The Death of CDPs: A Missionary Perspective on the Future of MarTech

Composable CDPs are a transitional step. AI is transforming core data & marketing infra & ecosystem.

Introduction

As I mark a decade as a CDP builder and practitioner, I reflect on my journey, including my role as the Founder of Zylotech (now acquired), a leading CDP firm recognized by Gartner and Forrester. Over the years, I've partnered with enterprise giants like Cisco, Google, Lowes, Dell, Travelers Insurance, P&G, Staples, and Palo Alto Networks to name a few, helping them harness CDPs to build high-growth GTM/CX engines rooted in first-party data.

A recent MarTech.org article, "Cloud Data Warehouses Set to Disrupt the MarTech Stack," has ignited crucial discussions in my network. It's clear we're witnessing not just a disruption, but a paradigm shift signalling the end of traditional Customer Data Platforms (CDPs).

The Rise of Cloud Data Warehouses in MarTech

The MarTech.org article highlights a significant trend: cloud data warehouses are increasingly becoming the foundational layer of modern marketing technology stacks. Marketing has realised the importance of a mature data platform instead of an all in one CDP approach. But this shift is driven by several other factors:

1. Scalability and Performance: Cloud data warehouses can handle massive volumes of both structured & unstructured data with impressive speed and efficiency.

2. Cost-Effectiveness: They offer more attractive pricing models compared to traditional data management solutions or CDPs

3. Flexibility: These platforms provide the ability to easily connect with various tools and services in the MarTech ecosystem.

This trend is not just a minor adjustment; it's a fundamental reimagining of how marketing/CX data should be managed and utilized. LLMs or AI advancement are further enabling the tail winds to this change.

The Downfall of Traditional CDPs

Traditional CDPs, once seen as the ultimate solution for unifying customer data, are quickly becoming obsolete due to several critical shortcomings:

1. Data Silos: Instead of eliminating silos, many CDPs inadvertently created new ones.

2. Scalability Issues: They often struggle to handle the volume and variety of modern data streams.

3. Lack of Flexibility: Monolithic CDP architectures fail to adapt quickly to changing business needs.

4. Complexity Overload: Marketing teams found themselves burdened with data management tasks beyond their expertise.

5. Self Service nightmare - Skills and workload required for self service data CDPs is close to MDMs and Data Warehousing skillset, so why this shadow data platform?

Composable CDPs: A Transitional Step 

Composable CDPs emerged about 1-2 years ago as a response to the challenges of traditional CDPs, advocating for integrating best-of-breed components rather than relying on a single platform, also just before the explosion of Large Language Models (LLMs) and Neuro Symbolic AI. It was an initial response to the challenges faced by traditional CDPs, advocating for the integration of best-of-breed components rather than relying on a single, all-encompassing platform.

While they offer more flexibility, they don't fully address the core issues in a cloud data warehouse-centric world. The real challenge for marketing and CX teams is intelligence activation, not just data activation, enabling them to make data-driven decisions without creating more workload. Marketers focus on outcomes, often relying on third-party data due to insufficient quality in their first-party data, which hinders effective decision-making. The solution lies in improving first-party data quality and usability while integrating it with the broader MarTech ecosystem.

The New Paradigm beyond CDPs: Data Cloud Platforms and Marketing Cloud Platforms

The future of marketing technology lies in the specialization & separation of two key areas:

1. Data Cloud Platforms: Platforms like Snowflake, Databricks, Google, Microsoft and AWS are becoming the central repositories for all customer data, Cloud native MDMs like Reltio handling complex tasks of data unification and governance. Enabling Customer 360 View as its too complex and heavy lift for marketing teams to handle.

2. Marketing Cloud Platforms: Tools focused on specific aspects of marketing execution, such as campaign management, journey, content personalization, and customer engagement, will connect to these platforms. I.e Salesforce, Hubspot, Adobe

Organizations are proactively shifting their dynamics as data teams increasingly collaborate with marketing and customer experience (CX) departments to address the limitations of traditional CDPs.

This partnership is driving the adoption of more advanced solutions, such as:

1. Identity & Data clean rooms for secure, privacy-compliant data collaboration

2. Robust privacy and compliance frameworks

3. AI-driven innovation initiatives

By leveraging mature data platforms already established by data teams, this collaborative approach makes a strong case for consolidating data infrastructure and eliminating the confusion caused by multiple, overlapping data platforms. As a result, organizations are moving towards a more streamlined, efficient, and powerful data ecosystem that better serves their marketing and CX objectives while maintaining strict data governance standards.

Decision Intelligence: The Missing Link

While data cloud platforms solve the data management, governance challenge, they don't inherently provide the marketing-specific insights needed for effective campaigns or insights to effective CX. This is where decision intelligence comes into play.

Decision intelligence is the application of machine learning (ML), artificial intelligence (AI), and automation to augment human decision-making. It enriches relevant data from multiple sources, analyzes it, and provides actionable insights to support strategic, tactical, and operational decisions.

 Decision intelligence systems focussed on marketing/ GTM operations serve as the crucial intermediary layer, translating unified customer data into actionable marketing insights and automated processes. These AI-powered decision intelligence systems offer:

1. Activate Intelligence - Agentic workflows to activate operating intelligence 

2. Flexible Enrichment - LLM powered data enrichment to each customer profiles from any data sources first, second or third party to enable data quality on auto pilot.

3. Automated Insight Generation: Continuous analysis of data, providing real-time, actionable insights. For eg: KYC + Segmentation via Inference as a service

4. Predictive and Prescriptive Analytics: Forecasting outcomes and suggesting optimal marketing strategies.

5. Continuous Learning: Refining models based on actual marketing outcomes and evolving customer behaviour.

Companies are already sitting on tons of Predictive Analytics models or libraries; they just need the enriched, contextual data and AI assisted Intelligence activation i.e inference as a service, to enable real time decision intelligence and now with a Chat UX marketeers can finally leverage all of this complex data, scenarios into decision making and actions, long pending dream of a data driven marketing/ GTM ops team.

Agentic Workflows: Empowering Marketing Teams

With decision intelligence, marketing and GTM teams can leverage agentic workflows—intelligent, semi-autonomous processes that initiate actions and adapt based on real-time intelligence.

This approach:

1. Frees marketers from data manipulation, wrangling & linear automation tasks 

2. AI Assistant to all list management or advances segmentation work

3. Enables rapid response to market or customer behaviour changes

4. Unlock Intelligence across entire tech stack, channels in a creative way

5. Ensures data-driven decision making without requiring marketers to become data scientists

Conclusion: Reliable data + Decision Augmentation + Content creativity = ROI

The rise of cloud data platforms and AI heralds a new era in marketing technology, promising greater market responsiveness, more personalized customer experiences, human-AI co-intelligence in marketing and data analytics, improved efficiency in marketing and CX operations, and better utilization and enrichment of first-party data. The future is not about housing all data in a CDP or turning marketers into data scientists; it's about leveraging cloud infrastructures and intelligent systems to deliver the right insights at the right time, enabling smart decisions and compelling customer experiences. Successful organizations will recognize the limitations of traditional CDPs and embrace this cloud-centric, intelligence-driven approach. Are you ready for this transformation?