Embedded CDPs Emerge in Marketing
Today’s marketing organizations continue to look for new and innovative ways to do more with their data. They seek to personalize offers and messages, build campaigns and customer journeys that enhance customer loyalty and satisfaction, and boost campaign effectiveness and bottom-line results.
MarTech is an important driver of these objectives, and the market for marketing technologies is moving quickly toward the concept of embedding customer data platform (CDP) capabilities within broader customer engagement and experience solutions. This is largely due to the convergence and consolidation of data, AI, and analytics technologies. And the trend is beginning to play out with recent acquisitions of stand-alone CDP vendors by bigger players in the space. Let’s talk about why this trend will continue and the value of the “embedded” approach.
CDP 101
Since the early 2010s, marketing departments have embraced the use of customer data platforms, or CDPs. Described by the CDP Institute as “packaged software that creates a persistent, unified customer database that is accessible to other systems,” CDPs have largely delivered on their initial promise.
They aim to provide a single, coherent view of customers by bringing disparate data sources together (email, social media, web, point of sale and more).
The critical capabilities of CDPs—data ingestion, identity management and resolution, audience management, and marketing and advertising channel activation—are now being merged with advanced analytics, enterprise decisioning, AI/genAI, data governance, and optimization to create the new “CDP nirvana.”
Challenges Remain
Yet even with their success, marketers are asking more and more of their data…and their CDPs.
A recent report from Harvard Business Review Analytic Services, sponsored by SAS, underscored the current challenges for marketing technologies like CDPs.
Survey respondents were 388 members of the Harvard Business Review community involved in marketing decisions at their organization and familiar with their marketing department’s use of MarTech.
- The report found most MarTech stacks were comprised of dozens of disparate solutions often deployed within siloed systems.
- When asked to identify the greatest barriers preventing MarTech from having a positive impact on customer trust for their organization, 38 percent of respondents cited difficulty integrating new technologies into their existing stack.
- Respondents also cited data-related barriers to building customer trust, including poor data quality (37 percent), silos of customer data (36 percent) and lack of real-time data access (29 percent).
This speaks to the trending need for both composability and consolidation within enterprise MarTech stacks, to take a best-of-breed approach to solving today’s more advanced marketing use cases.
Embedded AI
With the rise of artificial intelligence (AI) in the past few years, marketing organizations are eager to use these new technologies alongside customer data to drive positive outcomes for both the customer and the business.
While few AI-driven MarTech tools today use AI to conduct and navigate the entirety of the solution, many MarTech solutions have embedded AI capabilities. These help brands apply AI to make marketing processes easier and more efficient.
Whether detecting patterns in customer data using “traditional” AI or creating content and copy using “new” generative AI, these AI-embedded solutions support key marketing goals. The embedding of AI into marketing technologies is a trend that isn’t likely to slow.
Embedded CDP
In a similar way, CDP capabilities are increasing being embedded into broader marketing and customer engagement solutions.
The trend toward composable CDPs, for example, means it is easier for companies to build tailored solutions with flexible, modular components. This avoids the limitations of a one-size-fits-all CDP approach.
The overarching goal of a CDP is activating data with intelligence. Embedding CDP capabilities into broader MarTech solutions gives marketing organizations even more power and flexibility.
Benefits of Embedded CDP
An embedded CDP approach goes beyond traditional CDPs to deliver deeper customer understanding from embedded predictive analytics, contextual customer engagement across all channels, and compelling CX tailored to each customer's unique journey.
For example, while CDPs are excellent at data ingestion, they sometime fall short in detecting real-time events. By combining AI and advanced analytics with CDP’s data-ingestion capabilities, marketing organizations can capture events as they happen and deliver timely and relevant response via the device or channel used by the consumer. Capabilities such as real-time decisioning, triggering, and next best actions helps marketers detect and respond to real-time events, while data management and governance tools ensure that they respect and protect consumer data privacy.
Another example of embedded CDP capabilities extending the power of traditional CDPs is that they help marketers move beyond simple segmentation to AI-powered journey orchestration. Using reinforcement learning, customer engagement solutions can learn from patterns in data to prescribe the ideal actions consumers should take.
Customer engagement and experience solutions with embedded CDP capabilities can better activate unified customer data via AI-powered, right-time journey orchestration across all channels. AI and advanced analytics, next-best-offer capabilities, real-time decisioning and seamless integration with other marketing functions—such as planning, testing, and attribution analysis—will further unify and enhance customer understanding.
Future-Proofing the CDP
By looking to customer engagement solutions with embedded CDP capabilities, brands can begin to future-proof their MarTech efforts. As the days of physical customer data profile creation wane, the new trend will be toward virtual “just-in-time” customer profiles that are created in a hybrid fashion against a cloud data warehouse. This eliminates much of the costly data duplication and storage. It also ensures up-to-date accuracy and consistency of data.
Additionally, this API-based approach gives organizations the ability to maintain flexibility in their MarTech integrations, avoiding long-term vendor lock-in. This provides freedom in a rapidly evolving digital marketing landscape. More centralized decisioning and data orchestration against a more nimble, scalable and flexible data architecture will certainly drive improved long-term business results.
Jonathan Moran is head of MarTech solutions marketing at SAS, covering global product marketing activities with a focus on customer experience and marketing technologies. During his career, Moran has had the opportunity to not only architect, develop, demonstrate and implement analytical software solutions, but also work on-site with Fortune 500 customers across industries, helping them solve unique digital marketing analytics issues. Moran has more than 20 years of marketing and analytics industry experience, including roles at Earnix and the Teradata Corporation in presales, consulting, and marketing.