language, a standardized programming language used to manage, query, and manipulate relational databases, enabling the efficient retrieval and analysis of customer data.
Structured Data: Data that is organized in a fixed format or schema, making it easily searchable.
Semi-Structured Data: Data that does not conform to a rigid structure but contains tags or markers to separate elements, such as JSON, XML, or log files, allowing for more flexible data storage and analysis.
Sunsetting: A marketing decision to churn marketing messages to users that are completely unengaged with the brand.
T
Tag Management: The system that allows businesses to manage and deploy tracking tags or snippets of code on their websites or apps efficiently, enabling accurate data collection and integration without extensive manual coding.
Third-Party Data: Information collected by an entity that does not have a direct relationship with the user and sold to other organizations.
Third-Party Cookie: A piece of data stored on a user's device by a website other than the one they are currently visiting, typically used for tracking and advertising purposes across different sites. Learn more about the topic here.
Triggered Message: An automated outreach sent in response to a predetermined user event.
Touchpoint: Any point of interaction between a customer and a company, whether physical or digital.
U
Unstructured Data: Information that does not have a predefined data model, such as emails, social media posts, and videos.
User Profile: A digital collection of demographic and behavioral data on a given individual.
Unified Customer Profile: A comprehensive, single view of a customer created by aggregating and reconciling data from multiple sources, providing a complete and consistent understanding of the customer for personalized marketing and engagement.
V
Value Proposition: The unique value a company promises to deliver to its customers.
Voice of the Customer (VoC): The process of collecting, analyzing, and acting on customer feedback to improve products and services.
W
Web Analytics: The measurement, collection, analysis, and reporting of web data to understand and optimize web usage.
Webhooks: Notifications that enable user behaviors to trigger non-app actions like sending an SMS, or posting social media messages.
Y
Yield Management: The process of making frequent adjustments in the price of a product in response to certain market factors, such as demand or competition.
Z
Zero-Party Data: Data that a customer intentionally and proactively shares with a company, such as preferences and purchase intentions.
