Comparing DMPs, CDPs, and Predictive Analytics Platforms

The 2020 Chief Martech guide reveals over 1,200 data providers available to help marketers with mobile/web analytics, audience segmentation, marketing attribution, customer intelligence, data science, customer data, dashboards, and visualizations. It’s an understatement to say that marketing science and data platforms are exploding. 

But what do these tools do? And how do they differ?

What is a DMP?

A data management platform (DMP) activates first-party, second-party, and third-party audience data for marketing and advertising initiatives. DMPs combine data from online and offline sources for business intelligence and, today, for data science efforts.

Companies find DMPs useful because they allow them to collect, store, and analyze large amounts of data from various sources in a central location. This centralized data collection makes it easier for companies to gain insights into their customers and effectively target their marketing efforts. 

DMPs also allow companies to segment their audience and create more personalized experiences for each segment. That precise segmentation can lead to higher engagement and conversion rates, as consumers receive messages in the channels tailored to their preferences and interests. 

Additionally, DMPs can feed audiences to other marketing and advertising tools, such as demand-side platforms (DSPs) and ad networks or exchanges. This integration improves the efficiency and effectiveness of digital advertising campaigns. Furthermore, combining these tools makes it possible to automate the audience-building and targeting process. 

Without DMPs, it would be challenging for companies to organize and use their data for personalized advertisements, cross-sell/upsell initiatives, or other database-marketing initiatives.

DMP: data management platform, CDP: customer data platform, predictive analytics platform

What is a CDP?

On the other hand, a CDP or customer data platform focuses solely on first-party data. A CDP allows organizations to collect and build a single, coherent, complete view of each customer within their database. 

Companies gain value from customer data platforms (CDPs) because they centralize and manage first-party data about their customers and provide complete customer profiles. They draw on various types of data, such as website interactions, social media, marketing campaigns, and sales transactions and conversations. 

CDPs provide companies with a single, unified view of their customers. This unified view can then inform and improve personalization, segmentation, and other marketing and sales projects. Additionally, CDPs can integrate with other systems, such as marketing automation and analytics platforms, to enable more effective and efficient marketing and sales operations.

How do DMPs and CDPs differ?

Those descriptions may sound very similar! However, DMPs and CDPs are different. A CDP collects and organizes first-party un-anonymized customer data. A CDP aims to build out a 360-degree view of the customer to support marketing, sales, and customer service to deliver better customer experiences. 

DMPs, on the other hand, focus on a mix of data (first-, second-, and third-party) and rely on cookies and anonymized data. While CDPs allow everyone across marketing, sales, and customer service to get a better view of the customer, DMPs cater specifically to digital marketers to help support web and digital display goals.

What are marketing predictive analytics platforms?

A newly developed ‘blue ocean’ in the marketing science space consists of platforms designed to predict customer behavior. 

Predictive analytics is the use of data to predict future trends and events. Marketers are rapidly adopting predictive analytics to enhance user acquisition efforts, maximize ROAS, and optimize marketing campaigns.

Customer success teams are finding success in using predictive analytics to help anticipate customer retention, predicting churn, customer lifetime value, and chances for upselling/cross-selling.

Predictive analytics is distinct from CDPs and DMPs and usually works with the data gathered from those platforms. Predictive analytics platforms leverage machine learning and AI to predict future events rather than creating audiences like CDPs or DMPs. 

In many cases, marketing and customer success teams have used CDPs to build holistic and comprehensive views of their customer database. Then, teams will build out predictive analytics solutions to predict customer behavior that influences programs and revenue creation. For instance, teams can: 

  1. Predict what campaigns are driving higher ROAS 
  2. Understand what customers are about to churn
  3. What customers have the highest lifetime value for special outreach

 

Utilizing predictive analytics with a CDP is an effective way to boost the success of marketing, sales, and customer service efforts. By integrating these data solutions, a company can thoroughly understand its clients and anticipate how they will act. 

Instead of simply using spreadsheets or BI tools to analyze data from the past, teams can look at what will happen in the future. This future perspective allows them to be proactive in defining marketing strategy, sales efforts, and customer service refinements. Brands can now adapt quickly to market changes, privacy laws, personalization requests, third-party cookie deprecation, and supply chain issues.

Predictive analytics provides users with a better understanding of their customers’ future behavior and the performance of their ad campaigns. This in-depth knowledge helps teams manage their budgets more efficiently and gain higher returns on their marketing investments. 

 

If you’re looking to get started in predictive analytics, we created a helpful guide. The guide leads you and other stakeholders through gathering information and making decisions about your data-driven strategy.

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