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Customer Data Platforms 101 The Complete Guide to CDP

Customer Data Platforms 101 The Complete Guide to CDP

A CDP is an application that collects and stores customer data from across the organization and multiple channels into a single database for data unification and identity resolution, analyzation, and activation.

Essentially, a Customer Data Platform (CDP) is a database often used by marketers and customer experience teams to ingest customer data from relevant platforms, channels, and devices, including marketing, product, sales, and support systems to provide a single, unified view of your customers—a “golden record” or “unified customer profile.” Based on segmentation within the CDP, companies are able to activate hyper-personalized and data-driven consumer engagement across various marketing channels.

A visual representation of how a CDP is used to collect, unify, analyze and activate customer data.

What is a CDP’s Function?

CDPs manage first-party data, second-party data (from partners), as well as third-party data (e.g. demographic or location data that enriches first-party data). The CDP unifies customer profile data to create a single version of truth—the golden record—on any particular customer or account. It then makes this data available to other systems to create personalized, relevant customer experiences.

CDP functionality typically includes:

Data Ingestion: Ingests customer data from marketing, sales, and product systems, including CRM, web and mobile app logs, email marketing, ecommerce, IoT, and more
ID Unification: Unifies customer data under one unique identifier, including products purchased, web pages viewed, ads clicked, and resolves duplicate profiles by combining them into a single profile
Segmentation: Analyzes customer data using a rule-based model, or leverages artificial intelligence (AI) and machine learning (ML) to find key segments based on common attributes
Customer Data Analytics: Uses machine learning recommendations, affinity, and predictive scoring to analyze customer data and provide a better view of the customer journey across channels, uncovering trends and correlations to help improve the experience
Reporting: Enables customized dashboards that provide key insights on customer profiles, targets and segments, journey maps, and more
Activation: Provides real-time profile data to marketing systems to deliver personalized, targeted experiences across all channels and devices

What is a CDP’s Data Collection Capability?

A CDP connects to a wide range of systems and data sources across an organization using built-in connectors, SDKs, webhooks, and APIs. It collects all types of data, including profile data and real-time interaction data (behavioral, demographics, transactional), campaign data, product data, customer support data, mobile and device data, IoT data, and more

This customer data comes in many formats—structured, unstructured, semi-structured—and a CDP must integrate these sources to build a single customer profile. By using schemaless ingestion, the CDP can collect raw, event-level data without needing to create predefined tables. This speeds up the collection process as well as conforms to changes made at the data source.

Customer data is collected in several ways. It is collected in batches for a period of time and then loaded into the system in a single batch. Batch processing is automated through workflows as a part of a data pipeline. You can also set up incremental batch processing to only bring in the last set of data since the previous load.

Data can also be streamed into the CDP as it’s recorded in web logs and mobile applications, giving marketers real-time access to changes in customer profiles.

What Is a CDP’s Data Unification Methodology?

How does a CDP unify data? Once in a CDP, customer data must be unified into a single customer profile using a process known as customer identity resolution or data unification. Customer identity resolution includes sophisticated algorithms to stitch identifiers from multiple systems. Identity stitching automates identity graph creation and continuously unifies data into profiles as your customers continue to engage.

A CDP unifies customer identifiers and data sources to create a single customer profile.

During the unification process, customer data is validated, cleaned, and deduped to create a single customer profile. The identity resolution process is done in two ways:

  • Deterministically: Unique IDs for customer records in each system are matched using common information, such as an email address or name. This high confidence approach works best when first-party data is readily available.
  • Probabilistically: This approach analyzes a variety of customer data points to estimate the statistical likelihood that two identities are the same customer. While statistical connections aren’t as definitive as authenticated IDs, they can be extremely helpful when first-party data is limited.

Profiles are then enriched with second- and third-party data sources that fill in missing attributes and update other attributes with more recent information.

How Does a Customer Data Platform Work?

Step 1 ) Data Collection & Integration
The first step is getting first-party customer data into the CDP, including basic profile data, engagement data, and transaction data. First-party data comes from systems and channels such as web and mobile, email and marketing automation, CRM, surveys, ecommerce systems, and more. The data comes in many formats, structured and unstructured.

Most CDPs will offer pre-defined integrations to common data sources and systems from marketing, sales, and support. The data is ingested in real-time or batches, continually feeding the CDP with current customer data.

Step 2 ) Customer Data Cleansing/Transformation

Collecting data is the first part. Once ingested, some CDPs have the capability to clean the data, ensuring it’s consistent and correct. Data cleansing includes resolving identities, deduplicating profiles, discarding inaccurate data (including fake profiles), and resolving discrepancies. Some CDPs also include extract, transform, and load (ETL) capabilities that can be used to build data pipelines for these activities.

Customer Profile Enrichment
Once the profile is complete, a CDP can enrich the profile by integrating second- and third-party data sources. This type of data comes from organizations like Bombora and Dun & Bradstreet (business data), Acxiom and Neilsen (demographics data), weather, interest data, and other sources. Enriching the profile with this type of data helps fill in missing or inaccurate attributes and remove duplicate information. It also helps with building a richer set of seed segments for advertising platforms—enhancing prospecting activities with higher match rates and market reach.

Step 3 ) Customer Segmentation
A CDP provides tools for marketers to define audience segments based on attributes and behaviors. You use segments to improve targeting and personalization. Segments are rules-based, or they are built using machine learning and AI. Predictive scoring is one example of a machine learning algorithm. With predictive scores, marketers can enrich their profiles with data they wouldn’t be able to tabulate on their own and create more robust target audiences.

Using the segmentation capabilities of a CDP, you can do things like:

  • Identify advocates
  • Predict customer churn
  • Identify potential upsell and cross-sell opportunities
  • Identify top-performing customers
  • Deliver relevant recommendations based on a profile’s purchase history

Customer Data Platform Use Cases

Digital Marketing Use Cases

Real-time personalization: Delight your customer with the right message at the right time and right place.
Cross-channel orchestration: Identify the channels a customer or segment uses in a customer journey, ensuring the messaging and information is consistent across those channels.
Behavioral retargeting: Segment customers by shopping behaviors like products viewed, content read, or past purchases, and retarget them with new products and services.
Lookalike advertising: Define customer segments with similar product purchases, shopping behavior, demographics, and more to help find similar customers to target.
Account-based marketing: Segment accounts in the CDP to help you understand and prioritize where to focus, as well as track account and contact interactions with your company across channels and campaigns.

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