Is It Time to Ditch Your DMP?

Paul Cowman
Paul Cowman
Sr. Director, Demand Generation, Pecan AI

Over the past year, the digital marketing industry has seen numerous developments in ad tech, data processing, and audience building. The rapid pace of change has left many professionals wondering if it’s time to abandon their data management platforms (DMPs). 

In addition, the increased reliance on third-party data, changes by tech giants Apple and Google, privacy regulations, and new tools allowing brands and publishers to utilize first-party data have raised questions about the future of DMPs. 

So how could these tools be replaced by other rising technologies that displace many of the DMP’s typical functions and benefits? And what other advantages might those tools offer?

DMPs are being disrupted

I believe DMPs will become obsolete due to the emergence of more versatile and cutting-edge tools that offer predictive modeling and audience-building capabilities. Specifically, two new technologies hold great promise: Customer Data Platforms (CDPs) and predictive analytics/marketing science tools.

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The rise of CDPs

CDPs represent an exciting data trend that has expanded rapidly over the last few years. They enable business teams to understand customer profiles using multiple data sources. 

That deeper understanding can improve campaign management, guide more meaningful marketing analysis, and inform business decisions. CDPs also can contribute to personalized marketing initiatives across channels. With a CDP in place, a brand can create audience segments for ad campaigns and work within the ad platforms to create look-alike audiences. 

CDPs have been an excellent tool for marketers executing data-driven marketing strategies. For example, a recent Tealium report shows a 2x satisfaction rate in meeting marketing objectives for companies that use a CDP. There are other advantages, too. The report states that teams operating CDPs are better prepared to handle privacy regulations, demands for personalization, the loss of third-party cookies, and disrupted supply chains.”

However, the cost of implementing a CDP can be pretty staggering. Not all implementations are successful. For example, Salesforce’s CDP can cost up to $65,000 a month, and according to Action IQ, nearly a third of CDP deployments fail. As an executive, this can be a risky investment that could cost you your job if it’s unsuccessful.

As a result, a new type of CDP, called a composable CDP, has emerged. A composable CDP allows brands to purchase parts of a CDP to complement their existing technology stack. The idea is that this will help reduce costs and make customer profiling more accessible.

The problem is that many components needed to run a fully functional CDP still need to be implemented. The Harvard Business Review states that only 39% of businesses have a data warehouse. So, many companies would need to build all components of a CDP, making the ‘composable’ functionality not especially feasible.

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The unique role of predictive analytics and marketing science platforms

SaaS-based predictive analytics platforms are emerging data tools that enable marketers to analyze and model their first-party data to predict future behavior, events, or results. Using AI and machine learning, marketing, customer experience, and business intelligence teams can quickly create and launch predictive models. 

Unlike CDPs and DMPs, these tools allow for cost-effective and immediate time to value, with many businesses seeing a return in months, not years. Predictive analytics tools offer three unique advantages over CDPs and DMPs that make them the preferred choice in some situations: 

  1. Predictive analytics can uniquely foresee future customer behavior. Predictive models use data and machine learning algorithms to identify patterns and trends, allowing marketers to anticipate customer needs and tailor marketing efforts accordingly. This capability can lead to more effective marketing campaigns and increased customer engagement. In contrast, CDPs and DMPs primarily focus on aggregating and managing customer data. While they can provide valuable insights, they can’t make predictions about future customer behavior.
  2. Another advantage of predictive analytics is that teams can use it to inform a wide range of business decisions, from product development to customer service. In contrast, CDPs and DMPs are typically used for customer profiling initiatives. 
  3. Last but not least, predictive analytics tools should be highly flexible and customizable, allowing businesses to tailor their models to their specific needs and goals. CDPs and DMPs, on the other hand, may come with limitations and may not be able to meet the unique needs of every business fully.

In conclusion, while CDPs and DMPs can provide valuable customer data and insights, predictive analytics offers the added advantage of being able to make predictions about future customer behavior and inform a broader range of business decisions.

If you’re ready to learn more about how predictive analytics can help you ditch your DMP and understand customers’ future behavior, get in touch.

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