Customer Data Platform

A customer data platform creates a unified customer profile with information gathered from multiple sources. CDPs collect data on anonymous visitors to your website and analyze lifetime customer behavior and customer journeys, tracking both online and offline customer data. They’re designed to handle multiple data points from a large number of sources.

Skypoint’s CDP is built with integrated privacy compliance and sensitive data management. We use a proprietary identity resolution model to stitch your first-party customer data together to create a comprehensive customer profile and journey. 

We allow customers to configure role-based access to PII and other data that is labeled sensitive by our customers to ensure usability while protecting consumer privacy 

Skypoint’s CDP sits at the intersection of composable and unbundled, to provide the best of both worlds.

It’s an architectural paradigm — the platform is unbundled, meaning it has a decoupled Data warehouse / Lakehouse and it’s “composable” with other tools like Dbt, fivetran etc.

With a traditional CDP, the data warehouse is a separate entity from the CDP itself. Batch data moves through the ETL (Extract Transform Load) process to the data warehouse, while event data goes into the CDP.

In this situation, the CDP is completely separate from the data warehouse. This means that analyzing data outside of the CDP environment is fractured and creates yet another data silo.

With an unbundled CDP, the data warehouse or data lake serves as the center of the system and provides an easy-to-use interface to do data transformations under the covers for things like audience segmentation, metrics, and exporting lists to campaign tools like Mailchimp or Klaviyo.

From there, your organization doesn’t need to do extra gymnastics to properly analyze your data and implement marketing changes based on the insights collected.

By unbundling the CDP, you maintain the ability to analyze your data where and when you need it while also providing Marketing team-friendly capabilities you know and love from a traditional CDP. You also get more flexibility and control over your data, which is invaluable as your organization grows and develops.

In simple terms, the real value here is that you’re gaining a strong data foundation so you’re set up to solve a vast array of business needs. Read our blog post

Skypoint Cloud normalizes and standardizes customer data leveraging the Common Data Model (CDM). CDM is an open standard that creates an extensible collection of data schemas (entities, attributes, relationships) that translate into business concepts and activities. 

The Skypoint platform maps your customer data to CDM to power our proprietary identity resolution model which stitches data together to create a comprehensive profile of customer activities, interactions, transactions, and behaviors. 

With a growing list of native connectors, you can easily and quickly connect your data from existing systems directly into Skypoint’s platform. Our integrations list is always expanding.

Customer data platform (CDP) and master data management (MDM) are both data management technologies, but they serve different purposes. MDM is focused on creating a single source of truth for critical business data, while CDP is focused on creating a unified view of customer data to support customer interactions and experiences. MDM is typically used for back-office functions, while CDP is used for customer-facing activities.

Here’s a brief summary of what makes CDP different from MDM:

Skypoint Cloud uses a series of data quality checks to identify issues and ensure only the highest quality and most complete data is included in our identity resolution model—improving the overall accuracy of our matching model. 

We leverage pattern-based validations and standard regular expressions for normalizing data. Our platform also provides insights to help you continuously improve data quality and know which actions to take. Together, we are always increasing the completeness and accuracy of your data source systems.

customer 360 consolidates and organizes the information that a brand knows about its customers into comprehensive profiles. The information that these profiles contain varies from one business to another, but typically includes:

• Customers’ contact information and preferences
• Demographics shared by the customer (i.e. gender, age)
• Digital and offline engagement history (i.e. email, customer care)
• A customer’s transaction history—or treatment history in healthcare
• Relevant healthcare data details on a patient’s medical care and status (i.e. prescriptions, allergies)

Many customer 360s include not just raw data but a view of each customer that is enriched with relevant attributes and predictions. For example, a customer 360 view for a consumer brand might include derived fields like “number of last transactions in the last 12 months” and “likelihood to purchase in the next 3 months.” 

A customer 360 is a foundational tool for businesses that aims to personalize and improve the customer experience. Brands typically use their customer 360 to fuel use cases like:

Understanding of the overall customer base to design better products and experiences (What percentage of my customers have seen a primary care physician at least once over the past month?)

Measurement of customer health (Has my active customer base been trending up or down over time?)

Segmentation to identify the optimal channel, message, or promotion for groups of customers (Who are my customers who make frequent, low dollar-value purchases?)

Personalization of one-to-one customer interactions (What’s the clinical history of a particular patient engaging with a care rep on a digital health app?)