Data management and data-driven marketing strategies have been a top priority for business leaders across every industry in recent years. In the era of customer experience, access to and ownership of customer data is critical for brands looking to build relationships with their audiences and measure the impact of their efforts. Technologies such as data management platforms (DMPs) and customer data platforms (CDPs) allow companies to develop a more robust customer view so they can offer better informed and timely messaging. While each of these platforms collects and applies customer data to marketing initiatives, they’re not interchangeable. CDPs and DMPs each serve distinct functions in regards to their data focuses and functionalities.
DMPs primarily focus on 3rd party data sources such as cookie IDs and IP addresses to help marketers more effectively target different customer segments with paid ad campaigns. On the other hand, CDPs unify and use all data, including 1st party data, to create real customer profiles of users who have interacted with your brand across all channels. While both platforms serve an important purpose, marketers should understand the difference between the two.
DMP use cases are mainly ads-oriented and cookie-based. CDP use cases range across:
- CRM based customer experience
- Communication that is based on PII (Personally identifiable data)
- Customer analytics
In the past, Marketing Service Providers (MSPs) built customer or marketing databases that fulfilled a similar role but only included profile and transaction data. With the evolution of digital marketing, MSPs' highly customized approach and scaled-up database models have become too expensive and clunky when the size of data is at web-scale, and SaaS marketing clouds require modern API integration of customer data.
The Benefits of a CDP
CDPs have three main pieces of functionality:
Single view of the customer: integration; cleanse, standardize, dedupe, household, etc. customer data in one place across both online and offline sources (POS, call center, web, email, etc.), to create a complete single view of the customer. This includes profiles to be connected in real time to transactions and to events (web, IoT, email, calls, etc.).
Customer analytics and machine learning: the scale and granularity of atomic-level data is important, but marketers need intelligent data. For example, CDPs include LTV calculation of customers based on various metrics. Predictive analytics are also important to recognize patterns in the data and reduce the complexity and noise in the data to amplify the intelligence for marketers.
Connectivity to customer interaction systems: CDPs, either through batch or real-time APIs, serve as the intelligent customer data backbone to ensure a customer web browsing event, or a store return or a call center complaint is available for changing customer experience and communication.