Artificial intelligence and customer data platforms are increasingly intertwined. The CDP Institute recently asked industry leaders to describe best practices to maximize value in the enterprise.  Our third post in this series covers AI and CDPs.

AI Supports a Broad Range of Applications.  The rise of AI has left its mark on the Customer Data Platform industry. “[We see] AI for personalization & predictive analytics: Top companies use AI in CDPs to personalize experiences & predict customer behavior. By integrating machine learning, one can segment audiences based on behavioral data vs. static demographics, leading to more dynamic & effective campaigns. AI also enables real-time decisions & campaign optimization by adjusting strategies based on data in real time,” explains Janet A. Jaiswal, Global VP of Marketing, Blueshift.

AI Depends on Customer Data.  Enterprises are working with more customer data than ever before. “Artificial intelligence is transforming the way businesses handle customer data. Modern Customer Data Platforms will (or already do) leverage AI to manage, analyze, and utilize vast amounts of data more efficiently. By integrating AI into a CDP, users will receive deeper insights and more accurate customer profiles, allowing them to make more informed decisions,” explains Lindsay Hansen, Marketing Manager, Ascent360.

CDPs Support AI Models.  A Customer Data Platform can help build AI efforts. “Leading enterprises are realizing that CDP is the foundation for the brand’s “Customer AI” efforts, for training & fine-tuning their models and agents. The CDP is also critical in reducing real-time LLM inference costs, by pre-processing certain data using predictive AI (e.g. auto-segmenting customers), and feeding a manageable number of synthesized tokens to the inference layer,” explains Vijay Chittoor, Co-Founder & CEO, Blueshift.

AI Decisioning Engines Rely on CDPs   Enterprises can benefit from “[t]ying the CDP to a decisioning engine. [This] enables better automated decisions made by AI decisioning models with richer unified data. CDPs allow enterprises to usher in a Data-rich era leading to individualized journeys & hyper-personalization. It can only be achieved if the CDP and MMH are fully integrated and the decisioning engine can read each customer’s single view to decide the next best action,” observes Rony Vexelman, VP of Marketing, Optimove.

CDPs Speed AI Deployments.  CDPs can help enterprises work quickly with AI. “[We recommend that customers] start from data, including governance and reusability: To enable trusted AI and meet fast time-to-market demands, customers [should] focus on creating comprehensive 360 profiles with rigorous, automated processes that enable continuous curation of high-quality, real-time data,” observes Venki Subramanian, SVP, Product Management, Reltio.

CDPs Increasingly Include AI Features.  AI is becoming a part of existing CDP builds. “Enterprises are transitioning from standalone CDPs to fully integrated platforms where the CDP forms the foundation, with marketing automation and AI built on top. This shift empowers businesses to enhance customer experiences, improve operational decision-making, and deliver personalised, data-driven strategies that drive value and sustainable growth,” notes Maros Gardon, GTM Marketing Manager, Xtremepush.

AI is Applied Within CDPs.  AI can support internal CDP processes as well.  “[First], customer data is unified across touchpoints to create a 360-degree view, supporting targeted omnichannel strategies. Next, predictive insights driven by AI enable proactive marketing and customer engagement, improving CLV. Finally, translating insights into real-time engagement requires a content strategy, with teams using AI and creative campaigns to deliver personalized experiences,” says Deepak Narisety, Marketing Technology Practice Leader, Acxiom. 

Customer Data Platforms and AI in Retail.  Specific industries can benefit from a CDP and AI. “[We see] leveraging CDP data for AI model training: Retail enterprise clients use the rich, unified datasets within their CDPs to train advanced AI and machine learning models […]. This enables them to develop custom AI models that enhance predictive analytics, personalization, and automation, driving smarter decisions and more tailored customer experiences,” says Diganta Roy, Product Marketing Manager, Algonomy.

We hope you got a lot of insight from the experts on AI and CDPs. Also, see our other CDP Best Practices posts on these topics:

• CDP Use Cases
• Customer Data Unification
• Customer Data Privacy and Security
• Customer Data Platform Benefits

Thank you to our participants!