In the rapidly evolving landscape of marketing technology innovation, effectively using data through advanced platforms and tools is imperative for business success. The initial wave of customer data platforms (CDPs) revolutionized this by consolidating customer data across different channels and sources into a unified and comprehensive 360-degree view of customers.

This consolidation enabled organizations to gain deeper insights into customer behavior, promoting data-driven decisions and enhancing customer experiences across connected touchpoints. However, as CDP adoption scaled, organizations faced challenges with the increasing volume of data, interaction points, and use cases, coupled with limited data science resources to actually automate and activate the data collected.

AI continues to emerge as a pivotal driver for operational efficiency and customer engagement. Organizations are increasingly interested in CDPs that can leverage AI technologies to derive meaningful insights, predict customer behavior, and personalize experiences faster than ever.

One such example is Adobe Experience Platform's (AEP’s) Intelligent Services, with new AI tools called Customer AI and Attribution AI which emerged as a response to these challenges and ambitions. These tools are part of AEP’s Data Science Workspace and are designed to supercharge large-scale data analysis and modeling.

  • Adobe Customer AI employs proprietary machine learning algorithms to generate customer predictions, with insights around the influential factors that drive engagement, conversion, or customer retention.
  • Adobe Attribution AI employs multi-touch attribution models for measuring and optimizing marketing and advertising resources. It calculates the impact of every individual customer interaction across each phase of their journeys to drive engagement and conversions.

CDP + AI in action

Here are a few use cases illustrating how Adobe Customer AI and Attribution AI unlock significant business benefits within CDP implementations:

  • A retailer can use Customer AI to generate propensity scores for customers most likely to purchase in the next 30 days. The retailer could then use these scores to target them with personalized messages, such as email offers and promotions or in-app notifications.
  • A telecommunications company can use Customer AI to identify at-risk accounts. The company could then use this information to personalize rich offers at their service channels, like the call center, to prevent customers from leaving.
  • A financial services company can use Attribution AI to measure customer acquisition from its marketing campaigns. This information could then be used to optimize the company's campaigns and improve ROI.

While these capabilities are beneficial, it is vital to adhere to key principles of ethical AI when designing and implementing such solutions. Guiding principles surrounding transparency, explainability, data privacy, accountability, and human oversight must be followed to prevent potential drawbacks like unintended bias or discrimination. For instance, if historical sales data indicate a preference for targeting certain products or services to a specific demographic, the AI models may inadvertently generate bias, overlooking opportunities to promote these products to a broader audience.

How to get started

To start leveraging these new capabilities, organizations should consider several key factors:

  • Current CDP maturity: Conduct a maturity model assessment to help identify gaps and areas for improvement, providing a roadmap for the incremental adoption of advanced AI features.
  • Use cases and roadmap: Define use cases that address your business objectives, whether that's identifying customers most likely to purchase, reducing churn, or measuring the impact of marketing campaigns. These use cases will provide a framework for deploying and measuring the right AI capabilities.
  • Data science resourcing: Assess your organization's data science capabilities. If necessary, consider leveraging Adobe's Intelligent Services, which can simplify the integration of AI into your data strategy.
  • Data and organizational adoption: Ensure that your organization is prepared to adopt these new technologies. This involves securing buy-in from stakeholders and training staff and ensuring that the necessary infrastructure is in place.
  • Ethical AI: Adhere to key principles of Ethical AI when designing AI solutions to prevent potential drawbacks like unintended bias or discrimination.

Integrating AI capabilities in the new era of CDPs can facilitate personalized and enhanced customer experiences at scale. Adobe's Customer AI and Attribution AI help empower organizations to overcome scaling challenges, drive operational efficiency, expedite insights to market, and achieve remarkable ROI. By adhering to ethical AI principles, businesses can ensure responsible and unbiased AI implementation, fostering trust and long-term success in the martech landscape.

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