Carolyn Barrs, MarTech Strategy Consultant at Merkle, explains how generative AI could help marketers leverage vast data sets and create personalized content that connects deeply with each customer, improving the customer experience and increasing conversion rates. She further explains how this understanding extends to the use of specific technologies such as CDPs (customer data platforms), which are essential for providing personalized recommendations, segmentations and predictive insights.
In today’s digital age, customer experience (CX) is a business’s key differentiator. With the abundance of data available, marketers can leverage generative AI to create highly personalized experiences that resonate with each customer. However, for generative AI to truly make an impact, organizations must understand how to use this technology effectively. This involves knowing the appropriate use cases, understanding supporting models, and refining data inputs for optimal results.
Generative AI has expanded across many CX technologies and revolutionized chat, content personalization, and recommendation engine capabilities. The generative AI storm has impacted customer data platforms, and many major CDP vendors have established integrations with AI vendors to expand platform capabilities.
Examples of AI-powered CDP enhancements
Some CDP providers have already started implementing the following AI-based enhancements:
- Audience management: CDPs can leverage generative AI to create natural language audiences. Natural language prompt inputs can generate SQL queries as output to access specific segments of their customer base. This can simplify audience segmentation, reduce reliance on technical resources, and allow business users to be more self-sufficient in their audience management efforts.
- Customer knowledge: CDPs can provide real-time insights to marketers. By analyzing large volumes of customer data, generative AI algorithms can quickly identify trends, patterns, and opportunities for improvement. This information is then presented to marketers, allowing them to make data-driven decisions and optimize their marketing efforts in a timely manner.
- Orchestration of the journey: CDPs can leverage generative AI to automatically adapt and optimize customer journeys in real-time, ensuring customers receive relevant engagements across multiple touchpoints to enrich the customer experience.
- Content production: CDPs can offer content generation that leverages AI-based capabilities to optimize email text and images based on customer preferences and behavioral data. This ensures that the email content is highly engaging and increases the chances of customer interaction and conversion.
- Product recommendations: CDPs with AI-powered product recommendations can determine which items are most likely to drive purchases for individual customers. Each customer’s experience can be personalized with relevant product recommendations. Generative AI models can analyze customer data, purchase history, browsing behavior and other relevant factors to generate accurate and targeted product recommendations, thereby increasing the likelihood of conversion.
See more : How AI can evolve data analysis
Tips for Maximizing AI Benefits with CDPs
To achieve the best results with generative AI, marketers should consider several best practices. First, it is essential to have a thorough understanding of the AI model used and its capabilities. Different models have different strengths and limitations, and marketers must select the most appropriate model for their specific use case. This requires staying up to date with the latest advances in generative AI and continually evaluating and experimenting with different models. While pre-built models are available, some CDPs allow organizations to train their own machine learning (ML) models using real-time, identity-resolved customer data. This allows businesses to create highly personalized ML models that match their needs and goals. Additionally, CDPs can facilitate the activation of ML models from a data warehouse to downstream tools, making it quick and easy to deploy these models and leverage them across various marketing channels.
Another best practice is to invest in high-quality data inputs. The accuracy and relevance of the generated content is highly dependent on the quality of the data fed into the AI model. Organizations must ensure that data used for model training and testing meets data quality standards for accuracy, completeness, and recency. This can help avoid biased or misleading results and ensure that the content generated matches the desired customer experience.
Additionally, ethical considerations should be prioritized when using generative AI for customer experience. While the potential benefits of AI-generated content are vast, it is essential to be aware of the potential risks that accompany it. These risks include spreading false information or producing biased results. To address these concerns, it is crucial to implement safeguards and a thorough review process to mitigate these risks and ensure that generated content complies with ethical standards. This may involve regularly monitoring and evaluating the performance, accuracy and fairness of AI models. Additionally, it is important to prioritize transparency and disclosure regarding the use of AI-generated content in order to maintain customer trust. By proactively addressing ethical considerations, businesses can leverage generative AI to improve customer experiences while maintaining integrity.
If you’re a marketing leader wondering where to start upskilling your marketing team, a quick engineering education can be a good first step. Rapid engineering involves developing clear and specific instructions for the AI model. It is important to note that generative AI is not a magic solution that automatically delivers accurate results. This requires careful and rapid engineering to ensure that the AI model understands the desired outcome and generates relevant and accurate content.
Conclusion
In 2024, we expect to see an increasing number of CDP providers building features based on generative AI models. Early adopters will continue to iterate on the models and deepen the maturity of the platform by enriching content production, audience management, orchestration and decision-making. By staying informed of the latest advances in generative AI and focusing on ethical practices, organizations should be well-positioned to harness the full potential of CDPs to deliver exceptional customer experiences.
How have you used generative AI to improve your CDP? Share with us on Facebook, TwitterAnd LinkedIn. We would love to hear from you!
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